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array_correspondence

array_correspondence(A, B, axis=None)

Computes a map from A to the equal elements in B

Parameters:

Name Type Description Default
A (a,) or (a,k) numpy array (must be 1-dim or 2-dim) required
B (b,) or (b,k) numpy array (must be 1-dim or 2-dim) required
axis int or None, optional (default None)

If None, will treat A and B as flat arrays. If a number, will check for equality of the entire axis, in which case the dimension of A and B across that axis must be equal.

 None

Returns:

Name Type Description
f (a,) numpy int array

index list mapping from A to B, with -1 if there is no matching entry. If b contains multiple eligible entries, return an arbitrary one. If there are no -1s, b[f] == a

Examples:

Example with simple array correspondence:

>>> A = np.array([1,7,2,7,9,3,7,0])
>>> B = np.array([7,7,3,2,7,8])
>>> f = gpy.array_correspondence(A, B)
>>> f
array([-1,  0,  3,  0, -1,  2,  0, -1])

Example with row correspondence:

>>> A = np.array([[1,3],[5,2],[1,2],[5,2]])
>>> B = np.array([[1,2],[6,9],[5,2]])
>>> f = gpy.array_correspondence(A, B, axis=0)
>>> f
array([-1,  2,  0,  2])
Source code in src/gpytoolbox/array_correspondence.py 
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def array_correspondence(A,B,axis=None):
    """Computes a map from A to the equal elements in B

    Parameters
    ----------
    A : (a,) or (a,k) numpy array (must be 1-dim or 2-dim)
    B : (b,) or (b,k) numpy array (must be 1-dim or 2-dim)
    axis : int or None, optional (default None)
        If None, will treat A and B as flat arrays.
        If a number, will check for equality of the entire axis, in which case
        the dimension of A and B across that axis must be equal.

    Returns
    -------
    f : (a,) numpy int array
        index list mapping from A to B, with -1 if there is no
        matching entry.
        If b contains multiple eligible entries, return an arbitrary one.
        If there are no -1s, `b[f] == a`

    Examples
    --------
    Example with simple array correspondence:
    ```python
    >>> A = np.array([1,7,2,7,9,3,7,0])
    >>> B = np.array([7,7,3,2,7,8])
    >>> f = gpy.array_correspondence(A, B)
    >>> f
    array([-1,  0,  3,  0, -1,  2,  0, -1])
    ```
    Example with row correspondence:
    ```python
    >>> A = np.array([[1,3],[5,2],[1,2],[5,2]])
    >>> B = np.array([[1,2],[6,9],[5,2]])
    >>> f = gpy.array_correspondence(A, B, axis=0)
    >>> f
    array([-1,  2,  0,  2])
    ```

    """

    if axis not in (None, 0, 1, -1, -2):
        raise Exception("Axis can only be None, 0, 1, -1, -2")
    if len(A.shape) > 2 or len(B.shape) > 2:
        raise Exception("Inputs A, B can only be up to 2 dimensional")

    if axis==1 or axis==-1:
        # np.unique behaves weird with axis==1... but we only work with
        # up to dim 2, so always simply use the axis==0 case.
        A = A.T
        B = B.T
        axis = 0

    # While we have to keep track of duplicates in A (to map them to the
    # correct place), we do not care about duplicates in B

    # uB is deduplicated B. mapB allows us to map to the first occurence of each vector.
    # (contribution by Towaki Takikawa)
    uB, mapB = np.unique(B, return_index=True, axis=axis)

    # We concatenate uB with A. Any entries from A that get de-duped is a 'hit'.
    _, idx, inv = np.unique(np.concatenate((uB,A), axis=axis),
        return_index=True, return_inverse=True, axis=axis)

    # We don't care about the range of the output that is about uB- that was a decoy to
    # grill out the hits from A.
    imap = idx[inv[uB.shape[0]:]]
    imap[imap>=uB.shape[0]] = -1
    f = np.where(imap<0, -1, mapB[imap])

    return f