diff --git a/python/damask/Lambert.py b/python/damask/Lambert.py
new file mode 100644
index 000000000..9972b7965
--- /dev/null
+++ b/python/damask/Lambert.py
@@ -0,0 +1,122 @@
+####################################################################################################
+# Code below available according to below conditions on https://github.com/MarDiehl/3Drotations
+####################################################################################################
+# Copyright (c) 2017-2019, Martin Diehl, Max-Planck-Institut für Eisenforschung GmbH
+# Copyright (c) 2013-2014, Marc De Graef/Carnegie Mellon University
+# All rights reserved.
+#
+# Redistribution and use in source and binary forms, with or without modification, are 
+# permitted provided that the following conditions are met:
+#
+#     - Redistributions of source code must retain the above copyright notice, this list 
+#        of conditions and the following disclaimer.
+#     - Redistributions in binary form must reproduce the above copyright notice, this 
+#        list of conditions and the following disclaimer in the documentation and/or 
+#        other materials provided with the distribution.
+#     - Neither the names of Marc De Graef, Carnegie Mellon University nor the names 
+#        of its contributors may be used to endorse or promote products derived from 
+#        this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" 
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE 
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE 
+# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE 
+# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL 
+# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR 
+# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER 
+# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, 
+# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE 
+# USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+####################################################################################################
+import numpy as np
+
+sc   = np.pi**(1./6.)/6.**(1./6.)
+beta = np.pi**(5./6.)/6.**(1./6.)/2.
+R1   = (3.*np.pi/4.)**(1./3.)
+
+def CubeToBall(cube):
+  
+    if np.abs(np.max(cube))>np.pi**(2./3.) * 0.5:
+      raise ValueError
+  
+    # transform to the sphere grid via the curved square, and intercept the zero point
+    if np.allclose(cube,0.0,rtol=0.0,atol=1.0e-300):
+      ball = np.zeros(3)
+    else:
+      # get pyramide and scale by grid parameter ratio
+      p = GetPyramidOrder(cube)
+      XYZ = cube[p] * sc
+
+      # intercept all the points along the z-axis
+      if np.allclose(XYZ[0:2],0.0,rtol=0.0,atol=1.0e-300):
+        ball = np.array([0.0, 0.0, np.sqrt(6.0/np.pi) * XYZ[2]])
+      else:
+        order = [1,0] if np.abs(XYZ[1]) <= np.abs(XYZ[0]) else [0,1]
+        q = np.pi/12.0 * XYZ[order[0]]/XYZ[order[1]]
+        c = np.cos(q)
+        s = np.sin(q)
+        q = R1*2.0**0.25/beta * XYZ[order[1]] / np.sqrt(np.sqrt(2.0)-c)
+        T = np.array([ (np.sqrt(2.0)*c - 1.0), np.sqrt(2.0) * s]) * q
+
+        # transform to sphere grid (inverse Lambert)
+        # note that there is no need to worry about dividing by zero, since XYZ[2] can not become zero
+        c = np.sum(T**2)
+        s = c *         np.pi/24.0 /XYZ[2]**2
+        c = c * np.sqrt(np.pi/24.0)/XYZ[2]
+        q = np.sqrt( 1.0 - s )
+        ball = np.array([ T[order[1]] * q, T[order[0]] * q, np.sqrt(6.0/np.pi) * XYZ[2] - c ])
