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())
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
'''
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