コード例 #1
0
ファイル: check-fold.py プロジェクト: argriffing/yolo-tyrion
def wright_distn_helper(M, T, R_mut):
    """
    @param M: index to states
    @param T: states to index
    @param R_mut: scaled mutation rate matrix
    @return: stationary distribution of the process
    """
    #FIXME: remove dependence on T
    lmcs = wrightcore.get_lmcs(M)
    lps = wrightcore.create_selection_neutral(M)
    log_drift = wrightcore.create_neutral_drift(lmcs, lps, M)
    P_drift = np.exp(log_drift)
    P_mut = scipy.linalg.expm(R_mut)
    P = np.dot(P_mut, P_drift)
    v = MatrixUtil.get_stationary_distribution(P)
    return v
コード例 #2
0
ファイル: check-fold.py プロジェクト: argriffing/yolo-tyrion
def wright_distn_helper(M, T, R_mut):
    """
    @param M: index to states
    @param T: states to index
    @param R_mut: scaled mutation rate matrix
    @return: stationary distribution of the process
    """
    #FIXME: remove dependence on T
    lmcs = wrightcore.get_lmcs(M)
    lps = wrightcore.create_selection_neutral(M)
    log_drift = wrightcore.create_neutral_drift(lmcs, lps, M)
    P_drift = np.exp(log_drift)
    P_mut = scipy.linalg.expm(R_mut)
    P = np.dot(P_mut, P_drift)
    v = MatrixUtil.get_stationary_distribution(P)
    return v
コード例 #3
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def do_full_simplex_then_collapse(mutrate, popsize):
    #mutrate = 0.01
    #mutrate = 0.2
    #mutrate = 10
    #mutrate = 100
    #mutrate = 1
    N = popsize
    k = 4
    M = np.array(list(multinomstate.gen_states(N, k)), dtype=int)
    T = multinomstate.get_inverse_map(M)
    # Create the joint site pair mutation rate matrix.
    R = mutrate * wrightcore.create_mutation(M, T)
    # Create the joint site pair drift transition matrix.
    lmcs = wrightcore.get_lmcs(M)
    lps = wrightcore.create_selection_neutral(M)
    log_drift = wrightcore.create_neutral_drift(lmcs, lps, M)
    # Define the drift and mutation transition matrices.
    P_drift = np.exp(log_drift)
    P_mut = scipy.linalg.expm(R)
    # Define the composite per-generation transition matrix.
    P = np.dot(P_mut, P_drift)
    # Solve a system of equations to find the stationary distribution.
    v = MatrixUtil.get_stationary_distribution(P)
    for state, value in zip(M, v):
        print state, value
    # collapse the two middle states
    nstates_collapsed = multinomstate.get_nstates(N, k-1)
    M_collapsed = np.array(list(multinomstate.gen_states(N, k-1)), dtype=int)
    T_collapsed = multinomstate.get_inverse_map(M_collapsed)
    v_collapsed = np.zeros(nstates_collapsed)
    for i, bigstate in enumerate(M):
        AB, Ab, aB, ab = bigstate.tolist()
        Ab_aB = Ab + aB
        j = T_collapsed[AB, Ab_aB, ab]
        v_collapsed[j] += v[i]
    for state, value in zip(M_collapsed, v_collapsed):
        print state, value
    # draw an equilateral triangle
    #drawtri(M_collapsed, T_collapsed, v_collapsed)
    #test_mesh()
    return M_collapsed, T_collapsed, v_collapsed
コード例 #4
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def do_full_simplex_then_collapse(mutrate, popsize):
    #mutrate = 0.01
    #mutrate = 0.2
    #mutrate = 10
    #mutrate = 100
    #mutrate = 1
    N = popsize
    k = 4
    M = np.array(list(multinomstate.gen_states(N, k)), dtype=int)
    T = multinomstate.get_inverse_map(M)
    # Create the joint site pair mutation rate matrix.
    R = mutrate * wrightcore.create_mutation(M, T)
    # Create the joint site pair drift transition matrix.
    lmcs = wrightcore.get_lmcs(M)
    lps = wrightcore.create_selection_neutral(M)
    log_drift = wrightcore.create_neutral_drift(lmcs, lps, M)
    # Define the drift and mutation transition matrices.
    P_drift = np.exp(log_drift)
    P_mut = scipy.linalg.expm(R)
