def pop_arg(pop):
ev = []
for kk in range(100):
rec_events = 0
gclones = pop.get_genotypes()
nclones = pop.get_clone_sizes()
pop0 = h.haploid_highd(L)
pop0.track_locus_genealogy(range(L))
pop0.set_genotypes(gclones, nclones)
pop0.set_fitness_additive(ff)
for pair in epi_values:
pop0.add_fitness_coefficient(epi_values[pair], [pair[0], pair[1]])
pop0.recombination_model = h.CROSSOVERS
pop0.mutation_rate = 0 # mutation rate per site per generation
pop0.outcrossing_rate = 1 # probability of sexual reproduction per gen
pop0.crossover_rate = rec # probability of crossover per site per gen
pop0.evolve(0)
pop0.evolve(1)
for i in range(L - 1):
j = i + 1
tree0 = pop0.genealogy.get_tree(i).print_newick()
tree1 = pop0.genealogy.get_tree(j).print_newick()
rec_events += int(tree0 != tree1)
for kkk in range(1):
ev.append(rec_events)
return ev
def init_pop(add_sel, epi_values):
pop = h.haploid_highd(L)
pop.carrying_capacity = N
pop.recombination_model = h.CROSSOVERS
pop.mutation_rate = mu # mutation rate per site per generation
pop.outcrossing_rate = 1 # probability of sexual reproduction per gen
pop.crossover_rate = rec # probability of crossover per site per gen
pop.set_fitness_additive(add_sel)
for pair in epi_values:
pop.add_fitness_coefficient(epi_values[pair], [pair[0], pair[1]])
#g = np.random.choice([False,True],L,[0.5,0.5])
pop.set_wildtype(N)
pop.evolve(0) # it's crucial don't know why
return pop
i = epis1[kkk]
j = epis2[kkk]
if j < i: [i, j] = [j, i]
#for i in epis:
#for j in epis:
if i in L_dif and j in L_dif and j > i and True: #np.random.random(1)[0] < ppp: #i in L_dif:
#value = np.sqrt(Vi/float(L1))/float(L1-1)*float(N)
#value = np.random.gamma(np.sqrt(Vi/float(L1))/float(L1-1)*N,1.0,1)[0]
#value = np.random.normal(0,np.sqrt(Vi/float(L1))/float(L1-1),1)[0]
value = np.random.normal(
0,
np.sqrt(Vi / float(L1)) * ppp, 1)[0]
#value = np.sqrt(Vi/float(L1))/float(L1-1)
#value = value / float(N)
epi_values[(i, j)] = value
pop1 = h.haploid_highd(L)
pop1.carrying_capacity = N
pop1.set_wildtype(N)
pop1.recombination_model = h.CROSSOVERS
pop1.mutation_rate = mu # mutation rate per site per generation
pop1.outcrossing_rate = 1 # probability of sexual reproduction per gen
pop1.crossover_rate = rec # probability of crossover per site per gen
pop1.set_fitness_additive(ff)
for pair in epi_values:
i = pair[0]
j = pair[1]
pop1.add_fitness_coefficient(epi_values[(i, j)],
[i, j])
pop2 = h.haploid_highd(L)
pop2.carrying_capacity = N
pop2.set_wildtype(N)
csize,cmut = get_mut_count_subtree(C) sub_tree_size += csize tmp_mut_count.extend(cmut) tmp_mut_count.append([csize,C.branch_length]) return sub_tree_size, tmp_mut_count def get_SFS(T): sample_size,SFS = get_mut_count_subtree(T.root) SFS = np.asarray(SFS) SFS[:,0]/=sample_size return sample_size,SFS L=100 pop=h.haploid_highd(L) pop.outcrossing_rate=1.0 pop.crossover_rate=1.0/pop.L pop.mutation_rate=0.0/pop.L pop.carrying_capacity=100 #track the loci 10, 50 and 90 pop.track_locus_genealogy([10,50,90]) #initialize the populations pop.set_wildtype(pop.carrying_capacity) pop.status() #evolve population for several coalescent times pop.evolve(4*pop.N)
# Import module import sys sys.path.insert(0, '../pkg/python') import numpy as np import matplotlib.pyplot as plt import FFPopSim as h from Bio import Phylo # Globals L = 1000 # number of loci N = 300 # population size # Construct class pop = h.haploid_highd(L, all_polymorphic=False) # Set a growth rate explicitely pop.growth_rate = 2.5 # Start tracking genealogy of two loci pop.track_locus_genealogy([3, 60]) # Test fitness landscapes rep = np.zeros(L) rep[np.random.random(L) > 0.5] = -0.1 pop.set_trait_additive(rep) # Show the additive part of the fitness landscape print pop.get_trait_additive()
#neutral asexual: N=100 s=0.00001 r=0.0 #selected asexual: N=10000 s=0.01 r=0.0 #selected sexual: N=1000 s=0.01 r=1.0 L = 1000 #number of segregating sites s = 1e-2 #single site effect N = 10000 #population size r = 0.0 #outcrossing rate