"""
Created on Thu Aug 12 14:59:03 2010

Warning: not tried out or tested yet, Done

Author: josef-pktd
"""

import matplotlib.pyplot as plt
import numpy as np
from numpy import abs as np_abs, inf, where
from scipy import stats
from scipy.special import comb
from scipy.stats.distributions import rv_continuous


# Generalized Pareto  with reversed sign of c as in literature
class genpareto2_gen(rv_continuous):
    def _argcheck(self, c):
        c = np.asarray(c)
        self.b = where(c > 0, 1.0 / np_abs(c), inf)
        return where(c == 0, 0, 1)

    def _pdf(self, x, c):
        Px = np.power(1 - c * x, -1.0 + 1.0 / c)
        return Px

    def _logpdf(self, x, c):
        return (-1.0 + 1.0 / c) * np.log1p(-c * x)

    def _cdf(self, x, c):
        return 1.0 - np.power(1 - c * x, 1.0 / c)

    def _ppf(self, q, c):
        vals = -1.0 / c * (np.power(1 - q, c) - 1)
        return vals

    def _munp(self, n, c):
        k = np.arange(0, n + 1)
        val = (1.0 / c) ** n * np.sum(comb(n, k) * (-1) ** k / (1.0 + c * k), axis=0)
        return where(c * n > -1, val, inf)

    def _entropy(self, c):
        if c < 0:
            return 1 - c
        else:
            self.b = 1.0 / c
            return rv_continuous._entropy(self, c)


genpareto2 = genpareto2_gen(
    a=0.0,
    name="genpareto",
    longname="A generalized Pareto",
    shapes="c",
    #                           extradoc="""
    #
    # Generalized Pareto distribution
    #
    # genpareto2.pdf(x,c) = (1+c*x)**(-1-1/c)
    # for c != 0, and for x >= 0 for all c, and x < 1/abs(c) for c < 0.
    # """
)

shape, loc, scale = 0.5, 0, 1
rv = np.arange(5)
quant = [0.01, 0.1, 0.5, 0.9, 0.99]
for method, x in [("pdf", rv), ("cdf", rv), ("sf", rv), ("ppf", quant), ("isf", quant)]:
    print(getattr(genpareto2, method)(x, shape, loc, scale))
    print(getattr(stats.genpareto, method)(x, -shape, loc, scale))

print(genpareto2.stats(shape, loc, scale, moments="mvsk"))
print(stats.genpareto.stats(-shape, loc, scale, moments="mvsk"))
print(genpareto2.entropy(shape, loc, scale))
print(stats.genpareto.entropy(-shape, loc, scale))


def paramstopot(thresh, shape, scale):
    """transform shape scale for peak over threshold

    y = x-u|x>u ~ GPD(k, sigma-k*u) if x ~ GPD(k, sigma)
    notation of de Zea Bermudez, Kotz
    k, sigma is shape, scale
    """
    return shape, scale - shape * thresh


def paramsfrompot(thresh, shape, scalepot):
    return shape, scalepot + shape * thresh


def warnif(cond, msg):
    if not cond:
        print(msg, "does not hold")


def meanexcess(thresh, shape, scale):
    """mean excess function of genpareto

    assert are inequality conditions in de Zea Bermudez, Kotz
    """
    warnif(shape > -1, "shape > -1")
    warnif(thresh >= 0, "thresh >= 0")  # make it weak inequality
    warnif((scale - shape * thresh) > 0, "(scale - shape*thresh) > 0")
    return (scale - shape * thresh) / (1 + shape)


def meanexcess_plot(data, params=None, lidx=100, uidx=10, method="emp", plot=0):
    if method == "est":
        # does not make much sense yet,
        # estimate the parameters and use theoretical meanexcess
        if params is None:
            raise NotImplementedError
        else:
            pass  # estimate parames
    elif method == "emp":
        # calculate meanexcess from data
        datasorted = np.sort(data)
        meanexcess = (datasorted[::-1].cumsum()) / np.arange(
            1, len(data) + 1
        ) - datasorted[::-1]
        meanexcess = meanexcess[::-1]
        if plot:
            plt.plot(datasorted[:-uidx], meanexcess[:-uidx])
            if params is not None:
                shape, scale = params
                plt.plot(
                    datasorted[:-uidx],
                    (scale - datasorted[:-uidx] * shape) / (1.0 + shape),
                )
    return datasorted, meanexcess


