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DAMASK_EICMD
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introduction of orientation-relationship (OR) models: Kurdjumov-Sachs (KS), Nishiyama and Wassermann (NW), Greninger-Troiano(GT), Bain

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Yannick Naunheim 2015-05-08 14:14:44 +00:00
parent f561e2d12e
commit 90ae536204
1 changed files with 288 additions and 57 deletions
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345
lib/damask/orientation.py
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@ -1,26 +1,27 @@
# -*- coding: UTF-8 no BOM -*- # -*- coding: UTF-8 no BOM -*-
################################################### ###################################################
# NOTE: everything here needs to be a numpy array # # NOTE: everything here needs to be a np array #
################################################### ###################################################
import numpy,math,random import math,random
import numpy as np
# ****************************************************************************************** # ******************************************************************************************
class Rodrigues: class Rodrigues:
# ****************************************************************************************** # ******************************************************************************************
def __init__(self, vector = numpy.zeros(3)): def __init__(self, vector = np.zeros(3)):
self.vector = vector self.vector = vector
def asQuaternion(self): def asQuaternion(self):
norm = numpy.linalg.norm(self.vector) norm = np.linalg.norm(self.vector)
halfAngle = numpy.arctan(norm) halfAngle = np.arctan(norm)
return Quaternion(numpy.cos(halfAngle),numpy.sin(halfAngle)*self.vector/norm) return Quaternion(np.cos(halfAngle),np.sin(halfAngle)*self.vector/norm)
def asAngleAxis(self): def asAngleAxis(self):
norm = numpy.linalg.norm(self.vector) norm = np.linalg.norm(self.vector)
halfAngle = numpy.arctan(norm) halfAngle = np.arctan(norm)
return (2.0*halfAngle,self.vector/norm) return (2.0*halfAngle,self.vector/norm)
@ -69,7 +70,7 @@ class Quaternion:
self.x *= vRescale self.x *= vRescale
self.y *= vRescale self.y *= vRescale
self.z *= vRescale self.z *= vRescale
self.w = numpy.cos(exponent*omega) self.w = np.cos(exponent*omega)
return self return self
def __mul__(self, other): def __mul__(self, other):
@ -98,7 +99,7 @@ class Quaternion:
Vy = other[1] Vy = other[1]
Vz = other[2] Vz = other[2]
return numpy.array([\ return np.array([\
w * w * Vx + 2 * y * w * Vz - 2 * z * w * Vy + \ w * w * Vx + 2 * y * w * Vz - 2 * z * w * Vy + \
x * x * Vx + 2 * y * x * Vy + 2 * z * x * Vz - \ x * x * Vx + 2 * y * x * Vy + 2 * z * x * Vz - \
z * z * Vx - y * y * Vx, z * z * Vx - y * y * Vx,
@ -291,12 +292,12 @@ class Quaternion:
return [i for i in self] return [i for i in self]
def asM(self): # to find Averaging Quaternions (see F. Landis Markley et al.) def asM(self): # to find Averaging Quaternions (see F. Landis Markley et al.)
