import numpy as npimport matplotlib.pyplot as plt
browser_market_share = {    'browsers': ['firefox', 'chrome', 'safari', 'edge', 'ie', 'opera'],    'market_share': [8.61, 69.55, 8.36, 4.12, 2.76, 2.43],    'color': ['#5A69AF', '#579E65', '#F9C784', '#FC944A', '#F24C00', '#00B825']}

class BubbleChart:    def __init__(self, area, bubble_spacing=0):        """        Setup for bubble collapse.
        Parameters        ----------        area : array-like            Area of the bubbles.        bubble_spacing : float, default: 0            Minimal spacing between bubbles after collapsing.
        Notes        -----        If "area" is sorted, the results might look weird.        """        area = np.asarray(area)        r = np.sqrt(area / np.pi)
        self.bubble_spacing = bubble_spacing        self.bubbles = np.ones((len(area), 4))        self.bubbles[:, 2] = r        self.bubbles[:, 3] = area        self.maxstep = 2 * self.bubbles[:, 2].max() + self.bubble_spacing        self.step_dist = self.maxstep / 2
        # calculate initial grid layout for bubbles        length = np.ceil(np.sqrt(len(self.bubbles)))        grid = np.arange(length) * self.maxstep        gx, gy = np.meshgrid(grid, grid)        self.bubbles[:, 0] = gx.flatten()[:len(self.bubbles)]        self.bubbles[:, 1] = gy.flatten()[:len(self.bubbles)]
        self.com = self.center_of_mass()
    def center_of_mass(self):        return np.average(            self.bubbles[:, :2], axis=0, weights=self.bubbles[:, 3]        )
    def center_distance(self, bubble, bubbles):        return np.hypot(bubble[0] - bubbles[:, 0],                        bubble[1] - bubbles[:, 1])
    def outline_distance(self, bubble, bubbles):        center_distance = self.center_distance(bubble, bubbles)        return center_distance - bubble[2] - \            bubbles[:, 2] - self.bubble_spacing
    def check_collisions(self, bubble, bubbles):        distance = self.outline_distance(bubble, bubbles)        return len(distance[distance < 0])
    def collides_with(self, bubble, bubbles):        distance = self.outline_distance(bubble, bubbles)        idx_min = np.argmin(distance)        return idx_min if type(idx_min) == np.ndarray else [idx_min]
    def collapse(self, n_iterations=50):        """        Move bubbles to the center of mass.
        Parameters        ----------        n_iterations : int, default: 50            Number of moves to perform.        """        for _i in range(n_iterations):            moves = 0            for i in range(len(self.bubbles)):                rest_bub = np.delete(self.bubbles, i, 0)                # try to move directly towards the center of mass                # direction vector from bubble to the center of mass                dir_vec = self.com - self.bubbles[i, :2]
                # shorten direction vector to have length of 1                dir_vec = dir_vec / np.sqrt(dir_vec.dot(dir_vec))
                # calculate new bubble position                new_point = self.bubbles[i, :2] + dir_vec * self.step_dist                new_bubble = np.append(new_point, self.bubbles[i, 2:4])
                # check whether new bubble collides with other bubbles                if not self.check_collisions(new_bubble, rest_bub):                    self.bubbles[i, :] = new_bubble                    self.com = self.center_of_mass()                    moves += 1                else:                    # try to move around a bubble that you collide with                    # find colliding bubble                    for colliding in self.collides_with(new_bubble, rest_bub):                        # calculate direction vector                        dir_vec = rest_bub[colliding, :2] - self.bubbles[i, :2]                        dir_vec = dir_vec / np.sqrt(dir_vec.dot(dir_vec))                        # calculate orthogonal vector                        orth = np.array([dir_vec[1], -dir_vec[0]])                        # test which direction to go                        new_point1 = (self.bubbles[i, :2] + orth *                                      self