sample_size=30 #number of individuals whose genealogy is looked at nsamples = 3 #number of trees burnin = 2000 #either ~5*N or 5/s, depending on whether coalescence is dominated by drift or draft dt = 1000 #time between samples #set up population, switch on infinite sites mode pop=h.haploid_highd(L, all_polymorphic=True) #set the population size via the carrying capacity pop.carrying_capacity= N #set the crossover rate, outcrossing_rate and recombination model pop.outcrossing_rate = r pop.recombination_model = h.CROSSOVERS pop.crossover_rate = 1.0/pop.L #set the effect sizes of the mutations that are injected (the same at each site in this case) pop.set_fitness_additive(np.ones(L)*s) #track the genealogy at a central locus L/2 (which one doesn't matter in the asexual case) pop.track_locus_genealogy([L/2])
sys.path.insert(0, '../pkg/python')
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import FFPopSim as h
# specify parameters
N = 500000 # Population size
L = 4 # number of loci
mu = 0.0 # no new mutations
r = [0.01, 0.03, 0.05] # recombination rates
### set up
pop = h.haploid_lowd(L) # produce an instance of haploid_lowd with L loci
pop.carrying_capacity = N # set the steady-state population size
pop.set_recombination_rates(r, h.CROSSOVERS) # assign the recombination rate
pop.set_mutation_rates(mu) # assign the mutation rate
# initialize the population with
# - N/2 individuals with genotype 0, that is ----
# - N/2 with the opposite genotype, that is ++++
pop.set_genotypes([0, 2**L - 1],
[N/2, N/2])
# evolve for some time
pop.evolve(50)
# copy the population and evolve the two in parallel
pop2 = pop.copy()
'''
author: Fabio Zanini
date: 14/05/12
content: Test script for the python bindings to the low-dimensional
simulation
'''
# Import module
import sys
sys.path.insert(0, '../pkg/python')
import numpy as np
import matplotlib.pyplot as plt
import FFPopSim as h
# Construct class
pop = h.haploid_lowd(4)
# Test initialization
#pop.set_allele_frequencies([0,0.3,0.6,0.9], 1000)
pop.set_genotypes([1,2],[400,800])
# Test setting the recombination/mutation rates
pop.set_recombination_rates([0.01, 0.03, 0.02], h.SINGLE_CROSSOVER)
pop.set_mutation_rates([0.003,0.002,0.004,0.005],
[0.006,0.004,0.008,0.010])
# Test getting the mutation rate
print pop.get_mutation_rates(direction=0)
print pop.get_mutation_rates(direction=1)
# Test setting / getting fitness
# vim: fdm=indent
'''
author: Fabio Zanini
date: 25/04/12
content: Test script for the python bindings
'''
# Import module
import sys
sys.path.insert(0, '../pkg/python')
import numpy as np
import matplotlib.pyplot as plt
import FFPopSim as h
# Construct class
pop = h.hivpopulation(1000)
# Test I/O fitness landscapes
pop.set_replication_landscape(lethal_fraction=0.05,
number_valleys=0)
pop.read_replication_coefficients('hiv_model.dat')
rep = pop.get_replication_additive()
rep[np.random.random(10000) > 0.5] = -0.1
pop.set_replication_additive(rep)
# Show the additive part of the fitness landscape
print pop.get_trait_additive()
# Test population initialization
pop.set_allele_frequencies([0.3] * h.HIVGENOME, 1000)
# parse the command line arguments
import argparse
parser = argparse.ArgumentParser(description="Simulate a population on a mixed additive/epistatic fitness function")
parser.add_argument('--pop', default=10000, type=float, help='Population size (N)')
parser.add_argument('--rec', default=0,type=float, help='out-crossing rate (r)')
parser.add_argument('--sigma', default=0.05,type=float, help='Sigma')
parser.add_argument('--hsq', default=0,type=float, help='heritability')
parser.add_argument('--Ttraj', default=200,type=int, help='Length of trajectory in generations')
parser.add_argument('--dt', default=1,type =int, help='time increments of trajectory')
params=parser.parse_args()
# set up the population
L = 1000 # number of loci
pop = ffpop.haploid_highd(L) # create an instance of the class
pop.outcrossing_rate = params.rec # outcrossing rate
