def d2branchlk(probs1, probs2, seqlen, bgfreq, kappa, t):
model1 = spidir.make_hky_matrix(bgfreq, kappa, t)
model2 = spidir.make_hky_matrix(bgfreq, kappa, 0.0)
dmodel1 = spidir.make_hky_deriv_matrix(bgfreq, kappa, t)
dmodel2 = spidir.make_hky_deriv_matrix(bgfreq, kappa, 0.0)
d2model1 = spidir.make_hky_deriv2_matrix(bgfreq, kappa, t)
d2model2 = spidir.make_hky_deriv2_matrix(bgfreq, kappa, 0.0)
logl = 0.0
for j in xrange(seqlen):
g = sum(bgfreq[k] * sum(model1[k][x] * probs1[4 * j + x]
for x in xrange(4)) *
sum(model2[k][y] * probs2[4 * j + y]
for y in xrange(4)) for k in xrange(4))
dg = sum(bgfreq[k] * sum(dmodel1[k][x] * probs1[4 * j + x]
for x in xrange(4)) *
sum(model2[k][y] * probs2[4 * j + y]
for y in xrange(4)) for k in xrange(4))
d2g = sum(bgfreq[k] * sum(d2model1[k][x] * probs1[4 * j + x]
for x in xrange(4)) *
sum(model2[k][y] * probs2[4 * j + y]
for y in xrange(4)) for k in xrange(4))
logl += - safediv(dg*dg, g*g, INF) + \
safediv(d2g, g, INF)
return logl
def d2branchlk(probs1, probs2, seqlen, bgfreq, kappa, t):
model1 = spidir.make_hky_matrix(bgfreq, kappa, t)
model2 = spidir.make_hky_matrix(bgfreq, kappa, 0.0)
dmodel1 = spidir.make_hky_deriv_matrix(bgfreq, kappa, t)
dmodel2 = spidir.make_hky_deriv_matrix(bgfreq, kappa, 0.0)
d2model1 = spidir.make_hky_deriv2_matrix(bgfreq, kappa, t)
d2model2 = spidir.make_hky_deriv2_matrix(bgfreq, kappa, 0.0)
logl = 0.0
for j in xrange(seqlen):
g = sum(
bgfreq[k]
* sum(model1[k][x] * probs1[4 * j + x] for x in xrange(4))
* sum(model2[k][y] * probs2[4 * j + y] for y in xrange(4))
for k in xrange(4)
)
dg = sum(
bgfreq[k]
* sum(dmodel1[k][x] * probs1[4 * j + x] for x in xrange(4))
* sum(model2[k][y] * probs2[4 * j + y] for y in xrange(4))
for k in xrange(4)
)
d2g = sum(
bgfreq[k]
* sum(d2model1[k][x] * probs1[4 * j + x] for x in xrange(4))
* sum(model2[k][y] * probs2[4 * j + y] for y in xrange(4))
for k in xrange(4)
)
logl += -safediv(dg * dg, g * g, INF) + safediv(d2g, g, INF)
return logl
def test_hky_deriv2(self):
"""general test"""
bgfreq = [.3, .2, .3, .2]
kappa = 2.0
i = random.randint(0, 3)
j = random.randint(0, 3)
x = list(frange(0, 1.0, .01))
y = [spidir.make_hky_deriv_matrix(bgfreq, kappa, t)[i][j] for t in x]
dy = [spidir.make_hky_deriv2_matrix(bgfreq, kappa, t)[i][j] for t in x]
dy2 = [(spidir.make_hky_deriv_matrix(bgfreq, kappa, t + .01)[i][j] -
spidir.make_hky_deriv_matrix(bgfreq, kappa, t)[i][j]) / .01
for t in x]
prep_dir("test/output/hky2")
rplot_start("test/output/hky2/hky_deriv2.pdf")
rplot("plot", x, y, t="l", ylim=[min(dy2 + dy + y), max(dy2 + dy + y)])
rp.lines(x, dy, col="red")
rp.lines(x, dy2, col="blue")
rplot_end(True)
def test_hky_deriv2(self):
"""general test"""
bgfreq = [.3, .2, .3, .2]
kappa = 2.0
i = random.randint(0, 3)
j = random.randint(0, 3)
x = list(frange(0, 1.0, .01))
y = [spidir.make_hky_deriv_matrix(bgfreq, kappa, t)[i][j]
for t in x]
dy = [spidir.make_hky_deriv2_matrix(bgfreq, kappa, t)[i][j]
for t in x]
dy2 = [(spidir.make_hky_deriv_matrix(bgfreq, kappa, t+.01)[i][j] -
spidir.make_hky_deriv_matrix(bgfreq, kappa, t)[i][j]) / .01
for t in x]
prep_dir("test/output/hky2")
rplot_start("test/output/hky2/hky_deriv2.pdf")
rplot("plot", x, y, t="l", ylim=[min(dy2 + dy + y), max(dy2 + dy + y)])
rp.lines(x, dy, col="red")
rp.lines(x, dy2, col="blue")
rplot_end(True)