def sample(self, samples=[], bounds=None, closest=True, **kwargs):
        """Samples values at supplied coordinates.

        Allows sampling of element with a list of coordinates matching
        the key dimensions, returning a new object containing just the
        selected samples. Supports multiple signatures:

        Sampling with a list of coordinates, e.g.:

            ds.sample([(0, 0), (0.1, 0.2), ...])

        Sampling a range or grid of coordinates, e.g.:

            1D: ds.sample(3)
            2D: ds.sample((3, 3))

        Sampling by keyword, e.g.:

            ds.sample(x=0)

        Args:
            samples: List of nd-coordinates to sample
            bounds: Bounds of the region to sample
                Defined as two-tuple for 1D sampling and four-tuple
                for 2D sampling.
            closest: Whether to snap to closest coordinates
            **kwargs: Coordinates specified as keyword pairs
                Keywords of dimensions and scalar coordinates

        Returns:
            Element containing the sampled coordinates
        """
        if kwargs and samples != []:
            raise Exception('Supply explicit list of samples or kwargs, not both.')
        elif kwargs:
            sample = [slice(None) for _ in range(self.ndims)]
            for dim, val in kwargs.items():
                sample[self.get_dimension_index(dim)] = val
            samples = [tuple(sample)]
        elif isinstance(samples, tuple) or util.isscalar(samples):
            if self.ndims == 1:
                xlim = self.range(0)
                lower, upper = (xlim[0], xlim[1]) if bounds is None else bounds
                edges = np.linspace(lower, upper, samples+1)
                linsamples = [(l+u)/2.0 for l,u in zip(edges[:-1], edges[1:])]
            elif self.ndims == 2:
                (rows, cols) = samples
                if bounds:
                    (l,b,r,t) = bounds
                else:
                    l, r = self.range(0)
                    b, t = self.range(1)

                xedges = np.linspace(l, r, cols+1)
                yedges = np.linspace(b, t, rows+1)
                xsamples = [(lx+ux)/2.0 for lx,ux in zip(xedges[:-1], xedges[1:])]
                ysamples = [(ly+uy)/2.0 for ly,uy in zip(yedges[:-1], yedges[1:])]

                Y,X = np.meshgrid(ysamples, xsamples)
                linsamples = list(zip(X.flat, Y.flat))
            else:
                raise NotImplementedError("Regular sampling not implemented "
                                          "for elements with more than two dimensions.")
            samples = list(util.unique_iterator(self.closest(linsamples)))

        # Note: Special handling sampling of gridded 2D data as Curve
        # may be replaced with more general handling
        # see https://github.com/ioam/holoviews/issues/1173
        from ...element import Table, Curve
        if len(samples) == 1:
            sel = {kd.name: s for kd, s in zip(self.kdims, samples[0])}
            dims = [kd for kd, v in sel.items() if not np.isscalar(v)]
            selection = self.select(**sel)

            # If a 1D cross-section of 2D space return Curve
            if self.interface.gridded and self.ndims == 2 and len(dims) == 1:
                new_type = Curve
                kdims = [self.get_dimension(kd) for kd in dims]
            else:
                new_type = Table
                kdims = self.kdims

            if np.isscalar(selection):
                selection = [samples[0]+(selection,)]
            else:
                reindexed = selection.clone(new_type=Dataset).reindex(kdims)
                selection = tuple(reindexed.columns(kdims+self.vdims).values())

            datatype = list(util.unique_iterator(self.datatype+['dataframe', 'dict']))
            return self.clone(selection, kdims=kdims, new_type=new_type,
                              datatype=datatype)

        lens = set(len(util.wrap_tuple(s)) for s in samples)
        if len(lens) > 1:
            raise IndexError('Sample coordinates must all be of the same length.')

        if closest:
            try:
                samples = self.closest(samples)
            except NotImplementedError:
                pass
        samples = [util.wrap_tuple(s) for s in samples]
        return self.clone(self.interface.sample(self, samples), new_type=Table)