Python matplotlib 模块，markers() 实例源码

def convert_to_plot(self, plot, coord, offset=True):
        """
        Convert coordinates from projected data coordinates to PlotWindow
        plot coordinates.  Projected data coordinates are two dimensional
        and refer to the location relative to the specific axes being plotted,
        although still in simulation units.  PlotWindow plot coordinates
        are locations as found in the final plot, usually with the origin
        in the center of the image and the extent of the image defined by
        the final plot axis markers.
        """
        # coord should be a 2 x ncoord array-like datatype.
        try:
            ncoord = np.array(coord).shape[1]
        except IndexError:
            ncoord = 1

        # Convert the data and plot limits to tiled numpy arrays so that
        # convert_to_plot is automatically vectorized.

        x0 = np.array(np.tile(plot.xlim[0],ncoord))
        x1 = np.array(np.tile(plot.xlim[1],ncoord))
        xx0 = np.tile(plot._axes.get_xlim()[0],ncoord)
        xx1 = np.tile(plot._axes.get_xlim()[1],ncoord)

        y0 = np.array(np.tile(plot.ylim[0],ncoord))
        y1 = np.array(np.tile(plot.ylim[1],ncoord))
        yy0 = np.tile(plot._axes.get_ylim()[0],ncoord)
        yy1 = np.tile(plot._axes.get_ylim()[1],ncoord)

        ccoord = np.array(coord)

        # We need a special case for when we are only given one coordinate.
        if ccoord.shape == (2,):
            return ((ccoord[0]-x0)/(x1-x0)*(xx1-xx0) + xx0,
                    (ccoord[1]-y0)/(y1-y0)*(yy1-yy0) + yy0)
        else:
            return ((ccoord[0][:]-x0)/(x1-x0)*(xx1-xx0) + xx0,
                    (ccoord[1][:]-y0)/(y1-y0)*(yy1-yy0) + yy0)

def random_marker():
    markers = mks.MarkerStyle.markers
    num = len(markers.keys())
    idx = random.randint(0, num - 1)
    return markers.keys()[idx]