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from sympy import (Symbol, Eq, Ne, simplify, sqrt, exp, pi, symbols,
                Piecewise, factorial, gamma, IndexedBase, Add, Pow, Mul,
                Indexed, Integer, Integral, DiracDelta, Dummy, Sum, oo)
from sympy.functions.elementary.piecewise import ExprCondPair
from sympy.stats import (Poisson, Beta, Exponential, P,
                        Multinomial, MultivariateBeta)
from sympy.stats.crv_types import Normal
from sympy.stats.drv_types import PoissonDistribution
from sympy.stats.compound_rv import CompoundPSpace, CompoundDistribution
from sympy.stats.joint_rv import MarginalDistribution
from sympy.stats.rv import pspace, density
from sympy.testing.pytest import ignore_warnings

def test_density():
    x = Symbol('x')
    l = Symbol('l', positive=True)
    rate = Beta(l, 2, 3)
    X = Poisson(x, rate)
    assert isinstance(pspace(X), CompoundPSpace)
    assert density(X, Eq(rate, rate.symbol)) == PoissonDistribution(l)
    N1 = Normal('N1', 0, 1)
    N2 = Normal('N2', N1, 2)
    assert density(N2)(0).doit() == sqrt(10)/(10*sqrt(pi))
    assert simplify(density(N2, Eq(N1, 1))(x)) == \
        sqrt(2)*exp(-(x - 1)**2/8)/(4*sqrt(pi))
    assert simplify(density(N2)(x)) == sqrt(10)*exp(-x**2/10)/(10*sqrt(pi))

def test_MarginalDistribution():
    a1, p1, p2 = symbols('a1 p1 p2', positive=True)
    C = Multinomial('C', 2, p1, p2)
    B = MultivariateBeta('B', a1, C[0])
    MGR = MarginalDistribution(B, (C[0],))
    mgrc = Mul(Symbol('B'), Piecewise(ExprCondPair(Mul(Integer(2),
    Pow(Symbol('p1', positive=True), Indexed(IndexedBase(Symbol('C')),
    Integer(0))), Pow(Symbol('p2', positive=True),
    Indexed(IndexedBase(Symbol('C')), Integer(1))),
    Pow(factorial(Indexed(IndexedBase(Symbol('C')), Integer(0))), Integer(-1)),
    Pow(factorial(Indexed(IndexedBase(Symbol('C')), Integer(1))), Integer(-1))),
    Eq(Add(Indexed(IndexedBase(Symbol('C')), Integer(0)),
    Indexed(IndexedBase(Symbol('C')), Integer(1))), Integer(2))),
    ExprCondPair(Integer(0), True)), Pow(gamma(Symbol('a1', positive=True)),
    Integer(-1)), gamma(Add(Symbol('a1', positive=True),
    Indexed(IndexedBase(Symbol('C')), Integer(0)))),
    Pow(gamma(Indexed(IndexedBase(Symbol('C')), Integer(0))), Integer(-1)),
    Pow(Indexed(IndexedBase(Symbol('B')), Integer(0)),
    Add(Symbol('a1', positive=True), Integer(-1))),
    Pow(Indexed(IndexedBase(Symbol('B')), Integer(1)),
    Add(Indexed(IndexedBase(Symbol('C')), Integer(0)), Integer(-1))))
    assert MGR(C) == mgrc

def test_compound_distribution():
    Y = Poisson('Y', 1)
    Z = Poisson('Z', Y)
    assert isinstance(pspace(Z), CompoundPSpace)
    assert isinstance(pspace(Z).distribution, CompoundDistribution)
    assert Z.pspace.distribution.pdf(1).doit() == exp(-2)*exp(exp(-1))

def test_mix_expression():
    Y, E = Poisson('Y', 1), Exponential('E', 1)
    k = Dummy('k')
    expr1 = Integral(Sum(exp(-1)*Integral(exp(-k)*DiracDelta(k - 2), (k, 0, oo)
    )/factorial(k), (k, 0, oo)), (k, -oo, 0))
    expr2 = Integral(Sum(exp(-1)*Integral(exp(-k)*DiracDelta(k - 2), (k, 0, oo)
    )/factorial(k), (k, 0, oo)), (k, 0, oo))
    assert P(Eq(Y + E, 1)) == 0
    assert P(Ne(Y + E, 2)) == 1
    with ignore_warnings(UserWarning): ### TODO: Restore tests once warnings are removed
        assert P(E + Y < 2, evaluate=False).rewrite(Integral).dummy_eq(expr1)
        assert P(E + Y > 2, evaluate=False).rewrite(Integral).dummy_eq(expr2)

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