def __init__(self, crystal, experiment_ids=[0]):
# The state of the unit cell parameterisation is the reciprocal space
# orthogonalisation matrix 'B'. The initial state is irrelevant for
# this model, as composition of a new B matrix and derivatives can be
# done with just the values of 6 unit cell parameters, without
# defining axial directions (which are selected by choice of the PDB
# convention). For this reason also, the axes of the
# parameters are irrelevant and are set here to None.
### 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 = [Parameter(e * 1.e5, name = "g_param_%d" % i) \
for i, e in enumerate(X)]
# set up the base class
ModelParameterisation.__init__(self, crystal, istate, p_list,
experiment_ids=experiment_ids)
# call compose to calculate all the derivatives
self.compose()
return
def __init__(self, beam, goniometer=None, experiment_ids=None):
# The state of the beam model consists of the s0 vector that it is
# modelling. The initial state is the direction of this vector at the point
# of initialisation. Future states are composed by rotations around axes
# perpendicular to that direction and normalisation specified by the
# wavenumber (inverse wavelength).
# Set up the initial state
if experiment_ids is None:
experiment_ids = [0]
s0 = matrix.col(beam.get_s0())
s0dir = matrix.col(beam.get_unit_s0())
istate = s0dir
# build the parameter list
p_list = self._build_p_list(s0, goniometer)
# set up the base class
ModelParameterisation.__init__(self,
beam,
istate,
p_list,
experiment_ids=experiment_ids)
# call compose to calculate all the derivatives
self.compose()
return
def __init__(self, crystal, experiment_ids=None):
# The state of the unit cell parameterisation is the reciprocal space
# orthogonalisation matrix 'B'. The initial state is irrelevant for
# this model, as composition of a new B matrix and derivatives can be
# done with just the values of 6 unit cell parameters, without
# defining axial directions (which are selected by choice of the PDB
# convention). For this reason also, the axes of the
# parameters are irrelevant and are set here to None.
### Set up the initial state
if experiment_ids is None:
experiment_ids = [0]
istate = None
# build the parameter list
p_list = self._build_p_list(crystal)
# set up the base class
ModelParameterisation.__init__(self, crystal, istate, p_list,
experiment_ids=experiment_ids)
# call compose to calculate all the derivatives
self.compose()
return
def __init__(self, crystal, experiment_ids=[0]):
# The state of a crystal orientation parameterisation is an orientation
# matrix '[U]'. The initial state is a snapshot of the crystal
# orientation at the time of initialisation '[U0]'. Future states are
# composed by rotations around axes of the phi-axis frame by Tait-Bryan
# angles.
#
# [U] = [Phi3][Phi2][Phi1][U0]
### Set up the initial state
istate = crystal.get_U()
### Set up the parameters
phi1 = Parameter(.0, matrix.col((1, 0, 0)), 'angle (mrad)', 'Phi1')
phi2 = Parameter(.0, matrix.col((0, 1, 0)), 'angle (mrad)', 'Phi2')
phi3 = Parameter(.0, matrix.col((0, 0, 1)), 'angle (mrad)', 'Phi3')
# build the parameter list in a specific, maintained order
p_list = [phi1, phi2, phi3]
# set up the base class
ModelParameterisation.__init__(self, crystal, istate, p_list,
experiment_ids=experiment_ids)
# call compose to calculate all the derivatives
self.compose()
return
def __init__(self, crystal, experiment_ids=None):
