Starting 0.2.0

README.rst

@@ -21,84 +21,3 @@ If analysis using orix forms a part of published work please cite the github rep
 
 orix (this version) is released under the GPL v3 license.
 
-
-Getting started
----------------
-
-The use of orix should feel familiar to the use of numpy, but rather
-than cells of numbers, the cells contain single 3d objects, such as
-vectors or quaternions. They can all be created using tuples, lists,
-numpy arrays, or other numpy-compatible iterables, and will raise an
-error if constructed with the incorrect number of dimensions. Basic
-examples are given below.
-
-Vectors
-~~~~~~~
-
-Vectors are 3d objects representing positions or directions with
-"magnitude". They can be added and subtracted with integers, floats, or
-other vectors (provided the data are of compatible shapes) and have
-several further unique operations.
-
-.. code:: python
-
-    >>> import numpy as np
-    >>> from orix.vector import Vector3d
-    >>> v = Vector3d((1, 1, -1))
-    >>> w_array = np.array([[[1, 0, 0], [0, 0, -1]], [[1, 1, 0], [-1, 0, -1]]])
-    >>> w = Vector3d(w_array)
-    >>> v + w
-    # Vector3d (2, 2)
-    # [[[ 2  1 -1]
-    #   [ 1  1 -2]]
-    #
-    #  [[ 2  2 -1]
-    #   [ 0  1 -2]]]
-    >>> v.dot(w)
-    # array([[1, 1],
-    #        [2, 0]])
-    >>> v.cross(w)
-    # Vector3d (2, 2)
-    # [[[ 0 -1 -1]
-    #   [-1  1  0]]
-    #
-    #  [[ 1 -1  0]
-    #   [-1  2  1]]]
-    >>> v.unit
-    # Vector3d (1,)
-    # [[ 0.5774  0.5774 -0.5774]]
-    >>> w[0]
-    # Vector3d (2,)
-    # [[ 1  0  0]
-    #   [ 0  0 -1]]
-    >>> w[:, 0]
-    # Vector3d (2,)
-    # [[1 0 0]
-    #  [1 1 0]]
-
-
-Quaternions
-~~~~~~~~~~~
-
-Quaternions are four-dimensional data structures. Unit quaternions are
-often used for representing rotations in 3d. Quaternion multiplication
-is defined and can be applied to either other quaternions or vectors.
-
-.. code:: python
-
-    >>> from orix.quaternion.rotation import Rotation
-    >>> p = Rotation([0.5, 0.5, 0.5, 0.5])
-    >>> q = Rotation([0, 1, 0, 0])
-    >>> p.axis
-    # Vector3d (1,)
-    # [[0.5774 0.5774 0.5774]]
-    >>> p.angle
-    # array([2.0943951])
-    >>> p * q
-    # Rotation (1,)
-    # [[-0.5  0.5  0.5 -0.5]]
-    >>> p * ~p # (unit rotation)
-    # Rotation (1,)
-    # [[1. 0. 0. 0.]]
-    >>> p.to_euler() # (Euler angles in the Bunge convention)
-    # array([[1.57079633, 1.57079633, 0.        ]])