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realisticBottleneck.py~
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realisticBottleneck.py~
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"""
Realistic bottleneck model, no migration.
"""
"""
In words:
At time TF in the past, an equilibrium teosinte population will split into
a maize population and a teosinte population. The maize population will immediately go
through a bottleneck of size nuMB. Then, it will grow exponentially at
time TF. The Teosinte population will not go through a bottleneck. It will grow linearly to
end at size nuTeoF.
"""
import numpy
import dadi
def maizeBottleneck (params, ns, pts):
nuMB , nuMF , nuTeoF , TF , m12, m21 = params
# define the grid
xx = dadi.Numerics.default_grid(pts)
# phi for the equilibrium ancestral population
phi = dadi.PhiManip.phi_1D(xx)
# now do the population split
phi = dadi.PhiManip.phi_1D_to_2D(xx, phi)
# Send maize population through a bottleneck
#phi = dadi.Integration.two_pops(phi, xx, T = TMB , nu1 = 1, nu2 = nuMB)
# define the maize exponential growth function
nu_funcM = lambda t: nuMB*(nuMF/nuMB) ** (t/TF)
# Option 1: define the teosinte exponential growth function
nu_funcTeo = lambda t: 1*(nuTeoF/1) ** (t/TF)
# Recover maize population exponentially, allow migration
phi = dadi.Integration.two_pops(phi, xx, T = TF, nu1 = nu_funcTeo, nu2 = nu_funcM, m12 = m12, m21 = m21)
fs = dadi.Spectrum.from_phi(phi, ns, (xx,xx))
return fs
def oneD_expansion ((nu , T ), ns, pts):
# define the grid
xx = dadi.Numerics.default_grid(pts)
# phi for the equilibrium ancestral population
phi = dadi.PhiManip.phi_1D(xx)
# define the exponential growth function
nu_func = lambda t: numpy.exp(numpy.log(nu) * t/T)
# Grow population
phi = dadi.Integration.one_pop(phi, xx, T, nu_func)
fs = dadi.Spectrum.from_phi(phi, ns, (xx,))
return fs
def oneD_bottleneck ((nuB , nuF , TB, TF ), ns, pts):
# define the grid
xx = dadi.Numerics.default_grid(pts)
# phi for the equilibrium ancestral population
phi = dadi.PhiManip.phi_1D(xx)
# send maize through bottleneck
# phi = dadi.Integration.one_pop(phi, xx, TB, nuB)
# define the exponential growth function
nu_func = lambda t: nuB*(nuF/nuB) ** (t/TF)
# Grow population
phi = dadi.Integration.one_pop(phi, xx, TF, nu_func)
# phi = dadi.Integration.one_pop(phi, xx, TF, nuF)
fs = dadi.Spectrum.from_phi(phi, ns, (xx,))
return fs
def maizeBottleneck_selection (params, ns, pts):
nuMB , nuMF , nuTeoF , TF , m12, m21, sM = params
# define the grid
xx = dadi.Numerics.default_grid(pts)
# phi for the equilibrium ancestral population
phi = dadi.PhiManip.phi_1D(xx)
# now do the population split
phi = dadi.PhiManip.phi_1D_to_2D(xx, phi)
# Send maize population through a bottleneck
#phi = dadi.Integration.two_pops(phi, xx, T = TMB , nu1 = 1, nu2 = nuMB)
# define the maize exponential growth function
nu_funcM = lambda t: nuMB*(nuMF/nuMB) ** (t/TF)
# define the teosinte exponential growth function
# nu_funcTeo = lambda t: numpy.exp(numpy.log(nuTeoF) * t/T)
nu_funcTeo = lambda t: 1*(nuTeoF/1) ** (t/TF)
# Recover maize population exponentially, allow migration and selection
phi = dadi.Integration.two_pops(phi, xx, T = TF, nu1 = nu_funcTeo, nu2 = nu_funcM, m12 = m12, m21 = m21, gamma1 = 0, gamma2 =sM)
fs = dadi.Spectrum.from_phi(phi, ns, (xx,xx))
return fs