python 2.7 - Websocket Error Running Pygame -

i'm running siera on macbook pro. i've got code below in cell in jupyter notebook. i'm trying run code see part of project udacity machine learning engineer class. when hit shift enter run code pygame window pops up, , hangs , spins. message below in terminal. see issue might be? pygame has been little tricky going.

terminal message:  uncaught exception /api/kernels/6f220a14-725d-4484-8770-93a3a7f7d95d/channels?session_id=07775c8bbf074e4d8717d4135fcafded (::1)     httpserverrequest(protocol='http', host='localhost:8888', method='get', uri='/api/kernels/6f220a14-725d-4484-8770-93a3a7f7d95d/channels?session_id=07775c8bbf074e4d8717d4135fcafded', version='http/1.1', remote_ip='::1', headers={'origin': 'http://localhost:8888', 'upgrade': 'websocket', 'sec-websocket-extensions': 'x-webkit-deflate-frame', 'sec-websocket-version': '13', 'connection': 'upgrade', 'sec-websocket-key': 'rrothfnf2xt1xznyhkevrg==', 'user-agent': 'mozilla/5.0 (macintosh; intel mac os x 10_12_6) applewebkit/603.3.8 (khtml, gecko) version/10.1.2 safari/603.3.8', 'host': 'localhost:8888', 'cookie': 'username-localhost-8888="2|1:0|10:1503172058|23:username-localhost-8888|44:yzvmzdrhowjhmjc4ndlimmjhnzg3mjyxmtfkoti3m2m=|57cc29addda75eecbfbcc042014d8e21438ee1e1bd03287e905edf0ff195be0d"; _xsrf=2|c4cb02f3|6fa896295a4e403506de1a7168fe3f8a|1501449753', 'pragma': 'no-cache', 'cache-control': 'no-cache'})     traceback (most recent call last):       file "/users/myname/anaconda/lib/python2.7/site-packages/tornado/web.py", line 1425, in _stack_context_handle_exception         raise_exc_info((type, value, traceback))       file "/users/myname/anaconda/lib/python2.7/site-packages/tornado/stack_context.py", line 314, in wrapped         ret = fn(*args, **kwargs)       file "/users/myname/anaconda/lib/python2.7/site-packages/zmq/eventloop/zmqstream.py", line 191, in <lambda>         self.on_recv(lambda msg: callback(self, msg), copy=copy)       file "/users/myname/anaconda/lib/python2.7/site-packages/notebook/services/kernels/handlers.py", line 373, in _on_zmq_reply         super(zmqchannelshandler, self)._on_zmq_reply(stream, msg)       file "/users/myname/anaconda/lib/python2.7/site-packages/notebook/base/zmqhandlers.py", line 258, in _on_zmq_reply         self.write_message(msg, binary=isinstance(msg, bytes))       file "/users/myname/anaconda/lib/python2.7/site-packages/tornado/websocket.py", line 210, in write_message         raise websocketclosederror()     websocketclosederror   code:  import random import math environment import agent, environment planner import routeplanner simulator import simulator  class learningagent(agent):     """ agent learns drive in smartcab world.         object modifying. """       def __init__(self, env, learning=false, epsilon=1.0, alpha=0.5):         super(learningagent, self).__init__(env)     # set agent in evironment          self.planner = routeplanner(self.env, self)  # create route planner         self.valid_actions = self.env.valid_actions  # set of valid actions          # set parameters of learning agent         self.learning = learning # whether agent expected learn         self.q = dict()          # create q-table dictionary of tuples         self.epsilon = epsilon   # random exploration factor         self.alpha = alpha       # learning factor          ###########         ## ##         ###########         # set additional class parameters needed       def reset(self, destination=none, testing=false):         """ reset function called @ beginning of each trial.             'testing' set true if testing trials being used             once training trials have completed. """          # select destination new location route         self.planner.route_to(destination)          ###########          ## ##         ###########         # update epsilon using decay function of choice         # update additional class parameters needed         # if 'testing' true, set epsilon , alpha 0          return none      def build_state(self):         """ build_state function called when agent requests data              environment. next waypoint, intersection inputs, , deadline              features available agent. """          # collect data environment         waypoint = self.planner.next_waypoint() # next waypoint          inputs = self.env.sense(self)           # visual input - intersection light , traffic         deadline = self.env.get_deadline(self)  # remaining deadline          ###########          ## ##         ###########         # set 'state' tuple of relevant data agent                 state = none          return state       def get_maxq(self, state):         """ get_max_q function called when agent asked find             maximum q-value of actions based on 'state' smartcab in. """          ###########          ## ##         ###########         # calculate maximum q-value of actions given state          maxq = none          return maxq        def createq(self, state):         """ createq function called when state generated agent. """          ###########          ## ##         ###########         # when learning, check if 'state' not in q-table         # if not, create new dictionary state         #   then, each action available, set initial q-value 0.0          return       def choose_action(self, state):         """ choose_action function called when agent asked choose             action take, based on 'state' smartcab in. """          # set agent state , default action         self.state = state         self.next_waypoint = self.planner.next_waypoint()         action = none          ###########          ## ##         ###########         # when not learning, choose random action         # when learning, choose random action 'epsilon' probability         #   otherwise, choose action highest q-value current state          return action       def learn(self, state, action, reward):         """ learn function called after agent completes action ,             receives award. function not consider future rewards              when conducting learning. """          ###########          ## ##         ###########         # when learning, implement value iteration update rule         #   use learning rate 'alpha' (do not use discount factor 'gamma')          return       def update(self):         """ update function called when time step completed in              environment given trial. function build agent             state, choose action, receive reward, , learn if enabled. """          state = self.build_state()          # current state         self.createq(state)                 # create 'state' in q-table         action = self.choose_action(state)  # choose action         reward = self.env.act(self, action) # receive reward         self.learn(state, action, reward)   # q-learn          return   def run():     """ driving function running simulation.          press esc close simulation, or [space] pause simulation. """      ##############     # create environment     # flags:     #   verbose     - set true display additional output simulation     #   num_dummies - discrete number of dummy agents in environment, default 100     #   grid_size   - discrete number of intersections (columns, rows), default (8, 6)     env = environment()      ##############     # create driving agent     # flags:     #   learning   - set true force driving agent use q-learning     #    * epsilon - continuous value exploration factor, default 1     #    * alpha   - continuous value learning rate, default 0.5     agent = env.create_agent(learningagent)      ##############     # follow driving agent     # flags:     #   enforce_deadline - set true enforce deadline metric     env.set_primary_agent(agent)      ##############     # create simulation     # flags:     #   update_delay - continuous time (in seconds) between actions, default 2.0 seconds     #   display      - set false disable gui if pygame enabled     #   log_metrics  - set true log trial , simulation results /logs     #   optimized    - set true change default log file name     sim = simulator(env)      ##############     # run simulator     # flags:     #   tolerance  - epsilon tolerance before beginning testing, default 0.05      #   n_test     - discrete number of testing trials perform, default 0     sim.run()   if __name__ == '__main__':     run()