+    
+      # reverse the coordinates back to the regular order according to the original pyramid number
+      ball = ball[p]
+
+    return ball
+
+
+def BallToCube(ball):
+  
+    rs = np.linalg.norm(ball)
+    if rs > R1: 
+      raise ValueError
+  
+    if np.allclose(ball,0.0,rtol=0.0,atol=1.0e-300):
+      cube = np.zeros(3)
+    else:
+      p = GetPyramidOrder(ball)
+      xyz3 = ball[p] 
+
+      # inverse M_3
+      xyz2 = xyz3[0:2] * np.sqrt( 2.0*rs/(rs+np.abs(xyz3[2])) )
+      
+      # inverse M_2
+      qxy = np.sum(xyz2**2)
+      
+      if np.isclose(qxy,0.0,rtol=0.0,atol=1.0e-300):
+        Tinv = np.zeros(2)
+      else:
+        q2 = qxy + np.max(np.abs(xyz2))**2
+        sq2 = np.sqrt(q2)
+        q = (beta/np.sqrt(2.0)/R1) * np.sqrt(q2*qxy/(q2-np.max(np.abs(xyz2))*sq2))
+        tt = np.clip((np.min(np.abs(xyz2))**2+np.max(np.abs(xyz2))*sq2)/np.sqrt(2.0)/qxy,-1.0,1.0)
+        Tinv = np.array([1.0,np.arccos(tt)/np.pi*12.0]) if np.abs(xyz2[1]) <= np.abs(xyz2[0]) else \
+               np.array([np.arccos(tt)/np.pi*12.0,1.0])
+        Tinv = q * np.where(xyz2<0.0,-Tinv,Tinv)
+      
+      # inverse M_1
+      cube = np.array([ Tinv[0], Tinv[1],  (-1.0 if xyz3[2] < 0.0 else 1.0) * rs / np.sqrt(6.0/np.pi) ]) /sc
+
+      # reverst the coordinates back to the regular order according to the original pyramid number
+      cube = cube[p]
+      
+    return cube
+
+def GetPyramidOrder(xyz):
+ 
+    if   (abs(xyz[0])<= xyz[2]) and (abs(xyz[1])<= xyz[2]) or \
+         (abs(xyz[0])<=-xyz[2]) and (abs(xyz[1])<=-xyz[2]):
+      return [0,1,2]
+    elif (abs(xyz[2])<= xyz[0]) and (abs(xyz[1])<= xyz[0]) or \
+         (abs(xyz[2])<=-xyz[0]) and (abs(xyz[1])<=-xyz[0]):
+      return [1,2,0]
+    elif (abs(xyz[0])<= xyz[1]) and (abs(xyz[2])<= xyz[1]) or \
+         (abs(xyz[0])<=-xyz[1]) and (abs(xyz[2])<=-xyz[1]):
+      return [2,0,1]
diff --git a/python/damask/orientation.py b/python/damask/orientation.py
index 1bc850734..73b7620ca 100644
--- a/python/damask/orientation.py
+++ b/python/damask/orientation.py
@@ -6,6 +6,9 @@
 
 import math,os
 import numpy as np
+from . import Lambert
+
+P = -1
 
 # ******************************************************************************************
 class Quaternion:
@@ -1093,3 +1096,443 @@ class Orientation:
     rot=np.dot(otherMatrix,myMatrix.T)
 
     return Orientation(matrix=np.dot(rot,self.asMatrix()),symmetry=targetSymmetry)
+
+####################################################################################################
+# Code below available according to below conditions on https://github.com/MarDiehl/3Drotations
+####################################################################################################
+# Copyright (c) 2017-2019, Martin Diehl, Max-Planck-Institut für Eisenforschung GmbH
+# Copyright (c) 2013-2014, Marc De Graef/Carnegie Mellon University
+# All rights reserved.
+#
+# Redistribution and use in source and binary forms, with or without modification, are 
+# permitted provided that the following conditions are met:
+#
+#     - Redistributions of source code must retain the above copyright notice, this list 
+#        of conditions and the following disclaimer.
+#     - Redistributions in binary form must reproduce the above copyright notice, this 
+#        list of conditions and the following disclaimer in the documentation and/or 
+#        other materials provided with the distribution.
