def eval(cls, n, rho, v): if not n >= 0: raise ValueError("must have n >= 0") elif n == 0: return 1 else: return g(n-1, rho, v) \ + gammasimp(gamma(rho+2+n)/gamma(rho+2)) \ / gammasimp(gamma(3+n)/gamma(3))*v**n
def test_lerchphi(): from sympy import gammasimp, exp_polar, polylog, log, lerchphi assert hyperexpand(hyper([1, a], [a + 1], z) / a) == lerchphi(z, 1, a) assert hyperexpand(hyper([1, a, a], [a + 1, a + 1], z) / a**2) == lerchphi( z, 2, a) assert hyperexpand(hyper([1, a, a, a], [a + 1, a + 1, a + 1], z) / a**3) == lerchphi(z, 3, a) assert hyperexpand(hyper([1] + [a] * 10, [a + 1] * 10, z) / a**10) == lerchphi(z, 10, a) assert gammasimp( hyperexpand(meijerg([0, 1 - a], [], [0], [-a], exp_polar(-I * pi) * z))) == lerchphi(z, 1, a) assert gammasimp( hyperexpand( meijerg([0, 1 - a, 1 - a], [], [0], [-a, -a], exp_polar(-I * pi) * z))) == lerchphi(z, 2, a) assert gammasimp( hyperexpand( meijerg([0, 1 - a, 1 - a, 1 - a], [], [0], [-a, -a, -a], exp_polar(-I * pi) * z))) == lerchphi(z, 3, a) assert hyperexpand(z * hyper([1, 1], [2], z)) == -log(1 + -z) assert hyperexpand(z * hyper([1, 1, 1], [2, 2], z)) == polylog(2, z) assert hyperexpand(z * hyper([1, 1, 1, 1], [2, 2, 2], z)) == polylog(3, z) assert hyperexpand( hyper([1, a, 1 + S.Half], [a + 1, S.Half], z)) == -2 * a / (z - 1) + (-2 * a**2 + a) * lerchphi(z, 1, a) # Now numerical tests. These make sure reductions etc are carried out # correctly # a rational function (polylog at negative integer order) assert can_do([2, 2, 2], [1, 1]) # NOTE these contain log(1-x) etc ... better make sure we have |z| < 1 # reduction of order for polylog assert can_do([1, 1, 1, b + 5], [2, 2, b], div=10) # reduction of order for lerchphi # XXX lerchphi in mpmath is flaky assert can_do([1, a, a, a, b + 5], [a + 1, a + 1, a + 1, b], numerical=False) # test a bug from sympy import Abs assert hyperexpand( hyper( [S.Half, S.Half, S.Half, 1], [Rational(3, 2), Rational(3, 2), Rational(3, 2)], Rational(1, 4), )) == Abs(-polylog(3, exp_polar(I * pi) / 2) + polylog(3, S.Half))
def test_lerchphi(): from sympy import gammasimp, exp_polar, polylog, log, lerchphi assert hyperexpand(hyper([1, a], [a + 1], z)/a) == lerchphi(z, 1, a) assert hyperexpand( hyper([1, a, a], [a + 1, a + 1], z)/a**2) == lerchphi(z, 2, a) assert hyperexpand(hyper([1, a, a, a], [a + 1, a + 1, a + 1], z)/a**3) == \ lerchphi(z, 3, a) assert hyperexpand(hyper([1] + [a]*10, [a + 1]*10, z)/a**10) == \ lerchphi(z, 10, a) assert gammasimp(hyperexpand(meijerg([0, 1 - a], [], [0], [-a], exp_polar(-I*pi)*z))) == lerchphi(z, 1, a) assert gammasimp(hyperexpand(meijerg([0, 1 - a, 1 - a], [], [0], [-a, -a], exp_polar(-I*pi)*z))) == lerchphi(z, 2, a) assert gammasimp(hyperexpand(meijerg([0, 1 - a, 1 - a, 1 - a], [], [0], [-a, -a, -a], exp_polar(-I*pi)*z))) == lerchphi(z, 3, a) assert hyperexpand(z*hyper([1, 1], [2], z)) == -log(1 + -z) assert hyperexpand(z*hyper([1, 1, 1], [2, 2], z)) == polylog(2, z) assert hyperexpand(z*hyper([1, 1, 1, 1], [2, 2, 2], z)) == polylog(3, z) assert hyperexpand(hyper([1, a, 1 + S(1)/2], [a + 1, S(1)/2], z)) == \ -2*a/(z - 1) + (-2*a**2 + a)*lerchphi(z, 1, a) # Now numerical tests. These make sure reductions etc are carried out # correctly # a rational function (polylog at negative integer order) assert can_do([2, 2, 2], [1, 1]) # NOTE these contain log(1-x) etc ... better make sure we have |z| < 1 # reduction of order for polylog assert can_do([1, 1, 1, b + 5], [2, 2, b], div=10) # reduction of order for lerchphi # XXX lerchphi in mpmath is flaky assert can_do( [1, a, a, a, b + 5], [a + 1, a + 1, a + 1, b], numerical=False) # test a bug from sympy import Abs assert hyperexpand(hyper([S(1)/2, S(1)/2, S(1)/2, 1], [S(3)/2, S(3)/2, S(3)/2], S(1)/4)) == \ Abs(-polylog(3, exp_polar(I*pi)/2) + polylog(3, S(1)/2))
def test_meijerg_expand(): from sympy import gammasimp, simplify # from mpmath docs assert hyperexpand(meijerg([[], []], [[0], []], -z)) == exp(z) assert hyperexpand(meijerg([[1, 1], []], [[1], [0]], z)) == \ log(z + 1) assert hyperexpand(meijerg([[1, 1], []], [[1], [1]], z)) == \ z/(z + 1) assert hyperexpand(meijerg([[], []], [[S.Half], [0]], (z/2)**2)) \ == sin(z)/sqrt(pi) assert hyperexpand(meijerg([[], []], [[0], [S.Half]], (z/2)**2)) \ == cos(z)/sqrt(pi) assert can_do_meijer([], [a], [a - 1, a - S.Half], []) assert can_do_meijer([], [], [a/2], [-a/2], False) # branches... assert can_do_meijer([a], [b], [a], [b, a - 1]) # wikipedia assert hyperexpand(meijerg([1], [], [], [0], z)) == \ Piecewise((0, abs(z) < 1), (1, abs(1/z) < 