def get_fixation_unconstrained_kb(S, d, log_kb):
"""
This uses the Kacser and Burns effect instead of the sign function.
"""
soft_sign_S = algopy.tanh(algopy.exp(log_kb) * S)
D = d * soft_sign_S
H = algopy.zeros_like(S)
for i in range(H.shape[0]):
for j in range(H.shape[1]):
H[i, j] = 1. / kimrecessive.denom_piecewise(0.5 * S[i, j], D[i, j])
return H
def get_fixation_unconstrained_kb(S, d, log_kb):
"""
This uses the Kacser and Burns effect instead of the sign function.
"""
soft_sign_S = algopy.tanh(algopy.exp(log_kb)*S)
D = d * soft_sign_S
H = algopy.zeros_like(S)
for i in range(H.shape[0]):
for j in range(H.shape[1]):
H[i, j] = 1. / kimrecessive.denom_piecewise(
0.5*S[i, j], D[i, j])
return H
def get_fixation_unconstrained_kb_fquad(
S, d, log_kb, x, w, codon_neighbor_mask):
"""
This uses the Kacser and Burns effect instead of the sign function.
"""
#TODO: possibly use a mirror symmetry to double the speed
soft_sign_S = algopy.tanh(algopy.exp(log_kb)*S)
D = d * soft_sign_S
H = algopy.zeros_like(S)
for i in range(H.shape[0]):
for j in range(H.shape[1]):
if codon_neighbor_mask[i, j]:
H[i, j] = 1. / kimrecessive.denom_fixed_quad(
0.5*S[i, j], D[i, j], x, w)
return H
def get_fixation_unconstrained_kb_fquad(
S, d, log_kb, x, w, codon_neighbor_mask):
"""
This uses the Kacser and Burns effect instead of the sign function.
"""
#TODO: possibly use a mirror symmetry to double the speed
soft_sign_S = algopy.tanh(algopy.exp(log_kb)*S)
D = d * soft_sign_S
H = algopy.zeros_like(S)
for i in range(H.shape[0]):
for j in range(H.shape[1]):
if codon_neighbor_mask[i, j]:
H[i, j] = 1. / kimrecessive.denom_fixed_quad(
0.5*S[i, j], D[i, j], x, w)
return H