+#     - Neither the names of Marc De Graef, Carnegie Mellon University nor the names 
+#        of its contributors may be used to endorse or promote products derived from 
+#        this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" 
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE 
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE 
+# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE 
+# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL 
+# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR 
+# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER 
+# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, 
+# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE 
+# USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+####################################################################################################
+
+def isone(a):
+  return np.isclose(a,1.0,atol=1.0e-15,rtol=0.0)
+  
+def iszero(a):
+  return np.isclose(a,0.0,atol=1.0e-300,rtol=0.0)
+  
+  
+def eu2om(eu):
+  """Euler angles to orientation matrix"""
+  c = np.cos(eu)
+  s = np.sin(eu)
+
+  om = np.array([[+c[0]*c[2]-s[0]*s[2]*c[1], +s[0]*c[2]+c[0]*s[2]*c[1], +s[2]*s[1]],
+                 [-c[0]*s[2]-s[0]*c[2]*c[1], -s[0]*s[2]+c[0]*c[2]*c[1], +c[2]*s[1]],
+                 [+s[0]*s[1],                -c[0]*s[1],                +c[1]     ]])
+
+  om[np.where(iszero(om))] = 0.0
+  return om
+
+
+def eu2ax(eu):
+  """Euler angles to axis angle""" 
+  t = np.tan(eu[1]*0.5)
+  sigma = 0.5*(eu[0]+eu[2])
+  delta = 0.5*(eu[0]-eu[2])
+  tau   = np.linalg.norm([t,np.sin(sigma)])
+  alpha = np.pi if iszero(np.cos(sigma)) else \
+          2.0*np.arctan(tau/np.cos(sigma))
+  
+  if iszero(alpha):
+    ax = np.array([ 0.0, 0.0, 1.0, 0.0 ])
+  else:
+    ax = -P/tau * np.array([ t*np.cos(delta), t*np.sin(delta), np.sin(sigma) ])                     # passive axis-angle pair so a minus sign in front
+    ax = np.append(ax,alpha) 
+    if alpha < 0.0: ax *= -1.0                                                                      # ensure alpha is positive
+  
+  return ax
+
+
+def eu2ro(eu):
+  """Euler angles to Rodrigues vector"""
+  ro = eu2ax(eu)                                                                                    # convert to axis angle representation
+  if ro[3] >= np.pi:                                                                                # Differs from original implementation. check convention 5
+    ro[3] = np.inf
+  elif iszero(ro[3]):
+    ro = np.array([ 0.0, 0.0, P, 0.0 ])
+  else:
+    ro[3] = np.tan(ro[3]*0.5)
+  
+  return ro
+
+
+def eu2qu(eu):
+  """Euler angles to quaternion"""
+  ee = 0.5*eu
+  cPhi = np.cos(ee[1])
+  sPhi = np.sin(ee[1])
+  qu = np.array([     cPhi*np.cos(ee[0]+ee[2]),
+                   -P*sPhi*np.cos(ee[0]-ee[2]),
+                   -P*sPhi*np.sin(ee[0]-ee[2]),
+                   -P*cPhi*np.sin(ee[0]+ee[2]) ])
+  #if qu[0] < 0.0: qu.homomorph()                                      !ToDo: Check with original
+  return qu
+
+
+def om2eu(om):
+  """Euler angles to orientation matrix"""
+  if isone(om[2,2]**2):
+    eu = np.array([np.arctan2( om[0,1],om[0,0]), np.pi*0.5*(1-om[2,2]),0.0])                        # following the paper, not the reference implementation
+  else:
+    zeta = 1.0/np.sqrt(1.0-om[2,2]**2)
+    eu = np.array([np.arctan2(om[2,0]*zeta,-om[2,1]*zeta),
+                   np.arccos(om[2,2]),
+                   np.arctan2(om[0,2]*zeta, om[1,2]*zeta)])
+  
+  # reduce Euler angles to definition range, i.e a lower limit of 0.0
+  eu = np.where(eu<0, (eu+2.0*np.pi)%np.array([2.0*np.pi,np.pi,2.0*np.pi]),eu)
+  return eu
+
+
+def ax2om(ax):
+  """Axis angle to orientation matrix"""
+  c = np.cos(ax[3])
+  s = np.sin(ax[3])
+  omc = 1.0-c
+  om=np.diag(ax[0:3]**2*omc + c)
+
+  for idx in [[0,1,2],[1,2,0],[2,0,1]]:
+    q = omc*ax[idx[0]] * ax[idx[1]]
+    om[idx[0],idx[1]] = q + s*ax[idx[2]]
+    om[idx[1],idx[0]] = q - s*ax[idx[2]]
+
+  return om if P < 0.0 else om.T
+
+
+def qu2eu(qu):
+  """Quaternion to Euler angles""" 
+  q03 = qu[0]**2+qu[3]**2
+  q12 = qu[1]**2+qu[2]**2
+  chi = np.sqrt(q03*q12)
+  
+  if iszero(chi):
+    eu = np.array([np.arctan2(-P*2.0*qu[0]*qu[3],qu[0]**2-qu[3]**2), 0.0,   0.0]) if iszero(q12) else \
+         np.array([np.arctan2(2.0*qu[1]*qu[2],qu[1]**2-qu[2]**2),         np.pi, 0.0])
+  else:
+    #chiInv = 1.0/chi         ToDo: needed for what?
