def simplified_kpca(kpca, source_data):
    """
    Performs kernel PCA on the provided dataset and returns the familiar
    eigenvector, eigenvalue, and scree matrices.

    Note that the positions in the eigenvalues may need to be transposed

    Parameters
    ----------
    kpca : KernelPCA object
        configured Kernel PCA object ready to perform analysis
    source_data : 2D numpy array
        Data arranged as [iteration, features] example - [position, time]

    Returns
    -------
    eigenvalues : 2D numpy array
        Eigenvalues in the original space arranged as [component,iteration]
    scree : 1D numpy array
        S component
    eigenvector : 2D numpy array
        Eigenvectors in the original space arranged as [component,features]

    """
    X_kpca = kpca.fit(source_data.T)
    eigenvectors = X_kpca.alphas_.T
    eigenvalues = X_kpca.fit_transform(source_data)
    scree = kpca.lambdas_
    return eigenvalues, scree, eigenvectors
