def main(args):
alpha = args.alpha
N = args.N
k = 3
print 'alpha:', alpha
print 'N:', N
print 'k:', k
print
M = np.array(list(multinomstate.gen_states(N, k)), dtype=int)
T = multinomstate.get_inverse_map(M)
R_mut = wrightcore.create_mutation_abc(M, T)
R_drift = wrightcore.create_moran_drift_rate_k3(M, T)
Q = alpha * R_mut + R_drift
# pick out the correct eigenvector
W, V = scipy.linalg.eig(Q.T)
w, v = min(zip(np.abs(W), V.T))
print 'rate matrix:'
print Q
print
print 'transpose of rate matrix:'
print Q.T
print
print 'eigendecomposition of transpose of rate matrix as integers:'
print scipy.linalg.eig(Q.T)
print
print 'transpose of rate matrix in mathematica notation:'
print MatrixUtil.m_to_mathematica_string(Q.T.astype(int))
print
print 'abs eigenvector corresponding to smallest abs eigenvalue:'
print np.abs(v)
print
def main(args):
alpha = args.alpha
N = args.N
k = 3
print 'alpha:', alpha
print 'N:', N
print 'k:', k
print
M = np.array(list(multinomstate.gen_states(N, k)), dtype=int)
T = multinomstate.get_inverse_map(M)
R_mut = wrightcore.create_mutation_abc(M, T)
R_drift = wrightcore.create_moran_drift_rate_k3(M, T)
Q = alpha * R_mut + R_drift
# pick out the correct eigenvector
W, V = scipy.linalg.eig(Q.T)
w, v = min(zip(np.abs(W), V.T))
print 'rate matrix:'
print Q
print
print 'transpose of rate matrix:'
print Q.T
print
print 'eigendecomposition of transpose of rate matrix as integers:'
print scipy.linalg.eig(Q.T)
print
print 'transpose of rate matrix in mathematica notation:'
print MatrixUtil.m_to_mathematica_string(Q.T.astype(int))
print
print 'abs eigenvector corresponding to smallest abs eigenvalue:'
print np.abs(v)
print
def get_moran_drift(M, T):
k = M.shape[1]
if k == 2:
return wrightcore.create_moran_drift_rate_k2(M, T)
elif k == 3:
return wrightcore.create_moran_drift_rate_k3(M, T)
elif k == 4:
return wrightcore.create_moran_drift_rate_k4(M, T)
else:
raise NotImplementedError
def get_moran_drift(M, T):
k = M.shape[1]
if k == 2:
return wrightcore.create_moran_drift_rate_k2(M, T)
elif k == 3:
return wrightcore.create_moran_drift_rate_k3(M, T)
elif k == 4:
return wrightcore.create_moran_drift_rate_k4(M, T)
else:
raise NotImplementedError
def get_moran_drift(M, T):
#FIXME: this is dumb,
#FIXME: but I'm not sure how to use variable-dimension ndarrays in cython
k = M.shape[1]
if k == 2:
return wrightcore.create_moran_drift_rate_k2(M, T)
elif k == 3:
return wrightcore.create_moran_drift_rate_k3(M, T)
elif k == 4:
return wrightcore.create_moran_drift_rate_k4(M, T)
else:
raise NotImplementedError
def get_moran_drift(M, T):
#FIXME: this is dumb,
#FIXME: but I'm not sure how to use variable-dimension ndarrays in cython
k = M.shape[1]
if k == 2:
return wrightcore.create_moran_drift_rate_k2(M, T)
elif k == 3:
return wrightcore.create_moran_drift_rate_k3(M, T)
elif k == 4:
return wrightcore.create_moran_drift_rate_k4(M, T)
else:
raise NotImplementedError
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
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