+    eu = np.array([np.arctan2((-P*qu[0]*qu[2]+qu[1]*qu[3])*chi, (-P*qu[0]*qu[1]-qu[2]*qu[3])*chi ),
+                   np.arctan2( 2.0*chi, q03-q12 ), 
+                   np.arctan2(( P*qu[0]*qu[2]+qu[1]*qu[3])*chi, (-P*qu[0]*qu[1]+qu[2]*qu[3])*chi )])
+  
+  # reduce Euler angles to definition range, i.e a lower limit of 0.0
+  eu = np.where(eu<0, (eu+2.0*np.pi)%np.array([2.0*np.pi,np.pi,2.0*np.pi]),eu)
+  return eu
+
+
+def ax2ho(ax):
+  """Axis angle to homochoric""" 
+  f = (0.75 * ( ax[3] - np.sin(ax[3]) ))**(1.0/3.0)
+  ho = ax[0:3] * f
+  return ho
+
+
+def ho2ax(ho):
+  """Homochoric to axis angle"""
+  tfit = np.array([+1.0000000000018852,      -0.5000000002194847,
+                   -0.024999992127593126,    -0.003928701544781374,
+                   -0.0008152701535450438,   -0.0002009500426119712,
+                   -0.00002397986776071756,  -0.00008202868926605841,
+                   +0.00012448715042090092,  -0.0001749114214822577,
+                   +0.0001703481934140054,   -0.00012062065004116828,
+                   +0.000059719705868660826, -0.00001980756723965647,
+                   +0.000003953714684212874, -0.00000036555001439719544])
+  # normalize h and store the magnitude
+  hmag_squared = np.sum(ho**2.)
+  if iszero(hmag_squared):
+    ax = np.array([ 0.0, 0.0, 1.0, 0.0 ])
+  else:
+    hm = hmag_squared
+  
+  # convert the magnitude to the rotation angle
+    s = tfit[0] + tfit[1] * hmag_squared
+    for i in range(2,16):
+      hm *= hmag_squared
+      s  += tfit[i] * hm
+    ax = np.append(ho/np.sqrt(hmag_squared),2.0*np.arccos(s))                                       # ToDo: Check sanity check in reference implementation 
+
+  return ax
+
+
+def om2ax(om):
+  """Orientation matrix to axis angle"""
+  ax=np.empty(4)
+
+  # first get the rotation angle
+  t = 0.5*(om.trace() -1.0)
+  ax[3] = np.arccos(np.clip(t,-1.0,1.0))
+  
+  if iszero(ax[3]):
+    ax = [ 0.0, 0.0, 1.0, 0.0]
+  else:
+    w,vr = np.linalg.eig(om)
+  # next, find the eigenvalue (1,0j)
+    i = np.where(np.isclose(w,1.0+0.0j))[0][0]
+    ax[0:3] = np.real(vr[0:3,i])
+    diagDelta = np.array([om[1,2]-om[2,1],om[2,0]-om[0,2],om[0,1]-om[1,0]])
+    ax[0:3] = np.where(iszero(diagDelta), ax[0:3],np.abs(ax[0:3])*np.sign(-P*diagDelta))
+  
+  return np.array(ax)
+
+
+def ro2ax(ro):
+  """Rodrigues vector to axis angle"""
+  ta = ro[3]
+  
+  if iszero(ta):
+    ax = [ 0.0, 0.0, 1.0, 0.0 ]
+  elif not np.isfinite(ta):
+    ax = [ ro[0], ro[1], ro[2], np.pi ]
+  else:
+    angle = 2.0*np.arctan(ta)
+    ta = 1.0/np.linalg.norm(ro[0:3])
+    ax = [ ro[0]/ta, ro[1]/ta, ro[2]/ta, angle ]
+
+  return np.array(ax)
+
+
+def ax2ro(ax):
+  """Axis angle to Rodrigues vector""" 
+  if iszero(ax[3]):
+    ro = [ 0.0, 0.0, P, 0.0 ]
+  else:
+    ro = [ax[0], ax[1], ax[2]]
+    # 180 degree case
+    ro += [np.inf] if np.isclose(ax[3],np.pi,atol=1.0e-15,rtol=0.0) else \
