def __init__(self, detector, t_range, num_intervals, experiment_ids=None):
if experiment_ids is None:
experiment_ids = [0]
# Set up the smoother
smoother = GaussianSmoother(t_range, num_intervals)
nv = smoother.num_values()
# Factory function to provide to _init_core
def parameter_type(value, axis, ptype, name):
return ScanVaryingParameterSet(value, nv, axis, ptype, name)
# Set up the initial state and parameter list
dat = self._init_core(detector, parameter_type)
self._d_at_t = matrix.sqr(detector[0].get_d_matrix())
# set up the base class
ScanVaryingModelParameterisation.__init__(
self,
detector,
dat["istate"],
dat["p_list"],
smoother,
experiment_ids=experiment_ids,
)
return
def __init__(self, crystal, t_range, num_intervals, experiment_ids=None):
if experiment_ids is None:
experiment_ids = [0]
# The state of a scan-varying unit cell parameterisation is the
# reciprocal space orthogonalisation matrix '[B](t)', expressed as a
# function of image number 't' in a sequential scan.
# Other comments from CrystalUnitCellParameterisation are relevant here
# Set up the smoother
smoother = GaussianSmoother(t_range, num_intervals)
nv = smoother.num_values()
# Set up the initial state
istate = None
self._B_at_t = crystal.get_B()
# Factory function to provide to _build_p_list
def parameter_type(value, name):
return ScanVaryingParameterSet(value, nv, name=name)
# Build the parameter list
p_list = self._build_p_list(crystal, parameter_type)
# Set up the base class
ScanVaryingModelParameterisation.__init__(
self,
crystal,
istate,
p_list,
smoother,
experiment_ids=experiment_ids)
return
def __init__(self, crystal, t_range, num_intervals, experiment_ids=None):
if experiment_ids is None:
experiment_ids = [0]
# The state of a scan-varying unit cell parameterisation is the
# reciprocal space orthogonalisation matrix '[B](t)', expressed as a
# function of image number 't' in a sequential scan.
# Other comments from CrystalUnitCellParameterisation are relevant here
# Set up the smoother
smoother = GaussianSmoother(t_range, num_intervals)
nv = smoother.num_values()
# Set up the initial state
istate = None
self._B_at_t = crystal.get_B()
# Factory function to provide to _build_p_list
def parameter_type(value, name):
return ScanVaryingParameterSet(value, nv, name=name)
# Build the parameter list
p_list = self._build_p_list(crystal, parameter_type)
# Set up the base class
ScanVaryingModelParameterisation.__init__(
self, crystal, istate, p_list, smoother, experiment_ids=experiment_ids
)
return
def __init__(self, crystal, t_range, num_intervals, experiment_ids=[0]):
# The state of a scan-varying unit cell parameterisation is the
# reciprocal space orthogonalisation matrix '[B](t)', expressed as a
# function of image number 't' in a sequential scan.
# Other comments from CrystalUnitCellParameterisation are relevant here
# Set up the smoother
smoother = GaussianSmoother(t_range, num_intervals)
nv = smoother.num_values()
### Set up the initial state
istate = None
### Set up symmetrizing object
self._S = symmetrize_reduce_enlarge(crystal.get_space_group())
self._S.set_orientation(orientation=crystal.get_B())
X = self._S.forward_independent_parameters()
dB_dp = self._S.forward_gradients()
B = self._S.backward_orientation(independent=X).reciprocal_matrix()
### Set up the independent parameters, with a change of scale
p_list = [ScanVaryingParameterSet(e * 1.e5, nv, name = "g_param_%d" % i) \
for i, e in enumerate(X)]
# Set up the base class
ScanVaryingModelParameterisation.__init__(self, crystal, istate,
p_list, smoother,
experiment_ids=experiment_ids)
return
def __init__(self,
beam,
t_range,
num_intervals,
goniometer=None,
experiment_ids=None):
if experiment_ids is None:
experiment_ids = [0]