1), (meijerg([1], [], [], [0], z), True)) assert hyperexpand(meijerg([], [1], [0], [], z)) == \ Piecewise((1, abs(z) < 1), (0, abs(1/z) < 1), (meijerg([], [1], [0], [], z), True)) # The Special Functions and their Approximations assert can_do_meijer([], [], [a + b/2], [a, a - b/2, a + S.Half]) assert can_do_meijer( [], [], [a], [b], False) # branches only agree for small z assert can_do_meijer([], [S.Half], [a], [-a]) assert can_do_meijer([], [], [a, b], []) assert can_do_meijer([], [], [a, b], []) assert can_do_meijer([], [], [a, a + S.Half], [b, b + S.Half]) assert can_do_meijer([], [], [a, -a], [0, S.Half], False) # dito assert can_do_meijer([], [], [a, a + S.Half, b, b + S.Half], []) assert can_do_meijer([S.Half], [], [0], [a, -a]) assert can_do_meijer([S.Half], [], [a], [0, -a], False) # dito assert can_do_meijer([], [a - S.Half], [a, b], [a - S.Half], False) assert can_do_meijer([], [a + S.Half], [a + b, a - b, a], [], False) assert can_do_meijer([a + S.Half], [], [b, 2*a - b, a], [], False) # This for example is actually zero. assert can_do_meijer([], [], [], [a, b]) # Testing a bug: assert hyperexpand(meijerg([0, 2], [], [], [-1, 1], z)) == \ Piecewise((0, abs(z) < 1), (z/2 - 1/(2*z), abs(1/z) < 1), (meijerg([0, 2], [], [], [-1, 1], z), True)) # Test that the simplest possible answer is returned: assert gammasimp(simplify(hyperexpand( meijerg([1], [1 - a], [-a/2, -a/2 + S.Half], [], 1/z)))) == \ -2*sqrt(pi)*(sqrt(z + 1) + 1)**a/a # Test that hyper is returned assert hyperexpand(meijerg([1], [], [a], [0, 0], z)) == hyper( (a,), (a + 1, a + 1), z*exp_polar(I*pi))*z**a*gamma(a)/gamma(a + 1)**2 # Test place option f = meijerg(((0, 1), ()), ((S.Half,), (0,)), z**2) assert hyperexpand(f) == sqrt(pi)/sqrt(1 + z**(-2)) assert hyperexpand(f, place=0) == sqrt(pi)*z/sqrt(z**2 + 1)
def test_mellin_transform_bessel(): from sympy import Max MT = mellin_transform # 8.4.19 assert MT(besselj(a, 2*sqrt(x)), x, s) == \ (gamma(a/2 + s)/gamma(a/2 - s + 1), (-re(a)/2, S(3)/4), True) assert MT(sin(sqrt(x))*besselj(a, sqrt(x)), x, s) == \ (2**a*gamma(-2*s + S(1)/2)*gamma(a/2 + s + S(1)/2)/( gamma(-a/2 - s + 1)*gamma(a - 2*s + 1)), ( -re(a)/2 - S(1)/2, S(1)/4), True) assert MT(cos(sqrt(x))*besselj(a, sqrt(x)), x, s) == \ (2**a*gamma(a/2 + s)*gamma(-2*s + S(1)/2)/( gamma(-a/2 - s + S(1)/2)*gamma(a - 2*s + 1)), ( -re(a)/2, S(1)/4), True) assert MT(besselj(a, sqrt(x))**2, x, s) == \ (gamma(a + s)*gamma(S(1)/2 - s) / (sqrt(pi)*gamma(1 - s)*gamma(1 + a - s)), (-re(a), S(1)/2), True) assert MT(besselj(a, sqrt(x))*besselj(-a, sqrt(x)), x, s) == \ (gamma(s)*gamma(S(1)/2 - s) / (sqrt(pi)*gamma(1 - a - s)*gamma(1 + a - s)), (0, S(1)/2), True) # NOTE: prudnikov gives the strip below as (1/2 - re(a), 1). As far as # I can see this is wrong (since besselj(z) ~ 1/sqrt(z) for z large) assert MT(besselj(a - 1, sqrt(x))*besselj(a, sqrt(x)), x, s) == \ (gamma(1 - s)*gamma(a + s - S(1)/2) / (sqrt(pi)*gamma(S(3)/2 - s)*gamma(a - s + S(1)/2)), (S(1)/2 - re(a), S(1)/2), True) assert MT(besselj(a, sqrt(x))*besselj(b, sqrt(x)), x, s) == \ (4**s*gamma(1 - 2*s)*gamma((a + b)/2 + s) / (gamma(1 - s + (b - a)/2)*gamma(1 - s + (a - b)/2) *gamma( 1 - s + (a + b)/2)), (-(re(a) + re(b))/2, S(1)/2), True) assert MT(besselj(a, sqrt(x))**2 + besselj(-a, sqrt(x))**2, x, s)[1:] == \ ((Max(re(a), -re(a)), S(1)/2), True) # Section 8.4.20 assert MT(bessely(a, 2*sqrt(x)), x, s) == \ (-cos(pi*(a/2 - s))*gamma(s - a/2)*gamma(s + a/2)/pi, (Max(-re(a)/2, re(a)/2), S(3)/4), True) assert MT(sin(sqrt(x))*bessely(a, sqrt(x)), x, s) == \ (-4**s*sin(pi*(a/2 - s))*gamma(S(1)/2 - 2*s) * gamma((1 - a)/2 + s)*gamma((1 + a)/2 + s) / (sqrt(pi)*gamma(1 - s - a/2)*gamma(1 - s + a/2)), (Max(-(re(a) + 1)/2, (re(a) - 1)/2), S(1)/4), True) assert MT(cos(sqrt(x))*bessely(a, sqrt(x)), x, s) == \ (-4**s*cos(pi*(a/2 - s))*gamma(s - a/2)*gamma(s + a/2)*gamma(S(1)/2 - 2*s) / (sqrt(pi)*gamma(S(1)/2 - s - a/2)*gamma(S(1)/2 - s + a/2)), (Max(-re(a)/2, re(a)/2), S(1)/4), True) assert MT(besselj(a, sqrt(x))*bessely(a, sqrt(x)), x, s) == \ (-cos(pi*s)*gamma(s)*gamma(a + s)*gamma(S(1)/2 - s) / (pi**S('3/2')*gamma(1 + a - s)), (Max(-re(a), 0), S(1)/2), True) assert MT(besselj(a, sqrt(x))*bessely(b, sqrt(x)), x, s) == \ (-4**s*cos(pi*(a/2 - b/2 + s))*gamma(1 - 2*s) * gamma(a/2 - b/2 + s)*gamma(a/2 + b/2 + s) / (pi*gamma(a/2 - b/2 - s + 1)*gamma(a/2 + b/2 - s + 1)), (Max((-re(a) + re(b))/2, (-re(a) - re(b))/2), S(1)/2), True) # NOTE bessely(a, sqrt(x))**2 and bessely(a, sqrt(x))*bessely(b, sqrt(x)) # are a mess (no matter what way you look at it ...) assert MT(bessely(a, sqrt(x))**2, x, s)[1:] == \ ((Max(-re(a), 0, re(a)), S(1)/2), True) # Section 8.4.22 # TODO we can't do any of these (delicate cancellation) # Section 8.4.23 assert MT(besselk(a, 2*sqrt(x)), x, s) == \ (gamma( s - a/2)*gamma(s + a/2)/2, (Max(-re(a)/2, re(a)/2), oo), True) assert MT( besselj(a, 2 * sqrt(2 * sqrt(x))) * besselk(a, 2 * sqrt(2 * sqrt(x))), x, s) == (4**(-s) * gamma(2 * s) * gamma(a / 2 + s) / (2 * gamma(a / 2 - s + 1)), (Max(0, -re(a) / 2), oo), True) # TODO bessely(a, x)*besselk(a, x) is a mess assert MT(besseli(a, sqrt(x))*besselk(a, sqrt(x)), x, s) == \ (gamma(s)*gamma( a + s)*gamma(-s + S(1)/2)/(2*sqrt(pi)*gamma(a - s + 1)), (Max(-re(a), 0), S(1)/2), True) assert MT(besseli(b, sqrt(x))*besselk(a, sqrt(x)), x, s) == \ (2**(2*s - 1)*gamma(-2*s + 1)*gamma(-a/2 + b/2 + s)* \ gamma(a/2 + b/2 + s)/(gamma(-a/2 + b/2 - s + 1)* \ gamma(a/2 + b/2 - s + 1)), (Max(-re(a)/2 - re(b)/2, \ re(a)/2 - re(b)/2), S(1)/2), True) # TODO products of besselk are a mess mt = MT(exp(-x / 2) * besselk(a, x / 2), x, s) mt0 = gammasimp((trigsimp(gammasimp(mt[0].expand(func=True))))) assert mt0 == 2 * pi**(S(3) / 2) * cos(pi * s) * gamma(-s + S(1) / 2) / ( (cos(2 * pi * a) - cos(2 * pi * s)) * gamma(-a - s + 1) * gamma(a - s + 1)) assert mt[1:] == ((Max(-re(a), re(a)), oo), True)
def test_probability(): # various integrals from probability theory from sympy.abc import x, y from sympy import symbols, Symbol, Abs, expand_mul, gammasimp, powsimp, sin mu1, mu2 = symbols('mu1 mu2', nonzero=True) sigma1, sigma2 = symbols('sigma1 sigma2', positive=True) rate = Symbol('lambda', positive=True) def normal(x, mu, sigma): return 1/sqrt(2*pi*sigma**2)*exp(-(x - mu)**2/2/sigma**2) def exponential(x, rate): return rate*exp(-rate*x) assert integrate(normal(x, mu1, sigma1), (x, -oo, oo), meijerg=True) == 1 assert integrate(x*normal(x, mu1, sigma1), (x, -oo, oo), meijerg=True) == \ mu1 assert integrate(x**2*normal(x, mu1, sigma1), (x, -oo, oo), meijerg=True) \ == mu1**2 + sigma1**2 assert integrate(x**3*normal(x, mu1, sigma1), (x, -oo, oo), meijerg=True) \ == mu1**3 + 3*mu1*sigma1**2 assert integrate(normal(x, mu1, sigma1)*normal(y, mu2, sigma2), (x, -oo, oo), (y, -oo, oo), meijerg=True) == 1 assert integrate(x*normal(x, mu1, sigma1)*normal(y, mu2, sigma2), (x, -oo, oo), (y, -oo, oo), meijerg=True) == mu1 assert integrate(y*normal(x, mu1, sigma1)*normal(y, mu2, sigma2), (x, -oo, oo), (y, -oo, oo), meijerg=True) == mu2 assert integrate(x*y*normal(x, mu1, sigma1)*normal(y, mu2, sigma2), (x, -oo, oo), (y, -oo, oo), meijerg=True) == mu1*mu2 assert integrate((x + y + 1)*normal(x, mu1, sigma1)*normal(y, mu2, sigma2), (x, -oo, oo), (y, -oo, oo), meijerg=True) == 1 + mu1 + mu2 assert integrate((x + y - 1)*normal(x, mu1, sigma1)*normal(y, mu2, sigma2), (x, -oo, oo), (y, -oo, oo), meijerg=True) == \ -1 + mu1 + mu2 i = integrate(x**2*normal(x, mu1, sigma1)*normal(y, mu2, sigma2), (x, -oo, oo), (y, -oo, oo), meijerg=True) assert not i.has(Abs) assert simplify(i) == mu1**2 + sigma1**2 assert integrate(y**2*normal(x, mu1, sigma1)*normal(y, mu2, sigma2), (x, -oo, oo), (y, -oo, oo), meijerg=True) == \ sigma2**2 + mu2**2 assert integrate(exponential(x, rate), (x, 0, oo), meijerg=True) == 1 assert integrate(x*exponential(x, rate), (x, 0, oo), meijerg=True) == \ 1/rate assert integrate(x**2*exponential(x, rate), (x, 0, oo), meijerg=True) == \ 2/rate**2 def E(expr): res1 = integrate(expr*exponential(x, rate)*normal(y, mu1, sigma1), (x, 0, oo), (y, -oo, oo), meijerg=True) res2 = integrate(expr*exponential(x, rate)*normal(y, mu1, sigma1), (y, -oo, oo), (x, 0, oo), meijerg=True) assert expand_mul(res1) == expand_mul(res2) return res1 assert E(1) == 1 assert E(x*y) == mu1/rate assert E(x*y**2) == mu1**2/rate + sigma1**2/rate ans = sigma1**2 + 1/rate**2 assert simplify(E((x + y + 1)**2) - E(x + y + 1)**2) == ans assert simplify(E((x + y - 1)**2) - E(x + y - 1)**2) == ans assert simplify(E((x + y)**2) - E(x + y)**2) == ans # Beta' distribution alpha, beta = symbols('alpha beta', positive=True) betadist = x**(alpha - 1)*(1 + x)**(-alpha - beta)*gamma(alpha + beta) \ /gamma(alpha)/gamma(beta) assert integrate(betadist, (x, 0, oo), meijerg=True) == 1 i = integrate(x*betadist, (x, 0, oo), meijerg=True, conds='separate') assert (gammasimp(i[0]), i[1]) == (alpha/(beta - 1), 1 < beta) j = integrate(x**2*betadist, (x, 0, oo), meijerg=True, conds='separate') assert j[1] == (1 < beta - 1) assert gammasimp(j[0] - i[0]**2) == (alpha + beta - 1)*alpha \ /(beta - 2)/(beta - 1)**2 # Beta distribution # NOTE: this is evaluated using antiderivatives. It also tests that # meijerint_indefinite returns the simplest possible answer. a, b = symbols('a b', positive=True) betadist = x**(a - 1)*(-x + 1)**(b - 1)*gamma(a + b)/(gamma(a)*gamma(b)) assert simplify(integrate(betadist, (x, 0, 1), meijerg=True)) == 1 assert simplify(integrate(x*betadist, (x, 0, 1), meijerg=True)) == \ a/(a + b) assert simplify(integrate(x**2*betadist, (x, 0, 1), meijerg=True)) == \ a*(a + 1)/(a + b)/(a + b + 1) assert simplify(integrate(x**y*betadist, (x, 0, 1), meijerg=True)) == \ gamma(a + b)*gamma(a + y)/gamma(a)/gamma(a + b + y) # Chi distribution k = Symbol('k', integer=True, positive=True) chi = 2**(1 - k/2)*x**(k - 1)*exp(-x**2/2)/gamma(k/2) assert powsimp(integrate(chi, (x, 0, oo), meijerg=True)) == 1 assert simplify(integrate(x*chi, (x, 0, oo), meijerg=True)) == \ sqrt(2)*gamma((k + 1)/2)/gamma(k/2) assert simplify(integrate(x**2*chi, (x, 0, oo), meijerg=True)) == k # Chi^2 distribution chisquared = 2**(-k/2)/gamma(k/2)*x**(k/2 - 1)*exp(-x/2) assert powsimp(integrate(chisquared, (x, 0, oo), meijerg=True)) == 1 assert simplify(integrate(x*chisquared, (x, 0, oo), meijerg=True)) == k assert simplify(integrate(x**2*chisquared, (x, 0, oo), meijerg=True)) == \ k*(k + 2) assert gammasimp(integrate(((x - k)/sqrt(2*k))**3*chisquared, (x, 0, oo), meijerg=True)) == 2*sqrt(2)/sqrt(k) # Dagum distribution a, b, p = symbols('a b p', positive=True) # XXX (x/b)**a does not work dagum = a*p/x*(x/b)**(a*p)/(1 + x**a/b**a)**(p + 1) assert simplify(integrate(dagum, (x, 0, oo), meijerg=True)) == 1 # XXX conditions are a mess arg = x*dagum assert simplify(integrate(arg, (x, 0, oo), meijerg=True, conds='none') ) == a*b*gamma(1 - 1/a)*gamma(p + 1 + 1/a)/( (a*p + 1)*gamma(p)) assert simplify(integrate(x*arg, (x, 0, oo), meijerg=True, conds='none') ) == a*b**2*gamma(1 - 2/a)*gamma(p + 1 + 2/a)/( (a*p + 2)*gamma(p)) # F-distribution d1, d2 = symbols('d1 d2', positive=True) f = sqrt(((d1*x)**d1 * d2**d2)/(d1*x + d2)**(d1 + d2))/x \ /gamma(d1/2)/gamma(d2/2)*gamma((d1 + d2)/2) assert simplify(integrate(f, (x, 0, oo), meijerg=True)) == 1 # TODO conditions are a mess assert simplify(integrate(x*f, (x, 0, oo), meijerg=True, conds='none') ) == d2/(d2 - 2) assert simplify(integrate(x**2*f, (x, 0, oo), meijerg=True, conds='none') ) == d2**2*(d1 + 2)/d1/(d2 - 4)/(d2 - 2) # TODO gamma, rayleigh # inverse gaussian lamda, mu = symbols('lamda mu', positive=True) dist = sqrt(lamda/2/pi)*x**(-S(3)/2)*exp(-lamda*(x - mu)**2/x/2/mu**2) mysimp = lambda expr: simplify(expr.rewrite(exp)) assert mysimp(integrate(dist, (x, 0, oo))) == 1 assert mysimp(integrate(x*dist, (x, 0, oo))) == mu assert mysimp(integrate((x - mu)**2*dist, (x, 0, oo))) == mu**3/lamda assert mysimp(integrate((x - mu)**3*dist, (x, 0, oo))) == 3*mu**5/lamda**2 # Levi c = Symbol('c', positive=True) assert integrate(sqrt(c/2/pi)*exp(-c/2/(x - mu))/(x - mu)**S('3/2'), (x, mu, oo)) == 1 # higher moments oo # log-logistic alpha, beta = symbols('alpha beta', positive=True) distn = (beta/alpha)*x**(beta - 1)/alpha**(beta - 1)/ \ (1 + x**beta/alpha**beta)**2 # FIXME: If alpha, beta are not declared as finite the line below hangs # after the changes in: # https://github.com/sympy/sympy/pull/16603 assert simplify(integrate(distn, (x, 0, oo))) == 1 # NOTE the conditions are a mess, but correctly state beta > 1 assert simplify(integrate(x*distn, (x, 0, oo), conds='none')) == \ pi*alpha/beta/sin(pi/beta) # (similar comment for conditions applies) assert simplify(integrate(x**y*distn, (x, 0, oo), conds='none')) == \ pi*alpha**y*y/beta/sin(pi*y/beta) # weibull k = Symbol('k', positive=True) n = Symbol('n', positive=True) distn = k/lamda*(x/lamda)**(k - 1)*exp(-(x/lamda)**k) assert simplify(integrate(distn, (x, 0, oo))) == 1 assert simplify(integrate(x**n*distn, (x, 0, oo))) == \ lamda**n*gamma(1 + n/k) # rice distribution from sympy import besseli nu, sigma = symbols('nu sigma', positive=True) rice = x/sigma**2*exp(-(x**2 + nu**2)/2/sigma**2)*besseli(0, x*nu/sigma**2) assert integrate(rice, (x, 0, oo), meijerg=True) == 1 # can someone verify higher moments? # Laplace distribution mu = Symbol('mu', real=True) b = Symbol('b', positive=True) laplace = exp(-abs(x - mu)/b)/2/b assert integrate(laplace, (x, -oo, oo), meijerg=True) == 1 assert integrate(x*laplace, (x, -oo, oo), meijerg=True) == mu assert integrate(x**2*laplace, (x, -oo, oo), meijerg=True) == \ 2*b**2 + mu**2 # TODO are there other distributions supported on (-oo, oo) that we can do? # misc tests k = Symbol('k', positive=True) assert gammasimp(expand_mul(integrate(log(x)*x**(k - 1)*exp(-x)/gamma(k), (x, 0, oo)))) == polygamma(0, k)
