"""

adjusted from Denis on pystatsmodels mailing list

there might still be problems with loc and scale,

"""

from scipy import stats

__date__ = "2010-12-29 dec"


class MaxDist(stats.rv_continuous):
    """max of n of scipy.stats normal expon ...
    Example:
        maxnormal10 = RVmax( scipy.stats.norm, 10 )
        sample = maxnormal10( size=1000 )
        sample.cdf = cdf ^ n,  ppf ^ (1/n)
    """

    def __init__(self, dist, n):
        self.dist = dist
        self.n = n
        super().__init__(
            name="maxdist",
            a=dist.a,
            b=dist.b,
            longname="A maximumdistribution",
            # extradoc = extradoc
        )

    def _pdf(self, x, *args, **kw):
        return (
            self.n
            * self.dist.pdf(x, *args, **kw)
            * self.dist.cdf(x, *args, **kw) ** (self.n - 1)
        )

    def _cdf(self, x, *args, **kw):
        return self.dist.cdf(x, *args, **kw) ** self.n

    def _ppf(self, q, *args, **kw):
        # y = F(x) ^ n  <=>  x = F-1( y ^ 1/n)
        return self.dist.ppf(q ** (1.0 / self.n), *args, **kw)


#    def rvs( self, *args, **kw ):
#       size = kw.pop( "size", 1 )
#       u = np.random.uniform( size=size, **kw ) ** (1 / self.n)
#       return self.dist.ppf( u, **kw )


maxdistr = MaxDist(stats.norm, 10)

print(maxdistr.rvs(size=10))
print(maxdistr.stats(moments="mvsk"))

"""
>>> print maxdistr.stats(moments = 'mvsk')
(array(1.5387527308351818), array(0.34434382328492852), array(0.40990510188513779), array(0.33139861783918922))
>>> rvs = np.random.randn(1000,10)
>>> stats.describe(rvs.max(1))
(1000, (-0.028558517753519492, 3.6134958002753685), 1.5560520428553426, 0.34965234046170773, 0.48504309950278557, 0.17691859056779258)
>>> rvs2 = maxdistr.rvs(size=1000)
>>> stats.describe(rvs2)
(1000, (-0.015290995091401905, 3.3227019151170931), 1.5248146840651813, 0.32827518543128631, 0.23998620901199566, -0.080555658370268013)
>>> rvs2 = maxdistr.rvs(size=10000)
>>> stats.describe(rvs2)
(10000, (-0.15855091764294812, 4.1898138060896937), 1.532862047388899, 0.34361316060467512, 0.43128838106936973, 0.41115043864619061)

>>> maxdistr.pdf(1.5)
0.69513824417156755

#integrating the pdf
>>> maxdistr.expect()
1.5387527308351729
>>> maxdistr.expect(lambda x:1)
0.99999999999999956


"""