# The state of a crystal orientation parameterisation is an orientation
# matrix '[U]'. The initial state is a snapshot of the crystal
# orientation at the time of initialisation '[U0]'. Future states are
# composed by rotations around axes of the phi-axis frame by Tait-Bryan
# angles.
#
# [U] = [Phi3][Phi2][Phi1][U0]
# set up the initial state
if experiment_ids is None:
experiment_ids = [0]
istate = crystal.get_U()
# build the parameter list
p_list = self._build_p_list()
# set up the base class
ModelParameterisation.__init__(self, crystal, istate, p_list,
experiment_ids=experiment_ids)
# call compose to calculate all the derivatives
self.compose()
return
def __init__(self, beam, goniometer=None, experiment_ids=None):
# The state of the beam model consists of the s0 vector that it is
# modelling. The initial state is the direction of this vector at the point
# of initialisation. Future states are composed by rotations around axes
# perpendicular to that direction and normalisation specified by the
# wavenumber (inverse wavelength).
# Set up the initial state
if experiment_ids is None:
experiment_ids = [0]
s0 = matrix.col(beam.get_s0())
s0dir = matrix.col(beam.get_unit_s0())
istate = s0dir
# build the parameter list
p_list = self._build_p_list(s0, goniometer)
# set up the base class
ModelParameterisation.__init__(self, beam, istate, p_list,
experiment_ids=experiment_ids)
# call compose to calculate all the derivatives
self.compose()
return
def __init__(self, detector, experiment_ids=None):
# The state of a single Panel is its detector matrix d = (d1|d2|d0).
# However, for the purposes of parameterisation we choose a different
# vector than d0 to locate the Panel. That's because we want to perform
# rotations around a point on the detector surface, and d0 points to the
# corner of the Panel. To avoid excess correlations between 'tilt' and
# 'twist' angles with the detector distance, we prefer to perform
# rotations around a point located at the centre of the Panel. This is
# usually close to point of intersection with the plane normal drawn
# from the origin of the laboratory frame.
#
# Therefore we define:
#
# * a vector 'dorg' locating the centre of the single Panel
# * a pair of orthogonal unit directions 'd1' and 'd2' forming a plane
# with its origin at the end of the vector dorg
# * a third unit direction 'dn', orthogonal to both 'd1' & 'd2'.
# * offsets to locate the origin d0 of the Panel frame from the
# tip of the dorg vector, in terms of the coordinate system
# formed by d1, d2 and dn.
#
# Held separately in attribute 'models' are:
# * references to the detector objects contained in this model
#
# For this simplified class there is only a single Panel frame
# and the vector dn is not actually required, because the plane formed
# by d1 and d2 is coplanar with the sensor plane. Therefore the
# offset is fully in terms of d1 and d2
# set up the initial state of the detector parameterisation from the
# orientation of the single Panel it contains, in terms of the vectors
# dorg, d1 and d2.
if experiment_ids is None:
experiment_ids = [0]
dat = self._init_core(detector)
# set up the base class
ModelParameterisation.__init__(self, detector, dat['istate'], dat['p_list'],
experiment_ids=experiment_ids)
# call compose to calculate all the derivatives
self.compose()
return
def __init__(self, beam, goniometer=None, experiment_ids=[0]):
# The state of the beam model consists of the s0 vector that it is
# modelling. The initial state is the direction of this vector at the point
# of initialisation. Future states are composed by rotations around axes
# perpendicular to that direction and normalisation specified by the
# wavenumber (inverse wavelength).
#
# The 'models' attribute refers to the beam vector contained by this
# model.
### Set up the initial state
s0 = matrix.col(beam.get_s0())
s0dir = matrix.col(beam.get_unit_s0())
istate = s0dir
### Set up the parameters
if goniometer:
spindle = matrix.col(goniometer.get_rotation_axis())
s0_plane_dir2 = s0.cross(spindle).normalize()
s0_plane_dir1 = s0_plane_dir2.cross(s0).normalize()
else:
s0_plane_dir1 = s0.ortho().normalize()
s0_plane_dir2 = s0.cross(s0_plane_dir1).normalize()
# rotation around s0_plane_dir1
mu1 = Parameter(.0, s0_plane_dir1, 'angle (mrad)', 'Mu1')
# rotation around s0_plane_dir2
mu2 = Parameter(.0, s0_plane_dir2, 'angle (mrad)', 'Mu2')
# length of s0
nu = Parameter(s0.length(), ptype='wavenumber (Angstroem^-1)', name='nu')
# build the parameter list in a specific, maintained order
p_list = [mu1, mu2, nu]
# set up the base class
ModelParameterisation.__init__(self, beam, istate, p_list,
experiment_ids=experiment_ids)
# call compose to calculate all the derivatives
self.compose()
return
def __init__(self, detector):
