Module dadi.TwoLocus.demographics

Functions

def equilibrium(pts, ns, rho=0.0, dt=0.005, gammaA=0.0, gammaB=0.0, hA=0.5, hB=0.5, thetaA=1.0, thetaB=1.0)

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def equilibrium(pts, ns, rho=0.0, dt=0.005, gammaA=0.0, gammaB=0.0,
                 hA=0.5, hB=0.5, thetaA=1.0, thetaB=1.0):
    x = numerics.grid(pts)
    yA, yB, phi = equilibrium_phi(pts, rho, dt, gammaA, gammaB, hA, hB,
                                  thetaA, thetaB)

    fs = numerics.sample_cached(phi, ns, x)
    return fs

def equilibrium_phi(pts, rho, dt, gammaA=0, gammaB=0, hA=0, hB=0, thetaA=1.0, thetaB=1.0)

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def equilibrium_phi(pts, rho, dt, gammaA=0, gammaB=0, hA=0, hB=0,
                    thetaA=1.0, thetaB=1.0):
    rho=float(rho); gammaA=float(gammaA); gammaB=float(gammaB);
    hA=float(hA); hB=float(hB); thetaA=float(thetaA); thetaB=float(thetaB)
    fname_template = 'phis_pts{0}_dt{1}_rho{2}_gammaA{3}_gammaB{4}_hA{5}_hB{6}_thetaA{7}_thetaB{8}.npz'
    fname = fname_template.format(pts,rho,dt,gammaA,gammaB,hA,hB,thetaA,thetaB)
    fname = os.path.join(cache_path, fname)
    try:
        fid = np.load(fname)
        yA,yB,phi = fid['arr_0'], fid['arr_1'], fid['arr_2']
        fid.close()
    except IOError:
        print('Calculating equilibrium phi from scratch.')
        x = numerics.grid(pts)

        yA = dadi.PhiManip.phi_1D(x, gamma = gammaA, h = hA, theta0 = thetaA)
        yB = dadi.PhiManip.phi_1D(x, gamma = gammaB, h = hB, theta0 = thetaB)

        phi = np.zeros((len(x),len(x),len(x)))
        yA, yB, phi = integration.advance(phi, x, 20., yA, yB, 1.0,
                                          gammaA, gammaB, hA, hB, rho,
                                          thetaA, thetaB, dt)
        np.savez(fname, yA, yB, phi)
    return yA, yB, phi

def set_cache_path(path='~/.dadi/TwoLocus_cache')

Set directory in which demographic equilibrium phi spectra will be cached.

The collection of cached spectra can get large, so it may be helpful to store them outside the user's home directory.

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def set_cache_path(path='~/.dadi/TwoLocus_cache'):
    """
    Set directory in which demographic equilibrium phi spectra will be cached.

    The collection of cached spectra can get large, so it may be helpful to
    store them outside the user's home directory.
    """
    global cache_path
    cache_path = os.path.expanduser(path)
    if not os.path.isdir(cache_path):
        os.makedirs(cache_path)

def three_epoch(params, pts, ns, rho=0.0, dt=0.005, gammaA=0.0, gammaB=0.0, hA=0.5, hB=0.5, thetaA=1.0, thetaB=1.0)

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def three_epoch(params, pts, ns, rho=0.0, dt=0.005, gammaA=0.0, gammaB=0.0,
                hA=0.5, hB=0.5, thetaA=1.0, thetaB=1.0):
    nu1,nu2,T1,T2 = params
    x = numerics.grid(pts)

    yA, yB, phi = equilibrium_phi(pts, rho, dt, gammaA, gammaB, hA, hB,
                                  thetaA, thetaB)

    yA,yB,phi = integration.advance(phi, x, T1, yA, yB, nu=nu1,
                                    gammaA=gammaA, gammaB=gammaB,
                                    hA=hA, hB=hB, rho=rho,
                                    thetaA=thetaA, thetaB=thetaB, dt=dt)

    yA,yB,phi = integration.advance(phi, x, T2, yA, yB, nu=nu2,
                                    gammaA=gammaA, gammaB=gammaB,
                                    hA=hA, hB=hB, rho=rho,
                                    thetaA=thetaA, thetaB=thetaB, dt=dt)

    fs = numerics.sample_cached(phi, ns, x)
    return fs

def two_epoch(params, pts, ns, rho=0.0, dt=0.005, gammaA=0.0, gammaB=0.0, hA=0.5, hB=0.5, thetaA=1.0, thetaB=1.0)

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def two_epoch(params, pts, ns, rho=0.0, dt=0.005, gammaA=0.0, gammaB=0.0,
              hA=0.5, hB=0.5, thetaA=1.0, thetaB=1.0):
    nu,T = params

    x = numerics.grid(pts)
    yA, yB, phi = equilibrium_phi(pts, rho, dt, gammaA, gammaB, hA, hB,
                                  thetaA, thetaB)

    yA,yB,phi = integration.advance(phi, x, T, yA, yB, nu=nu,
                                    gammaA=gammaA, gammaB=gammaB,
                                    hA=hA, hB=hB, rho=rho,
                                    thetaA=thetaA, thetaB=thetaB, dt=dt)

    fs = numerics.sample_cached(phi, ns, x)
    return fs