Issue with sampling uniform for mvslice

mvslice_broken.py

def mvslice(pdf, x0, widths, sampleSize=1000, dims=2, burnin=0, thin=0):
    """
    :param pdf:  function we're trying to sample
    :param x0:   inital point
    :param widths:  prior for widths of our hyperrectangle
    :param sampleSize:  number of samples to generate
    :param dims:  dimension of our multivariate space
    :param burnin:  number of samples to get rid of at beginning
    :param thin:   number of samples to keep
    """
    y0 = np.random.uniform(low=0, high=pdf(x0))
    samples = []

    # get hyperrectangle
    rectUnifs = np.random.uniform(size=dims)
    rectLefts = x0 - widths*rectUnifs
    rectRights = rectLefts + widths

    # Get our samples
    for i in range(sampleSize):

        while (True):

            # new proposal
            xstarUnifs = np.random.uniform(size=dims)
            xstar = rectLefts + xstarUnifs*(rectRights - rectLefts)

            if y0 < pdf(xstar):
                break
            else:

                # update rectangle
                for j in range(dims):

                    if xstar[j] < x0[j]:
                        rectLefts[j] = xstar[j]
                    else:
                        rectRights[j] = xstar[j]


        # save our sample
        samples.append(xstar)

        # Our last sample x0 is now our proposal
        x0 = xstar

        # Get our new y0 for next step
        y0 = np.random.uniform(low=0, high=pdf(x0))

        # reset our rectangle
        rectLefts = x0 - widths*rectUnifs
        rectRights = rectLefts + widths

    return samples

pdf = lambda x: np.e**(-(x[0]**2)/2 - ((-x[1])**2)/2)
X = mvslice(pdf,[0,0], [10,10], sampleSize=10000)
Xs = [a[1] for a in X]
Ys = [a[0] for a in X]

mvslice_fixed.py

def mvslice(pdf, x0, widths, sampleSize=1000, dims=2, burnin=0, thin=0):
    """
    :param pdf:  function we're trying to sample
    :param x0:   inital point
    :param widths:  prior for widths of our hyperrectangle
    :param sampleSize:  number of samples to generate
    :param dims:  dimension of our multivariate space
    :param burnin:  number of samples to get rid of at beginning
    :param thin:   number of samples to keep
    """
    y0 = np.random.uniform(low=0, high=pdf(x0))
    samples = []

    # get hyperrectangle
    rectUnifs = np.random.uniform(size=dims)
    rectLefts = x0 - widths*rectUnifs
    rectRights = rectLefts + widths

    # Get our samples
    for i in range(sampleSize):

        while (True):

            # new proposal
            xstarUnifs = np.random.uniform(size=dims)
            xstar = rectLefts + xstarUnifs*(rectRights - rectLefts)

            if y0 < pdf(xstar):
                break
            else:

                # update rectangle
                for j in range(dims):

                    if xstar[j] < x0[j]:
                        rectLefts[j] = xstar[j]
                    else:
                        rectRights[j] = xstar[j]


        # save our sample
        samples.append(xstar)

        # Our last sample x0 is now our proposal
        x0 = xstar

        # Get our new y0 for next step
        y0 = np.random.uniform(low=0, high=pdf(x0))

        # reset our rectangle
        ### rect unifs should be sampled again
        rectUnifs = np.random.uniform(size=dims)
        rectLefts = x0 - widths*rectUnifs
        rectRights = rectLefts + widths

    return samples

pdf = lambda x: np.e**(-(x[0]**2)/2 - ((-x[1])**2)/2)
X = mvslice(pdf,[0,0], [10,10], sampleSize=10000)
Xs = [a[1] for a in X]
Ys = [a[0] for a in X]