Plot does not update when zooming in about wxmplot HOT 6 CLOSED

class ConvexHullPlot(wxmplot.PlotPanel):
    # TODO: The Convex Hull plot should be visible along with the point, test the outliers
    """" Plots the convex hull plot """
    def __init__(self, parent_panel, bm_torsion=None,bm_torsion_array=None, table_id="1"):
        super().__init__(parent=parent_panel, size=(600, 600))
        #########################################
        tor_bend_object = OpenThreshold(table_id)        # this class creates threshold object
        torsion_array = tor_bend_object.get_torsion()
        bm_array = tor_bend_object.get_bending_moment()
        data_list = [Point(int(x), int(y)) for x, y in zip(torsion_array,bm_array)]
        ###########################################
        # create a stack of convex hull object,
        stack_object = ConvexHull(data_list)        # creates a convexhull class
        # creating a deep-copy of the stack to plot the graph
        stacks = copy.deepcopy(stack_object.hull)
        # check if the stack object is empty or not
        assert not stack_object.hull.isEmpty
        t = []
        s = []
        # check the size of the stack
        length_stack = stacks.size
        assert length_stack >= 3
        for i in range(length_stack):
            temp_point = stacks.pop()
            t.append(temp_point.x)
            s.append(temp_point.y)
        #########################################
        t = np.array(t)
        s = np.array(s)
        #############################################
        # plot outliers in red and non-outliers in green
        if bm_torsion is not None:
            outlier_detector = DetectOutlier(bm_torsion, stack_object.hull)
            if outlier_detector.is_outlier():
                # plot the outlier
                duration = 1000  # millisecond
                freq = 1000 # Hz
                x, y = np.array([bm_torsion.get_x()]), np.array([bm_torsion.get_y()])
                self.scatterplot(x, y, marker='*', s=100, color='red', xlabel="Bending Moment", ylabel="Torsion Moment")
                self.oplot(t, s, alpha=0.6, color="red")
                winsound.Beep(freq, duration)  # play the sound
                self.status_message = "Outlier detected"
            else:
                # plot the inlier
                x, y = np.array([bm_torsion.get_x()]), np.array([bm_torsion.get_y()])
                self.scatterplot(x, y, marker='*', s=100, color='green', xlabel="Bending Moment", ylabel="Torsion Moment")
                self.oplot(t, s,  alpha=0.6, color="red")
                self.status_message = "Data is within threshold"

        elif bm_torsion is None and bm_torsion_array is not None:
            bending_moment_all = list()
            torsion_moment_all = list()
            points_on_the_hull = copy.deepcopy(stack_object.hull)
            for point in bm_torsion_array:
                # create an outlier detector object
                # refill the convex hull because it gets emptied after outlier detection
                # points_on_the_hull = copy.deepcopy(stack_object.hull)
                # outlier_detector = DetectOutlier(point, points_on_the_hull)
                bending_moment_all.append(point.get_x())
                torsion_moment_all.append(point.get_y())
                # check if the point in the array is outlier
                #if outlier_detector.is_outlier():
                #    pass
                #else:
                #    pass
            bending_moment_all = np.array(bending_moment_all)
            torsion_moment_all = np.array(torsion_moment_all)
            self.scatterplot(bending_moment_all, torsion_moment_all, marker="*")
            self.oplot(t, s, color="red")
            self.set_xlabel("Bending Moment")
            self.set_ylabel("Torsion Moment")
        else:
            self.plot(t, s, color="blue")