def extract(text='', start=0, results=15, buckets=None, limit=False, max_familiarity=None, min_familiarity=None,
                max_hotttnesss=None, min_hotttnesss=None):

    buckets = buckets or []
    kwargs = {}
    
    kwargs['text'] = text
    
    if max_familiarity is not None:
        kwargs['max_familiarity'] = max_familiarity
    if min_familiarity is not None:
        kwargs['min_familiarity'] = min_familiarity
    if max_hotttnesss is not None:
        kwargs['max_hotttnesss'] = max_hotttnesss
    if min_hotttnesss is not None:
        kwargs['min_hotttnesss'] = min_hotttnesss
    if start:
        kwargs['start'] = start
    if results:
        kwargs['results'] = results
    if buckets:
        kwargs['bucket'] = buckets
    if limit:
        kwargs['limit'] = 'true'
    
    result = util.callm("%s/%s" % ('artist', 'extract'), kwargs)
    
    return [Artist(**util.fix(a_dict)) for a_dict in result['response']['artists']]
Solves Ax = B efficiently, where A is a diagonal Matrix,
dataset/ETHPy150Open echonest/pyechonest/pyechonest/artist.py/extract--crossover with--dataset/ETHPy150Open sympy/sympy/sympy/matrices/matrices.py/MatrixBase.diagonal_solve