def test_gammasimp(): R = Rational # was part of test_combsimp_gamma() in test_combsimp.py assert gammasimp(gamma(x)) == gamma(x) assert gammasimp(gamma(x + 1)/x) == gamma(x) assert gammasimp(gamma(x)/(x - 1)) == gamma(x - 1) assert gammasimp(x*gamma(x)) == gamma(x + 1) assert gammasimp((x + 1)*gamma(x + 1)) == gamma(x + 2) assert gammasimp(gamma(x + y)*(x + y)) == gamma(x + y + 1) assert gammasimp(x/gamma(x + 1)) == 1/gamma(x) assert gammasimp((x + 1)**2/gamma(x + 2)) == (x + 1)/gamma(x + 1) assert gammasimp(x*gamma(x) + gamma(x + 3)/(x + 2)) == \ (x + 2)*gamma(x + 1) assert gammasimp(gamma(2*x)*x) == gamma(2*x + 1)/2 assert gammasimp(gamma(2*x)/(x - S(1)/2)) == 2*gamma(2*x - 1) assert gammasimp(gamma(x)*gamma(1 - x)) == pi/sin(pi*x) assert gammasimp(gamma(x)*gamma(-x)) == -pi/(x*sin(pi*x)) assert gammasimp(1/gamma(x + 3)/gamma(1 - x)) == \ sin(pi*x)/(pi*x*(x + 1)*(x + 2)) assert gammasimp(factorial(n + 2)) == gamma(n + 3) assert gammasimp(binomial(n, k)) == \ gamma(n + 1)/(gamma(k + 1)*gamma(-k + n + 1)) assert powsimp(gammasimp( gamma(x)*gamma(x + S(1)/2)*gamma(y)/gamma(x + y))) == \ 2**(-2*x + 1)*sqrt(pi)*gamma(2*x)*gamma(y)/gamma(x + y) assert gammasimp(1/gamma(x)/gamma(x - S(1)/3)/gamma(x + S(1)/3)) == \ 3**(3*x - S(3)/2)/(2*pi*gamma(3*x - 1)) assert simplify( gamma(S(1)/2 + x/2)*gamma(1 + x/2)/gamma(1 + x)/sqrt(pi)*2**x) == 1 assert gammasimp(gamma(S(-1)/4)*gamma(S(-3)/4)) == 16*sqrt(2)*pi/3 assert powsimp(gammasimp(gamma(2*x)/gamma(x))) == \ 2**(2*x - 1)*gamma(x + S(1)/2)/sqrt(pi) # issue 6792 e = (-gamma(k)*gamma(k + 2) + gamma(k + 1)**2)/gamma(k)**2 assert gammasimp(e) == -k assert gammasimp(1/e) == -1/k e = (gamma(x) + gamma(x + 1))/gamma(x) assert gammasimp(e) == x + 1 assert gammasimp(1/e) == 1/(x + 1) e = (gamma(x) + gamma(x + 2))*(gamma(x - 1) + gamma(x))/gamma(x) assert gammasimp(e) == (x**2 + x + 1)*gamma(x + 1)/(x - 1) e = (-gamma(k)*gamma(k + 2) + gamma(k + 1)**2)/gamma(k)**2 assert gammasimp(e**2) == k**2 assert gammasimp(e**2/gamma(k + 1)) == k/gamma(k) a = R(1, 2) + R(1, 3) b = a + R(1, 3) assert gammasimp(gamma(2*k)/gamma(k)*gamma(k + a)*gamma(k + b)) 3*2**(2*k + 1)*3**(-3*k - 2)*sqrt(pi)*gamma(3*k + R(3, 2))/2 # issue 9699 assert gammasimp((x + 1)*factorial(x)/gamma(y)) == gamma(x + 2)/gamma(y) assert gammasimp(rf(x + n, k)*binomial(n, k)) == gamma(n + 1)*gamma(k + n + x)/(gamma(k + 1)*gamma(n + x)*gamma(-k + n + 1)) A, B = symbols('A B', commutative=False) assert gammasimp(e*B*A) == gammasimp(e)*B*A # check iteration assert gammasimp(gamma(2*k)/gamma(k)*gamma(-k - R(1, 2))) == ( -2**(2*k + 1)*sqrt(pi)/(2*((2*k + 1)*cos(pi*k)))) assert gammasimp( gamma(k)*gamma(k + R(1, 3))*gamma(k + R(2, 3))/gamma(3*k/2)) == ( 3*2**(3*k + 1)*3**(-3*k - S.Half)*sqrt(pi)*gamma(3*k/2 + S.Half)/2) # issue 6153 assert gammasimp(gamma(S(1)/4)/gamma(S(5)/4)) == 4 # was part of test_combsimp() in test_combsimp.py assert gammasimp(binomial(n + 2, k + S(1)/2)) == gamma(n + 3)/ \ (gamma(k + S(3)/2)*gamma(-k + n + S(5)/2)) assert gammasimp(binomial(n + 2, k + 2.0)) == \ gamma(n + 3)/(gamma(k + 3.0)*gamma(-k + n + 1)) # issue 11548 assert gammasimp(binomial(0, x)) == sin(pi*x)/(pi*x) e = gamma(n + S(1)/3)*gamma(n + S(2)/3) assert gammasimp(e) == e assert gammasimp(gamma(4*n + S(1)/2)/gamma(2*n - S(3)/4)) == \ 2**(4*n - S(5)/2)*(8*n - 3)*gamma(2*n + S(3)/4)/sqrt(pi) i, m = symbols('i m', integer = True) e = gamma(exp(i)) assert gammasimp(e) == e e = gamma(m + 3) assert gammasimp(e) == e e = gamma(m + 1)/(gamma(i + 1)*gamma(-i + m + 1)) assert gammasimp(e) == e p = symbols("p", integer=True, positive=True) assert gammasimp(gamma(-p+4)) == gamma(-p+4)
def test_gammasimp(): assert gammasimp(A*B - B*A) == A*B - B*A
def test_probability(): # various integrals from probability theory from sympy.abc import x, y from sympy import symbols, Symbol, Abs, expand_mul, gammasimp, powsimp, sin mu1, mu2 = symbols('mu1 mu2', real=True, nonzero=True, finite=True) sigma1, sigma2 = symbols('sigma1 sigma2', real=True, nonzero=True, finite=True, positive=True) rate = Symbol('lambda', real=True, positive=True, finite=True) def normal(x, mu, sigma): return 1/sqrt(2*pi*sigma**2)*exp(-(x - mu)**2/2/sigma**2) def exponential(x, rate): return rate*exp(-rate*x) assert integrate(normal(x, mu1, sigma1), (x, -oo, oo), meijerg=True) == 1 assert integrate(x*normal(x, mu1, sigma1), (x, -oo, oo), meijerg=True) == \ mu1 assert