+          [np.tan(ax[3]*0.5)]
+
+  return np.array(ro)
+
+
+def ax2qu(ax):
+  """Axis angle to quaternion"""
+  if iszero(ax[3]):
+    qu = np.array([ 1.0, 0.0, 0.0, 0.0 ])
+  else:
+    c = np.cos(ax[3]*0.5)
+    s = np.sin(ax[3]*0.5)
+    qu = np.array([ c, ax[0]*s, ax[1]*s, ax[2]*s ])
+  
+  return qu
+
+
+def ro2ho(ro):
+  """Rodrigues vector to homochoric""" 
+  if iszero(np.sum(ro[0:3]**2.0)):
+    ho = [ 0.0, 0.0, 0.0 ]
+  else:
+    f = 2.0*np.arctan(ro[3]) -np.sin(2.0*np.arctan(ro[3])) if np.isfinite(ro[3]) else np.pi
+    ho = ro[0:3] * (0.75*f)**(1.0/3.0)
+
+  return np.array(ho)
+
+
+def qu2om(qu):
+  """Quaternion to orientation matrix"""
+  qq = qu[0]**2-(qu[1]**2 + qu[2]**2 + qu[3]**2)
+  om = np.diag(qq + 2.0*np.array([qu[1],qu[2],qu[3]])**2)
+
+  om[1,0] = 2.0*(qu[2]*qu[1]+qu[0]*qu[3])
+  om[0,1] = 2.0*(qu[1]*qu[2]-qu[0]*qu[3])
+  om[2,1] = 2.0*(qu[3]*qu[2]+qu[0]*qu[1])
+  om[1,2] = 2.0*(qu[2]*qu[3]-qu[0]*qu[1])
+  om[0,2] = 2.0*(qu[1]*qu[3]+qu[0]*qu[2])
+  om[2,0] = 2.0*(qu[3]*qu[1]-qu[0]*qu[2])
+  return om if P > 0.0 else om.T
+
+
+def om2qu(om):
+  """Orientation matrix to quaternion"""
+  s = [+om[0,0] +om[1,1] +om[2,2] +1.0, 
+       +om[0,0] -om[1,1] -om[2,2] +1.0,
+       -om[0,0] +om[1,1] -om[2,2] +1.0, 
+       -om[0,0] -om[1,1] +om[2,2] +1.0]
+  s = np.maximum(np.zeros(4),s)
+  qu = np.sqrt(s)*0.5*np.array([1.0,P,P,P])
+  # verify the signs (q0 always positive)
+  #ToDo: Here I donot understand the original shortcut from paper to implementation
+
+  qu /= np.linalg.norm(qu)
+  if any(isone(abs(qu))): qu[np.where(np.logical_not(isone(qu)))] = 0.0
+  if om[2,1] < om[1,2]: qu[1] *= -1.0
+  if om[0,2] < om[2,0]: qu[2] *= -1.0
+  if om[1,0] < om[0,1]: qu[3] *= -1.0
+  if any(om2ax(om)[0:3]*qu[1:4] < 0.0): print(om2ax(om),qu) # something is wrong here
+  return qu
+
+def qu2ax(qu):
+  """Quaternion to axis angle"""
+  omega = 2.0 * np.arccos(qu[0])
+  if iszero(omega):                                                                                 #  return axis as [001]  if the angle is zero
+    ax = [ 0.0, 0.0, 1.0, 0.0 ]
+  elif not iszero(qu[0]):
+    s = np.sign(qu[0])/np.sqrt(qu[1]**2+qu[2]**2+qu[3]**2)
+    ax = [ qu[1]*s, qu[2]*s, qu[3]*s, omega ]
+  else:
+    ax = [ qu[1], qu[2], qu[3], np.pi]
+
+  return np.array(ax)
+
+
+def qu2ro(qu):
+  """Quaternion to Rodrigues vector"""
+  if iszero(qu[0]):
+    ro = [qu[1], qu[2], qu[3], np.inf]
+  else:
+    s = np.linalg.norm([qu[1],qu[2],qu[3]])
+    ro = [0.0,0.0,P,0.0] if iszero(s) else \
+         [ qu[1]/s,  qu[2]/s,  qu[3]/s, np.tan(np.arccos(qu[0]))]
+ 
+  return np.array(ro)
+
+
+def qu2ho(qu):
+  """Quaternion to homochoric"""
+  omega = 2.0 * np.arccos(qu[0])
+  
+  if iszero(omega):
+    ho = np.array([ 0.0, 0.0, 0.0 ])
+  else:
+    ho = np.array([qu[1], qu[2], qu[3]])
+    f  = 0.75 * ( omega - np.sin(omega) )
+    ho = ho/np.linalg.norm(ho) * f**(1./3.)