return numpy.outer([i for i in self],[i for i in self]) return np.outer([i for i in self],[i for i in self])
def asMatrix(self): def asMatrix(self):
return numpy.array([[1.0-2.0*(self.y*self.y+self.z*self.z), 2.0*(self.x*self.y-self.z*self.w), 2.0*(self.x*self.z+self.y*self.w)], return np.array([[1.0-2.0*(self.y*self.y+self.z*self.z), 2.0*(self.x*self.y-self.z*self.w), 2.0*(self.x*self.z+self.y*self.w)],
[ 2.0*(self.x*self.y+self.z*self.w), 1.0-2.0*(self.x*self.x+self.z*self.z), 2.0*(self.y*self.z-self.x*self.w)], [ 2.0*(self.x*self.y+self.z*self.w), 1.0-2.0*(self.x*self.x+self.z*self.z), 2.0*(self.y*self.z-self.x*self.w)],
[ 2.0*(self.x*self.z-self.y*self.w), 2.0*(self.x*self.w+self.y*self.z), 1.0-2.0*(self.x*self.x+self.y*self.y)]]) [ 2.0*(self.x*self.z-self.y*self.w), 2.0*(self.x*self.w+self.y*self.z), 1.0-2.0*(self.x*self.x+self.y*self.y)]])
def asAngleAxis(self): def asAngleAxis(self):
if self.w > 1: if self.w > 1:
@ -309,15 +310,15 @@ class Quaternion:
angle = math.atan2(y,x) angle = math.atan2(y,x)
if angle < 1e-3: if angle < 1e-3:
return angle, numpy.array([1.0, 0.0, 0.0]) return angle, np.array([1.0, 0.0, 0.0])
else: else:
return angle, numpy.array([self.x / s, self.y / s, self.z / s]) return angle, np.array([self.x / s, self.y / s, self.z / s])
def asRodrigues(self): def asRodrigues(self):
if self.w != 0.0: if self.w != 0.0:
return numpy.array([self.x, self.y, self.z])/self.w return np.array([self.x, self.y, self.z])/self.w
else: else:
return numpy.array([float('inf')]*3) return np.array([float('inf')]*3)
def asEulers(self,type='bunge'): def asEulers(self,type='bunge'):
''' '''
@ -384,8 +385,8 @@ class Quaternion:
@classmethod @classmethod
def fromRodrigues(cls, rodrigues): def fromRodrigues(cls, rodrigues):
if not isinstance(rodrigues, numpy.ndarray): rodrigues = numpy.array(rodrigues) if not isinstance(rodrigues, np.ndarray): rodrigues = np.array(rodrigues)
halfangle = math.atan(numpy.linalg.norm(rodrigues)) halfangle = math.atan(np.linalg.norm(rodrigues))
c = math.cos(halfangle) c = math.cos(halfangle)
w = c w = c
x,y,z = c*rodrigues x,y,z = c*rodrigues
@ -394,8 +395,8 @@ class Quaternion:
@classmethod @classmethod
def fromAngleAxis(cls, angle, axis): def fromAngleAxis(cls, angle, axis):
if not isinstance(axis, numpy.ndarray): axis = numpy.array(axis) if not isinstance(axis, np.ndarray): axis = np.array(axis)
axis /= numpy.linalg.norm(axis) axis /= np.linalg.norm(axis)
s = math.sin(angle / 2.0) s = math.sin(angle / 2.0)
w = math.cos(angle / 2.0) w = math.cos(angle / 2.0)
x = axis[0] * s x = axis[0] * s
@ -677,52 +678,52 @@ class Symmetry:
Check whether given vector falls into standard stereographic triangle of own symmetry. Check whether given vector falls into standard stereographic triangle of own symmetry.
