def reproduction(self,ind):
self.environment.delete(ind.drawing) #delete the drawings of all parents (they are about to die)
if max(ind.kernelshape) > self.max_kernel:
self.max_kernel = max(ind.kernelshape)
elif min(ind.kernelshape) < self.min_kernel:
self.min_kernel = min(ind.kernelshape)
if not ind.male:
male_gene=self.densloc.get_male_gene(ind.x,ind.y)
if male_gene!=None:
#b=6
#a=((self.growthrate**(1/float(b))-1)/float(equilibrium_density))
#mean_n_seeds=(self.growthrate)/(1+(a*(self.density.densloc_check(self.x,self.y)-1))**b)
self.growthrate = 5
a=(self.growthrate-1)/float(self.equilibrium_density)
mean_n_seeds=self.growthrate/(1+a*(self.densloc.densloc_check(ind.x,ind.y)-1))
n_seeds=poisson(mean_n_seeds)
offspring=[]
for r in xrange(n_seeds): #For all seeds check if they mutate on one of the two genes and transfer the parent traits into the new individual (+ the mutation)
if rnd.random()<0.5:
mother_allele = ind.kernelshape[0]
else:
mother_allele = ind.kernelshape[1]
if rnd.random()<0.5:
father_allele = male_gene[0][0]
else:
father_allele = male_gene[0][1]
if np.random.random()>self.sexratio:
male=True
else:
male=False
offspring.append(individual(ind.x, ind.y,
[mother_allele,
father_allele],
male,
male_gene[1],
ind.random_gene))
self.individuals.extend(offspring)
else:
#a=((self.growthrate**(1/float(b))-1)/float(equilibrium_density))
#mean_n_seeds=(self.growthrate)/(1+(a*(self.density.densloc_check(self.x,self.y)-1))**b)
self.growthrate = 2.5
a=(self.growthrate-1)/float(self.equilibrium_density)
mean_n_seeds=self.growthrate/(1+a*(self.densloc.densloc_check(ind.x,ind.y)-1))
n_seeds=poisson(mean_n_seeds)
offspring=[]
for r in xrange(n_seeds): #For all seeds check if they mutate on one of the two genes and transfer the parent traits into the new individual (+ the mutation)
mother_allele1 = ind.kernelshape[0]
mother_allele2 = ind.kernelshape[1]
if np.random.random()>self.sexratio:
male=True
else:
male=False
offspring.append(individual(ind.x, ind.y,
[mother_allele1,
mother_allele2],
male,
ind.random_gene,
ind.random_gene))
self.individuals.extend(offspring)
def reproduction(self,ind):
self.environment.delete(ind.drawing) #delete the drawings of all parents (they are about to die)
if not ind.male:
male_gene=self.densloc.get_male_gene(ind.x,ind.y)
if male_gene!=None:
#b=6
#a=((self.growthrate**(1/float(b))-1)/float(equilibrium_density))
#mean_n_seeds=(self.growthrate)/(1+(a*(self.density.densloc_check(self.x,self.y)-1))**b)
self.growthrate = 5
a=(self.growthrate-1)/float(self.equilibrium_density)
mean_n_seeds=self.growthrate/(1+a*(self.densloc.densloc_check(ind.x,ind.y)-1))
n_seeds=poisson(mean_n_seeds)
offspring=[]
for r in xrange(n_seeds): #For all seeds check if they mutate on one of the two genes and transfer the parent traits into the new individual (+ the mutation)
if rnd.random()<0.5:
mother_allele = ind.kernelshape[0]
else:
mother_allele = ind.kernelshape[1]
if rnd.random()<0.5:
father_allele = male_gene[0][0]
else:
father_allele = male_gene[0][1]
if np.random.random()>self.sexratio:
male=True
else:
male=False
offspring.append(individual(ind.x, ind.y,
[mother_allele,
father_allele],
male,
male_gene[1],
ind.random_gene))
self.individuals.extend(offspring)
else:
#a=((self.growthrate**(1/float(b))-1)/float(equilibrium_density))
#mean_n_seeds=(self.growthrate)/(1+(a*(self.density.densloc_check(self.x,self.y)-1))**b)
self.growthrate = 2.5
a=(self.growthrate-1)/float(self.equilibrium_density)
mean_n_seeds=self.growthrate/(1+a*(self.densloc.densloc_check(ind.x,ind.y)-1))
n_seeds=poisson(mean_n_seeds)
offspring=[]
for r in xrange(n_seeds): #For all seeds check if they mutate on one of the two genes and transfer the parent traits into the new individual (+ the mutation)
mother_allele1 = ind.kernelshape[0]
mother_allele2 = ind.kernelshape[1]
if np.random.random()>self.sexratio:
male=True
else:
male=False
offspring.append(individual(ind.x, ind.y,
[mother_allele1,
mother_allele2],
male,
ind.random_gene,
ind.random_gene))
self.individuals.extend(offspring)
def reproduction(self,ind):
self.environment.delete(ind.drawing) #delete the drawings of all parents (they are about to die)
if ind.kernelshape > self.max_kernel:
self.max_kernel = ind.kernelshape
