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