pop.recombination_model = ffpop.CROSSOVERS # recombination model
# set random epistatic fitness lanscape
pop.set_random_epistasis(params.sigma * np.sqrt(1 - params.hsq))
# set heritability
if (params.hsq > 0):
pop.set_trait_additive(np.ones(L) * params.sigma * sqrt(params.hsp / L))
# initialize population in linkage equilibrium with all
# allele frequencies at 0.5
pop.set_allele_frequencies(np.ones(L) * 0.5, params.pop)
import numpy as np
import matplotlib.pyplot as plt
import FFPopSim as h
# specify parameters
N = 10000 # population size
adaptive_fraction = 0.01 # fraction of beneficial/adaptive sites
effect_size_adap = 0.03 # mean selection coefficient of beneficial alleles
# script
if __name__ == '__main__':
pop = h.hivpopulation(N) # create population with default parameters
# set random replication/fitness landscape
pop.set_replication_landscape(adaptive_fraction=adaptive_fraction,
effect_size_adaptive=effect_size_adap)
# prepare figure
fig = plt.figure()
ax = fig.add_subplot(1,1,1)
colors = ['b', 'r', 'g', 'cyan']
# evolve and plot histograms
x0 = pop.get_fitness_statistics().mean
for i in xrange(4):
pop.evolve(250)
h = pop.get_fitness_histogram()
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import FFPopSim as h
# specify parameters
L = 4 # number of loci
N = 1e10 # population size
mu = 1e-5 # mutation rate
r = 1e-4 # recombination rate
f = [0.3, 0.2, 0.1, 0.05] # fitness (additive effects)
# Script
if __name__ == '__main__':
pop = h.haploid_lowd(L) # produce an instance of haploid_lowd with L loci
pop.set_genotypes([0], [N]) # start with a wildtype population
pop.set_recombination_rates(r) # set recombination rate
pop.set_mutation_rates(mu) # set mutation rate
pop.set_fitness_additive(f) # set fitness landscape
# evolve while tracking genotype frequencies
times = []
genotype_frequencies = []
while (pop.get_genotype_frequency(0b1111) < 0.99) and (pop.generation <
1e7):
pop.evolve()
# get genotype frequencies and time
genotype_frequencies.append(pop.get_genotype_frequencies())
help='out-crossing rate (r)')
parser.add_argument('--sigma', default=0.05, type=float, help='Sigma')
parser.add_argument('--hsq', default=0, type=float, help='heritability')
parser.add_argument('--Ttraj',
default=200,
type=int,
help='Length of trajectory in generations')
parser.add_argument('--dt',
default=1,
type=int,
help='time increments of trajectory')
params = parser.parse_args()
# set up the population
L = 1000 # number of loci
pop = ffpop.haploid_highd(L) # create an instance of the class
pop.outcrossing_rate = params.rec # outcrossing rate
pop.recombination_model = ffpop.CROSSOVERS # recombination model
# set random epistatic fitness lanscape
pop.set_random_epistasis(params.sigma * np.sqrt(1 - params.hsq))
# set heritability
if (params.hsq > 0):
pop.set_trait_additive(np.ones(L) * params.sigma * sqrt(params.hsp / L))
# initialize population in linkage equilibrium with all
# allele frequencies at 0.5
pop.set_allele_frequencies(np.ones(L) * 0.5, params.pop)
# evolve and store statistics along the way
''' # Import module import sys sys.path.insert(0, '../pkg/python') import numpy as np import matplotlib.pyplot as plt import FFPopSim as h from Bio import Phylo # Globals L = 1000 # number of loci N = 300 # population size # Construct class pop = h.haploid_highd(L, all_polymorphic=False) # Set a growth rate explicitely pop.growth_rate = 2.5 # Start tracking genealogy of two loci pop.track_locus_genealogy([3, 60]) # Test fitness landscapes rep = np.zeros(L) rep[np.random.random(L) > 0.5] = -0.1 pop.set_trait_additive(rep) # Show the additive part of the fitness landscape print pop.get_trait_additive()
F_matrix = np.zeros((L, L))
k = 0
for i in range(0, L):
for j in range(0, L):
if i != j:
F_matrix[i][j] = Fij[k]
k += 1
return [mu_true, sigma_true, F_matrix]
[mu_tr, sigma_tr, F] = RFM(0.0, sigma, L)
params = [L, N, n_savings, sv_int, mu, r, rho, sigma, mu_tr,
sigma_tr] # to pass as argument to functions !AVOID MODIFY!
