import numpy as np


def reshapeF(x, size):
    return np.reshape(x, size, order='F')


def mkvc(x, numDims=1):
    """Creates a vector with the number of dimension specified

    e.g.:

        a = np.array([1, 2, 3])

        mkvc(a, 1).shape
            > (3, )

        mkvc(a, 2).shape
            > (3, 1)

        mkvc(a, 3).shape
            > (3, 1, 1)

    """
    assert type(x) == np.ndarray, "Vector must be a numpy array"

    if numDims == 1:
        return x.flatten(order='F')
    elif numDims == 2:
        return x.flatten(order='F')[:, np.newaxis]
    elif numDims == 3:
        return x.flatten(order='F')[:, np.newaxis, np.newaxis]


def ndgrid(*args, **kwargs):
    """
    Form tensorial grid for 1, 2, or 3 dimensions.

    Returns as column vectors by default.

    To return as matrix input:

        ndgrid(..., vector=False)

    The inputs can be a list or separate arguments.

    e.g.

        a = np.array([1, 2, 3])
        b = np.array([1, 2])

        XY = ndgrid(a, b)
            > [[1 1]
               [2 1]
               [3 1]
               [1 2]
               [2 2]
               [3 2]]

        X, Y = ndgrid(a, b, vector=False)
            > X = [[1 1]
                   [2 2]
                   [3 3]]
            > Y = [[1 2]
                   [1 2]
                   [1 2]]

    """

    # Read the keyword arguments, and only accept a vector=True/False
    vector = kwargs.pop('vector', True)
    assert type(vector) == bool, "'vector' keyword must be a bool"
    assert len(kwargs) == 0, "Only 'vector' keyword accepted"

    # you can either pass a list [x1, x2, x3] or each seperately
    if type(args[0]) == list:
        xin = args[0]
    else:
        xin = args

    # Each vector needs to be a numpy array
    assert np.all([type(x) == np.ndarray for x in xin]), "All vectors must be numpy arrays."

    if len(xin) == 1:
        return xin[0]
    elif len(xin) == 2:
        XY = np.broadcast_arrays(mkvc(xin[1], 1), mkvc(xin[0], 2))
        if vector:
            X2, X1 = [mkvc(x) for x in XY]
            return np.c_[X1, X2]
        else:
            return XY[1], XY[0]
    elif len(xin) == 3:
        XYZ = np.broadcast_arrays(mkvc(xin[2], 1), mkvc(xin[1], 2), mkvc(xin[0], 3))
        if vector:
            X3, X2, X1 = [mkvc(x) for x in XYZ]
            return np.c_[X1, X2, X3]
        else:
            return XYZ[2], XYZ[1], XYZ[0]