elif ind.kernelshape < self.min_kernel:
self.max_kernel = ind.kernelshape
if not ind.male:
male_gene=self.densloc.get_male_gene(ind.x,ind.y)
if male_gene!=None:
#b=6
#a=((self.growthrate**(1/float(b))-1)/float(equilibrium_density))
#mean_n_seeds=(self.growthrate)/(1+(a*(self.density.densloc_check(self.x,self.y)-1))**b)
self.growthrate = 20
a=(self.growthrate-1)/float(self.equilibrium_density)
mean_n_seeds=self.growthrate/(1+a*(self.densloc.densloc_check(ind.x,ind.y)-1))
n_seeds=poisson(mean_n_seeds)
offspring=[]
new_gene=(ind.kernelshape+male_gene[0])/float(2)
for r in xrange(n_seeds): #For all seeds check if they mutate on one of the two genes and transfer the parent traits into the new individual (+ the mutation)
mutation=0
if np.random.random()<self.mutationrate:
mutation=np.random.uniform(-0.5,0.5)
if new_gene+mutation<0:
mutation=-mutation
if np.random.random()>self.sexratio:
male=True
else:
male=False
offspring.append(individual(ind.x, ind.y,
new_gene+mutation,
male,
male_gene[1],
ind.random_gene))
self.individuals.extend(offspring)
def reproduction(self, ind):
self.environment.delete(ind.drawing) # delete the drawings of all parents (they are about to die)
if ind.kernelshape > self.max_kernel:
self.max_kernel = ind.kernelshape
elif ind.kernelshape < self.min_kernel:
self.max_kernel = ind.kernelshape
if not ind.male:
male_gene = self.densloc.get_male_gene(ind.x, ind.y)
if male_gene != None:
# b=6
# a=((self.growthrate**(1/float(b))-1)/float(equilibrium_density))
# mean_n_seeds=(self.growthrate)/(1+(a*(self.density.densloc_check(self.x,self.y)-1))**b)
self.growthrate = 20
a = (self.growthrate - 1) / float(self.equilibrium_density)
mean_n_seeds = self.growthrate / (1 + a * (self.densloc.densloc_check(ind.x, ind.y) - 1))
n_seeds = poisson(mean_n_seeds)
offspring = []
new_gene = (ind.kernelshape + male_gene[0]) / float(2)
for r in xrange(
n_seeds
): # For all seeds check if they mutate on one of the two genes and transfer the parent traits into the new individual (+ the mutation)
mutation = 0
if np.random.random() < self.mutationrate:
mutation = np.random.uniform(-0.5, 0.5)
if new_gene + mutation < 0:
mutation = -mutation
if np.random.random() > self.sexratio:
male = True
else:
male = False
offspring.append(individual(ind.x, ind.y, new_gene + mutation, male, male_gene[1], ind.random_gene))
self.individuals.extend(offspring)
def reproduction(self, equilibrium_density, mutationrate, generalists):
self.density.add_gendispersal_allele(self.kernelshape)
self.environment.delete(
self.drawing
) #delete the drawings of all parents (they are about to die)
if not self.male_sex:
male_gene = self.density.get_male_gene(self.x, self.y)
if male_gene != None:
self.generalists = generalists
#b=6
#a=((self.growthrate**(1/float(b))-1)/float(equilibrium_density))
#mean_n_seeds=(self.growthrate)/(1+(a*(self.density.densloc_check(self.x,self.y)-1))**b)
a = (self.growthrate - 1) / float(equilibrium_density)
mean_n_seeds = self.growthrate / (
1 + a * (self.density.densloc_check(self.x, self.y) - 1))
n_seeds = poisson(mean_n_seeds)
offspring = []
new_gene = (self.kernelshape + male_gene) / float(2)
for r in xrange(
n_seeds
): #For all seeds check if they mutate on one of the two genes and transfer the parent traits into the new individual (+ the mutation)
mutation = 0
if np.random.random() < mutationrate:
mutation = np.random.uniform(-0.5, 0.5)
if new_gene + mutation < 0:
mutation = -mutation
anchestor_info = self.anchestor_info[:]
offspring.append(
Generalist(self.x, self.y, self.max_y,
new_gene + mutation, self.environment,
self.density, anchestor_info,
self.sexratio))
self.generalists.extend(offspring)
def reproduction(self,equilibrium_density,mutationrate,generalists):
self.generalists=generalists
self.density.add_gendispersal_allele(self.kernelshape)
#self.environment.delete(self.drawing) #delete the drawings of all parents (they are about to die)
#b=6
#a=((self.growthrate**(1/float(b))-1)/float(equilibrium_density))
#mean_n_seeds=(self.growthrate)/(1+(a*(self.density.densloc_check(self.x,self.y)-1))**b)
a=(self.growthrate-1)/float(equilibrium_density)
mean_n_seeds=self.growthrate/(1+a*(self.density.densloc_check(self.x,self.y)-1))
n_seeds=poisson(mean_n_seeds)
offspring=[]
