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# coding: UTF8
# Copyright 2009 Thomas Jourdan
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program; if not, write to the Free Software
# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA
import random
import exon_color
import model_layer
import ka_random
class MarkovChainLayer(model_layer.Layer):
"""Markov chain layer
inv: self.cell_colors is not None and len(self.cell_colors) == self.states
inv: self.probability is not None and len(self.probability) == self.states
inv: 1 <= self.square_base <= 7
"""
def __init__(self, trunk):
"""Markov chain layer constructor"""
super(MarkovChainLayer, self).__init__(trunk)
self.states = 4
self.square_base = 3
self.cell_colors = [None] * self.states
for cix in range(len(self.cell_colors)):
self.cell_colors[cix] = exon_color.Color(self.path, 0, 0, 0, 1)
self.probability = [[1.0 / self.states] * self.states for i in range(self.states)]
# for row, row_probabilities in enumerate(other.probability):
# for col, cell_probability in enumerate(row_probabilities):
# self.probability[row][col] = 1.0 / self.states
def __eq__(self, other):
"""Equality based on the cells color components."""
equal = isinstance(other, MarkovChainLayer) \
and super(MarkovChainLayer, self).__eq__(other) \
and self.states == other.states \
and self.square_base == other.square_base
if equal:
for cix, cell_color in enumerate(self.cell_colors):
equal = equal and cell_color == other.cell_colors[cix]
if equal:
for row, row_probabilities in enumerate(self.probability):
for col, cell_probability in enumerate(row_probabilities):
equal = equal and cell_probability == other.probability[row][col]
return equal
def randomize(self):
"""Randomize the layers components."""
super(MarkovChainLayer, self).randomize()
for cix in range(len(self.cell_colors)):
self.cell_colors[cix].randomize()
for row, row_probabilities in enumerate(self.probability):
for col in range(len(row_probabilities)):
self.probability[row][col] = random.random() / self.states
self.square_base = random.randint(1, 7)
def mutate(self):
"""Make small random changes to the layers components."""
super(MarkovChainLayer, self).mutate()
for cix in range(len(self.cell_colors)):
self.cell_colors[cix].mutate()
for row, row_probabilities in enumerate(self.probability):
for col, cell_probability in enumerate(row_probabilities):
val = ka_random.limitate(cell_probability \
+ ka_random.jitter(1.0 / self.states))
self.probability[row][col] = val
if ka_random.is_mutating():
self.square_base += random.randint(-1, 1)
self.square_base = ka_random.limitate_range(self.square_base, 1, 7)
def shuffle(self):
"""Shuffle similar componets.
For example exchange foreground and background color."""
super(MarkovChainLayer, self).shuffle()
if ka_random.is_shuffling():
random.shuffle(self.cell_colors)
if ka_random.is_shuffling():
random.shuffle(self.probability)
for row_probabilities in self.probability:
if ka_random.is_shuffling():
random.shuffle(row_probabilities)
def crossingover(self, other):
"""
pre: isinstance(other, MarkovChainLayer)
pre: isinstance(self, MarkovChainLayer)
# check for distinct references, needs to copy content, not references
post: __return__ is not self
post: __return__ is not other
"""
new_one = MarkovChainLayer(self.get_trunk())
cross_sequence = self.crossingover_base(new_one, other, \
1 + len(self.cell_colors))
if cross_sequence[0]:
new_one.square_base = self.square_base
else:
new_one.square_base = other.square_base
for cix in range(len(self.cell_colors)):
if cross_sequence[1 + cix]:
new_one.cell_colors[cix] = self.cell_colors[cix].copy()
else:
new_one.cell_colors[cix] = other.cell_colors[cix].copy()
return new_one
def draw(self, ctx, width, height):
"""
pre: ctx is not None
pre: width > 0
pre: height > 0
pre: width == height
"""
self.begin_draw(ctx, width, height)
cell_state = 0
dim = self.square_base**2
delta_x, delta_y = 1.0 / dim, 1.0 / dim
for y_index in range(dim):
for x_index in range(dim):
rgba = self.cell_colors[cell_state].rgba
ctx.set_source_rgba(rgba[0], rgba[1], rgba[2], rgba[3])
ctx.rectangle((x_index-0.5*dim)*delta_x, \
(y_index-0.5*dim)*delta_y, \
delta_x, delta_y)
ctx.fill()
cell_state = (cell_state+1) % self.states
def explain(self, formater):
# super(MarkovChainLayer, self).explain(formater)
formater.color_array(self.cell_colors, 'cell colors:')
def copy(self):
"""The Markov chain layer copy constructor
# check for distinct references, needs to copy content, not references
post: __return__ is not self
"""
new_one = MarkovChainLayer(self.get_trunk())
self.copy_base(new_one)
new_one.states = self.states
new_one.square_base = self.square_base
new_one.cell_colors = [None] * self.states
for cix in range(len(self.cell_colors)):
new_one.cell_colors[cix] = self.cell_colors[cix].copy()
new_one.probability = [[0.0] * self.states for i in range(self.states)]
for row, row_probabilities in enumerate(self.probability):
for col, cell_probability in enumerate(row_probabilities):
new_one.probability[row][col] = cell_probability
return new_one
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