def replayPopFromText(param, pop, caracs):
txtpop = []
with open('replay.txt', 'r') as txt:
for l in txt:
txtpop.append(l)
for i in range(len(txtpop)):
if txtpop[i][:4] == 'nom:':
ind = classes.indiv(param, caracs)
ind.ADN.setNbNodeInt(int(txtpop[i].split()[13][:-1]))
j = i + 1
while txtpop[j] != '\n':
g = classes.gene()
g.inNode = int(txtpop[j].split()[0][:-1])
g.outNode = int(txtpop[j].split()[1][:-1])
g.weight = float(txtpop[j].split()[2][:-1])
g.active = bool(txtpop[j].split()[3][:-1] == 'True')
g.code = int(txtpop[j].split()[4])
ind.ADN.addGene(g)
ind.ADN.siInLibre[g.inNode] = False
if g.outNode < ind.ADN.encOffset:
ind.ADN.siOutLibre[g.outNode] = False
else:
ind.ADN.siEncrLibre[g.outNode] = False
j += 1
ind.nom = pop.getProchNom()
pop.addIndiv(ind)
def loadPopForAnalysis(param, pop, caracs):
txtpop = []
with open('population.txt', 'r') as txt:
for l in txt:
txtpop.append(l)
for i in range(len(txtpop)):
if txtpop[i][:4] == 'nom:':
ind = classes.indiv(param, caracs)
ind.nom = int(txtpop[i].split()[1][:-1])
ind.espece = int(txtpop[i].split()[9][:-1])
ind.score = float(txtpop[i].split()[15][:-1])
ind.classement = int(txtpop[i].split()[17])
j = i + 1
while txtpop[j] != '\n':
g = classes.gene()
g.inNode = int(txtpop[j].split()[0][:-1])
g.outNode = int(txtpop[j].split()[1][:-1])
g.weight = float(txtpop[j].split()[2][:-1])
g.active = bool(txtpop[j].split()[3][:-1] == 'True')
g.code = int(txtpop[j].split()[4])
ind.ADN.addGene(g)
j += 1
pop.addIndiv(ind)
def creationNodeInt(param, pop, ind): nbConnexEligible = ind.ADN.compteGene() connexEligible = [] for g in ind.ADN.seqGene: if g.weight == 0 or not g.active: nbConnexEligible -= 1 connexEligible.append(0) else: connexEligible.append(1) if nbConnexEligible == 0: changementPoids(ind) else: rndGene = random.choices(ind.ADN.seqGene, connexEligible)[0] rndGene.active = False g1 = classes.gene() g1.inNode = rndGene.inNode g1.outNode = param['nbOutput'] + ind.ADN.nbNodeInt g1.weight = 1 g1.active = True if pop.arbreGen[g1.inNode][g1.outNode] == 0: pop.setGeneCode(g1.inNode, g1.outNode) g1.code = pop.arbreGen[g1.inNode][g1.outNode] g2 = classes.gene() g2.inNode = param['nbInput'] + ind.ADN.nbNodeInt g2.outNode = rndGene.outNode g2.weight = rndGene.weight g2.active = True if pop.arbreGen[g2.inNode][g2.outNode] == 0: pop.setGeneCode(g2.inNode, g2.outNode) g2.code = pop.arbreGen[g2.inNode][g2.outNode] ind.ADN.addGene(g1) ind.ADN.addGene(g2) ind.ADN.addNodeInt() oldEncr = rndGene.code + ind.ADN.encOffset if not ind.ADN.siEncrLibre[oldEncr]: newEncr = g2.code + ind.ADN.encOffset for g in ind.ADN.seqGene: if g.outNode == oldEncr: g.outNode = newEncr ind.ADN.siEncrLibre[oldEncr] = True ind.ADN.siEncrLibre[newEncr] = False