# the state of the detector model is comprised of:
#
# * a vector 'dorg' locating the origin of the detector model
# * two orthogonal unit directions 'd1' and 'd2' forming a plane
# with its origin at the end of the vector dorg
# * a third unit direction 'dn', orthogonal to both 'd1' & 'd2'.
# * offsets to locate the origin of each sensor frame from the
# tip of the dorg vector, in terms of the coordinate system
# formed by d1, d2 and dn.
#
# Held separately in attribute 'models' are:
# * references to the sensor objects contained in this model
#
# For this simplified class there is only a single sensor frame
# and the vector dn is not required, because the plane formed by
# d1 and d2 is coplanar with the sensor plane. Therefore the
# offset is fully in terms of d1 and d2
# set up the initial state of the detector model from the
# orientation of the single sensor it contains
# get some vectors we need from the Panel
panel = detector[0]
so = matrix.col(panel.get_origin())
d1 = matrix.col(panel.get_fast_axis())
d2 = matrix.col(panel.get_slow_axis())
dn = matrix.col(panel.get_normal())
# we choose the dorg vector to terminate in the centre of the
# sensor, and the offset between the end of the dorg vector and
# the sensor origin is a coordinate matrix with elements in the
# basis d1, d2, dn
panel_lim = panel.get_image_size_mm()
offset = matrix.col(
(-1.0 * panel_lim[0] / 2.0, -1.0 * panel_lim[1] / 2.0, 0.0))
dorg = so - offset[0] * d1 - offset[1] * d2
# Set up the initial state. There are multiple items of interest, so
# use a dictionary here (note for a single sensor model we can do
# without the first 3 of these, but will need them for multiple sensors)
istate = {"d1": d1, "d2": d2, "dn": dn, "offset": offset}
# set up the parameters.
# distance from lab origin to detector model plane along its
# normal, in initial orientation
distance = panel.get_directed_distance()
dist = Parameter(distance, dn, "length")
# shift in the detector model plane to locate dorg, in initial
# orientation
shift = dorg - dn * distance
shift1 = Parameter(shift.dot(d1), d1, "length")
shift2 = Parameter(shift.dot(d2), d2, "length")
# rotations of the plane through its origin about:
# 1) axis normal to initial orientation
# 2) d1 axis of initial orientation
# 3) d2 axis of initial orientation
tau1 = Parameter(0, dn, "angle")
tau2 = Parameter(0, d1, "angle")
tau3 = Parameter(0, d2, "angle")
# build the parameter list in a specific, maintained order
p_list = [dist, shift1, shift2, tau1, tau2, tau3]
# set up the base class
ModelParameterisation.__init__(self, detector, istate, p_list)
# call compose to calculate all the derivatives
self.compose()
def __init__(self, detector):
# the state of the detector model is comprised of:
#
# * a vector 'dorg' locating the origin of the detector model
# * two orthogonal unit directions 'd1' and 'd2' forming a plane
# with its origin at the end of the vector dorg
# * a third unit direction 'dn', orthogonal to both 'd1' & 'd2'.
# * offsets to locate the origin of each sensor frame from the
# tip of the dorg vector, in terms of the coordinate system
# formed by d1, d2 and dn.
#
# Held separately in attribute 'models' are:
# * references to the sensor objects contained in this model
#
# For this simplified class there is only a single sensor frame
# and the vector dn is not required, because the plane formed by
# d1 and d2 is coplanar with the sensor plane. Therefore the
# offset is fully in terms of d1 and d2
# set up the initial state of the detector model from the
# orientation of the single sensor it contains
# get some vectors we need from the Panel
panel = detector[0]
so = matrix.col(panel.get_origin())
d1 = matrix.col(panel.get_fast_axis())
d2 = matrix.col(panel.get_slow_axis())
dn = matrix.col(panel.get_normal())
# we choose the dorg vector to terminate in the centre of the
# sensor, and the offset between the end of the dorg vector and
# the sensor origin is a coordinate matrix with elements in the
# basis d1, d2, dn
panel_lim = panel.get_image_size_mm()
offset = matrix.col((-1. * panel_lim[0] / 2.,
-1. * panel_lim[1] / 2.,
0.))