+
+  return ho
+
+
+def ho2cu(ho):
+  """Homochoric to cubochoric"""
+  return  Lambert.BallToCube(ho)
+
+
+def cu2ho(cu):
+  """Cubochoric to homochoric"""
+  return  Lambert.CubeToBall(cu)
+
+
+def ro2eu(ro):
+  """Rodrigues vector to orientation matrix"""
+  return om2eu(ro2om(ro))
+
+
+def eu2ho(eu):
+  """Euler angles to homochoric"""
+  return ax2ho(eu2ax(eu))
+
+
+def om2ro(om):
+  """Orientation matrix to Rodriques vector"""
+  return eu2ro(om2eu(om))
+
+
+def om2ho(om):
+  """Orientation matrix to homochoric"""
+  return ax2ho(om2ax(om))
+
+
+def ax2eu(ax):
+  """Orientation matrix to Euler angles"""
+  return om2eu(ax2om(ax))
+
+
+def ro2om(ro):
+ """Rodgrigues vector to orientation matrix"""
+ return ax2om(ro2ax(ro))
+
+
+def ro2qu(ro):
+  """Rodrigues vector to quaternion"""
+  return ax2qu(ro2ax(ro))
+
+
+def ho2eu(ho):
+  """Homochoric to Euler angles"""
+  return ax2eu(ho2ax(ho))
+
+
+def ho2om(ho):
+  """Homochoric to orientation matrix"""
+  return ax2om(ho2ax(ho))
+
+
+def ho2ro(ho):
+  """Axis angle to Rodriques vector"""
+  return ax2ro(ho2ax(ho))
+
+
+def ho2qu(ho):
+  """Homochoric to quaternion"""
+  return ax2qu(ho2ax(ho))
+
+
+def eu2cu(eu):
+  """Euler angles to cubochoric"""
+  return ho2cu(eu2ho(eu))
+
+
+def om2cu(om):
+  """Orientation matrix to cubochoric"""
+  return ho2cu(om2ho(om))
+
+
+def ax2cu(ax):
+  """Axis angle to cubochoric"""
+  return ho2cu(ax2ho(ax))
+
+
+def ro2cu(ro):
+  """Rodrigues vector to cubochoric"""
+  return ho2cu(ro2ho(ro))
+
+
+def qu2cu(qu):
+  """Quaternion to cubochoric"""
+  return ho2cu(qu2ho(qu))
+
+
+def cu2eu(cu):
+  """Cubochoric to Euler angles"""
+  return ho2eu(cu2ho(cu))
+
+
+def cu2om(cu):
+  """Cubochoric to orientation matrix"""
+  return ho2om(cu2ho(cu))
+
+
+def cu2ax(cu):
+  """Cubochoric to axis angle"""
+  return ho2ax(cu2ho(cu))
+
+
+def cu2ro(cu):
+  """Cubochoric to Rodrigues vector"""
+  return ho2ro(cu2ho(cu))
+
+
+def cu2qu(cu):
+  """Cubochoric to quaternion"""
+  return ho2qu(cu2ho(cu))