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demographics

Equilibrium, two epoch, and three epoch (bottleneck) model with selection (sig1, sig2) on the two derived alleles

bottlegrowth(params, ns, pts, sig1=0.0, sig2=0.0, theta1=1.0, theta2=1.0, misid=0.0, dt=0.005, folded=False)

Three epoch demography - two instantaneous population size changes in the past Args: params (list[float]): [nu1,nu2,T1,T2] - nuB,nuF - relative poplulation size changes to ancestral population size (nu1 occurs before nu2, historically)

    - T - time for which population had relative sizes nuB, nuF (scaled by 2N generations)
sig1 (float): population scaled selection coefficient for the first derived allele.
sig2 (float): population scaled selection coefficients for the second derived allele.
theta1 (float): population scaled mutation rates for the first derived allele.
theta2 (float): population scaled mutation rates for the second derived allele.
misid(float): ancestral misidentification parameter
dt (float): time step to use for integration
folded (bool): If True, fold the frequency spectrum (if we assume we don't know the order that derived alleles appeared).
Source code in dadi/Triallele/demographics.py 
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def bottlegrowth(params, ns, pts, sig1 = 0.0, sig2 = 0.0, theta1 = 1.0, theta2 = 1.0, misid = 0.0, dt = 0.005, folded = False):
    """
    Three epoch demography - two instantaneous population size changes in the past
    Args:
        params (list[float]): [nu1,nu2,T1,T2]
            - nuB,nuF - relative poplulation size changes to ancestral population size (nu1 occurs before nu2, historically)

            - T - time for which population had relative sizes nuB, nuF (scaled by 2N generations)
        sig1 (float): population scaled selection coefficient for the first derived allele.
        sig2 (float): population scaled selection coefficients for the second derived allele.
        theta1 (float): population scaled mutation rates for the first derived allele.
        theta2 (float): population scaled mutation rates for the second derived allele.
        misid(float): ancestral misidentification parameter
        dt (float): time step to use for integration
        folded (bool): If True, fold the frequency spectrum (if we assume we don't know the order that derived alleles appeared).
    """
    nuB,nuF,T = params
    if nuB == nuF:
        nu = nuB
    else:
        nu = lambda t: nuB*np.exp(np.log(nuF/nuB) * t/T)

    x = np.linspace(0,1,pts+1)
    sig1,sig2 = np.float(sig1),np.float(sig2)

    y1 = dadi.PhiManip.phi_1D(x,gamma=sig1)
    y2 = dadi.PhiManip.phi_1D(x,gamma=sig2)
    phi = np.zeros((len(x),len(x)))

    # integrate to equilibrium first
    if sig1 == sig2 == 0.0 and theta1 == theta2 == 1:
        phi = integration.equilibrium_neutral_exact(x)
    else:
        phi = integration.equilibrium_neutral_exact(x)
        phi,y1,y2 = integration.advance(phi, x, 2, y1, y2, nu=1., sig1=sig1, sig2=sig2, theta1=theta1, theta2=theta2, dt=dt)

    phi,y1,y2 = integration.advance(phi, x, T, y1, y2, nu, sig1, sig2, theta1, theta2, dt=dt)

    dx = numerics.grid_dx(x)

    try:
        ns = int(ns)
    except TypeError:
        ns = ns[0]

    fs = numerics.sample(phi, ns, x)
    fs.extrap_t = dt

    if folded == True:
        fs = fs.fold_major()

    if misid > 0.0:
        fs = numerics.misidentification(fs,misid)

    return fs

equilibrium(params, ns, pts, sig1=0.0, sig2=0.0, theta1=1.0, theta2=1.0, misid=0.0, dt=0.005, folded=False)

Integrate the density function to equilibrium Args: params (list): unused sig1 (float): population scaled selection coefficient for the first derived allele. sig2 (float): population scaled selection coefficients for the second derived allele. theta1 (float): population scaled mutation rates for the first derived allele. theta2 (float): population scaled mutation rates for the second derived allele. misid(float): ancestral misidentification parameter dt (float): time step to use for integration folded (bool): If True, fold the frequency spectrum (if we assume we don't know the order that derived alleles appeared).