def creationConnexion(param, pop, ind, caracs): while True: inNode = ind.ADN.selectInActif() while True: outNode = ind.ADN.selectOutActif() if inNode < param['nbInput'] or outNode < param['nbOutput']\ or inNode - param['nbInput'] != outNode - param['nbOutput']: break if not ind.ADN.tellSiGene(pop.arbreGen[inNode][outNode]): break #prevents self-connexions and redundant genes # if inNode >= param['nbInput'] and outNode >= param['nbOutput']: if inNode >= param['nbInput']: if func.siCreationBoucle(param, ind, inNode, outNode, caracs): changementPoids(ind) else: g = classes.gene() g.inNode = inNode g.outNode = outNode g.weight = 0 g.active = True if pop.arbreGen[g.inNode][g.outNode] == 0: pop.setGeneCode(g.inNode, g.outNode) g.code = pop.arbreGen[g.inNode][g.outNode] ind.ADN.addGene(g) else: g = classes.gene() g.inNode = inNode g.outNode = outNode g.weight = 0 g.active = True if pop.arbreGen[g.inNode][g.outNode] == 0: pop.setGeneCode(g.inNode, g.outNode) g.code = pop.arbreGen[g.inNode][g.outNode] ind.ADN.addGene(g)
def creationInput(pop, ind): g = classes.gene() g.inNode = ind.ADN.selectInLibre() g.outNode = ind.ADN.selectOutActif() g.weight = 0 g.active = True if pop.arbreGen[g.inNode][g.outNode] == 0: pop.setGeneCode(g.inNode, g.outNode) g.code = pop.arbreGen[g.inNode][g.outNode] ind.ADN.addGene(g) ind.ADN.siInLibre[g.inNode] = False
def siCreationBoucle(param, ind, inNode, outNode, caracs):
mannequin = classes.indiv(param, caracs)
for g in ind.ADN.seqGene:
gmanq = classes.gene()
g.copyTo(gmanq)
mannequin.ADN.addGene(gmanq)
gmanq = classes.gene()
gmanq.inNode = inNode
gmanq.outNode = outNode
gmanq.active = True
mannequin.ADN.addGene(gmanq)
boolCreationBoucle = False
for node in range(param['nbOutput']):
boolCreationBoucle = boolCreationBoucle or\
recSiBoucle(param, mannequin, node, 1)
for encrNode in range(mannequin.ADN.encOffset, mannequin.maxOutputTotal):
boolCreationBoucle = boolCreationBoucle or\
recSiBoucle(param, mannequin, encrNode, 1)
return boolCreationBoucle
def creationOutput(param, pop, ind, caracs): inNode = ind.ADN.selectInActif() outNode = ind.ADN.selectOutLibre() if func.siCreationBoucle(param, ind, inNode, outNode, caracs): changementPoids(ind) else: g = classes.gene() g.inNode = inNode g.outNode = outNode g.weight = 0 g.active = True if pop.arbreGen[g.inNode][g.outNode] == 0: pop.setGeneCode(g.inNode, g.outNode) g.code = pop.arbreGen[g.inNode][g.outNode] ind.ADN.addGene(g) ind.ADN.siOutLibre[g.outNode] = False
def creationEncryption(param, pop, ind, caracs): inNode = ind.ADN.selectInActif() outNode = ind.ADN.selectEncryptionTarget().code + ind.ADN.encOffset if func.siCreationBoucle(param, ind, inNode, outNode, caracs): changementPoids(ind) else: g = classes.gene() g.inNode = inNode g.outNode = outNode g.weight = 0 g.active = True if pop.arbreGen[g.inNode][g.outNode] == 0: pop.setGeneCode(g.inNode, g.outNode) g.code = pop.arbreGen[g.inNode][g.outNode] ind.ADN.addGene(g) ind.ADN.siEncrLibre[g.outNode] = False
def creerPopulation(param, pop, caracs):
for i in range(param['nbIndiv']):
ind = classes.indiv(param, caracs)
for connexion in range(param['connexInit']):
g = classes.gene()
unique = False
