Method hlines

lib/matplotlib/axes/_axes.py:1116–1203

Plot horizontal lines at each *y* from *xmin* to *xmax*. Parameters ---------- y : float or array-like y-indexes where to plot the lines. xmin, xmax : float or array-like Respective beginning and end of each line. If scalars are

(self, y, xmin, xmax, colors=None, linestyles='solid',
               label='', **kwargs)

    @_preprocess_data(replace_names=["y", "xmin", "xmax", "colors"],
                      label_namer="y")
    def hlines(self, y, xmin, xmax, colors=None, linestyles='solid',
               label='', **kwargs):
        """
        Plot horizontal lines at each *y* from *xmin* to *xmax*.

        Parameters
        ----------
        y : float or array-like
            y-indexes where to plot the lines.

        xmin, xmax : float or array-like
            Respective beginning and end of each line. If scalars are
            provided, all lines will have the same length.

        colors : :mpltype:`color` or list of color , default: :rc:`lines.color`

        linestyles : {'solid', 'dashed', 'dashdot', 'dotted'}, default: 'solid'

        label : str, default: ''

        Returns
        -------
        `~matplotlib.collections.LineCollection`

        Other Parameters
        ----------------
        data : indexable object, optional
            DATA_PARAMETER_PLACEHOLDER
        **kwargs :  `~matplotlib.collections.LineCollection` properties.

        See Also
        --------
        vlines : vertical lines
        axhline : horizontal line across the Axes
        """

        # We do the conversion first since not all unitized data is uniform
        xmin, xmax, y = self._process_unit_info(
            [("x", xmin), ("x", xmax), ("y", y)], kwargs)

        if not np.iterable(y):
            y = [y]
        if not np.iterable(xmin):
            xmin = [xmin]
        if not np.iterable(xmax):
            xmax = [xmax]

        # Create and combine masked_arrays from input
        y, xmin, xmax = cbook._combine_masks(y, xmin, xmax)
        y = np.ravel(y)
        xmin = np.ravel(xmin)
        xmax = np.ravel(xmax)

        masked_verts = np.ma.empty((len(y), 2, 2))
        masked_verts[:, 0, 0] = xmin
        masked_verts[:, 0, 1] = y
        masked_verts[:, 1, 0] = xmax
        masked_verts[:, 1, 1] = y