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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def get_logx(nlive, simulate=False):
 r"""Returns a logx vector showing the expected or simulated logx positions of points. The shrinkage factor between two points .. math:: t_i = X_{i-1} / X_{i} is distributed as the largest of :math:`n_i` uniform random variables between 1 and 0, where :math:`n_i` is the local number of live points. We are interested in .. math:: \log(t_i) = \log X_{i-1} - \log X_{i} which has expected value :math:`-1/n_i`. Parameters nlive_array: 1d numpy array Ordered local number of live points present at each point's iso-likelihood contour. simulate: bool, optional Should log prior volumes logx be simulated from their distribution (if False their expected values are used). Returns ------- logx: 1d numpy array log X values for points. """

    assert nlive.min() > 0, (
        'nlive contains zeros or negative values! nlive = ' + str(nlive))
    if simulate:
        logx_steps = np.log(np.random.random(nlive.shape)) / nlive
    else:
        logx_steps = -1 * (nlive.astype(float) ** -1)
    return np.cumsum(logx_steps)