for r in xrange(n_seeds): #For all seeds check if they mutate on one of the two genes and transfer the parent traits into the new individual (+ the mutation)
mutation=0
if random()<mutationrate:
mutation=uniform(-0.5,0.5)
if self.kernelshape+mutation<0:
mutation=-mutation
anchestor_info=self.anchestor_info[:]
offspring.append(Generalist(self.x, self.y,self.kernelshape+mutation,self.environment,self.density,anchestor_info))
self.generalists.extend(offspring)
#f=fits.open(c.psffile)
#c.psf = f[0].data
#f.close()
# normalise the psf
#c.psf = c.psf/c.psf.sum()
# Initialise the arrays that will form the N Tuple for minimization
#x = np.zeros(c.nxpts*c.nypts)
#y = np.arange(c.nxpts*c.nypts)
#xerr = np.zeros(c.nxpts*c.nypts)
#yerr = np.zeros(c.nxpts*c.nypts)
numra.seed(11198205, 120980)# Set random number seed
# compute errors assuming poisson fluctuations in electrons
zerr = np.sqrt((abs(numra.poisson(z)-z)*c.gain)**2.0+c.rdnoise**2.0)
# Populate x and y arrays
#k = 0
#for i in range(c.nxpts) :
# for j in range(c.nypts) :
# y[k] = j
# x[k] = i
# k += 1
# Generate the N-Tuple for the input data. Hippodraw needs it in this form
#from hippo import NTuple
#nt = NTuple () # empty one
#nt.addColumn ( 'x', x )
#nt.addColumn ( 'y', y )
def reproduction(self, ind):
self.environment.delete(
ind.drawing
) #delete the drawings of all parents (they are about to die)
if max(ind.kernelshape) > self.max_kernel:
self.max_kernel = max(ind.kernelshape)
elif min(ind.kernelshape) < self.min_kernel:
self.max_kernel = min(ind.kernelshape)
if not ind.male:
male_gene = self.densloc.get_male_gene(ind.x, ind.y)
if male_gene != None:
#b=6
#a=((self.growthrate**(1/float(b))-1)/float(equilibrium_density))
#mean_n_seeds=(self.growthrate)/(1+(a*(self.density.densloc_check(self.x,self.y)-1))**b)
self.growthrate = 5
a = (self.growthrate - 1) / float(self.equilibrium_density)
mean_n_seeds = self.growthrate / (
1 + a * (self.densloc.densloc_check(ind.x, ind.y) - 1))
n_seeds = poisson(mean_n_seeds)
offspring = []
for r in xrange(
n_seeds
): #For all seeds check if they mutate on one of the two genes and transfer the parent traits into the new individual (+ the mutation)
#sample a new father for each offspring (already selected for the first one)
if r > 0:
male_gene = self.densloc.get_male_gene(ind.x, ind.y)
if rnd.random() < 0.5:
mother_allele = ind.kernelshape[0]
else:
mother_allele = ind.kernelshape[1]
if rnd.random() < 0.5:
father_allele = male_gene[0][0]
else:
father_allele = male_gene[0][1]
mutation1 = 0
if np.random.random() < self.mutationrate:
mutation1 = np.random.uniform(-0.5, 0.5)
if mother_allele + mutation1 < 0:
mutation1 = -mutation1
mutation2 = 0
if np.random.random() < self.mutationrate:
mutation2 = np.random.uniform(-0.5, 0.5)
if father_allele + mutation2 < 0:
mutation2 = -mutation2
if np.random.random() > self.sexratio:
male = True
else:
male = False
offspring.append(
individual(ind.x, ind.y, [
mother_allele + mutation1,
father_allele + mutation2
], male, male_gene[1], ind.random_gene))
self.individuals.extend(offspring)
else:
#a=((self.growthrate**(1/float(b))-1)/float(equilibrium_density))
#mean_n_seeds=(self.growthrate)/(1+(a*(self.density.densloc_check(self.x,self.y)-1))**b)
self.growthrate = 2.5
a = (self.growthrate - 1) / float(self.equilibrium_density)
mean_n_seeds = self.growthrate / (
1 + a * (self.densloc.densloc_check(ind.x, ind.y) - 1))
n_seeds = poisson(mean_n_seeds)
offspring = []
for r in xrange(
n_seeds
): #For all seeds check if they mutate on one of the two genes and transfer the parent traits into the new individual (+ the mutation)
mother_allele1 = ind.kernelshape[0]
mother_allele2 = ind.kernelshape[1]
mutation1 = 0
if np.random.random() < self.mutationrate:
mutation1 = np.random.uniform(-0.5, 0.5)
if mother_allele1 + mutation1 < 0:
mutation1 = -mutation1
mutation2 = 0
if np.random.random() < self.mutationrate:
mutation2 = np.random.uniform(-0.5, 0.5)
if mother_allele2 + mutation2 < 0:
mutation2 = -mutation2
if np.random.random() > self.sexratio:
male = True
else:
male = False
offspring.append(
individual(ind.x, ind.y, [
mother_allele1 + mutation1,
mother_allele2 + mutation2
], male, ind.random_gene, ind.random_gene))
self.individuals.extend(offspring)