# [0 1 2 3 4 5 6 7 8 9 ]
hpop = pop.haploid_highd(L)
def declare_initial(params):
"""
Print of initial parameters of the system.
"""
print('*--*--*--*--*--*--*--*--*')
print('|')
print('* L = %d' % params[0])
print('| N = %d' % params[1])
print('* T = %d' % (params[2] * params[3]))
print('| mu = %.5f' % params[4])
print('* r = %.5f' % params[5])
print('| rho = %.5f' % params[6])
print('* sigma = %.5f' % params[7])
'''
author: Fabio Zanini
date: 14/05/12
content: Test script for the python bindings to the low-dimensional
simulation
'''
# Import module
import sys
sys.path.insert(0, '../pkg/python')
import numpy as np
import matplotlib.pyplot as plt
import FFPopSim as h
# Construct class
pop = h.haploid_lowd(4)
# Test initialization
#pop.set_allele_frequencies([0,0.3,0.6,0.9], 1000)
pop.set_genotypes([1, 2], [400, 800])
# Test setting the recombination/mutation rates
pop.set_recombination_rates([0.01, 0.03, 0.02], h.SINGLE_CROSSOVER)
pop.set_mutation_rates([0.003, 0.002, 0.004, 0.005],
[0.006, 0.004, 0.008, 0.010])
# Test getting the mutation rate
print pop.get_mutation_rates(direction=0)
print pop.get_mutation_rates(direction=1)
# Test setting / getting fitness
sys.path.insert(0, '../pkg/python')
import numpy as np
import matplotlib.pyplot as plt
import FFPopSim as h
# specify parameters
N = 10000 # population size
adaptive_fraction = 0.01 # fraction of beneficial/adaptive sites
effect_size_adap = 0.03 # mean selection coefficient of beneficial alleles
# script
if __name__ == '__main__':
pop = h.hivpopulation(N) # create population with default parameters
# set random replication/fitness landscape
pop.set_replication_landscape(adaptive_fraction=adaptive_fraction,
effect_size_adaptive=effect_size_adap)
# prepare figure
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
colors = ['b', 'r', 'g', 'cyan']
# evolve and plot histograms
x0 = pop.get_fitness_statistics().mean
for i in xrange(4):
pop.evolve(250)
h = pop.get_fitness_histogram()
#asexual: N=10000 s=0.01 r=0.0 U=0.1 #sexual: N=1000 s=0.01 r=1.0 U=0.1 L = 1000 #number of segregating sites s = -1e-6 #single site effect N = 200 #population size U = 0.1 #genome wide mutation rate r = 0.0 #outcrossing rate sample_size = 30 #number of individuals whose genealogy is looked at nsamples = 3 #number of trees burnin = 2000 #either ~5*N or 5/s, depending on whether coalescence is dominated by drift or draft dt = 1000 #time between samples #set up population, switch to all_polymorphic mode pop = h.haploid_highd(L) #set the per-site mutation rate pop.mutation_rate = U / pop.L #set the population size via the carrying capacity pop.carrying_capacity = N #set the crossover rate, outcrossing_rate and recombination model pop.outcrossing_rate = r pop.recombination_model = h.CROSSOVERS pop.crossover_rate = 1.0 / pop.L #set the effect sizes of the mutations that are injected (the same at each site in this case) pop.set_fitness_additive(np.ones(L) * s)
import sys
sys.path.insert(0, '../pkg/python')
import numpy as np
import matplotlib.pyplot as plt
import FFPopSim as h
# specify parameters
N = 10000 # population size
L = 100 # number of loci
mu = 0.0 # no new mutations
r = 0.1/L # crossover rate
# set up population
pop = h.haploid_highd(L) # produce an instance of haploid_highd
pop.carrying_capacity = N # set the steady-state population size
pop.recombination_model = h.CROSSOVERS # specify crossover recombination
pop.outcrossing_rate = 1 # obligate sexual
pop.crossover_rate = r # assign the crossover rate per locus
pop.mutation_rate = mu # assign the mutation rate per locus
# initialize the population with
# - N/2 individuals with genotype 00..00 or --..--
# - N/2 with the opposite genotype 11..11 or ++..++
pop.set_genotypes([np.zeros(L,dtype='int'),
np.ones(L,dtype='int')],
[N/2, N/2])
# locus pairs for which LD is to be tracked
locus_pairs = [[0,10],
