def exercise_isotropic_adp():
i_seqs = (0,)
weight = 2
u_cart = ((1,2,3,5,2,8),)
u_iso = (0,)
use_u_aniso = (True,)
p = adp_restraints.isotropic_adp_proxy(
i_seqs=i_seqs,
weight=weight)
assert p.i_seqs == i_seqs
assert approx_equal(p.weight, weight)
i = adp_restraints.isotropic_adp(u_cart=u_cart[0], weight=weight)
expected_deltas = (-1, 0, 1, 5, 2, 8)
expected_gradients = (-4, 0, 4, 40, 16, 64)
assert approx_equal(i.weight, weight)
assert approx_equal(i.deltas(), expected_deltas)
assert approx_equal(i.rms_deltas(), 4.5704364002673632)
assert approx_equal(i.residual(), 376.0)
assert approx_equal(i.gradients(), expected_gradients)
gradients_aniso_cart = flex.sym_mat3_double(1, (0,0,0,0,0,0))
gradients_iso = flex.double(1,0)
proxies = adp_restraints.shared_isotropic_adp_proxy([p,p])
u_cart = flex.sym_mat3_double(u_cart)
u_iso = flex.double(u_iso)
use_u_aniso = flex.bool(use_u_aniso)
params = adp_restraint_params(u_cart=u_cart, u_iso=u_iso, use_u_aniso=use_u_aniso)
residuals = adp_restraints.isotropic_adp_residuals(params, proxies=proxies)
assert approx_equal(residuals, (i.residual(),i.residual()))
deltas_rms = adp_restraints.isotropic_adp_deltas_rms(params, proxies=proxies)
assert approx_equal(deltas_rms, (i.rms_deltas(),i.rms_deltas()))
residual_sum = adp_restraints.isotropic_adp_residual_sum(
params,
proxies=proxies,
gradients_aniso_cart=gradients_aniso_cart
)
assert approx_equal(residual_sum, 752.0)
fd_grads_aniso, fd_grads_iso = finite_difference_gradients(
restraint_type=adp_restraints.isotropic_adp,
proxy=p,
u_cart=u_cart,
u_iso=u_iso,
use_u_aniso=use_u_aniso
)
for g,e in zip(gradients_aniso_cart, fd_grads_aniso):
assert approx_equal(g, matrix.col(e)*2)
#
# check frame invariance of residual
#
u_cart = matrix.sym(sym_mat3=(0.1,0.2,0.05,0.03,0.02,0.01))
a = adp_restraints.isotropic_adp(
u_cart=u_cart.as_sym_mat3(), weight=1)
expected_residual = a.residual()
gen = flex.mersenne_twister()
for i in range(20):
R = matrix.rec(gen.random_double_r3_rotation_matrix(),(3,3))
u_cart_rot = R * u_cart * R.transpose()
a = adp_restraints.isotropic_adp(
u_cart=u_cart_rot.as_sym_mat3(), weight=1)
assert approx_equal(a.residual(), expected_residual)
def exercise_isotropic_adp():
i_seqs = (0,)
weight = 2
u_cart = ((1,2,3,5,2,8),)
u_iso = (0,)
use_u_aniso = (True,)
p = adp_restraints.isotropic_adp_proxy(
i_seqs=i_seqs,
weight=weight)
assert p.i_seqs == i_seqs
assert approx_equal(p.weight, weight)
i = adp_restraints.isotropic_adp(u_cart=u_cart[0], weight=weight)
expected_deltas = (-1, 0, 1, 5, 2, 8)
expected_gradients = (-4, 0, 4, 40, 16, 64)
assert approx_equal(i.weight, weight)
assert approx_equal(i.deltas(), expected_deltas)
assert approx_equal(i.rms_deltas(), 4.5704364002673632)
assert approx_equal(i.residual(), 376.0)
assert approx_equal(i.gradients(), expected_gradients)
gradients_aniso_cart = flex.sym_mat3_double(1, (0,0,0,0,0,0))
gradients_iso = flex.double(1,0)
proxies = adp_restraints.shared_isotropic_adp_proxy([p,p])
u_cart = flex.sym_mat3_double(u_cart)
u_iso = flex.double(u_iso)
use_u_aniso = flex.bool(use_u_aniso)
params = adp_restraint_params(u_cart=u_cart, u_iso=u_iso, use_u_aniso=use_u_aniso)
residuals = adp_restraints.isotropic_adp_residuals(params, proxies=proxies)
assert approx_equal(residuals, (i.residual(),i.residual()))
deltas_rms = adp_restraints.isotropic_adp_deltas_rms(params, proxies=proxies)
assert approx_equal(deltas_rms, (i.rms_deltas(),i.rms_deltas()))
residual_sum = adp_restraints.isotropic_adp_residual_sum(
params,
proxies=proxies,
gradients_aniso_cart=gradients_aniso_cart
)
assert approx_equal(residual_sum, 752.0)
fd_grads_aniso, fd_grads_iso = finite_difference_gradients(
restraint_type=adp_restraints.isotropic_adp,
proxy=p,
u_cart=u_cart,
u_iso=u_iso,
use_u_aniso=use_u_aniso
)
for g,e in zip(gradients_aniso_cart, fd_grads_aniso):
assert approx_equal(g, matrix.col(e)*2)
#
# check frame invariance of residual
#
u_cart = matrix.sym(sym_mat3=(0.1,0.2,0.05,0.03,0.02,0.01))
a = adp_restraints.isotropic_adp(
u_cart=u_cart.as_sym_mat3(), weight=1)
expected_residual = a.residual()
gen = flex.mersenne_twister()
for i in range(20):
R = matrix.rec(gen.random_double_r3_rotation_matrix(),(3,3))
u_cart_rot = R * u_cart * R.transpose()
a = adp_restraints.isotropic_adp(
u_cart=u_cart_rot.as_sym_mat3(), weight=1)
assert approx_equal(a.residual(), expected_residual)
def isotropic_adp_deviation(self):
if (self.n_isotropic_adp_proxies is not None):
isotropic_adp_deltas_rms = adp_restraints.isotropic_adp_deltas_rms(
u_cart=self.u_cart, proxies=self.isotropic_adp_proxies)
i_sq = isotropic_adp_deltas_rms * isotropic_adp_deltas_rms
i_ave = math.sqrt(flex.mean_default(i_sq, 0))
i_max = math.sqrt(flex.max_default(i_sq, 0))
i_min = math.sqrt(flex.min_default(i_sq, 0))
return i_min, i_max, i_ave
def isotropic_adp_deviation(self):
if (self.n_isotropic_adp_proxies is not None):
isotropic_adp_deltas_rms = adp_restraints.isotropic_adp_deltas_rms(
u_cart=self.u_cart,
proxies=self.isotropic_adp_proxies)
i_sq = isotropic_adp_deltas_rms * isotropic_adp_deltas_rms
i_ave = math.sqrt(flex.mean_default(i_sq, 0))
i_max = math.sqrt(flex.max_default(i_sq, 0))
i_min = math.sqrt(flex.min_default(i_sq, 0))
return i_min, i_max, i_ave