integrate(x**2*normal(x, mu1, sigma1), (x, -oo, oo), meijerg=True) \ == mu1**2 + sigma1**2 assert integrate(x**3*normal(x, mu1, sigma1), (x, -oo, oo), meijerg=True) \ == mu1**3 + 3*mu1*sigma1**2 assert integrate(normal(x, mu1, sigma1)*normal(y, mu2, sigma2), (x, -oo, oo), (y, -oo, oo), meijerg=True) == 1 assert integrate(x*normal(x, mu1, sigma1)*normal(y, mu2, sigma2), (x, -oo, oo), (y, -oo, oo), meijerg=True) == mu1 assert integrate(y*normal(x, mu1, sigma1)*normal(y, mu2, sigma2), (x, -oo, oo), (y, -oo, oo), meijerg=True) == mu2 assert integrate(x*y*normal(x, mu1, sigma1)*normal(y, mu2, sigma2), (x, -oo, oo), (y, -oo, oo), meijerg=True) == mu1*mu2 assert integrate((x + y + 1)*normal(x, mu1, sigma1)*normal(y, mu2, sigma2), (x, -oo, oo), (y, -oo, oo), meijerg=True) == 1 + mu1 + mu2 assert integrate((x + y - 1)*normal(x, mu1, sigma1)*normal(y, mu2, sigma2), (x, -oo, oo), (y, -oo, oo), meijerg=True) == \ -1 + mu1 + mu2 i = integrate(x**2*normal(x, mu1, sigma1)*normal(y, mu2, sigma2), (x, -oo, oo), (y, -oo, oo), meijerg=True) assert not i.has(Abs) assert simplify(i) == mu1**2 + sigma1**2 assert integrate(y**2*normal(x, mu1, sigma1)*normal(y, mu2, sigma2), (x, -oo, oo), (y, -oo, oo), meijerg=True) == \ sigma2**2 + mu2**2 assert integrate(exponential(x, rate), (x, 0, oo), meijerg=True) == 1 assert integrate(x*exponential(x, rate), (x, 0, oo), meijerg=True) == \ 1/rate assert integrate(x**2*exponential(x, rate), (x, 0, oo), meijerg=True) == \ 2/rate**2 def E(expr): res1 = integrate(expr*exponential(x, rate)*normal(y, mu1, sigma1), (x, 0, oo), (y, -oo, oo), meijerg=True) res2 = integrate(expr*exponential(x, rate)*normal(y, mu1, sigma1), (y, -oo, oo), (x, 0, oo), meijerg=True) assert expand_mul(res1) == expand_mul(res2) return res1 assert E(1) == 1 assert E(x*y) == mu1/rate assert E(x*y**2) == mu1**2/rate + sigma1**2/rate ans = sigma1**2 + 1/rate**2 assert simplify(E((x + y + 1)**2) - E(x + y + 1)**2) == ans assert simplify(E((x + y - 1)**2) - E(x + y - 1)**2) == ans assert simplify(E((x + y)**2) - E(x + y)**2) == ans # Beta' distribution alpha, beta = symbols('alpha beta', positive=True) betadist = x**(alpha - 1)*(1 + x)**(-alpha - beta)*gamma(alpha + beta) \ /gamma(alpha)/gamma(beta) assert integrate(betadist, (x, 0, oo), meijerg=True) == 1 i = integrate(x*betadist, (x, 0, oo), meijerg=True, conds='separate') assert (gammasimp(i[0]), i[1]) == (alpha/(beta - 1), 1 < beta) j = integrate(x**2*betadist, (x, 0, oo), meijerg=True, conds='separate') assert j[1] == (1 < beta - 1) assert gammasimp(j[0] - i[0]**2) == (alpha + beta - 1)*alpha \ /(beta - 2)/(beta - 1)**2 # Beta distribution # NOTE: this is evaluated using antiderivatives. It also tests that # meijerint_indefinite returns the simplest possible answer. a, b = symbols('a b', positive=True) betadist = x**(a - 1)*(-x + 1)**(b - 1)*gamma(a + b)/(gamma(a)*gamma(b)) assert simplify(integrate(betadist, (x, 0, 1), meijerg=True)) == 1 assert simplify(integrate(x*betadist, (x, 0, 1), meijerg=True)) == \ a/(a + b) assert simplify(integrate(x**2*betadist, (x, 0, 1), meijerg=True)) == \ a*(a + 1)/(a + b)/(a + b + 1) assert simplify(integrate(x**y*betadist, (x, 0, 1), meijerg=True)) == \ gamma(a + b)*gamma(a + y)/gamma(a)/gamma(a + b + y) # Chi distribution k = Symbol('k', integer=True, positive=True) chi = 2**(1 - k/2)*x**(k - 1)*exp(-x**2/2)/gamma(k/2) assert powsimp(integrate(chi, (x, 0, oo), meijerg=True)) == 1 assert simplify(integrate(x*chi, (x, 0, oo), meijerg=True)) == \ sqrt(2)*gamma((k + 1)/2)/gamma(k/2) assert simplify(integrate(x**2*chi, (x, 0, oo), meijerg=True)) == k # Chi^2 distribution chisquared = 2**(-k/2)/gamma(k/2)*x**(k/2 - 1)*exp(-x/2) assert powsimp(integrate(chisquared, (x, 0, oo), meijerg=True)) == 1 assert simplify(integrate(x*chisquared, (x, 0, oo), meijerg=True)) == k assert simplify(integrate(x**2*chisquared, (x, 0, oo), meijerg=True)) == \ k*(k + 2) assert gammasimp(integrate(((x - k)/sqrt(2*k))**3*chisquared, (x, 0, oo), meijerg=True)) == 2*sqrt(2)/sqrt(k) # Dagum distribution a, b, p = symbols('a b p', positive=True) # XXX (x/b)**a does not work dagum = a*p/x*(x/b)**(a*p)/(1 + x**a/b**a)**(p + 1) assert simplify(integrate(dagum, (x, 0, oo), meijerg=True)) == 1 # XXX conditions are a mess arg = x*dagum assert simplify(integrate(arg, (x, 0, oo), meijerg=True, conds='none') ) == a*b*gamma(1 - 1/a)*gamma(p + 1 + 1/a)/( (a*p + 1)*gamma(p)) assert simplify(integrate(x*arg, (x, 0, oo), meijerg=True, conds='none') ) == a*b**2*gamma(1 - 2/a)*gamma(p + 1 + 2/a)/( (a*p + 2)*gamma(p)) # F-distribution d1, d2 = symbols('d1 