dorg = so - offset[0] * d1 - offset[1] * d2
# Set up the initial state. There are multiple items of interest, so
# use a dictionary here (note for a single sensor model we can do
# without the first 3 of these, but will need them for multiple sensors)
istate = {'d1':d1,
'd2':d2,
'dn':dn,
'offset':offset}
# set up the parameters.
# distance from lab origin to detector model plane along its
# normal, in initial orientation
distance = panel.get_directed_distance()
dist = Parameter(distance, dn, 'length')
# shift in the detector model plane to locate dorg, in initial
# orientation
shift = dorg - dn * distance
shift1 = Parameter(shift.dot(d1), d1, 'length')
shift2 = Parameter(shift.dot(d2), d2, 'length')
# rotations of the plane through its origin about:
# 1) axis normal to initial orientation
# 2) d1 axis of initial orientation
# 3) d2 axis of initial orientation
tau1 = Parameter(0, dn, 'angle')
tau2 = Parameter(0, d1, 'angle')
tau3 = Parameter(0, d2, 'angle')
# build the parameter list in a specific, maintained order
p_list = [dist, shift1, shift2, tau1, tau2, tau3]
# set up the base class
ModelParameterisation.__init__(self, detector, istate, p_list)
# call compose to calculate all the derivatives
self.compose()
def __init__(self, detector, experiment_ids=None, level=0):
"""The additional 'level' argument selects which level of the detector
hierarchy is chosen to determine panel groupings that are treated as
separate rigid blocks."""
if experiment_ids is None:
experiment_ids = [0]
try:
h = detector.hierarchy()
except AttributeError:
print "This detector does not have a hierarchy"
raise
# list the panel groups at the chosen level
try:
self._groups = get_panel_groups_at_depth(h, level)
except AttributeError:
print "Cannot access the hierarchy at the depth level={0}".format(level)
raise
# collect the panel ids for each Panel within the groups
panels = [p for p in detector]
self._panel_ids_by_group = [get_panel_ids_at_root(panels, g) for g in self._groups]
p_list = []
self._group_ids_by_parameter = []
istate = []
self._offsets = []
self._dir1s = []
self._dir2s = []
# loop over the groups, collecting initial parameters and states
for igp, pnl_ids in enumerate(self._panel_ids_by_group):
panel_centres_in_lab_frame = []
for i in pnl_ids:
pnl = detector[i]
im_size = pnl.get_image_size_mm()
cntr = matrix.col(pnl.get_origin()) + \
0.5 * matrix.col(pnl.get_fast_axis()) * im_size[0] + \
0.5 * matrix.col(pnl.get_slow_axis()) * im_size[1]
panel_centres_in_lab_frame.append(cntr)
# get some vectors we need from the group
go = matrix.col(self._groups[igp].get_origin())
d1 = matrix.col(self._groups[igp].get_fast_axis())
d2 = matrix.col(self._groups[igp].get_slow_axis())
dn = matrix.col(self._groups[igp].get_normal())
# we choose the dorg vector for this group to terminate on the group's
# frame, at a point that we consider close to the centre of the group of
# panels. This point is defined by taking the 3D centroid of the panel
# centres then projecting that point onto the group frame.
centroid = reduce(lambda a,b: a+b, panel_centres_in_lab_frame) / len(
panel_centres_in_lab_frame)
try:
gp_centroid = matrix.col(self._groups[igp].get_ray_intersection(centroid))
dorg = go + gp_centroid[0] * d1 + gp_centroid[1] * d2
except RuntimeError: # workaround for a group frame that passes through
# the origin
dorg = matrix.col((0., 0., 0.))