# The state of a scan varying beam parameterisation is a vector
# '[s0](t)', expressed as a function of image number 't'
# in a sequential scan.
#
# The initial state is a snapshot of the beam unit direction
# at the point of initialisation '[s0dir]', which is independent of
# image number.
#
# Future states are composed by rotations around axes orthogonal to the
# initial direction and scaling by the wavenumber.
#
# [s0](t) = nu(t) * [Mu2](t)[Mu1](t)[s0dir]
# Set up the smoother
smoother = GaussianSmoother(t_range, num_intervals)
nv = smoother.num_values()
# Set up the initial state
s0 = matrix.col(beam.get_s0())
s0dir = matrix.col(beam.get_unit_s0())
istate = s0dir
self._s0_at_t = s0
# Factory function to provide to _build_p_list
def parameter_type(value, axis, ptype, name):
return ScanVaryingParameterSet(value, nv, axis, ptype, name)
# Build the parameter list
p_list = self._build_p_list(s0,
goniometer,
parameter_type=parameter_type)
# Set up the base class
ScanVaryingModelParameterisation.__init__(
self,
beam,
istate,
p_list,
smoother,
experiment_ids=experiment_ids)
return
def __init__(self,
goniometer,
t_range,
num_intervals,
beam=None,
experiment_ids=None):
if experiment_ids is None:
experiment_ids = [0]
# The state of a scan varying goniometer parameterisation is a matrix
# '[S](t)', expressed as a function of image number 't'
# in a sequential scan.
#
# The initial state is a snapshot of the setting matrix
# at the point of initialisation '[S0]', which is independent of
# image number.
#
# Future states are composed by rotations around two axes orthogonal to the
# initial spindle axis direction.
#
# [S](t) = [G2](t)[G1](t)[S0]
# Set up the smoother
smoother = GaussianSmoother(t_range, num_intervals)
nv = smoother.num_values()
# Set up the initial state
e_lab = matrix.col(goniometer.get_rotation_axis())
istate = matrix.sqr(goniometer.get_setting_rotation())
self._S_at_t = istate
# Factory function to provide to _build_p_list
def parameter_type(value, axis, ptype, name):
return ScanVaryingParameterSet(value, nv, axis, ptype, name)
# Build the parameter list
p_list = self._build_p_list(e_lab, beam, parameter_type=parameter_type)
# Set up the base class
ScanVaryingModelParameterisation.__init__(
self,
goniometer,
istate,
p_list,
smoother,
experiment_ids=experiment_ids)
return
def __init__(self, crystal, t_range, num_intervals, experiment_ids=None):
if experiment_ids is None:
experiment_ids = [0]
# The state of a scan varying crystal orientation parameterisation
# is an orientation
# matrix '[U](t)', expressed as a function of image number 't'
# in a sequential scan.
#
# The initial state is a snapshot of the crystal orientation
# at the point of initialisation '[U0]', which is independent of
# image number.
#
# Future states are composed by
# rotations around axes of the phi-axis frame by Tait-Bryan angles.
#
# [U](t) = [Phi3](t)[Phi2](t)[Phi1](t)[U0]
# Set up the smoother
smoother = GaussianSmoother(t_range, num_intervals)
nv = smoother.num_values()
# Set up the initial state
istate = matrix.sqr(crystal.get_U())
self._U_at_t = istate
# Factory function to provide to _build_p_list
def parameter_type(value, axis, ptype, name):
return ScanVaryingParameterSet(value, nv, axis, ptype, name)
# Build the parameter list
p_list = self._build_p_list(parameter_type)
# Set up the base class
ScanVaryingModelParameterisation.__init__(
self,
crystal,
istate,
p_list,
smoother,
experiment_ids=experiment_ids)
return
def __init__(self, beam, t_range, num_intervals, goniometer=None,
experiment_ids=None):
if experiment_ids is None:
experiment_ids = [0]