d2', positive=True) f = sqrt(((d1*x)**d1 * d2**d2)/(d1*x + d2)**(d1 + d2))/x \ /gamma(d1/2)/gamma(d2/2)*gamma((d1 + d2)/2) assert simplify(integrate(f, (x, 0, oo), meijerg=True)) == 1 # TODO conditions are a mess assert simplify(integrate(x*f, (x, 0, oo), meijerg=True, conds='none') ) == d2/(d2 - 2) assert simplify(integrate(x**2*f, (x, 0, oo), meijerg=True, conds='none') ) == d2**2*(d1 + 2)/d1/(d2 - 4)/(d2 - 2) # TODO gamma, rayleigh # inverse gaussian lamda, mu = symbols('lamda mu', positive=True) dist = sqrt(lamda/2/pi)*x**(-S(3)/2)*exp(-lamda*(x - mu)**2/x/2/mu**2) mysimp = lambda expr: simplify(expr.rewrite(exp)) assert mysimp(integrate(dist, (x, 0, oo))) == 1 assert mysimp(integrate(x*dist, (x, 0, oo))) == mu assert mysimp(integrate((x - mu)**2*dist, (x, 0, oo))) == mu**3/lamda assert mysimp(integrate((x - mu)**3*dist, (x, 0, oo))) == 3*mu**5/lamda**2 # Levi c = Symbol('c', positive=True) assert integrate(sqrt(c/2/pi)*exp(-c/2/(x - mu))/(x - mu)**S('3/2'), (x, mu, oo)) == 1 # higher moments oo # log-logistic distn = (beta/alpha)*x**(beta - 1)/alpha**(beta - 1)/ \ (1 + x**beta/alpha**beta)**2 assert simplify(integrate(distn, (x, 0, oo))) == 1 # NOTE the conditions are a mess, but correctly state beta > 1 assert simplify(integrate(x*distn, (x, 0, oo), conds='none')) == \ pi*alpha/beta/sin(pi/beta) # (similar comment for conditions applies) assert simplify(integrate(x**y*distn, (x, 0, oo), conds='none')) == \ pi*alpha**y*y/beta/sin(pi*y/beta) # weibull k = Symbol('k', positive=True) n = Symbol('n', positive=True) distn = k/lamda*(x/lamda)**(k - 1)*exp(-(x/lamda)**k) assert simplify(integrate(distn, (x, 0, oo))) == 1 assert simplify(integrate(x**n*distn, (x, 0, oo))) == \ lamda**n*gamma(1 + n/k) # rice distribution from sympy import besseli nu, sigma = symbols('nu sigma', positive=True) rice = x/sigma**2*exp(-(x**2 + nu**2)/2/sigma**2)*besseli(0, x*nu/sigma**2) assert integrate(rice, (x, 0, oo), meijerg=True) == 1 # can someone verify higher moments? # Laplace distribution mu = Symbol('mu', real=True) b = Symbol('b', positive=True) laplace = exp(-abs(x - mu)/b)/2/b assert integrate(laplace, (x, -oo, oo), meijerg=True) == 1 assert integrate(x*laplace, (x, -oo, oo), meijerg=True) == mu assert integrate(x**2*laplace, (x, -oo, oo), meijerg=True) == \ 2*b**2 + mu**2 # TODO are there other distributions supported on (-oo, oo) that we can do? # misc tests k = Symbol('k', positive=True) assert gammasimp(expand_mul(integrate(log(x)*x**(k - 1)*exp(-x)/gamma(k), (x, 0, oo)))) == polygamma(0, k)
def test_meijerg_expand(): from sympy import gammasimp, simplify # from mpmath docs assert hyperexpand(meijerg([[], []], [[0], []], -z)) == exp(z) assert hyperexpand(meijerg([[1, 1], []], [[1], [0]], z)) == \ log(z + 1) assert hyperexpand(meijerg([[1, 1], []], [[1], [1]], z)) == \ z/(z + 1) assert hyperexpand(meijerg([[], []], [[S(1)/2], [0]], (z/2)**2)) \ == sin(z)/sqrt(pi) assert hyperexpand(meijerg([[], []], [[0], [S(1)/2]], (z/2)**2)) \ == cos(z)/sqrt(pi) assert can_do_meijer([], [a], [a - 1, a - S.Half], []) assert can_do_meijer([], [], [a/2], [-a/2], False) # branches... assert can_do_meijer([a], [b], [a], [b, a - 1]) # wikipedia assert hyperexpand(meijerg([1], [], [], [0], z)) == \ Piecewise((0, abs(z) < 1), (1, abs(1/z) < 1), (meijerg([1], [], [], [0], z), True)) assert hyperexpand(meijerg([], [1], [0], [], z)) == \ Piecewise((1, abs(z) < 1), (0, abs(1/z) < 1), (meijerg([], [1], [0], [], z), True)) # The Special Functions and their Approximations assert can_do_meijer([], [], [a + b/2], [a, a - b/2, a + S.Half]) assert can_do_meijer( [], [], [a], [b], False) # branches only agree for small z assert can_do_meijer([], [S.Half], [a], [-a]) assert can_do_meijer([], [], [a, b], []) assert can_do_meijer([], [], [a, b], []) assert can_do_meijer([], [], [a, a + S.Half], [b, b + S.Half]) assert can_do_meijer([], [], [a, -a], [0, S.Half], False) # dito assert can_do_meijer([], [], [a, a + S.Half, b, b + S.Half], []) assert can_do_meijer([S.Half], [], [0], [a, -a]) assert can_do_meijer([S.Half], [], [a], [0, -a], False) # dito assert can_do_meijer([], [a - S.Half], [a, b], [a - S.Half], False) assert can_do_meijer([], [a + S.Half], [a + b, a - b, a], [], False) assert can_do_meijer([a + S.Half], [], [b, 2*a - b, a], [], False) # This for example is actually zero. assert can_do_meijer([], [], [], [a, b]) # Testing a bug: assert hyperexpand(meijerg([0, 2], [], [], [-1, 1], z)) == \ Piecewise((0, abs(z) < 1), (z/2 - 1/(2*z), abs(1/z) < 1), (meijerg([0, 2], [], [], [-1, 1], z), True)) # Test that the simplest possible answer is returned: assert gammasimp(simplify(hyperexpand( meijerg([1], [1 - a], [-a/2, -a/2 + S(1)/2], [], 1/z)))) == \ -2*sqrt(pi)*(sqrt(z + 1) + 1)**a/a # Test that hyper is returned assert hyperexpand(meijerg([1], [], [a], [0, 0], z)) == hyper( (a,), (a + 1, a + 1), z*exp_polar(I*pi))*z**a*gamma(a)/gamma(a + 1)**2 # Test place option f = meijerg(((0, 1), ()), ((S(1)/2,), (0,)), z**2) assert hyperexpand(f) == sqrt(pi)/sqrt(1 + z**(-2)) assert hyperexpand(f, place=0) == sqrt(pi)*z/sqrt(z**2 + 1)