# The offset between the end of the dorg vector and
# each Panel origin is a coordinate matrix with elements in the basis d1,
# d2, dn. We need also each Panel's plane directions dir1 and dir2 in
# terms of d1, d2 and dn.
offsets, dir1s, dir2s = [], [], []
#FIXME these dot products would be more efficiently done using a change of
# basis matrix instead
for p in [detector[i] for i in pnl_ids]:
offset = matrix.col(p.get_origin()) - dorg
offsets.append(matrix.col((offset.dot(d1),
offset.dot(d2),
offset.dot(dn))))
dir1 = matrix.col(p.get_fast_axis())
dir1_new_basis = matrix.col((dir1.dot(d1),
dir1.dot(d2),
dir1.dot(dn)))
dir1s.append(dir1_new_basis)
dir2 = matrix.col(p.get_slow_axis())
dir2_new_basis = matrix.col((dir2.dot(d1),
dir2.dot(d2),
dir2.dot(dn)))
dir2s.append(dir2_new_basis)
# The offsets and directions in the d1, d2, dn basis are fixed
# quantities, not dependent on parameter values. Keep these as separate
# sub-lists for each group
self._offsets.append(offsets)
self._dir1s.append(dir1s)
self._dir2s.append(dir2s)
# Set up the initial state for this group. This is the basis d1, d2, dn,
# plus the offset locating the origin of the initial group frame
gp_offset = go - dorg # lab frame basis
#FIXME another set of dot products better done by a matrix multiplication
gp_offset = matrix.col((gp_offset.dot(d1),
gp_offset.dot(d2),
gp_offset.dot(dn))) # d1,d2,dn basis
istate.append({'d1':d1, 'd2':d2, 'dn':dn, 'gp_offset':gp_offset})
# set up the parameters.
# distance from lab origin to ref_panel plane along its normal,
# in initial orientation
distance = self._groups[igp].get_directed_distance()
dist = Parameter(distance,
dn, 'length (mm)', 'Group{0}Dist'.format(igp + 1))
# shift in the detector model plane to locate dorg, in initial
# orientation
shift = dorg - dn * distance
shift1 = Parameter(shift.dot(d1), d1,
'length (mm)', 'Group{0}Shift1'.format(igp + 1))
shift2 = Parameter(shift.dot(d2), d2,
'length (mm)', 'Group{0}Shift2'.format(igp + 1))
# rotations of the plane through its origin about:
# 1) axis normal to initial orientation
# 2) d1 axis of initial orientation
# 3) d2 axis of initial orientation
tau1 = Parameter(0, dn, 'angle (mrad)', 'Group{0}Tau1'.format(igp + 1))
tau2 = Parameter(0, d1, 'angle (mrad)', 'Group{0}Tau2'.format(igp + 1))
tau3 = Parameter(0, d2, 'angle (mrad)', 'Group{0}Tau3'.format(igp + 1))
# extend the parameter list with those pertaining to this group
p_list.extend([dist, shift1, shift2, tau1, tau2, tau3])
self._group_ids_by_parameter.extend([igp] * 6)
# set up the base class
ModelParameterisation.__init__(self, detector, istate, p_list,
experiment_ids=experiment_ids,
is_multi_state=True)
# call compose to calculate all the derivatives
self.compose()
return
def __init__(self, detector, beam, experiment_ids=None):
