def compute_functional_and_gradients(self):
u = self.unpack()
f = scaling.wilson_total_nll_aniso(self.hkl,
self.f_obs,
self.sigma_f_obs,
self.epsilon,
self.sigma_prot_sq,
self.gamma_prot,
self.centric,
self.x[0],
self.unit_cell,
u)
self.f=f
g_full_exact = flex.double( scaling.wilson_total_nll_aniso_gradient(
self.hkl,
self.f_obs,
self.sigma_f_obs,
self.epsilon,
self.sigma_prot_sq,
self.gamma_prot,
self.centric,
self.x[0],
self.unit_cell,
u ))
g = self.pack(g_full_exact)
return f, g
def compute_functional_and_gradients(self):
u = self.unpack()
f = scaling.wilson_total_nll_aniso(self.hkl,
self.f_obs,
self.sigma_f_obs,
self.epsilon,
self.sigma_prot_sq,
self.gamma_prot,
self.centric,
self.x[0],
self.unit_cell,
u)
self.f=f
g_full_exact = flex.double( scaling.wilson_total_nll_aniso_gradient(
self.hkl,
self.f_obs,
self.sigma_f_obs,
self.epsilon,
self.sigma_prot_sq,
self.gamma_prot,
self.centric,
self.x[0],
self.unit_cell,
u ))
g = self.pack(g_full_exact)
return f, g
def test_likelihood_aniso():
u_star = [0, 0, 0, 0, 0, 0]
d_star_sq = flex.double(2, 0.25)
f_obs = flex.double(2, 1.0)
centric_array = flex.bool(2, True)
sigma_f_obs = f_obs / 10.0
sigma_sq = flex.double(2, 1.0)
epsilon = flex.double(2, 1.0)
gamma = flex.double(2, 0.0)
unit_cell = uctbx.unit_cell('20, 30, 40, 90.0, 90.0, 90.0')
mi = flex.miller_index(((1, 2, 3), (1, 2, 3)))
xs = crystal.symmetry((20, 30, 40), "P 2 2 2")
ms = miller.set(xs, mi)
nll_centric_aniso = scaling.wilson_single_nll_aniso(
ms.indices()[0], f_obs[0], sigma_f_obs[0], epsilon[0], sigma_sq[0],
gamma[0], centric_array[0], 0.0, unit_cell, u_star)
assert approx_equal(nll_centric_aniso, 1.07239) ## from Mathematica
nll_acentric_aniso = scaling.wilson_single_nll_aniso(
ms.indices()[0], f_obs[0], sigma_f_obs[0], epsilon[0], sigma_sq[0],
gamma[0], centric_array[0], 0.0, unit_cell, u_star)
centric_array = flex.bool(2, False)
nll_acentric_aniso = scaling.wilson_single_nll_aniso(
ms.indices()[0], f_obs[0], sigma_f_obs[0], epsilon[0], sigma_sq[0],
gamma[0], centric_array[0], 0.0, unit_cell, u_star)
assert approx_equal(nll_acentric_aniso, 0.306902) ## from Mathematica
centric_array = flex.bool(2, True)
u_star = [1, 1, 1, 0, 0, 0]
nll_centric_aniso = scaling.wilson_single_nll_aniso(
ms.indices()[0], f_obs[0], sigma_f_obs[0], epsilon[0], sigma_sq[0],
gamma[0], centric_array[0], 0.0, unit_cell, u_star)
assert approx_equal(nll_centric_aniso, 1.535008) ## from Mathematica
centric_array = flex.bool(2, False)
nll_acentric_aniso = scaling.wilson_single_nll_aniso(
ms.indices()[0], f_obs[0], sigma_f_obs[0], epsilon[0], sigma_sq[0],
gamma[0], centric_array[0], 0.0, unit_cell, u_star)
assert approx_equal(nll_acentric_aniso, 0.900003) ## from Mathematica
centric_array[1] = True
nll_total_aniso = scaling.wilson_total_nll_aniso(ms.indices(), f_obs,
sigma_f_obs, epsilon,
sigma_sq, gamma,
centric_array, 0.0,
unit_cell, u_star)
assert approx_equal(nll_total_aniso, 2.435011)
def test_likelihood_aniso():
u_star = [0,0,0,0,0,0]
d_star_sq = flex.double(2,0.25)
f_obs = flex.double(2,1.0)
centric_array = flex.bool(2,True)
sigma_f_obs = f_obs/10.0
sigma_sq = flex.double(2,1.0)
epsilon = flex.double(2,1.0)
gamma =flex.double(2,0.0)
unit_cell = uctbx.unit_cell('20, 30, 40, 90.0, 90.0, 90.0')
mi = flex.miller_index(((1,2,3), (1,2,3)))
xs = crystal.symmetry((20,30,40), "P 2 2 2")
ms = miller.set(xs, mi)
nll_centric_aniso = scaling.wilson_single_nll_aniso(ms.indices()[0],
f_obs[0],
sigma_f_obs[0],
epsilon[0],
sigma_sq[0],
gamma[0],
centric_array[0],
0.0,
unit_cell,
u_star)
assert approx_equal(nll_centric_aniso, 1.07239 ) ## from Mathematica
nll_acentric_aniso = scaling.wilson_single_nll_aniso(ms.indices()[0],
f_obs[0],
sigma_f_obs[0],
epsilon[0],
sigma_sq[0],
gamma[0],
centric_array[0],
0.0,
unit_cell,
u_star)
centric_array = flex.bool(2,False)
nll_acentric_aniso = scaling.wilson_single_nll_aniso(ms.indices()[0],
f_obs[0],
sigma_f_obs[0],
epsilon[0],
sigma_sq[0],
gamma[0],
centric_array[0],
0.0,
unit_cell,
u_star)
assert approx_equal(nll_acentric_aniso,0.306902 ) ## from Mathematica
centric_array = flex.bool(2,True)
u_star = [1,1,1,0,0,0]
nll_centric_aniso = scaling.wilson_single_nll_aniso(ms.indices()[0],
f_obs[0],
sigma_f_obs[0],
epsilon[0],
sigma_sq[0],
gamma[0],
centric_array[0],
0.0,
unit_cell,
u_star)
assert approx_equal(nll_centric_aniso, 1.535008 ) ## from Mathematica
centric_array = flex.bool(2,False)
nll_acentric_aniso = scaling.wilson_single_nll_aniso(ms.indices()[0],
f_obs[0],
sigma_f_obs[0],
epsilon[0],
sigma_sq[0],
gamma[0],
centric_array[0],
0.0,
unit_cell,
u_star)
assert approx_equal(nll_acentric_aniso, 0.900003 ) ## from Mathematica
centric_array[1]=True
nll_total_aniso = scaling.wilson_total_nll_aniso(ms.indices(),
f_obs,
sigma_f_obs,
epsilon,
sigma_sq,
gamma,
centric_array,
0.0,
unit_cell,
u_star)
assert approx_equal(nll_total_aniso, 2.435011)