def test_mellin_transform_bessel(): from sympy import Max MT = mellin_transform # 8.4.19 assert MT(besselj(a, 2*sqrt(x)), x, s) == \ (gamma(a/2 + s)/gamma(a/2 - s + 1), (-re(a)/2, S(3)/4), True) assert MT(sin(sqrt(x))*besselj(a, sqrt(x)), x, s) == \ (2**a*gamma(-2*s + S(1)/2)*gamma(a/2 + s + S(1)/2)/( gamma(-a/2 - s + 1)*gamma(a - 2*s + 1)), ( -re(a)/2 - S(1)/2, S(1)/4), True) assert MT(cos(sqrt(x))*besselj(a, sqrt(x)), x, s) == \ (2**a*gamma(a/2 + s)*gamma(-2*s + S(1)/2)/( gamma(-a/2 - s + S(1)/2)*gamma(a - 2*s + 1)), ( -re(a)/2, S(1)/4), True) assert MT(besselj(a, sqrt(x))**2, x, s) == \ (gamma(a + s)*gamma(S(1)/2 - s) / (sqrt(pi)*gamma(1 - s)*gamma(1 + a - s)), (-re(a), S(1)/2), True) assert MT(besselj(a, sqrt(x))*besselj(-a, sqrt(x)), x, s) == \ (gamma(s)*gamma(S(1)/2 - s) / (sqrt(pi)*gamma(1 - a - s)*gamma(1 + a - s)), (0, S(1)/2), True) # NOTE: prudnikov gives the strip below as (1/2 - re(a), 1). As far as # I can see this is wrong (since besselj(z) ~ 1/sqrt(z) for z large) assert MT(besselj(a - 1, sqrt(x))*besselj(a, sqrt(x)), x, s) == \ (gamma(1 - s)*gamma(a + s - S(1)/2) / (sqrt(pi)*gamma(S(3)/2 - s)*gamma(a - s + S(1)/2)), (S(1)/2 - re(a), S(1)/2), True) assert MT(besselj(a, sqrt(x))*besselj(b, sqrt(x)), x, s) == \ (4**s*gamma(1 - 2*s)*gamma((a + b)/2 + s) / (gamma(1 - s + (b - a)/2)*gamma(1 - s + (a - b)/2) *gamma( 1 - s + (a + b)/2)), (-(re(a) + re(b))/2, S(1)/2), True) assert MT(besselj(a, sqrt(x))**2 + besselj(-a, sqrt(x))**2, x, s)[1:] == \ ((Max(re(a), -re(a)), S(1)/2), True) # Section 8.4.20 assert MT(bessely(a, 2*sqrt(x)), x, s) == \ (-cos(pi*(a/2 - s))*gamma(s - a/2)*gamma(s + a/2)/pi, (Max(-re(a)/2, re(a)/2), S(3)/4), True) assert MT(sin(sqrt(x))*bessely(a, sqrt(x)), x, s) == \ (-4**s*sin(pi*(a/2 - s))*gamma(S(1)/2 - 2*s) * gamma((1 - a)/2 + s)*gamma((1 + a)/2 + s) / (sqrt(pi)*gamma(1 - s - a/2)*gamma(1 - s + a/2)), (Max(-(re(a) + 1)/2, (re(a) - 1)/2), S(1)/4), True) assert MT(cos(sqrt(x))*bessely(a, sqrt(x)), x, s) == \ (-4**s*cos(pi*(a/2 - s))*gamma(s - a/2)*gamma(s + a/2)*gamma(S(1)/2 - 2*s) / (sqrt(pi)*gamma(S(1)/2 - s - a/2)*gamma(S(1)/2 - s + a/2)), (Max(-re(a)/2, re(a)/2), S(1)/4), True) assert MT(besselj(a, sqrt(x))*bessely(a, sqrt(x)), x, s) == \ (-cos(pi*s)*gamma(s)*gamma(a + s)*gamma(S(1)/2 - s) / (pi**S('3/2')*gamma(1 + a - s)), (Max(-re(a), 0), S(1)/2), True) assert MT(besselj(a, sqrt(x))*bessely(b, sqrt(x)), x, s) == \ (-4**s*cos(pi*(a/2 - b/2 + s))*gamma(1 - 2*s) * gamma(a/2 - b/2 + s)*gamma(a/2 + b/2 + s) / (pi*gamma(a/2 - b/2 - s + 1)*gamma(a/2 + b/2 - s + 1)), (Max((-re(a) + re(b))/2, (-re(a) - re(b))/2), S(1)/2), True) # NOTE bessely(a, sqrt(x))**2 and bessely(a, sqrt(x))*bessely(b, sqrt(x)) # are a mess (no matter what way you look at it ...) assert MT(bessely(a, sqrt(x))**2, x, s)[1:] == \ ((Max(-re(a), 0, re(a)), S(1)/2), True) # Section 8.4.22 # TODO we can't do any of these (delicate cancellation) # Section 8.4.23 assert MT(besselk(a, 2*sqrt(x)), x, s) == \ (gamma( s - a/2)*gamma(s + a/2)/2, (Max(-re(a)/2, re(a)/2), oo), True) assert MT(besselj(a, 2*sqrt(2*sqrt(x)))*besselk( a, 2*sqrt(2*sqrt(x))), x, s) == (4**(-s)*gamma(2*s)* gamma(a/2 + s)/(2*gamma(a/2 - s + 1)), (Max(0, -re(a)/2), oo), True) # TODO bessely(a, x)*besselk(a, x) is a mess assert MT(besseli(a, sqrt(x))*besselk(a, sqrt(x)), x, s) == \ (gamma(s)*gamma( a + s)*gamma(-s + S(1)/2)/(2*sqrt(pi)*gamma(a - s + 1)), (Max(-re(a), 0), S(1)/2), True) assert MT(besseli(b, sqrt(x))*besselk(a, sqrt(x)), x, s) == \ (2**(2*s - 1)*gamma(-2*s + 1)*gamma(-a/2 + b/2 + s)* \ gamma(a/2 + b/2 + s)/(gamma(-a/2 + b/2 - s + 1)* \ gamma(a/2 + b/2 - s + 1)), (Max(-re(a)/2 - re(b)/2, \ re(a)/2 - re(b)/2), S(1)/2), True) # TODO products of besselk are a mess mt = MT(exp(-x/2)*besselk(a, x/2), x, s) mt0 = gammasimp((trigsimp(gammasimp(mt[0].expand(func=True))))) assert mt0 == 2*pi**(S(3)/2)*cos(pi*s)*gamma(-s + S(1)/2)/( (cos(2*pi*a) - cos(2*pi*s))*gamma(-a - s + 1)*gamma(a - s + 1)) assert mt[1:] == ((Max(-re(a), re(a)), oo), True)