def plot_data(self, proj, ax):
        """
        Creates and plots the contourplot of the original data. This is done
        by evaluating the density of projected datapoints on a grid.
        """
        x, y = proj
        x_data = self.ig.independent_data[x]
        y_data = self.ig.dependent_data[y]
        projected_data = np.column_stack((x_data, y_data)).T
        kde = gaussian_kde(projected_data)

        xx, yy = np.meshgrid(self.ig._x_points[x], self.ig._y_points[y])
        x_grid = xx.flatten()
        y_grid = yy.flatten()

        contour_grid = kde.pdf(np.column_stack((x_grid, y_grid)).T)
        # This is an fugly kludge, but it seems nescessary to make low density
        # areas show up.
        if self.ig.log_contour:
            contour_grid = np.log(contour_grid)
            vmin = -7
        else:
            vmin = None
        ax.contourf(xx, yy, contour_grid.reshape(xx.shape),
                    50, vmin=vmin, cmap='Blues')
Creates and plots the contourplot of the original data. This is done
        by evaluating the density of projected datapoints on a grid.