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# coding: UTF-8
# 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 math
import ka_debug
import model_random
import model_locus
import model_allele
import model_constraintpool
import exon_direction
from gettext import gettext as _
SECTIONS_CONSTRAINT = 'sectionsconstraint'
class RandomWalkSampler(model_allele.Allele):
"""RandomWalkSampler: Reverse the layer horizontally or vertically.
inv: len(self.direction_steps) > 0
"""
cdef = [{'bind' : SECTIONS_CONSTRAINT,
'name' : 'Number of sections.',
'domain': model_constraintpool.INT_RANGE,
'min' : 1, 'max': 100},
]
def __init__(self, trunk):
"""Constructor for a random walk."""
super(RandomWalkSampler, self).__init__(trunk)
self.direction_steps = [exon_direction.Direction(self.path, 0.0, 0.0)]
def dot(self):
result = ""
anchor = ka_debug.dot_id(self) + ' -> '
for ref in self.direction_steps:
result += ka_debug.dot_ref(anchor, ref)
return result
def __eq__(self, other):
"""Equality."""
equal = isinstance(other, RandomWalkSampler) \
and len(self.direction_steps) == len(other.direction_steps)
if equal:
for index, direction in enumerate(self.direction_steps):
equal = equal and direction == other.direction_steps[index]
return equal
def randomize(self):
"""Randomizes the walk."""
cpool = model_constraintpool.ConstraintPool.get_pool()
sections_constraint = cpool.get(self, SECTIONS_CONSTRAINT)
for dummy in range(model_random.randint_constrained(sections_constraint)):
direction = exon_direction.Direction(self.path, 0.0, 0.0)
direction.randomize()
self.direction_steps.append(direction)
def mutate(self):
"""Mutates the random walk."""
cpool = model_constraintpool.ConstraintPool.get_pool()
sections_constraint = cpool.get(self, SECTIONS_CONSTRAINT)
if model_random.is_mutating():
model_random.mutate_list(self.direction_steps, sections_constraint,
exon_direction.Direction(self.path, 0.0, 0.0))
def swap_places(self):
"""Reorder steps."""
model_random.swap_places(self.direction_steps)
def crossingover(self, other):
"""
pre: isinstance(other, RandomWalkSampler)
pre: isinstance(self, RandomWalkSampler)
# check for distinct references, needs to copy content, not references
post: __return__ is not self
post: __return__ is not other
post: model_locus.unique_check(__return__, self, other) == ''
"""
new_one = RandomWalkSampler(self.get_trunk())
new_one.direction_steps = model_random.crossingover_list(self.direction_steps,
other.direction_steps)
return new_one
def get_sample_points(self):
""" Produces a list of sampling points.
The points describes an random walk starting near (0.0, 0.0).
"""
sample_points = []
px, py = 0.0, 0.0
for step in self.direction_steps:
px += step.offset * math.cos(step.radian)
py += step.offset * math.sin(step.radian)
sample_points.append( (px, py) )
return sample_points
def get_sample_extent(self):
"""'Size' of one sample as a fraction of 1.
"""
aproximately = 1.0 / math.sqrt(1.0*len(self.direction_steps))
return aproximately, aproximately
def explain(self):
"""
post: len(__return__) == 3
"""
return u'Random walk sampler: %d points' \
% (len(self.direction_steps)), \
None, \
None
def copy(self):
"""A copy constructor.
post: isinstance(__return__, RandomWalkSampler)
# check for distinct references, needs to copy content, not references
post: __return__ is not self
"""
new_one = RandomWalkSampler(self.get_trunk())
new_one.direction_steps = model_random.copy_list(self.direction_steps)
return new_one
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