    # Define the composite per-generation transition matrix.
    P = np.dot(P_mut, P_drift)
    # Solve a system of equations to find the stationary distribution.
    v = MatrixUtil.get_stationary_distribution(P)
    for state, value in zip(M, v):
        print state, value
    # collapse the two middle states
    nstates_collapsed = multinomstate.get_nstates(N, k - 1)
    M_collapsed = np.array(list(multinomstate.gen_states(N, k - 1)), dtype=int)
    T_collapsed = multinomstate.get_inverse_map(M_collapsed)
    v_collapsed = np.zeros(nstates_collapsed)
    for i, bigstate in enumerate(M):
        AB, Ab, aB, ab = bigstate.tolist()
        Ab_aB = Ab + aB
        j = T_collapsed[AB, Ab_aB, ab]
        v_collapsed[j] += v[i]
    for state, value in zip(M_collapsed, v_collapsed):
        print state, value
    # draw an equilateral triangle
    #drawtri(M_collapsed, T_collapsed, v_collapsed)
    #test_mesh()
    return M_collapsed, T_collapsed, v_collapsed
コード例 #5
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def do_collapsed_simplex(scaled_mut, N):
    """
    @param N: population size
    """
    k = 3
    M = np.array(list(multinomstate.gen_states(N, k)), dtype=int)
    T = multinomstate.get_inverse_map(M)
    # Create the joint site pair mutation rate matrix.
    # This is scaled so that there are about popsize mutations per generation.
    R_mut_raw = wrightcore.create_mutation_collapsed(M, T)
    R_mut = (scaled_mut / float(N)) * R_mut_raw
    # Create the joint site pair drift transition matrix.
    lmcs = wrightcore.get_lmcs(M)
    lps = wrightcore.create_selection_neutral(M)
    #log_drift = wrightcore.create_neutral_drift(lmcs, lps, M)
    # Define the drift and mutation transition matrices.
    #P_drift = np.exp(log_drift)
    #P_mut = scipy.linalg.expm(R)
    # Define the composite per-generation transition matrix.
    #P = np.dot(P_mut, P_drift)
    # Solve a system of equations to find the stationary distribution.
    #v = MatrixUtil.get_stationary_distribution(P)
    # Try a new thing.
    # The raw drift matrix is scaled so that there are about N*N
    # replacements per generation.
    generation_rate = 1.0
    R_drift_raw = wrightcore.create_moran_drift_rate_k3(M, T)
    R_drift = (generation_rate / float(N)) * R_drift_raw
    #FIXME: you should get the stationary distn directly from the rate matrix
    P = scipy.linalg.expm(R_mut + R_drift)
    v = MatrixUtil.get_stationary_distribution(P)
    """
    for state, value in zip(M, v):
        print state, value
    """
    # draw an equilateral triangle
    #drawtri(M, T, v)
    return M, T, v
コード例 #6
0
def do_collapsed_simplex(scaled_mut, N):
    """
    @param N: population size
    """
    k = 3
    M = np.array(list(multinomstate.gen_states(N, k)), dtype=int)
    T = multinomstate.get_inverse_map(M)
    # Create the joint site pair mutation rate matrix.
    # This is scaled so that there are about popsize mutations per generation.
    R_mut_raw = wrightcore.create_mutation_collapsed(M, T)
    R_mut = (scaled_mut / float(N)) * R_mut_raw
    # Create the joint site pair drift transition matrix.
    lmcs = wrightcore.get_lmcs(M)
    lps = wrightcore.create_selection_neutral(M)
    #log_drift = wrightcore.create_neutral_drift(lmcs, lps, M)
    # Define the drift and mutation transition matrices.
    #P_drift = np.exp(log_drift)
    #P_mut = scipy.linalg.expm(R)
    # Define the composite per-generation transition matrix.
    #P = np.dot(P_mut, P_drift)
    # Solve a system of equations to find the stationary distribution.
    #v = MatrixUtil.get_stationary_distribution(P)
    # Try a new thing.
    # The raw drift matrix is scaled so that there are about N*N
    # replacements per generation.
    generation_rate = 1.0
    R_drift_raw = wrightcore.create_moran_drift_rate_k3(M, T)
    R_drift = (generation_rate / float(N)) * R_drift_raw
    #FIXME: you should get the stationary distn directly from the rate matrix
    P = scipy.linalg.expm(R_mut + R_drift)
    v = MatrixUtil.get_stationary_distribution(P)
    """
    for state, value in zip(M, v):
        print state, value
    """
    # draw an equilateral triangle
    #drawtri(M, T, v)
    return M, T, v