Source code in dadi/Triallele/demographics.py 
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def equilibrium(params, ns, pts, sig1 = 0.0, sig2 = 0.0, theta1 = 1.0, theta2 = 1.0, misid = 0.0, dt = 0.005, folded = False):
    """
    Integrate the density function to equilibrium
    Args:
        params (list): unused
        sig1 (float): population scaled selection coefficient for the first derived allele.
        sig2 (float): population scaled selection coefficients for the second derived allele.
        theta1 (float): population scaled mutation rates for the first derived allele.
        theta2 (float): population scaled mutation rates for the second derived allele.
        misid(float): ancestral misidentification parameter
        dt (float): time step to use for integration
        folded (bool): If True, fold the frequency spectrum (if we assume we don't know the order that derived alleles appeared).
    """
    x = np.linspace(0,1,pts+1)
    sig1,sig2 = np.float(sig1),np.float(sig2)

    y1 = dadi.PhiManip.phi_1D(x,gamma=sig1)
    y2 = dadi.PhiManip.phi_1D(x,gamma=sig2)
    phi = np.zeros((len(x),len(x)))
    if sig1 == sig2 == 0.0 and theta1 == theta2 == 1:
        phi = integration.equilibrium_neutral_exact(x)
    else:
        phi = integration.equilibrium_neutral_exact(x)
        phi,y1,y2 = integration.advance(phi, x, 2, y1, y2, nu=1., sig1=sig1, sig2=sig2, theta1=theta1, theta2=theta2, dt=dt)

    dx = numerics.grid_dx(x)

    try:
        ns = int(ns)
    except TypeError:
        ns = ns[0]

    fs = numerics.sample(phi, ns, x)
    fs.extrap_t = dt

    if folded == True:
        fs = fs.fold_major()

    if misid > 0.0:
        fs = numerics.misidentification(fs,misid)

    return fs

three_epoch(params, ns, pts, sig1=0.0, sig2=0.0, theta1=1.0, theta2=1.0, misid=0.0, dt=0.005, folded=False)

Three epoch demography - two instantaneous population size changes in the past Args: params (list[float]): [nu1,nu2,T1,T2] - nu1,nu2 - relative poplulation size changes to ancestral population size (nu1 occurs before nu2, historically)

    - T1,T2 - time for which population had relative sizes nu1, nu2 (scaled by 2N generations)
sig1 (float): population scaled selection coefficient for the first derived allele.
sig2 (float): population scaled selection coefficients for the second derived allele.
theta1 (float): population scaled mutation rates for the first derived allele.
theta2 (float): population scaled mutation rates for the second derived allele.
misid(float): ancestral misidentification parameter
dt (float): time step to use for integration
folded (bool): If True, fold the frequency spectrum (if we assume we don't know the order that derived alleles appeared).
Source code in dadi/Triallele/demographics.py 
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def three_epoch(params, ns, pts, sig1 = 0.0, sig2 = 0.0, theta1 = 1.0, theta2 = 1.0, misid = 0.0, dt = 0.005, folded = False):
    """
    Three epoch demography - two instantaneous population size changes in the past
    Args:
        params (list[float]): [nu1,nu2,T1,T2]
            - nu1,nu2 - relative poplulation size changes to ancestral population size (nu1 occurs before nu2, historically)

            - T1,T2 - time for which population had relative sizes nu1, nu2 (scaled by 2N generations)
        sig1 (float): population scaled selection coefficient for the first derived allele.
        sig2 (float): population scaled selection coefficients for the second derived allele.
        theta1 (float): population scaled mutation rates for the first derived allele.
        theta2 (float): population scaled mutation rates for the second derived allele.
        misid(float): ancestral misidentification parameter
        dt (float): time step to use for integration
        folded (bool): If True, fold the frequency spectrum (if we assume we don't know the order that derived alleles appeared).
    """
    nu1,nu2,T1,T2 = params
    x = np.linspace(0,1,pts+1)
    sig1,sig2 = np.float(sig1),np.float(sig2)

    y1 = dadi.PhiManip.phi_1D(x,gamma=sig1)
    y2 = dadi.PhiManip.phi_1D(x,gamma=sig2)
    phi = np.zeros((len(x),len(x)))