Return inverse pole figure color if requested. Return inverse pole figure color if requested.
''' '''
# basis = {'cubic' : numpy.linalg.inv(numpy.array([[0.,0.,1.], # direction of red # basis = {'cubic' : np.linalg.inv(np.array([[0.,0.,1.], # direction of red
# [1.,0.,1.]/numpy.sqrt(2.), # direction of green # [1.,0.,1.]/np.sqrt(2.), # direction of green
# [1.,1.,1.]/numpy.sqrt(3.)]).transpose()), # direction of blue # [1.,1.,1.]/np.sqrt(3.)]).transpose()), # direction of blue
# 'hexagonal' : numpy.linalg.inv(numpy.array([[0.,0.,1.], # direction of red # 'hexagonal' : np.linalg.inv(np.array([[0.,0.,1.], # direction of red
# [1.,0.,0.], # direction of green # [1.,0.,0.], # direction of green
# [numpy.sqrt(3.),1.,0.]/numpy.sqrt(4.)]).transpose()), # direction of blue # [np.sqrt(3.),1.,0.]/np.sqrt(4.)]).transpose()), # direction of blue
# 'tetragonal' : numpy.linalg.inv(numpy.array([[0.,0.,1.], # direction of red # 'tetragonal' : np.linalg.inv(np.array([[0.,0.,1.], # direction of red
# [1.,0.,0.], # direction of green # [1.,0.,0.], # direction of green
# [1.,1.,0.]/numpy.sqrt(2.)]).transpose()), # direction of blue # [1.,1.,0.]/np.sqrt(2.)]).transpose()), # direction of blue
# 'orthorhombic' : numpy.linalg.inv(numpy.array([[0.,0.,1.], # direction of red # 'orthorhombic' : np.linalg.inv(np.array([[0.,0.,1.], # direction of red
# [1.,0.,0.], # direction of green # [1.,0.,0.], # direction of green
# [0.,1.,0.]]).transpose()), # direction of blue # [0.,1.,0.]]).transpose()), # direction of blue
# } # }
if self.lattice == 'cubic': if self.lattice == 'cubic':
basis = numpy.array([ [-1. , 0. , 1. ], basis = np.array([ [-1. , 0. , 1. ],
[ numpy.sqrt(2.), -numpy.sqrt(2.), 0. ], [ np.sqrt(2.), -np.sqrt(2.), 0. ],
[ 0. , numpy.sqrt(3.), 0. ] ]) [ 0. , np.sqrt(3.), 0. ] ])
elif self.lattice == 'hexagonal': elif self.lattice == 'hexagonal':
basis = numpy.array([ [ 0. , 0. , 1. ], basis = np.array([ [ 0. , 0. , 1. ],
[ 1. , -numpy.sqrt(3.), 0. ], [ 1. , -np.sqrt(3.), 0. ],
[ 0. , 2. , 0. ] ]) [ 0. , 2. , 0. ] ])
elif self.lattice == 'tetragonal': elif self.lattice == 'tetragonal':
basis = numpy.array([ [ 0. , 0. , 1. ], basis = np.array([ [ 0. , 0. , 1. ],
[ 1. , -1. , 0. ], [ 1. , -1. , 0. ],
[ 0. , numpy.sqrt(2.), 0. ] ]) [ 0. , np.sqrt(2.), 0. ] ])
elif self.lattice == 'orthorhombic': elif self.lattice == 'orthorhombic':
basis = numpy.array([ [ 0., 0., 1.], basis = np.array([ [ 0., 0., 1.],
[ 1., 0., 0.], [ 1., 0., 0.],
[ 0., 1., 0.] ]) [ 0., 1., 0.] ])
else: else:
basis = None basis = None
if basis == None: if basis == None:
theComponents = -numpy.ones(3,'d') theComponents = -np.ones(3,'d')
else: else:
