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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def profile_stats(adapter, threshold = 0.9):
 """ Compares the pairwise hamming distances for all the sample profiles in the database. Returns a table of the number of distances within given ranges. Args: adapter (MongoAdapter):
 Adapter to mongodb threshold (float):
 If any distance is found above this threshold a warning will be given, stating the two matching samples. Returns: distance_dict (dict):
 dictionary with ranges as keys, and the number of distances that are within these ranges as values. """

    profiles = []
    samples = []

    #Instatiate the distance dictionary with a count 0 for all the ranges
    distance_dict = {key: 0 for key in HAMMING_RANGES.keys()}

    for case in adapter.cases():

        for individual in case['individuals']:

            if individual.get('profile'):
                #Make sample name <case_id>.<sample_id>
                sample_id = f"{case['case_id']}.{individual['ind_id']}"
                ind_profile = individual['profile']

                #Numpy array to hold all the distances for this samples profile
                distance_array = np.array([], dtype=np.float)

                for sample, profile in zip(samples, profiles):

                    #Get distance and append to distance array
                    distance = compare_profiles(ind_profile, profile)
                    distance_array = np.append(distance_array, distance)

                    #Issue warning if above threshold
                    if distance >= threshold:
                        LOG.warning(f"{sample_id} is {distance} similar to {sample}")

                #Check number of distances in each range and add to distance_dict
                for key,range in HAMMING_RANGES.items():

                    #Calculate the number of hamming distances found within the
                    #range for current individual
                    distance_dict[key] += np.sum(
                        (distance_array >= range[0]) & (distance_array < range[1])
                    )

                #Append profile and sample_id for this sample for the next
                #iteration
                profiles.append(ind_profile)
                samples.append(sample_id)

    return distance_dict