print(meanexcess(5, -0.5, 10))
print(meanexcess(5, -2, 10))

data = genpareto2.rvs(-0.75, scale=5, size=1000)
# data = np.random.uniform(50, size=1000)
# data = stats.norm.rvs(0, np.sqrt(50), size=1000)
# data = stats.pareto.rvs(1.5, np.sqrt(50), size=1000)
tmp = meanexcess_plot(data, params=(-0.75, 5), plot=1)
print(tmp[1][-20:])
print(tmp[0][-20:])


# plt.show()


def meanexcess_emp(data):
    datasorted = np.sort(data).astype(float)
    meanexcess = (datasorted[::-1].cumsum()) / np.arange(1, len(data) + 1) - datasorted[
        ::-1
    ]
    meancont = (datasorted[::-1].cumsum()) / np.arange(1, len(data) + 1)
    meanexcess = meanexcess[::-1]
    return datasorted, meanexcess, meancont[::-1]


def meanexcess_dist(self, lb, *args, **kwds):
    # default function in expect is identity
    # need args in call
    if np.ndim(lb) == 0:
        return self.expect(lb=lb, conditional=True)
    else:
        return np.array([self.expect(lb=lbb, conditional=True) for lbb in lb])


ds, me, mc = meanexcess_emp(1.0 * np.arange(1, 10))
print(ds)
print(me)
print(mc)

print(meanexcess_dist(stats.norm, lb=0.5))
print(meanexcess_dist(stats.norm, lb=[-np.inf, -0.5, 0, 0.5]))
rvs = stats.norm.rvs(size=100000)
rvs = rvs - rvs.mean()
print(rvs.mean(), rvs[rvs > -0.5].mean(), rvs[rvs > 0].mean(), rvs[rvs > 0.5].mean())

