diff --git a/pytasa/fundamental.py b/pytasa/fundamental.py
index c92f3b4..b469336 100644
--- a/pytasa/fundamental.py
+++ b/pytasa/fundamental.py
@@ -12,3 +12,91 @@
 #        calculation or tensor rotation. Basic rule should be that
 #        the functions take a 6x6 np. array (or matrix?) as input
 #        and return something. We can then build an OO interface on top
+
+def mat2tens(cij_mat, compl=False):
+    """Convert from Voigt to full tensor notation 
+
+       Convert from the 6*6 elastic constants matrix to 
+       the 3*3*3*3 tensor representation. Recoded from 
+       the Fortran implementation in DRex. Use the optional 
+       argument "compl" for the elastic compliance (not 
+       stiffness) tensor to deal with the multiplication 
+       of elements needed to keep the Voigt and full 
+       notation consistant.
+
+    """
+    cij_tens = np.zeros((3,3,3,3))
+    m2t = np.array([[0,5,4],[5,1,3],[4,3,2]])
+    if compl:
+        cij_mat = cij_mat / np.array([[1.0, 1.0, 1.0, 2.0, 2.0, 2.0],
+                                      [1.0, 1.0, 1.0, 2.0, 2.0, 2.0],
+                                      [1.0, 1.0, 1.0, 2.0, 2.0, 2.0],
+                                      [2.0, 2.0, 2.0, 4.0, 4.0, 4.0],
+                                      [2.0, 2.0, 2.0, 4.0, 4.0, 4.0],
+                                      [2.0, 2.0, 2.0, 4.0, 4.0, 4.0]])
+    for i in range(3):
+        for j in range(3):
+            for k in range(3):
+                for l in range(3):
+                    cij_tens[i,j,k,l] = cij_mat[m2t[i,j],m2t[k,l]]
+
+    return cij_tensA
+
+def tens2mat(cij_tens, compl=False):
+    """Convert from full tensor to Voigt notation
+
+       Convert from the 3*3*3*3 elastic constants tensor to 
+       to 6*6 matrix representation. Recoded from the Fortran
+       implementation in DRex. Use the optional 
+       argument "compl" for the elastic compliance (not 
+       stiffness) tensor to deal with the multiplication 
+       of elements needed to keep the Voigt and full 
+       notation consistant.
+
+    """
+    t2m = np.array([[0,1,2,1,2,0],[0,1,2,2,0,1]])
+    cij_mat = np.zeros((6,6))
+    # Convert back to matrix form
+    for i in range(6):
+        for j in range(6):
+            cij_mat[i,j] = cij_tens[t2m[0,i],t2m[1,i],t2m[0,j],t2m[1,j]]
+    
+    if compl:
+        cij_mat = cij_mat * np.array([[1.0, 1.0, 1.0, 2.0, 2.0, 2.0],
+                                      [1.0, 1.0, 1.0, 2.0, 2.0, 2.0],
+                                      [1.0, 1.0, 1.0, 2.0, 2.0, 2.0],
+                                      [2.0, 2.0, 2.0, 4.0, 4.0, 4.0],
+                                      [2.0, 2.0, 2.0, 4.0, 4.0, 4.0],
+                                      [2.0, 2.0, 2.0, 4.0, 4.0, 4.0]])
+
+    return cij_mat
+
+
+def rotT(T, g):
+    """Rotate a rank 4 tensor, T, using a rotation matrix, g
+       
+       Tensor rotation involves a summation over all combinations
+       of products of elements of the unrotated tensor and the 
+       rotation matrix. Like this for a rank 3 tensor:
+
+           T'(ijk) -> Sum g(i,p)*g(j,q)*g(k,r)*T(pqr)
+       
+       with the summation over p, q and r. The obvious implementation
+       involves (2*rank) length 3 loops building up the summation in the
+       inner set of loops. This optimized implementation >100 times faster 
+       than that obvious implementaton using 8 nested loops. Returns a 
+       3*3*3*3 numpy array representing the rotated tensor, Tprime. 
+
+       NB: For Voigt notation we could use Bond transform, as used in MSAT,
+       which is probably quicker.
+
+    """
+    gg = np.outer(g, g) # Flatterns input and returns 9*9 array
+                        # of all possible products
+    gggg = np.outer(gg, gg).reshape(4 * g.shape)
+                        # 81*81 array of double products reshaped
+                        # to 3*3*3*3*3*3*3*3 array...
+    axes = ((0, 2, 4, 6), (0, 1, 2, 3)) # We only need a subset 
+                                        # of gggg in tensordot...
+    return np.tensordot(gggg, T, axes)
+