def normalize(points):
    """
    Return the normalized version of a given vector of points.

    For a given array of n-dimensions, normalize each dimension by removing the
    initial offset and normalizing the points in a proportional interval: [0,1]
    on y, maintining the original ratio on x.
    """
    ratio = (points.x.max() - points.x.min()) / (points.y.max() - points.y.min()), 1
    ratio = np.array(ratio) / max(ratio)
    norm = points.apply(lambda c: (c - c.min()) / (c.max() - c.min()))
    return norm.apply(lambda p: ratio * p, axis=1)
Return the normalized version of a given vector of points.

For a given array of n-dimensions, normalize each dimension by removing the
initial offset and normalizing the points in a proportional interval: [0,1]
on y, maintining the original ratio on x.
src/io_helper.py