# The state of each Panel in the detector model is its matrix
# d = (d1|d2|d0). We need to define a new coordinate system rigidly
# attached to the detector model in which to express the
# parameterisation and compose each of the Panel states.
#
# We define:
#
# * a vector 'dorg' locating a point in laboratory space that moves with
# the rigid body of the detector and thus is fixed wrt each of the
# Panels.
# * A pair of orthogonal unit directions 'd1' and 'd2' forming a plane
# with its origin at the end of the vector dorg.
# * a third unit direction 'dn', orthogonal to both 'd1' & 'd2'.
# * offsets to locate the origin of each panel frame from the
# tip of the dorg vector, in terms of the coordinate system
# formed by d1, d2 and dn.
#
# Held separately in attribute 'models' are:
# * references to detector objects contained in this model
# set up the initial state of the detector model from the
# orientation of whichever Panel has its centre most closely
# located to the direct beam intersection. Call this 'mid_panel'
if experiment_ids is None:
experiment_ids = [0]
beam_centres = [matrix.col(p.get_beam_centre(beam.get_unit_s0())) \
for p in detector]
panel_centres = [0.5 * matrix.col(p.get_image_size_mm())
for p in detector]
beam_to_centres = [(a - b).length() for a, b in \
zip(beam_centres, panel_centres)]
mid_panel_id = beam_to_centres.index(min(beam_to_centres))
mid_panel = detector[mid_panel_id]
# get some vectors we need from the mid_panel
so = matrix.col(mid_panel.get_origin())
d1 = matrix.col(mid_panel.get_fast_axis())
d2 = matrix.col(mid_panel.get_slow_axis())
dn = matrix.col(mid_panel.get_normal())
# we choose the dorg vector to terminate in the centre of the mid_panel,
# and the offset between the end of the dorg vector and each Panel
# origin is a coordinate matrix with elements in the basis d1, d2, dn.
# We need also each Panel's plane directions dir1 and dir2 in terms of
# d1, d2 and dn.
mid_panel_centre = panel_centres[mid_panel_id]
dorg = so + mid_panel_centre[0] * d1 + mid_panel_centre[1] * d2
offsets, dir1s, dir2s = [], [], []
for p in detector:
offset = matrix.col(p.get_origin()) - dorg
offsets.append(matrix.col((offset.dot(d1),
offset.dot(d2),
offset.dot(dn))))
dir1 = matrix.col(p.get_fast_axis())
dir1_new_basis = matrix.col((dir1.dot(d1),
dir1.dot(d2),
dir1.dot(dn)))
dir1s.append(dir1_new_basis)
dir2 = matrix.col(p.get_slow_axis())
dir2_new_basis = matrix.col((dir2.dot(d1),
dir2.dot(d2),
dir2.dot(dn)))
dir2s.append(dir2_new_basis)
# The offsets and directions in the d1, d2, dn basis are fixed
# quantities, not dependent on parameter values.
self._offsets = offsets
self._dir1s = dir1s
self._dir2s = dir2s
# Set up the initial state. This is the basis d1, d2, dn.
istate = {'d1':d1, 'd2':d2, 'dn':dn}
# set up the parameters.
# distance from lab origin to mid_panel plane along its normal,
# in initial orientation
distance = mid_panel.get_directed_distance()
dist = Parameter(distance, dn, 'length (mm)', 'Dist')
# shift in the detector model plane to locate dorg, in initial
# orientation
shift = dorg - dn * distance
shift1 = Parameter(shift.dot(d1), d1, 'length (mm)', 'Shift1')
shift2 = Parameter(shift.dot(d2), d2, 'length (mm)', 'Shift2')
# rotations of the plane through its origin about:
# 1) axis normal to initial orientation
# 2) d1 axis of initial orientation
# 3) d2 axis of initial orientation
tau1 = Parameter(0, dn, 'angle (mrad)', 'Tau1')
tau2 = Parameter(0, d1, 'angle (mrad)', 'Tau2')
tau3 = Parameter(0, d2, 'angle (mrad)', 'Tau3')
# build the parameter list in a specific, maintained order
p_list = [dist, shift1, shift2, tau1, tau2, tau3]
# set up the base class
ModelParameterisation.__init__(self, detector, istate, p_list,
experiment_ids=experiment_ids,
is_multi_state=True)
# call compose to calculate all the derivatives
self.compose()
return
