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joachim |
#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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#=======================================================================
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#
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# ca_prng.py
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# ---------
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# Fast and simple ca_prng conformant cellular automata model in
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# Python. This model is actually implented as a general 1D CA class
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# and the rule and size of the CA array is provided as parameters.
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#
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#
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# Author: Joachim Strömbergson
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# Copyright (c) 2008, Kryptologik
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# All rights reserved.
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#
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# Redistribution and use in source and binary forms, with or without
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# modification, are permitted provided that the following conditions
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# are met:
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# * Redistributions of source code must retain the above copyright
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# notice, this list of conditions and the following disclaimer.
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#
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# * Redistributions in binary form must reproduce the above
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# copyright notice, this list of conditions and the following
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# disclaimer in the documentation and/or other materials
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# provided with the distribution.
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#
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# THIS SOFTWARE IS PROVIDED BY Kryptologik ''AS IS'' AND ANY EXPRESS
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# OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
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# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
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# ARE DISCLAIMED. IN NO EVENT SHALL Kryptologik BE LIABLE FOR ANY
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# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
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# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE
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# GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
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# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
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# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
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# NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
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# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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#
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#=======================================================================
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#-------------------------------------------------------------------
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# Python module imports.
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#-------------------------------------------------------------------
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import sys
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import math
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import optparse
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#-------------------------------------------------------------------
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# class CellularAutomata()
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#
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# This class implements a 1D cellular automata. The class expects
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# to be initalized with an array of arbitrary length with initial
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# cell state values (0 or 1) as well as an array with update rules.
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#
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# The update rule is expected to contain eight values (0 or 1)
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# that define the update value for a cell given by the current
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# state of the cell and two nearest neighbours. Note that nearest
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# neighbour is calculated with wrap around, that is the cell
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# array is treated as a ring.
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#-------------------------------------------------------------------
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class CellularAutomata():
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def __init__(self, rule, init_state, verbosity):
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self.my_rule = rule
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self.my_state = init_state
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self.verbose = verbosity
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def print_state(self):
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"""Print the current state of the cellular automata."""
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print self.my_state
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def update_ca_state(self):
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"""Update the cells in the cellular automata array."""
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# Create a new CA array to store the updated state.
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new_state = [x for x in range(len(self.my_state))]
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# For each cell we extract three consequtive bits from the
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# current state and use wrap around at the edges.
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for curr_bit in range(len(self.my_state)):
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if curr_bit == 0:
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bit_left = self.my_state[-1]
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bit_mid = self.my_state[0]
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bit_right = self.my_state[1]
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elif curr_bit == (len(self.my_state) - 1):
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bit_left = self.my_state[(curr_bit - 1)]
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bit_mid = self.my_state[curr_bit]
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bit_right = self.my_state[0]
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else:
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bit_left = self.my_state[(curr_bit - 1)]
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bit_mid = self.my_state[curr_bit]
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bit_right = self.my_state[(curr_bit + 1)]
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# Use the extraxted bits to calculate an index for
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# the update rule array and update the cell.
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rule_index = 4 * bit_left + 2 * bit_mid + bit_right
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if self.verbose:
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print "rule_index = %d " % rule_index
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new_state[curr_bit] = self.my_rule[rule_index]
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# Replace the old state array with the new array.
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self.my_state = new_state
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#-------------------------------------------------------------------
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# main()
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#
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# Main function.
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#-------------------------------------------------------------------
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def main():
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# Create an update rule array. This is Wolframs rule 30.
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# We also create a CA array with a given init state.
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my_update_rules = [0, 1, 1, 1, 1, 0, 0, 0]
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my_init_state = [1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0]
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# Create a CA object.
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my_ca = CellularAutomata(my_update_rules, my_init_state, False)
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# Run a few iterations printing the state before each update.
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for iteration in range(1000):
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my_ca.print_state()
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my_ca.update_ca_state()
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#-------------------------------------------------------------------
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# __name__
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# Python thingy which allows the file to be run standalone as
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# well as parsed from within a Python interpreter.
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#-------------------------------------------------------------------
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if __name__=="__main__":
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# Run the main function.
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sys.exit(main())
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#=======================================================================
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# EOF ca_prng.py
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#=======================================================================
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