def test_spherical_relp_stills_pred_param(tc):
print(
"Testing derivatives for SphericalRelpStillsPredictionParameterisation"
)
print(
"====================================================================="
)
# Build a prediction parameterisation for the stills experiment
pred_param = SphericalRelpStillsPredictionParameterisation(
tc.stills_experiments,
detector_parameterisations=[tc.det_param],
beam_parameterisations=[tc.s0_param],
xl_orientation_parameterisations=[tc.xlo_param],
xl_unit_cell_parameterisations=[tc.xluc_param],
)
# Predict the reflections in place. Must do this ahead of calculating
# the analytical gradients so quantities like s1 are correct
from dials.algorithms.refinement.prediction.managed_predictors import (
ExperimentsPredictorFactory, )
ref_predictor = ExperimentsPredictorFactory.from_experiments(
tc.stills_experiments, force_stills=True, spherical_relp=True)
ref_predictor(tc.reflections)
# get analytical gradients
an_grads = pred_param.get_gradients(tc.reflections)
fd_grads = tc.get_fd_gradients(pred_param, ref_predictor)
# compare FD with analytical calculations
for i, (an_grad, fd_grad) in enumerate(zip(an_grads, fd_grads)):
print("\nParameter {0}: {1}".format(i, fd_grad["name"]))
for name in ["dX_dp", "dY_dp", "dDeltaPsi_dp"]:
print(name)
for a, b in zip(an_grad[name], fd_grad[name]):
if name == "dDeltaPsi_dp":
# DeltaPsi errors are much worse than X, Y errors!
# FIXME, look into this further
assert a == pytest.approx(b, abs=5e-3)
else:
assert a == pytest.approx(b, abs=5e-6)
print("OK")
def run_spherical_relp_stills_pred_param(self, verbose=True):
if verbose:
print 'Testing derivatives for SphericalRelpStillsPredictionParameterisation'
print '====================================================================='
# Build a prediction parameterisation for the stills experiment
pred_param = SphericalRelpStillsPredictionParameterisation(
self.stills_experiments,
detector_parameterisations = [self.det_param],
beam_parameterisations = [self.s0_param],
xl_orientation_parameterisations = [self.xlo_param],
xl_unit_cell_parameterisations = [self.xluc_param])
# Predict the reflections in place. Must do this ahead of calculating
# the analytical gradients so quantities like s1 are correct
from dials.algorithms.refinement.prediction import ExperimentsPredictor
ref_predictor = ExperimentsPredictor(self.stills_experiments,
spherical_relp=True)
ref_predictor.update()
ref_predictor.predict(self.reflections)
# get analytical gradients
an_grads = pred_param.get_gradients(self.reflections)
fd_grads = self.get_fd_gradients(pred_param, ref_predictor)
# compare FD with analytical calculations
for i, (an_grad, fd_grad) in enumerate(zip(an_grads, fd_grads)):
if verbose: print "\nParameter {0}: {1}". format(i, fd_grad['name'])
for idx, name in enumerate(["dX_dp", "dY_dp", "dDeltaPsi_dp"]):
if verbose: print name
for a, b in zip(an_grad[name], fd_grad[name]):
if name == 'dDeltaPsi_dp':
# DeltaPsi errors are much worse than X, Y errors!
# FIXME, look into this further
assert approx_equal(a,b, eps=5e-3)
else:
assert approx_equal(a,b, eps=5e-6)
if verbose: print "OK"
if verbose: print
def run_spherical_relp_stills_pred_param(self, verbose=True):
if verbose:
print 'Testing derivatives for SphericalRelpStillsPredictionParameterisation'
print '====================================================================='
# Build a prediction parameterisation for the stills experiment
pred_param = SphericalRelpStillsPredictionParameterisation(
self.stills_experiments,
detector_parameterisations=[self.det_param],
beam_parameterisations=[self.s0_param],
xl_orientation_parameterisations=[self.xlo_param],
xl_unit_cell_parameterisations=[self.xluc_param])
# Predict the reflections in place. Must do this ahead of calculating
# the analytical gradients so quantities like s1 are correct
from dials.algorithms.refinement.prediction import ExperimentsPredictor
ref_predictor = ExperimentsPredictor(self.stills_experiments,
spherical_relp=True)
ref_predictor(self.reflections)
# get analytical gradients
an_grads = pred_param.get_gradients(self.reflections)
fd_grads = self.get_fd_gradients(pred_param, ref_predictor)
# compare FD with analytical calculations
for i, (an_grad, fd_grad) in enumerate(zip(an_grads, fd_grads)):
if verbose: print "\nParameter {0}: {1}".format(i, fd_grad['name'])
for idx, name in enumerate(["dX_dp", "dY_dp", "dDeltaPsi_dp"]):
if verbose: print name
for a, b in zip(an_grad[name], fd_grad[name]):
if name == 'dDeltaPsi_dp':
# DeltaPsi errors are much worse than X, Y errors!
# FIXME, look into this further
assert approx_equal(a, b, eps=5e-3)
else:
assert approx_equal(a, b, eps=5e-6)
if verbose: print "OK"
if verbose: print