theComponents = numpy.dot(basis,numpy.array([vector[0],vector[1],abs(vector[2])])) theComponents = np.dot(basis,np.array([vector[0],vector[1],abs(vector[2])]))
inSST = numpy.all(theComponents >= 0.0) inSST = np.all(theComponents >= 0.0)
if color: # have to return color array if color: # have to return color array
if inSST: if inSST:
rgb = numpy.power(theComponents/numpy.linalg.norm(theComponents),0.5) # smoothen color ramps rgb = np.power(theComponents/np.linalg.norm(theComponents),0.5) # smoothen color ramps
rgb = numpy.minimum(numpy.ones(3,'d'),rgb) # limit to maximum intensity rgb = np.minimum(np.ones(3,'d'),rgb) # limit to maximum intensity
rgb /= max(rgb) # normalize to (HS)V = 1 rgb /= max(rgb) # normalize to (HS)V = 1
else: else:
rgb = numpy.zeros(3,'d') rgb = np.zeros(3,'d')
return (inSST,rgb) return (inSST,rgb)
else: else:
return inSST return inSST
@ -749,17 +750,17 @@ class Orientation:
): ):
if random: # produce random orientation if random: # produce random orientation
self.quaternion = Quaternion.fromRandom() self.quaternion = Quaternion.fromRandom()
elif isinstance(Eulers, numpy.ndarray) and Eulers.shape == (3,): # based on given Euler angles elif isinstance(Eulers, np.ndarray) and Eulers.shape == (3,): # based on given Euler angles
self.quaternion = Quaternion.fromEulers(Eulers,'bunge') self.quaternion = Quaternion.fromEulers(Eulers,'bunge')
elif isinstance(matrix, numpy.ndarray) and matrix.shape == (3,3): # based on given rotation matrix elif isinstance(matrix, np.ndarray) and matrix.shape == (3,3): # based on given rotation matrix
self.quaternion = Quaternion.fromMatrix(matrix) self.quaternion = Quaternion.fromMatrix(matrix)
elif isinstance(angleAxis, numpy.ndarray) and angleAxis.shape == (4,): # based on given angle and rotation axis elif isinstance(angleAxis, np.ndarray) and angleAxis.shape == (4,): # based on given angle and rotation axis
self.quaternion = Quaternion.fromAngleAxis(angleAxis[0],angleAxis[1:4]) self.quaternion = Quaternion.fromAngleAxis(angleAxis[0],angleAxis[1:4])
elif isinstance(Rodrigues, numpy.ndarray) and Rodrigues.shape == (3,): # based on given Rodrigues vector elif isinstance(Rodrigues, np.ndarray) and Rodrigues.shape == (3,): # based on given Rodrigues vector
self.quaternion = Quaternion.fromRodrigues(Rodrigues) self.quaternion = Quaternion.fromRodrigues(Rodrigues)
elif isinstance(quaternion, Quaternion): # based on given quaternion elif isinstance(quaternion, Quaternion): # based on given quaternion
self.quaternion = quaternion.homomorphed() self.quaternion = quaternion.homomorphed()
elif isinstance(quaternion, numpy.ndarray) and quaternion.shape == (4,): # based on given quaternion elif isinstance(quaternion, np.ndarray) and quaternion.shape == (4,): # based on given quaternion