# The state of a scan varying beam parameterisation is a vector
# '[s0](t)', expressed as a function of image number 't'
# in a sequential scan.
#
# The initial state is a snapshot of the beam unit direction
# at the point of initialisation '[s0dir]', which is independent of
# image number.
#
# Future states are composed by rotations around axes orthogonal to the
# initial direction and scaling by the wavenumber.
#
# [s0](t) = nu(t) * [Mu2](t)[Mu1](t)[s0dir]
# Set up the smoother
smoother = GaussianSmoother(t_range, num_intervals)
nv = smoother.num_values()
# Set up the initial state
s0 = matrix.col(beam.get_s0())
s0dir = matrix.col(beam.get_unit_s0())
istate = s0dir
self._s0_at_t = s0
# Factory function to provide to _build_p_list
def parameter_type(value, axis, ptype, name):
return ScanVaryingParameterSet(value, nv, axis, ptype, name)
# Build the parameter list
p_list = self._build_p_list(s0, goniometer, parameter_type=parameter_type)
# Set up the base class
ScanVaryingModelParameterisation.__init__(self, beam, istate,
p_list, smoother,
experiment_ids=experiment_ids)
return
def __init__(self, crystal, t_range, num_intervals, experiment_ids=[0]):
# The state of a scan varying crystal orientation parameterisation
# is an orientation
# matrix '[U](t)', expressed as a function of image number 't'
# in a sequential scan.
#
# The initial state is a snapshot of the crystal orientation
# at the point of initialisation '[U0]', which is independent of
# image number.
#
# Future states are composed by
# rotations around axes of the phi-axis frame by Tait-Bryan angles.
#
# [U](t) = [Phi3](t)[Phi2](t)[Phi1](t)[U0]
# Set up the smoother
smoother = GaussianSmoother(t_range, num_intervals)
nv = smoother.num_values()
# Set up the initial state
istate = crystal.get_U()
# Set up the parameters
phi1 = ScanVaryingParameterSet(0.0, nv,
matrix.col((1., 0., 0.)), 'angle (mrad)', 'Phi1')
phi2 = ScanVaryingParameterSet(0.0, nv,
matrix.col((0., 1., 0.)), 'angle (mrad)', 'Phi2')
phi3 = ScanVaryingParameterSet(0.0, nv,
matrix.col((0., 0., 1.)), 'angle (mrad)', 'Phi3')
# Build the list of parameter sets in a specific, maintained order
p_list = [phi1, phi2, phi3]
# Set up the base class
ScanVaryingModelParameterisation.__init__(self, crystal, istate,
p_list, smoother,
experiment_ids=experiment_ids)
return
def __init__(self, crystal, t_range, num_intervals, experiment_ids=None):
if experiment_ids is None:
experiment_ids = [0]
# The state of a scan varying crystal orientation parameterisation
# is an orientation
# matrix '[U](t)', expressed as a function of image number 't'
# in a sequential scan.
#
# The initial state is a snapshot of the crystal orientation
# at the point of initialisation '[U0]', which is independent of
# image number.
#
# Future states are composed by
# rotations around axes of the phi-axis frame by Tait-Bryan angles.
#
# [U](t) = [Phi3](t)[Phi2](t)[Phi1](t)[U0]
# Set up the smoother
smoother = GaussianSmoother(t_range, num_intervals)
nv = smoother.num_values()
# Set up the initial state
istate = crystal.get_U()
self._U_at_t = istate
# Factory function to provide to _build_p_list
def parameter_type(value, axis, ptype, name):
return ScanVaryingParameterSet(value, nv, axis, ptype, name)
# Build the parameter list
p_list = self._build_p_list(parameter_type)
# Set up the base class
ScanVaryingModelParameterisation.__init__(
self, crystal, istate, p_list, smoother, experiment_ids=experiment_ids
)
return