    # integrate to equilibrium first
    if sig1 == sig2 == 0.0 and theta1 == theta2 == 1:
        phi = integration.equilibrium_neutral_exact(x)
    else:
        phi = integration.equilibrium_neutral_exact(x)
        phi,y1,y2 = integration.advance(phi, x, 2, y1, y2, nu=1., sig1=sig1, sig2=sig2, theta1=theta1, theta2=theta2, dt=dt)

    phi,y1,y2 = integration.advance(phi, x, T1, y1, y2, nu1, sig1, sig2, theta1, theta2, dt=dt)
    phi,y1,y2 = integration.advance(phi, x, T2, y1, y2, nu2, sig1, sig2, theta1, theta2, dt=dt)

    dx = numerics.grid_dx(x)

    try:
        ns = int(ns)
    except TypeError:
        ns = ns[0]

    fs = numerics.sample(phi, ns, x)
    fs.extrap_t = dt

    if folded == True:
        fs = fs.fold_major()

    if misid > 0.0:
        fs = numerics.misidentification(fs,misid)

    return fs

two_epoch(params, ns, pts, sig1=0.0, sig2=0.0, theta1=1.0, theta2=1.0, misid=0.0, dt=0.005, folded=False)

Two epoch demography - a single population size change at some point in the past Args: params (list[float]): [nu,T,sig1,sig2,theta1,theta2,misid,dt] - nu - relative poplulation size change to ancestral population size

    - T - time in past that size change occured (scaled by 2N generations)
sig1 (float): population scaled selection coefficient for the first derived allele.
sig2 (float): population scaled selection coefficients for the second derived allele.
theta1 (float): population scaled mutation rates for the first derived allele.
theta2 (float): population scaled mutation rates for the second derived allele.
misid(float): ancestral misidentification parameter
dt (float): time step to use for integration
folded (bool): If True, fold the frequency spectrum (if we assume we don't know the order that derived alleles appeared).
Source code in dadi/Triallele/demographics.py 
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def two_epoch(params, ns, pts, sig1 = 0.0, sig2 = 0.0, theta1 = 1.0, theta2 = 1.0, misid = 0.0, dt = 0.005, folded = False):
    """
    Two epoch demography - a single population size change at some point in the past
    Args:
        params (list[float]): [nu,T,sig1,sig2,theta1,theta2,misid,dt]
            - nu - relative poplulation size change to ancestral population size

            - T - time in past that size change occured (scaled by 2N generations)
        sig1 (float): population scaled selection coefficient for the first derived allele.
        sig2 (float): population scaled selection coefficients for the second derived allele.
        theta1 (float): population scaled mutation rates for the first derived allele.
        theta2 (float): population scaled mutation rates for the second derived allele.
        misid(float): ancestral misidentification parameter
        dt (float): time step to use for integration
        folded (bool): If True, fold the frequency spectrum (if we assume we don't know the order that derived alleles appeared).
    """
    nu,T = params

    x = np.linspace(0,1,pts+1)
    sig1,sig2 = np.float(sig1),np.float(sig2)

    y1 = dadi.PhiManip.phi_1D(x,gamma=sig1)
    y2 = dadi.PhiManip.phi_1D(x,gamma=sig2)
    phi = np.zeros((len(x),len(x)))

    # integrate to equilibrium first
    if sig1 == sig2 == 0.0 and theta1 == theta2 == 1:
        phi = integration.equilibrium_neutral_exact(x)
    else:
        phi = integration.equilibrium_neutral_exact(x)
        phi,y1,y2 = integration.advance(phi, x, 2, y1, y2, nu=1., sig1=sig1, sig2=sig2, theta1=theta1, theta2=theta2, dt=dt)

    phi,y1,y2 = integration.advance(phi, x, T, y1, y2, nu=nu, sig1=sig1, sig2=sig2, theta1=theta1, theta2=theta2, dt=dt)

    dx = numerics.grid_dx(x)

    try:
        ns = int(ns)
    except TypeError:
        ns = ns[0]

    fs = numerics.sample(phi, ns, x)
    fs.extrap_t = dt

    if folded == True:
        fs = fs.fold_major()

    if misid > 0.0:
        fs = numerics.misidentification(fs,misid)

    return fs