self.quaternion = Quaternion(quaternion).homomorphed() self.quaternion = Quaternion(quaternion).homomorphed()
self.symmetry = Symmetry(symmetry) self.symmetry = Symmetry(symmetry)
@ -774,7 +775,7 @@ class Orientation:
return 'Symmetry: %s\n' % (self.symmetry) + \ return 'Symmetry: %s\n' % (self.symmetry) + \
'Quaternion: %s\n' % (self.quaternion) + \ 'Quaternion: %s\n' % (self.quaternion) + \
'Matrix:\n%s\n' % ( '\n'.join(['\t'.join(map(str,self.asMatrix()[i,:])) for i in range(3)]) ) + \ 'Matrix:\n%s\n' % ( '\n'.join(['\t'.join(map(str,self.asMatrix()[i,:])) for i in range(3)]) ) + \
'Bunge Eulers / deg: %s' % ('\t'.join(map(lambda x:str(numpy.degrees(x)),self.asEulers('bunge'))) ) 'Bunge Eulers / deg: %s' % ('\t'.join(map(lambda x:str(np.degrees(x)),self.asEulers('bunge'))) )
def asQuaternion(self): def asQuaternion(self):
return self.quaternion.asList() return self.quaternion.asList()
@ -845,7 +846,7 @@ class Orientation:
TSL color of inverse pole figure for given axis TSL color of inverse pole figure for given axis
''' '''
color = numpy.zeros(3,'d') color = np.zeros(3,'d')
for i,q in enumerate(self.symmetry.equivalentQuaternions(self.quaternion)): for i,q in enumerate(self.symmetry.equivalentQuaternions(self.quaternion)):
pole = q.conjugated()*axis # align crystal direction to axis pole = q.conjugated()*axis # align crystal direction to axis
@ -871,5 +872,235 @@ class Orientation:
tmp_m = orientationList.pop(0).quaternion.asM() tmp_m = orientationList.pop(0).quaternion.asM()
for tmp_o in orientationList: for tmp_o in orientationList:
tmp_m += tmp_o.quaternion.asM() tmp_m += tmp_o.quaternion.asM()
eig, vec = numpy.linalg.eig(tmp_m/n) eig, vec = np.linalg.eig(tmp_m/n)
return Orientation( quaternion=Quaternion(quatArray=vec.T[eig.argmax()]) ) return Orientation( quaternion=Quaternion(quatArray=vec.T[eig.argmax()]) )
def related(self, relationModel, direction, targetSymmetry):
if relationModel not in ['KS','GT',"GT'",'NW','Bain']: return None
variant = int(abs(direction))
me = 0 if direction > 0 else 1
other = 1 if direction > 0 else 0
planes = {'KS': \
np.array([[[ 1, 1, 1],[ 0, 1, 1]],\
[[ 1, 1, 1],[ 0, 1, 1]],\
[[ 1, 1, 1],[ 0, 1, 1]],\
[[ 1, 1, 1],[ 0, 1, 1]],\
[[ 1, 1, 1],[ 0, 1, 1]],\
[[ 1, 1, 1],[ 0, 1, 1]],\
[[ 1, -1, 1],[ 0, 1, 1]],\
[[ 1, -1, 1],[ 0, 1, 1]],\
[[ 1, -1, 1],[ 0, 1, 1]],\
[[ 1, -1, 1],[ 0, 1, 1]],\
[[ 1, -1, 1],[ 0, 1, 1]],\
[[ 1, -1, 1],[ 0, 1, 1]],\
[[ -1, 1, 1],[ 0, 1, 1]],\
[[ -1, 1, 1],[ 0, 1, 1]],\
[[ -1, 1, 1],[ 0, 1, 1]],\
[[ -1, 1, 1],[ 0, 1, 1]],\
[[ -1, 1, 1],[ 0, 1, 1]],\
[[ -1, 1, 1],[ 0, 1, 1]],\
[[ 1, 1, 1],[ 0, 1, 1]],\
[[ 1, 1, 1],[ 0, 1, 1]],\
[[ 1, 1, 1],[ 0, 1, 1]],\
[[ 1, 1, 1],[ 0, 1, 1]],\
[[ 1, 1, 1],[ 0, 1, 1]],\
[[ 1, 1, 1],[ 0, 1, 1]]]),
'GT': \
np.array([[[ 1, 1, 1],[ 1, 1, 0]],\
