def test_ConditionalDistribution(testdata):
def check(
mosaicity_parameterisation,
wavelength_parameterisation,
fix_mosaic_spread=False,
fix_wavelength_spread=False,
fix_unit_cell=False,
fix_orientation=False,
):
experiment = testdata.experiment
models = testdata.models
h = testdata.h
s0 = np.array(testdata.s0).reshape(3, 1)
sp = np.array(testdata.sp).reshape(3, 1)
# ctot = testdata.ctot
# mobs = testdata.mobs
# Sobs = testdata.Sobs
U_params = models[1].get_param_vals()
B_params = models[2].get_param_vals()
M_params = models[0][:mosaicity_parameterisation.num_parameters()]
L_params = models[3]
state = ModelState(
experiment,
mosaicity_parameterisation,
wavelength_parameterisation,
fix_mosaic_spread=fix_mosaic_spread,
fix_wavelength_spread=fix_wavelength_spread,
fix_unit_cell=fix_unit_cell,
fix_orientation=fix_orientation,
)
state.U_params = np.array(U_params, dtype=np.float64)
state.B_params = np.array(B_params, dtype=np.float64)
state.M_params = np.array(M_params, dtype=np.float64)
state.L_params = L_params
def get_conditional(state):
conditional = ReflectionLikelihood(
state,
s0,
sp,
h,
0,
np.array([0.0, 0.0]),
np.array([0.0, 0.0, 0.0, 0.0]).reshape(2, 2),
).conditional
return conditional
conditional = get_conditional(state)
step = 1e-6
dm_dp = conditional.first_derivatives_of_mean()
dS_dp = conditional.first_derivatives_of_sigma()
parameters = state.active_parameters
def compute_sigma(parameters):
state.active_parameters = parameters
return get_conditional(state).sigma()
def compute_mean(parameters):
state.active_parameters = parameters
return get_conditional(state).mean()
dm_num = []
for i in range(len(parameters)):
def f(x):
p = copy.copy(parameters)
p[i] = x
return compute_mean(p)
dm_num.append(first_derivative(f, parameters[i], step))
for n, c in zip(dm_num, dm_dp):
for nn, cc in zip(n, c):
print(nn)
print(cc)
assert abs(nn - cc) < 1e-7
# assert all(abs(nn - cc) < 1e-7 for nn, cc in zip(n, c))
ds_num = []
for i in range(len(parameters)):
def f(x):
p = copy.copy(parameters)
p[i] = x
return compute_sigma(p)
ds_num.append(first_derivative(f, parameters[i], step))
for n, c in zip(ds_num, dS_dp):
for nn, cc in zip(n.flatten(), c.flatten()):
print(nn)
print(cc)
assert abs(nn - cc) < 1e-7
# assert all(abs(nn - cc) < 1e-7 for nn, cc in zip(n, c))
S1 = Simple1MosaicityParameterisation()
S6 = Simple6MosaicityParameterisation()
check(S1, None, fix_wavelength_spread=True)
check(S1, None, fix_wavelength_spread=True, fix_mosaic_spread=True)
check(S1, None, fix_wavelength_spread=True, fix_unit_cell=True)
check(S1, None, fix_wavelength_spread=True, fix_orientation=True)
check(S6, None, fix_wavelength_spread=True)
check(S6, None, fix_wavelength_spread=True, fix_mosaic_spread=True)
check(S6, None, fix_wavelength_spread=True, fix_unit_cell=True)
check(S6, None, fix_wavelength_spread=True, fix_orientation=True)
def test_ReflectionLikelihood(testdata):
def check(
mosaicity_parameterisation,
wavelength_parameterisation,
fix_mosaic_spread=False,
fix_wavelength_spread=False,
fix_unit_cell=False,
fix_orientation=False,
):
experiment = testdata.experiment
models = testdata.models
s0 = np.array(testdata.s0)
sp = np.array(testdata.sp)
h = testdata.h
ctot = testdata.ctot
mobs = testdata.mobs
Sobs = testdata.Sobs
U_params = models[1].get_param_vals()
B_params = models[2].get_param_vals()
M_params = np.array(
models[0][:mosaicity_parameterisation.num_parameters()])
L_params = flex.double(models[3])
state = ModelState(
experiment,
mosaicity_parameterisation,
wavelength_parameterisation,
fix_mosaic_spread=fix_mosaic_spread,
fix_wavelength_spread=fix_wavelength_spread,
fix_unit_cell=fix_unit_cell,
fix_orientation=fix_orientation,
)
state.U_params = U_params
state.B_params = B_params
state.M_params = M_params
state.L_params = L_params
def get_reflection_likelihood(state):
return ReflectionLikelihood(state, s0, sp, h, ctot, mobs, Sobs)
likelihood = get_reflection_likelihood(state)
step = 1e-6
dL_dp = likelihood.first_derivatives()
parameters = state.active_parameters
assert len(dL_dp) == len(parameters)
def compute_likelihood(parameters):
state.active_parameters = parameters
likelihood = get_reflection_likelihood(state)
return likelihood.log_likelihood()
dL_num = []
for i in range(len(parameters)):
def f(x):
p = copy.copy(parameters)
p[i] = x
return compute_likelihood(p)
dL_num.append(first_derivative(f, parameters[i], step))
assert len(dL_num) == len(parameters)
print(dL_num)
print(list(dL_dp))
for n, c in zip(dL_num, dL_dp):
print(n, c)
assert n == pytest.approx(c, abs=1e-4)
S1 = Simple1MosaicityParameterisation()
S6 = Simple6MosaicityParameterisation()
check(S1, None, fix_wavelength_spread=True)
check(S1, None, fix_wavelength_spread=True, fix_mosaic_spread=True)
check(S1, None, fix_wavelength_spread=True, fix_unit_cell=True)
check(S1, None, fix_wavelength_spread=True, fix_orientation=True)
check(S6, None, fix_wavelength_spread=True, fix_mosaic_spread=True)
check(S6, None, fix_wavelength_spread=True, fix_unit_cell=True)
check(S6, None, fix_wavelength_spread=True, fix_orientation=True)
check(S6, None, fix_wavelength_spread=True)
def parameterisation(self):
"""
Get the parameterisation
"""
return Simple1MosaicityParameterisation(self.params)