"""
[ 1.   0.5  0.   0.   0. ]
[ 1.   0.5  0.   0.   0. ]
[ 0.    0.75  1.    1.    1.  ]
[ 0.    0.75  1.    1.    1.  ]
[ 1.    0.25  0.    0.    0.  ]
[ 1.    0.25  0.    0.    0.  ]
[ 0.01002513  0.1026334   0.58578644  1.36754447  1.8       ]
[ 0.01002513  0.1026334   0.58578644  1.36754447  1.8       ]
[ 1.8         1.36754447  0.58578644  0.1026334   0.01002513]
[ 1.8         1.36754447  0.58578644  0.1026334   0.01002513]
(array(0.66666666666666674), array(0.22222222222222243), array(0.56568542494923058), array(-0.60000000000032916))
(array(0.66666666666666674), array(0.22222222222222243), array(0.56568542494923058), array(-0.60000000000032916))
0.5
0.5
25.0
shape > -1 does not hold
-20
[  41.4980671    42.83145298   44.24197578   45.81622844   47.57145212
   49.52692287   51.70553275   54.0830766    56.61358997   59.53409167
   62.8970042    66.73494156   71.04227973   76.24015612   82.71835988
   89.79611663   99.4252195   106.2372462    94.83432424    0.        ]
[  15.79736355   16.16373531   17.44204268   17.47968055   17.73264951
   18.23939099   19.02638455   20.79746264   23.7169161    24.48807136
   25.90496638   28.35556795   32.27623618   34.65714495   37.37093362
   47.32957609   51.27970515   78.98913941  129.04309012  189.66864848]
>>> np.arange(10)
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
>>> meanexcess_emp(np.arange(10))
(array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]), array([4, 4, 5, 5, 5, 6, 6, 5, 4, 0]), array([9, 8, 8, 7, 7, 6, 6, 5, 5, 4]))
>>> meanexcess_emp(1*np.arange(10))
(array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]), array([4, 4, 5, 5, 5, 6, 6, 5, 4, 0]), array([9, 8, 8, 7, 7, 6, 6, 5, 5, 4]))
>>> meanexcess_emp(1.*np.arange(10))
(array([ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9.]), array([ 4.5       ,  4.88888889,  5.25      ,  5.57142857,  5.83333333,
        6.        ,  6.        ,  5.66666667,  4.5       ,  0.        ]), array([ 9. ,  8.5,  8. ,  7.5,  7. ,  6.5,  6. ,  5.5,  5. ,  4.5]))
>>> meanexcess_emp(0.5**np.arange(10))
(array([ 0.00195313,  0.00390625,  0.0078125 ,  0.015625  ,  0.03125   ,
        0.0625    ,  0.125     ,  0.25      ,  0.5       ,  1.        ]), array([ 0.19960938,  0.22135417,  0.24804688,  0.28125   ,  0.32291667,
        0.375     ,  0.4375    ,  0.5       ,  0.5       ,  0.        ]), array([ 1.        ,  0.75      ,  0.58333333,  0.46875   ,  0.3875    ,
        0.328125  ,  0.28348214,  0.24902344,  0.22178819,  0.19980469]))
>>> meanexcess_emp(np.arange(10)**0.5)
(array([ 0.        ,  1.        ,  1.41421356,  1.73205081,  2.        ,
        2.23606798,  2.44948974,  2.64575131,  2.82842712,  3.        ]), array([ 1.93060005,  2.03400006,  2.11147337,  2.16567659,  2.19328936,
        2.18473364,  2.11854461,  1.94280904,  1.5       ,  0.        ]), array([ 3.        ,  2.91421356,  2.82472615,  2.73091704,  2.63194723,
        2.52662269,  2.41311242,  2.28825007,  2.14511117,  1.93060005]))
>>> meanexcess_emp(np.arange(10)**-2)
(array([-2147483648,           0,           0,           0,           0,
                 0,           0,           0,           0,           1]), array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0]), array([         1,          0,          0,          0,          0,
                0,          0,          0,          0, -214748365]))
>>> meanexcess_emp(np.arange(10)**(-0.5))
(array([ 0.33333333,  0.35355339,  0.37796447,  0.40824829,  0.4472136 ,
        0.5       ,  0.57735027,  0.70710678,  1.        ,         Inf]), array([ Inf,  Inf,  Inf,  Inf,  Inf,  Inf,  Inf,  Inf,  Inf,  NaN]), array([ Inf,  Inf,  Inf,  Inf,  Inf,  Inf,  Inf,  Inf,  Inf,  Inf]))
>>> np.arange(10)**(-0.5)
array([        Inf,  1.        ,  0.70710678,  0.57735027,  0.5       ,
        0.4472136 ,  0.40824829,  0.37796447,  0.35355339,  0.33333333])
>>> meanexcess_emp(np.arange(1,10)**(-0.5))
(array([ 0.33333333,  0.35355339,  0.37796447,  0.40824829,  0.4472136 ,
        0.5       ,  0.57735027,  0.70710678,  1.        ]), array([ 0.4857152 ,  0.50223543,  0.51998842,  0.53861177,  0.55689141,
        0.57111426,  0.56903559,  0.5       ,  0.        ]), array([ 1.        ,  0.85355339,  0.76148568,  0.69611426,  0.64633413,
        0.60665316,  0.57398334,  0.5464296 ,  0.52275224]))
>>> meanexcess_emp(np.arange(1,10))
(array([1, 2, 3, 4, 5, 6, 7, 8, 9]), array([4, 5, 5, 5, 6, 6, 5, 4, 0]), array([9, 8, 8, 7, 7, 6, 6, 5, 5]))
>>> meanexcess_emp(1.*np.arange(1,10))
(array([ 1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9.]), array([ 4.88888889,  5.25      ,  5.57142857,  5.83333333,  6.        ,
        6.        ,  5.66666667,  4.5       ,  0.        ]), array([ 9. ,  8.5,  8. ,  7.5,  7. ,  6.5,  6. ,  5.5,  5. ]))
>>> datasorted = np.sort(1.*np.arange(1,10))
>>> (datasorted[::-1].cumsum()-datasorted[::-1])
array([  0.,   9.,  17.,  24.,  30.,  35.,  39.,  42.,  44.])
>>> datasorted[::-1].cumsum()
array([  9.,  17.,  24.,  30.,  35.,  39.,  42.,  44.,  45.])
>>> datasorted[::-1]
array([ 9.,  8.,  7.,  6.,  5.,  4.,  3.,  2.,  1.])
>>>
"""