[[ 1, 1, 1],[ 1, 0, 1]],\
[[ -1, -1, 1],[ -1, -1, 0]],\
[[ -1, -1, 1],[ -1, 0, 1]],\
[[ -1, 1, 1],[ -1, 1, 0]],\
[[ -1, 1, 1],[ -1, 0, 1]],\
[[ 1, -1, 1],[ 1, -1, 0]],\
[[ 1, -1, 1],[ 1, 0, 1]],\
[[ 1, 1, 1],[ 0, 1, 1]],\
[[ 1, 1, 1],[ 1, 1, 0]],\
[[ -1, -1, 1],[ 0, -1, 1]],\
[[ -1, -1, 1],[ -1, -1, 0]],\
[[ -1, 1, 1],[ 0, 1, 1]],\
[[ -1, 1, 1],[ -1, 1, 0]],\
[[ 1, -1, 1],[ 0, -1, 1]],\
[[ 1, -1, 1],[ 1, -1, 0]],\
[[ 1, 1, 1],[ 1, 0, 1]],\
[[ 1, 1, 1],[ 0, 1, 1]],\
[[ -1, -1, 1],[ -1, 0, 1]],\
[[ -1, -1, 1],[ 0, -1, 1]],\
[[ -1, 1, 1],[ -1, 0, 1]],\
[[ -1, 1, 1],[ 0, 1, 1]],\
[[ 1, -1, 1],[ 1, 0, 1]],\
[[ 1, -1, 1],[ 0, -1, 1]]]),
"GT'": \
np.array([[[ 17, 7, 17],[ 17, 12, 5]],\
[[-17, 7,-17],[-17, 12, -5]],\
[[-17, -7, 17],[-17,-12, 5]],\
[[ 17, -7,-17],[ 17,-12, -5]],\
[[ 17, 17, 7],[ 17, 5, 12]],\
[[-17,-17, 7],[-17, -5, 12]],\
[[ 17,-17, -7],[ 17, -5,-12]],\
[[-17, 17, -7],[-17, 5,-12]],\
[[ 17, 17, 7],[ 5, 17, 12]],\
[[-17,-17, 7],[ -5,-17, 12]],\
[[ 17,-17, -7],[ 5,-17,-12]],\
[[-17, 17, -7],[ -5, 17,-12]],\
[[ 7, 17, 17],[ 12, 17, 5]],\
[[ 7,-17,-17],[ 12,-17, -5]],\
[[ -7,-17, 17],[-12,-17, 5]],\
[[ -7, 17,-17],[-12, 17, -5]],\
[[ 7, 17, 17],[ 12, 5, 17]],\
[[ 7,-17,-17],[ 12, -5,-17]],\
[[ -7,-17, 17],[-12, -5, 17]],\
[[ -7, 17,-17],[-12, 5,-17]],\
[[ 17, 7, 17],[ 5, 12, 17]],\
[[-17, 7,-17],[ -5, 12,-17]],\
[[-17, -7, 17],[ -5,-12, 17]],\
[[ 17, -7,-17],[ 5,-12,-17]]]),
'NW': \
np.array([[[ 1, 1, 1],[ 0, 1, 1]],\
[[ 1, 1, 1],[ 0, 1, 1]],\
[[ 1, 1, 1],[ 0, 1, 1]],\
[[ -1, 1, 1],[ 0, 1, 1]],\
[[ -1, 1, 1],[ 0, 1, 1]],\
[[ -1, 1, 1],[ 0, 1, 1]],\
[[ 1, -1, 1],[ 0, 1, 1]],\
[[ 1, -1, 1],[ 0, 1, 1]],\
[[ 1, -1, 1],[ 0, 1, 1]],\
[[ 1, 1, -1],[ 0, 1, 1]],\
[[ 1, 1, -1],[ 0, 1, 1]],\
[[ 1, 1, -1],[ 0, 1, 1]]]),
'Bain': \
np.array([[[ 1, 0, 0],[ 1, 0, 0]],\
[[ 0, 1, 0],[ 0, 1, 0]],\
[[ 0, 0, 1],[ 0, 0, 1]]]),
}
normals = {'KS': \
np.array([[[ -1, 0, 1],[ -1, -1, 1]],\
[[ -1, 0, 1],[ -1, 1, -1]],\
[[ 0, 1, -1],[ -1, -1, 1]],\
[[ 0, 1, -1],[ -1, 1, -1]],\
[[ 1, -1, 0],[ -1, -1, 1]],\
[[ 1, -1, 0],[ -1, 1, -1]],\
[[ -1, 0, 1],[ -1, -1, 1]],\
[[ -1, 0, 1],[ -1, 1, -1]],\
[[ 0, 1, -1],[ -1, -1, 1]],\
[[ 0, 1, -1],[ -1, 1, -1]],\
[[ 1, -1, 0],[ -1, -1, 1]],\
[[ 1, -1, 0],[ -1, 1, -1]],\
[[ -1, 0, 1],[ -1, -1, 1]],\
[[ -1, 0, 1],[ -1, 1, -1]],\
[[ 0, 1, -1],[ -1, -1, 1]],\
[[ 0, 1, -1],[ -1, 1, -1]],\
[[ 1, -1, 0],[ -1, -1, 1]],\
[[ 1, -1, 0],[ -1, 1, -1]],\
[[ -1, 0, 1],[ -1, -1, 1]],\
[[ -1, 0, 1],[ -1, 1, -1]],\
[[ 0, 1, -1],[ -1, -1, 1]],\
[[ 0, 1, -1],[ -1, 1, -1]],\
[[ 1, -1, 0],[ -1, -1, 1]],\
[[ 1, -1, 0],[ -1, 1, -1]]]),
'GT': \
np.array([[[ 17, -5,-12],[ 17,-17, -7]],\
[[ 17,-12, -5],[ 17, -7,-17]],\
[[-17, 5,-12],[-17, 17, -7]],\
[[-17, 12, -5],[-17, 7,-17]],\
[[ 17, 5, 12],[ 17, 17, 7]],\
