def exercise_singular_least_squares():
obs = flex.double([1.234])
weights_2345 = flex.double([2.345])
weights_zero = flex.double([0])
r_free_flags = flex.bool([False])
a = flex.double([0])
b = flex.double([0])
for obs_type in ["F", "I"]:
for weights, scale_factor in [(weights_2345, 3.456),
(weights_zero, 0)]:
tg = ext.targets_least_squares(compute_scale_using_all_data=False,
obs_type=obs_type,
obs=obs,
weights=weights,
r_free_flags=r_free_flags,
f_calc=flex.complex_double(a, b),
derivatives_depth=2,
scale_factor=scale_factor)
if (weights is weights_2345):
assert approx_equal(tg.scale_factor(), scale_factor)
assert list(tg.gradients_work()) == [0j]
assert list(tg.hessians_work()) == [(1, 1, 1)]
else:
assert tg.scale_factor() is None
assert tg.target_work() is None
assert tg.target_test() is None
assert tg.gradients_work().size() == 0
assert tg.hessians_work().size() == 0
def exercise_singular_least_squares():
obs = flex.double([1.234])
weights_2345 = flex.double([2.345])
weights_zero = flex.double([0])
r_free_flags = flex.bool([False])
a = flex.double([0])
b = flex.double([0])
for obs_type in ["F", "I"]:
for weights,scale_factor in [
(weights_2345, 3.456),
(weights_zero, 0)]:
tg = ext.targets_least_squares(
compute_scale_using_all_data=False,
obs_type=obs_type,
obs=obs,
weights=weights,
r_free_flags=r_free_flags,
f_calc=flex.complex_double(a, b),
derivatives_depth=2,
scale_factor=scale_factor)
if (weights is weights_2345):
assert approx_equal(tg.scale_factor(), scale_factor)
assert list(tg.gradients_work()) == [0j]
assert list(tg.hessians_work()) == [(1,1,1)]
else:
assert tg.scale_factor() is None
assert tg.target_work() is None
assert tg.target_test() is None
assert tg.gradients_work().size() == 0
assert tg.hessians_work().size() == 0
def __call__(self, f_calc, compute_gradients):
assert f_calc.unit_cell().is_similar_to(self.f_obs.unit_cell())
assert f_calc.space_group() == self.f_obs.space_group()
return ext.targets_least_squares(
compute_scale_using_all_data=self.compute_scale_using_all_data,
obs_type="F",
obs=self.f_obs.data(),
weights=self.weights,
r_free_flags=self.r_free_flags.data(),
f_calc=f_calc.data(),
derivatives_depth=int(compute_gradients),
scale_factor=self.scale_factor)
def __call__(self, f_calc, compute_gradients):
assert f_calc.unit_cell().is_similar_to(self.f_obs.unit_cell())
assert f_calc.space_group() == self.f_obs.space_group()
return ext.targets_least_squares(
compute_scale_using_all_data=self.compute_scale_using_all_data,
obs_type="F",
obs=self.f_obs.data(),
weights=self.weights,
r_free_flags=self.r_free_flags.data(),
f_calc=f_calc.data(),
derivatives_depth=int(compute_gradients),
scale_factor=self.scale_factor)
def get(O, derivatives_depth=0):
if (O.target_type == "ls"):
return ext.targets_least_squares(
compute_scale_using_all_data=False,
obs_type=O.obs_type,
obs=O.obs,
weights=O.weights,
r_free_flags=O.r_free_flags,
f_calc=flex.complex_double(O.a, O.b),
derivatives_depth=derivatives_depth,
scale_factor=O.scale_factor)
if (O.target_type == "cc"):
return ext.targets_correlation(obs_type=O.obs_type,
obs=O.obs,
weights=O.weights,
r_free_flags=O.r_free_flags,
f_calc=flex.complex_double(
O.a, O.b),
derivatives_depth=derivatives_depth)
raise RuntimeError("Unknown target_type.")
def get(O, derivatives_depth=0):
if (O.target_type == "ls"):
return ext.targets_least_squares(
compute_scale_using_all_data=False,
obs_type=O.obs_type,
obs=O.obs,
weights=O.weights,
r_free_flags=O.r_free_flags,
f_calc=flex.complex_double(O.a, O.b),
derivatives_depth=derivatives_depth,
scale_factor=O.scale_factor)
if (O.target_type == "cc"):
return ext.targets_correlation(
obs_type=O.obs_type,
obs=O.obs,
weights=O.weights,
r_free_flags=O.r_free_flags,
f_calc=flex.complex_double(O.a, O.b),
derivatives_depth=derivatives_depth)
raise RuntimeError("Unknown target_type.")
