Hints: 1. First figure out the relations between the groups in the scatterplot and the geographical descriptions. A. Find out the area on the ipyleaflet map based on the description. B. An area corresponds to a group in the scatterplot if they share the same color. 2. There are several ways that you can do to figure out which group each waveform of the yearly temperature profiles corresponds to. A. Plot the yearly temperature profile by yourself for each group using profile DataFrame and compare the shapes of waveforms in your plot and in the plot we showed. B. Decompose the yearly temperature profile vectors to get the first 2 coefficients. See where the coefficients are in the scatterplot and which group the coefficients are near to. In [45]: # THIS IS JUST A TEMPLATE. COPY THE DICTIONARIES AND CHANGE THE VALUES. DO NOT CHANGE VARIABLE NAMES! group_answer = {'A' : 0, 'B' : 1, E' : 5} waveform_answer = {'A' : 'a', 'B': "D' : 'd', 'B': 'e', " F ' : . f ' } ### ### YOUR CODE HERE ### In [46]: assert len(group_answer) == 6, `group_answer should only have 6 keys/values assert len(waveform_answer) == 6, 'waveform_answer should only have 6 keys/ values' for region in ['A', 'B' , "C', "D', 'B', 'F" ]: assert region in group_answer, f'Your group_answer is missing region {r egion}' for region in ['A' , 'B', 'C' , "D', 'E', 'F']: assert region in waveform_answer, f'your waveform_answer is missing reg ion {region}' unique_group_answer = np. unique(list(group_answer . values()), return_counts= True) unique_waveform_answer = np . unique(list(waveform_answer . values () ), return_c punts=True) assert (unique_group_answer [1] == 1).all(), "Your group_answer contains dup licate answers' assert (unique_waveform_answer [1] == 1). all(), 'Your waveform_answer contai ns duplicate answers In [47]: ## Hidden test cases here # ## ### AUTOGRADER TEST - DO NOT REMOVE ###