def plot_lines(f, x, samples, ax=None, **kwargs):
    r"""
    Plot a representative set of functions to sample

    Additionally, if a list of log-evidences are passed, along with list of
    functions, and list of samples, this function plots the probability mass
    function for all models marginalised according to the evidences.

    Parameters
    ----------
    f: function
        function :math:`f(x;\theta)` (or list of functions for each model) with
        dependent variable :math:`x`, parameterised by :math:`\theta`.

    x: 1D array-like
        `x` values to evaluate :math:`f(x;\theta)` at.

    samples: 2D array-like
        :math:`\theta` samples (or list of :math:`\theta` samples) to evaluate
        :math:`f(x;\theta)` at.
        `shape = (nsamples, npars)`

    ax: axes object, optional
        :class:`matplotlib.axes._subplots.AxesSubplot` to plot the contours
        onto. If unsupplied, then :func:`matplotlib.pyplot.gca()` is used to
        get the last axis used, or create a new one.

    logZ: 1D array-like, optional
        log-evidences of each model if multiple models are passed.
        Should be same length as the list `f`, and need not be normalised.
        Default: `numpy.ones_like(f)`

    weights: 1D array-like, optional
        sample weights (or list of weights), if desired. Should have length
        same as `samples.shape[0]`.
        Default: `numpy.ones_like(samples)`

    ntrim: int, optional
        Approximate number of samples to trim down to, if desired. Useful if
        the posterior is dramatically oversampled.
        Default: None

    cache: str, optional
        File root for saving previous calculations for re-use

    parallel, tqdm_args:
        see docstring for :func:`fgivenx.parallel.parallel_apply`

    kwargs: further keyword arguments
        Any further keyword arguments are plotting keywords that are passed to
        :func:`fgivenx.plot.plot_lines`.
    """
    logZ = kwargs.pop('logZ', None)
    weights = kwargs.pop('weights', None)
    ntrim = kwargs.pop('ntrim', None)
    cache = kwargs.pop('cache', '')
    parallel = kwargs.pop('parallel', False)
    tqdm_kwargs = kwargs.pop('tqdm_kwargs', {})

    fsamps = compute_samples(f, x, samples, logZ=logZ,
                             weights=weights, ntrim=ntrim,
                             parallel=parallel, cache=cache,
                             tqdm_kwargs=tqdm_kwargs)
    fgivenx.plot.plot_lines(x, fsamps, ax, **kwargs)
r"""
    Plot a representative set of functions to sample

    Additionally, if a list of log-evidences are passed, along with list of
    functions, and list of samples, this function plots the probability mass
    function for all models marginalised according to the evidences.

    Parameters
    ----------
    f: function
        function :math:`f(x;\theta)` (or list of functions for each model) with
        dependent variable :math:`x`, parameterised by :math:`\theta`.

    x: 1D array-like
        `x` values to evaluate :math:`f(x;\theta)` at.

    samples: 2D array-like
        :math:`\theta` samples (or list of :math:`\theta` samples) to evaluate
        :math:`f(x;\theta)` at.
        `shape = (nsamples, npars)`

    ax: axes object, optional
        :class:`matplotlib.axes._subplots.AxesSubplot` to plot the contours
        onto. If unsupplied, then :func:`matplotlib.pyplot.gca()` is used to
        get the last axis used, or create a new one.

    logZ: 1D array-like, optional
        log-evidences of each model if multiple models are passed.
        Should be same length as the list `f`, and need not be normalised.
        Default: `numpy.ones_like(f)`

    weights: 1D array-like, optional
        sample weights (or list of weights), if desired. Should have length
        same as `samples.shape[0]`.
        Default: `numpy.ones_like(samples)`

    ntrim: int, optional
        Approximate number of samples to trim down to, if desired. Useful if
        the posterior is dramatically oversampled.
        Default: None

    cache: str, optional
        File root for saving previous calculations for re-use

    parallel, tqdm_args:
        see docstring for :func:`fgivenx.parallel.parallel_apply`

    kwargs: further keyword arguments
        Any further keyword arguments are plotting keywords that are passed to
        :func:`fgivenx.plot.plot_lines`.