class View(QtGui.QWidget):
def __init__(self, bodySize):
super(View, self).__init__()
self.phenotype = Phenotype(bodySize)
self.initUI()
def initUI(self):
self.setGeometry(200, 200, WIDTH, HEIGHT)
self.setWindowTitle('Organism')
self.show()
def paintEvent(self, e):
qp = QtGui.QPainter()
qp.begin(self)
self.drawMatrix(qp)
qp.end()
def drawMatrix(self, qp):
rgbColors = self.chooseColors()
horizBoxSide = WIDTH // self.phenotype.size
vertBoxSide = HEIGHT // self.phenotype.size
boxSide = min(horizBoxSide, vertBoxSide)
color = QtGui.QColor(0, 0, 0)
qp.setPen(color)
for col in range(self.phenotype.size):
for row in range(self.phenotype.size):
color = rgbColors[self.phenotype.body[col][row]]
qp.setBrush(QtGui.QColor(color[0], color[1], color[2]))
qp.drawRect(row * boxSide, col * boxSide, boxSide, boxSide)
def chooseColors(self):
rgbColors = []
colorsNo = self.phenotype.GetConnectedComponentsNo()
rgbColors.append((255, 255, 255))
for i in range(1, colorsNo + 1):
r = random.randint(0, 255)
g = random.randint(0, 255)
b = random.randint(0, 255)
rgbColors.append((r, g, b))
return rgbColors
def UpdateData(self, genotype):
self.phenotype.UseGenotype(genotype)
self.update()
def __init__(self, name, chromosomes):
self.name = name
self.chromosomes = chromosomes
self.phenotype = Phenotype(self)
self.parents = []
self.children = []
def ChooseBestIndividal(self):
phenotype = Phenotype(self.bodySize)
indexBest = -1
adaptationBest = 0.0
for i in range(self.muValue):
phenotype.UseGenotype(self.population[i])
if phenotype.GetAdaptation() > adaptationBest:
indexBest = i
adaptationBest = phenotype.GetAdaptation()
self.lastAdaptationValue = self.currentAdaptationValue
self.currentAdaptationValue = adaptationBest
print("#" + str(self.lastAdaptationValuesBelowDelta) +
"\tCurrent best: " + str(adaptationBest))
self.view.UpdateData(self.population[indexBest])
def CreateRouletteWheel(self, individualsToChoose):
sum = 0.0
adaptationValue = []
for i in range(len(individualsToChoose)):
phenotype = Phenotype(self.bodySize)
phenotype.UseGenotype(individualsToChoose[i])
adaptation = phenotype.GetAdaptation()
adaptationValue.append(math.exp(adaptation))
sum += math.exp(adaptation)
rouletteWheel = []
for i in range(len(individualsToChoose)):
rouletteWheel.append(adaptationValue[i] / sum)
for i in range(1, len(individualsToChoose)):
rouletteWheel[i] += rouletteWheel[i - 1]
return rouletteWheel
def __init__(self, bodySize):
super(View, self).__init__()
self.phenotype = Phenotype(bodySize)
self.initUI()
class Being:
"""
Represent a being
Can be human or else
@args:
name: string, being name
chromosomes:
"""
def __init__(self, name, chromosomes):
self.name = name
self.chromosomes = chromosomes
self.phenotype = Phenotype(self)
self.parents = []
self.children = []
def getName(self):
return self.name
def getChildren(self):
return self.children
def getParents(self):
return self.parents
def getPhenotype(self):
return self.phenotype
def addParent(self, being):
self.parents.append(being)
def addChild(self, being):
self.children.append(being)
def removeChild(self, being):
self.children.remove(being)
def removeParent(self, being):
self.children.remove(being)
def getChromosomes(self):
return self.chromosomes
def addChromosome(self, chromosome):
self.chromosomes.append(chromosome)
def removeChromosome(self, chromosome):
self.chromosomes.remove(chromosome)
def getChromosomesByNames(self):
chromosomesByNames = {}
for chromosome in self.getChromosomes():
if chromosome.getName() in chromosomesByNames:
chromosomesByNames[chromosome.getName()].append(chromosome)
else:
#Python implicitely creates a key whenever you declare a property on a key that doesn't exist
chromosomesByNames[chromosome.getName()] = [chromosome]
return chromosomesByNames
def getChromosomesNameSet(self):
return set(self.getChromosomesByNames().keys())
def getSpecie(self):
return self.phenotype.getSpecie()
def isDescendant(self, being, maxDepth = 10, excludedBeings = []):
if self == being:
raise Exception("isDescendant cannot be called with self as argument")
if being in self.parents:
return True
if not self.parents or not maxDepth:
return False
cleanedParents = [parent for parent in self.parents if not parent in excludedBeings]
if not cleanedParents:
return False
return any([parent.isDescendant(being = being, maxDepth = maxDepth - 1, excludedBeings = excludedBeings + [self]) for parent in cleanedParents])
