def smoothness_p(n, m=-1, power=0, visual=None):
    from sympy.utilities import flatten

    # visual must be True, False or other (stored as None)
    if visual in (1, 0):
        visual = bool(visual)
    elif visual not in (True, False):
        visual = None

    if type(n) is str:
        if visual:
            return n
        d = {}
        for li in n.splitlines():
            k, v = [int(i) for i in
                    li.split('has')[0].split('=')[1].split('**')]
            d[k] = v
        if visual is not True and visual is not False:
            return d
        return smoothness_p(d, visual=False)
    elif type(n) is not tuple:
        facs = factorint(n, visual=False)

    if power:
        k = -1
    else:
        k = 1
    if type(n) is not tuple:
        rv = (m, sorted([(f,
                          tuple([M] + list(smoothness(f + m))))
                         for f, M in [i for i in facs.items()]],
                        key=lambda x: (x[1][k], x[0])))
    else:
        rv = n

    if visual is False or (visual is not True) and (type(n) in [int, Mul]):
        return rv
    lines = []
    for dat in rv[1]:
        dat = flatten(dat)
        dat.insert(2, m)
        lines.append('p**i=%i**%i has p%+i B=%i, B-pow=%i' % tuple(dat))
    return '\n'.join(lines)
Return a list of [m, (p, (M, sm(p + m), psm(p + m)))...]
dataset/ETHPy150Open sympy/sympy/sympy/ntheory/factor_.py/smoothness_p