while not unique:
if connexion == 0:
inNode = 0
else:
inNode = random.randrange(param['nbInput'])
outNode = random.randrange(param['nbOutput'])
if pop.arbreGen[inNode][outNode] == 0:
unique = True
elif not ind.ADN.tellSiGene(pop.arbreGen[inNode][outNode]):
unique = True
g.inNode = inNode
g.outNode = outNode
g.weight = random.uniform(-1, 1)
g.active = True
if pop.arbreGen[inNode][outNode] == 0:
pop.setGeneCode(inNode, outNode)
g.code = pop.arbreGen[inNode][outNode]
ind.ADN.addGene(g)
ind.ADN.siInLibre[inNode] = False
ind.ADN.siOutLibre[outNode] = False
ind.nom = pop.getProchNom()
pop.addIndiv(ind)
from classes import gene from pyfaidx import Fasta import os APP_ROOT = os.path.dirname(os.path.abspath(__file__)) data = read_json_compressed() a = data[1] b = data[2] name = a['name'] chrom = a['chrom'] start = a['start'] end = a['end'] ori = a['orientation'] exons = a['exons'] test_gene = gene(**a) tg2 = gene(**b) fasta_path = os.path.join(APP_ROOT, 'static', 'data', 'AT9.fa') genome = Fasta(fasta_path) tg2.get_sequence(genome) print(name,start,end) print(tg2.exon_list[0].start) """ LOCUS Example 24 bp DNA UNK 01-JAN-1980 DEFINITION An example GenBank file generated by BioPython ACCESSION 123456789 VERSION 123456789 KEYWORDS . SOURCE . ORGANISM .
def creerPopulationAvecCouches(param, pop, caracs):
for i in range(param['nbIndiv']):
ind = classes.indiv(param, caracs)
for transverse in range(param['transversesInit']):
unique = False
while not unique:
if transverse == 0:
inNode = 0
else:
inNode = random.randrange(param['nbInput'])
outNode = random.randrange(param['nbOutput'])
if pop.arbreGen[inNode][outNode] == 0:
unique = True
elif not ind.ADN.tellSiGene(pop.arbreGen[inNode][outNode]):
unique = True
for couche in range(param['couchesInit']):
g = classes.gene()
g.inNode = inNode
g.outNode = param['nbOutput'] + ind.ADN.nbNodeInt
g.weight = random.uniform(-1, 1)
g.active = True
if pop.arbreGen[g.inNode][g.outNode] == 0:
pop.setGeneCode(g.inNode, g.outNode)
g.code = pop.arbreGen[g.inNode][g.outNode]
ind.ADN.addGene(g)
inNode = param['nbInput'] + ind.ADN.nbNodeInt
ind.ADN.addNodeInt()
g = classes.gene()
g.inNode = inNode
g.outNode = outNode
g.weight = random.uniform(-1, 1)
g.active = True
if pop.arbreGen[g.inNode][g.outNode] == 0:
pop.setGeneCode(g.inNode, g.outNode)
g.code = pop.arbreGen[g.inNode][g.outNode]
ind.ADN.addGene(g)
ind.ADN.siInLibre[inNode] = False
ind.ADN.siOutLibre[outNode] = False
for nvlInput in range(param['inputSupInit']):
mutation.creationInput(pop, ind)
ind.ADN.seqGene[ind.ADN.compteGene() - 1].weight = random.uniform(
-1, 1)
for connexion in range(param['connexInit']):
mutation.creationConnexion(param, pop, ind, caracs)
ind.ADN.seqGene[ind.ADN.compteGene() - 1].weight = random.uniform(
-1, 1)
for encryption in range(param['encrInit']):
mutation.creationEncryption(param, pop, ind, caracs)
ind.ADN.seqGene[ind.ADN.compteGene() - 1].weight = random.uniform(
-1, 1)