[[ 17, 12, 5],[ 17, 7, 17]],\
[[-17, -5, 12],[-17,-17, 7]],\
[[-17,-12, 5],[-17, -7, 17]],\
[[-12, 17, -5],[ -7, 17,-17]],\
[[ -5, 17,-12],[-17, 17, -7]],\
[[ 12,-17, -5],[ 7,-17,-17]],\
[[ 5,-17,-12],[ 17,-17, -7]],\
[[-12,-17, 5],[ -7,-17, 17]],\
[[ -5,-17, 12],[-17,-17, 7]],\
[[ 12, 17, 5],[ 7, 17, 17]],\
[[ 5, 17, 12],[ 17, 17, 7]],\
[[ -5,-12, 17],[-17, -7, 17]],\
[[-12, -5, 17],[ -7,-17, 17]],\
[[ 5, 12, 17],[ 17, 7, 17]],\
[[ 12, 5, 17],[ 7, 17, 17]],\
[[ -5, 12,-17],[-17, 7,-17]],\
[[-12, 5,-17],[ -7, 17,-17]],\
[[ 5,-12,-17],[ 17, -7,-17]],\
[[ 12, -5,-17],[ 7,-17,-17]]]),
"GT'": \
np.array([[[ -1, 0, 1],[ -1, 1, 1]],\
[[ -1, 0, 1],[ -1, -1, 1]],\
[[ 1, 0, 1],[ 1, -1, 1]],\
[[ 1, 0, 1],[ 1, 1, 1]],\
[[ -1, 1, 0],[ -1, 1, 1]],\
[[ -1, 1, 0],[ -1, 1, -1]],\
[[ 1, 1, 0],[ 1, 1, 1]],\
[[ 1, 1, 0],[ 1, 1, -1]],\
[[ 1, -1, 0],[ 1, -1, 1]],\
[[ 1, -1, 0],[ 1, -1, -1]],\
[[ -1, -1, 0],[ -1, -1, 1]],\
[[ -1, -1, 0],[ -1, -1, -1]],\
[[ 0, -1, 1],[ 1, -1, 1]],\
[[ 0, -1, 1],[ -1, -1, 1]],\
[[ 0, 1, 1],[ -1, 1, 1]],\
[[ 0, 1, 1],[ 1, 1, 1]],\
[[ 0, 1, -1],[ 1, 1, -1]],\
[[ 0, 1, -1],[ -1, 1, -1]],\
[[ 0, -1, -1],[ -1, -1, -1]],\
[[ 0, -1, -1],[ 1, -1, -1]],\
[[ 1, 0, -1],[ 1, 1, -1]],\
[[ 1, 0, -1],[ 1, -1, -1]],\
[[ -1, 0, -1],[ -1, -1, -1]],\
[[ -1, 0, -1],[ -1, 1, -1]]]),
'NW': \
np.array([[[ 1, -1, 0],[ 1, 0, 0]],\
[[ 1, 0, -1],[ 1, 0, 0]],\
[[ 0, -1, 1],[ 1, 0, 0]],\
[[ 1, 1, 0],[ 1, 0, 0]],\
[[ 0, 1, -1],[ 1, 0, 0]],\
[[ 1, 0, 1],[ 1, 0, 0]],\
[[ 1, 1, 0],[ 1, 0, 0]],\
[[ 0, 1, 1],[ 1, 0, 0]],\
[[ -1, 0, 1],[ 1, 0, 0]],\
[[ 1, 0, 1],[ 1, 0, 0]],\
[[ 1, -1, 0],[ 1, 0, 0]],\
[[ 0, 1, 1],[ 1, 0, 0]]]),
'Bain': \
np.array([[[ 0, 1, 0],[ 0, 1, 1]],
[[ 0, 0, 1],[ 1, 0, 1]],
[[ 1, 0, 0],[ 1, 1, 0]]])]
}
myMatrix = np.array([[planes [relationModel][variant,me]],\
[normals[relationModel][variant,me]],\
[np.cross(normals[relationModel][variant,me],planes[relationModel][variant,me])]])
otherMatrix = np.array([[planes [relationModel][variant,other]],\
[normals[relationModel][variant,other]],\
[np.cross(normals[relationModel][variant,other],planes[relationModel][variant,other])]])
def getRotation(self,variant):
fccN=np.array([1.,1.,1.])
fccN=fccN/np.linalg.norm(fccN)
fccN=self.orientation.asMatrix().dot(fccN)
fccD=np.array([17.,-5.,-12.])
fccD=fccD/np.linalg.norm(fccD)
fccD=self.orientation.asMatrix().dot(fccD)
bccN=np.array([1.,1.,0.])
bccN=bccN/np.linalg.norm(bccN)
bccN=self.orientation.asMatrix().dot(bccN)
bccD=np.array([17.,-17.,-7.])
bccD=bccD/np.linalg.norm(bccD)
bccD=self.orientation.asMatrix().dot(bccD)
B = np.array(np.outer(bccN,fccN.T)+np.outer(bccD,fccD.T))*0.5
U,S,VT = np.linalg.svd(B)
M=np.diag([1,1,np.linalg.det(U)*np.linalg.det(VT)])
R=(U.dot(M)).dot(VT)
return Orientation(matrix=R)