def isAncestor(self, being, maxDepth = 10, excludedBeings = []):
if self == being:
raise Exception("isAncestor cannot be called with self as argument")
if being in self.children:
return True
if not self.children or not maxDepth:
return False
cleanedChildren = [child for child in self.children if not child in excludedBeings]
if not cleanedChildren:
return False
return any([child.isAncestor(being = being, maxDepth = maxDepth - 1, excludedBeings = excludedBeings + [self]) for child in cleanedChildren])
def isConsanguineous(self, being, maxDepth = 10, excludedBeings = [], firstCall = True):
if self == being:
if firstCall:
raise Exception("isConsanguineous cannot be called with self as argument")
else:
return True
if self.isAncestor(being = being, maxDepth = maxDepth, excludedBeings = excludedBeings):
return True
if not self.parents or not maxDepth:
return False
cleanedParents = [parent for parent in self.parents if not parent in excludedBeings]
if not cleanedParents:
return False
return any([parent.isConsanguineous(being = being, maxDepth = maxDepth - 1, excludedBeings = excludedBeings + [self], firstCall = False) for parent in cleanedParents])
def isInbred(self):
return any([self.parents[0].isConsanguineous(otherParent) for otherParent in self.parents if not otherParent == self.parents[0]])
def getGender(self):
return Phenotype(self).getGender()
def mate(self, being, name):
child = Being(name, [])
# Declaration of sets of chromosomes for each being
intersectionSet = self.getChromosomesNameSet().intersection(being.getChromosomesNameSet())
selfUniqueSet = self.getChromosomesNameSet().difference(being.getChromosomesNameSet())
beingUniqueSet = being.getChromosomesNameSet().difference(self.getChromosomesNameSet())
for chromosomeName in intersectionSet:
for individual in [self, being]:
chromosomes = individual.getChromosomesByNames()[chromosomeName]
chromosomesMean = len(chromosomes) / 2
#selectionSize is the number of chromosomes each parent is giving to his child for a certain chromosome type. It is used to create trisomic children
if (chromosomesMean % 2 == 0):
selectionSize = int(chromosomesMean)
else:
selectionSize = random.choice([int(chromosomesMean), int(chromosomesMean) + 1])
#Randomly choose a certain number of chromosomes (number = selectionSize)
for chromosome in random.sample(chromosomes, selectionSize):
child.addChromosome(chromosome)
for individual, individualUniqueSet in [(self, selfUniqueSet), (being, selfUniqueSet)]:
for chromosomeName in individualUniqueSet:
chromosomes = individual.getChromosomesByNames()[chromosomeName]
for chromosome in chromosomes:
child.addChromosome(chromosome)
self.addChild(child)
being.addChild(child)
child.addParent(self)
child.addParent(being)
return child
def get_phenotypes(self,
gene,
diplotypes,
sample_variants,
uncallable,
extra_variants=None):
g = gene.name
# Load phenotype file into object
phenotypes = Phenotype()
results = []
for sample, diplotype in diplotypes.items():
haps = split_genotype(diplotype)
haplotype_data = {}
for i in range(len(haps)):
h = haps[i]
h_function = phenotypes.get_haplotype_function(g, h)
h_presumptive = phenotypes.get_presumptive_haplotype_function(
g, h)
haplotype_data["hap_%s" % i] = h
haplotype_data["hap_%s_function" % i] = h_function
haplotype_data["hap_%s_presumptive" % i] = h_presumptive
phenotype = phenotypes.get_diplotype_function(g, haps[0], haps[1])
presumptive_phenotype = phenotypes.get_diplotype_function(
g, haps[0], haps[1], presumptive=True)
activity_score = phenotypes.get_activity_score(g,
haps[0],
haps[1],
presumptive=True)
#if phenotype != presumptive_phenotype:
# print(phenotype, presumptive_phenotype)
# exit()
results.append({
"sample":
sample,
"gene":
g,
"diplotype":
diplotype,
"hap_1":
haplotype_data["hap_0"],
"hap_2":
haplotype_data["hap_1"],
"hap_1_function":
haplotype_data["hap_0_function"],
"hap_2_function":
haplotype_data["hap_1_function"],
"hap_1_presumptive_function":
haplotype_data["hap_0_presumptive"],
"hap_2_presumptive_function":
haplotype_data["hap_1_presumptive"],
"hap_1_variants":
sample_variants[sample][0],
"hap_2_variants":
sample_variants[sample][1],
"phenotype":
phenotype,
"phenotype_presumptive":
presumptive_phenotype,
"activity_score":
activity_score,
"uncallable":
";".join(x.split("%")[0] for x in uncallable[sample]),
"extra_variants":
None if extra_variants is None else ";".join(
x.split("%")[0] for x in extra_variants[sample])
})
return results