for connexion in range(param['connexInit']):
mutation.creationConnexion(param, pop, ind, caracs)
ind.ADN.seqGene[ind.ADN.compteGene() - 1].weight = random.uniform(
-1, 1)
print(i)
#for g in ind.ADN.seqGene:
# print(str(g.inNode) + '; ' + str(g.outNode) + '; ' + str(g.weight) + '; ' + str(g.active) + '; ' + str(g.code))
ind.nom = pop.getProchNom()
pop.addIndiv(ind)
def seedPopFromCore(param, pop, caracs):
txtpop = []
with open('core.txt', 'r') as txt:
for l in txt:
txtpop.append(l)
for i in range(len(txtpop)):
if txtpop[i][:4] == 'nom:':
ind = classes.indiv(param, caracs)
ind.ADN.setNbNodeInt(int(txtpop[i].split()[13][:-1]))
j = i + 1
while txtpop[j] != '\n':
g = classes.gene()
g.inNode = int(txtpop[j].split()[0][:-1])
g.outNode = int(txtpop[j].split()[1][:-1])
g.weight = float(txtpop[j].split()[2][:-1])
g.active = bool(txtpop[j].split()[3][:-1] == 'True')
g.code = int(txtpop[j].split()[4])
if pop.arbreGen[g.inNode][g.outNode] == 0:
pop.arbreGen[g.inNode][g.outNode] = g.code
if g.code > pop.prochCode:
pop.prochCode = g.code
ind.ADN.addGene(g)
ind.ADN.siInLibre[g.inNode] = False
if g.outNode < ind.ADN.encOffset:
ind.ADN.siOutLibre[g.outNode] = False
else:
ind.ADN.siEncrLibre[g.outNode] = False
j += 1
ind.nom = pop.getProchNom()
pop.addIndiv(ind)
for i in range(param['nbIndiv'] - 1):
clone = classes.indiv(param, caracs)
clone.ADN.setNbNodeInt(ind.ADN.nbNodeInt)
for g0 in ind.ADN.seqGene:
g = classes.gene()
g0.copyTo(g)
clone.ADN.addGene(g)
clone.ADN.siInLibre[g.inNode] = False
if g.outNode < clone.ADN.encOffset:
clone.ADN.siOutLibre[g.outNode] = False
else:
clone.ADN.siEncrLibre[g.outNode] = False
for nvlInput in range(param['inputSupInit']):
mutation.creationInput(pop, clone)
for nvlOutput in range(param['outputSupInit']):
mutation.creationOutput(param, pop, clone, caracs)
for connexion in range(param['connexInit']):
mutation.creationConnexion(param, pop, clone, caracs)
for encryption in range(param['encrInit']):
mutation.creationEncryption(param, pop, clone, caracs)
for chgtPoids in range(param['chgtPoidsInit']):
mutation.changementPoids(clone)
clone.nom = pop.getProchNom()
pop.addIndiv(clone)
print(clone.nom)
def reproduction(param, pop, caracs):
for ind in pop.indiv:
if ind.classement > param['nbSurv']:
ind.reset(param)
for parent1 in pop.indiv:
if parent1.classement != 0:
parent2 = random.choices(pop.indiv, parent1.ptsMemeEspece)[0]
for enfant in pop.indiv:
if enfant.nom == 0:
break
for g in parent1.ADN.seqGene:
genf = classes.gene()
if parent2.ADN.tellSiGene(g.code):
if random.randrange(2):
g.copyTo(genf)
else:
parent2.ADN.findGene(g.code).copyTo(genf)
else:
g.copyTo(genf)
enfant.ADN.addGene(genf)
for g in parent2.ADN.seqGene:
if not enfant.ADN.tellSiGene(g.code):
genf = classes.gene()
g.copyTo(genf)
enfant.ADN.addGene(genf)
if func.siCreationBoucleChezEnfant(param, enfant, caracs):
enfant.reset(param)
parent2 = parent1
for g in parent1.ADN.seqGene:
genf = classes.gene()
g.copyTo(genf)
enfant.ADN.addGene(genf)
enfant.ADN.setNbNodeInt\
(max(parent1.ADN.nbNodeInt, parent2.ADN.nbNodeInt))
for g in enfant.ADN.seqGene:
enfant.ADN.siInLibre[g.inNode] = False
if g.outNode < enfant.ADN.encOffset:
enfant.ADN.siOutLibre[g.outNode] = False
else:
enfant.ADN.siEncrLibre[g.outNode] = False
enfant.nom = pop.getProchNom()
enfant.parent1 = parent1.nom
enfant.parent2 = parent2.nom
if parent1.nom == parent2.nom:
parent1.descendants += 1
else:
parent1.descendants += 1
parent2.descendants += 1
for regen in pop.indiv:
if regen.nom == 0:
regen.nom = pop.getProchNom()
for transverse in range(param['transversesInit']):
unique = False
while not unique:
if transverse == 0:
inNode = 0
else:
inNode = random.randrange(param['nbInput'])
outNode = random.randrange(param['nbOutput'])
if not ind.ADN.tellSiGene(pop.arbreGen[inNode][outNode]):
unique = True
for couche in range(param['couchesInit']):
g = classes.gene()
g.inNode = inNode
g.outNode = param['nbOutput'] + regen.ADN.nbNodeInt
g.weight = random.uniform(-1, 1)
g.active = True
if pop.arbreGen[g.inNode][g.outNode] == 0:
pop.setGeneCode(g.inNode, g.outNode)
g.code = pop.arbreGen[g.inNode][g.outNode]
regen.ADN.addGene(g)
inNode = param['nbInput'] + regen.ADN.nbNodeInt
regen.ADN.addNodeInt()
g = classes.gene()
g.inNode = inNode
g.outNode = outNode
g.weight = random.uniform(-1, 1)
g.active = True
if pop.arbreGen[g.inNode][g.outNode] == 0:
pop.setGeneCode(g.inNode, g.outNode)
g.code = pop.arbreGen[g.inNode][g.outNode]
regen.ADN.addGene(g)
regen.ADN.siInLibre[inNode] = False
regen.ADN.siOutLibre[outNode] = False
for nvlInput in range(param['inputSupInit']):
mutation.creationInput(pop, regen)
regen.ADN.seqGene[regen.ADN.compteGene() -
1].weight = random.uniform(-1, 1)
for connexion in range(param['connexInit']):
mutation.creationConnexion(param, pop, regen, caracs)
regen.ADN.seqGene[regen.ADN.compteGene() -
1].weight = random.uniform(-1, 1)
for encryption in range(param['encrInit']):
mutation.creationEncryption(param, pop, regen, caracs)
regen.ADN.seqGene[regen.ADN.compteGene() -
1].weight = random.uniform(-1, 1)
for connexion in range(param['connexInit']):
mutation.creationConnexion(param, pop, regen, caracs)
regen.ADN.seqGene[regen.ADN.compteGene() -
1].weight = random.uniform(-1, 1)
from classes import gene
from pyfaidx import Fasta
import os
APP_ROOT = os.path.dirname(os.path.abspath(__file__))
data = read_json_compressed()
a = data[1]
b = data[2]
name = a['name']
chrom = a['chrom']
start = a['start']
end = a['end']
ori = a['orientation']
exons = a['exons']
test_gene = gene(**a)
tg2 = gene(**b)
fasta_path = os.path.join(APP_ROOT, 'static', 'data', 'AT9.fa')
genome = Fasta(fasta_path)
tg2.get_sequence(genome)
print(name, start, end)
print(tg2.exon_list[0].start)
"""
LOCUS Example 24 bp DNA UNK 01-JAN-1980
DEFINITION An example GenBank file generated by BioPython
ACCESSION 123456789
VERSION 123456789
KEYWORDS .
SOURCE .
ORGANISM .
.
