def pearson4cdf(X, m, nu, a, _lambda, mu, sigma):
# pearson4pdf
# p = pearson4pdf(X,m,nu,a,lambda)
#
# Returns the pearson type IV probability density function with
# parameters m, nu, a and lambda at the values of X.
#
# Example
#
# See also
# pearson4pdf betapdf normpdf
# pearspdf pearsrnd
#
Xx = (X - _lambda) / a
if Xx < -sqrt(3):
p1 = fx(X, m, nu, a, _lambda) * a / (2 * m - 1) * (1j - Xx) * hyp2f1(1, m + nu / 2 * 1j, 2 * m, 2 / (1 - 1j * Xx))
p = float(p1.real)
elif Xx > sqrt(3):
p1 = 1 - fx(-X, m, -nu, a, -_lambda) * a / (2 * m - 1) * (1j + Xx) * hyp2f1(1, m - nu / 2 * 1j, 2 * m, 2 / (1 + 1j * Xx))
p = float(p1.real)
elif Xx < 0 and Xx > -sqrt(3) and abs(nu) < (4 - 2 * sqrt(3)) * m:
p1 = norm.cdf(X, mu, sigma)
p = float(p1.real)
elif Xx < 0 and Xx > -sqrt(3) and abs(nu) > (4 - 2 * sqrt(3)) * m:
p1 = (1 - exp(-(nu + 1j * 2 * m) * pi)) ** (-1) - (1j * a * fx(X, m, nu, a, _lambda)) / (1j * nu - 2 * m + 2) * (1 + Xx ** 2) * hyp2f1(1, 2 - 2 * m, 2 - m + 1j * nu / 2, (1 + 1j * Xx) / 2)
p = float(p1.real)
elif Xx > 0 and Xx < sqrt(3) and abs(nu) < (4 - 2 * sqrt(3)) * m:
p1 = norm.cdf(X, mu, sigma)
p = float(p1.real)
else:
p1 = 1 - (1 - exp(-(-nu + 1j * 2 * m) * pi)) ** (-1) + (1j * a * fx(-X, m, -nu, a, -_lambda)) / (1j * (-nu) - 2 * m + 2) * (1 + (-Xx) ** 2) * hyp2f1(1,2-2*m,2-m-1j*nu/2,(1-1j*Xx)/2)
p = float(p1.real)
return p
def e_ratio(a, b, e, x):
# Get S
bt2 = mp.beta(a, b - 1.0) # Beta function
bix = mp.betainc(a, b + 1.0, 0.0, e) # Incomplete Beta function
hf = mp.hyp2f1(1.0, a, a + b - 1.0,
-1.0) # 2F1, Gauss' hypergeometric function
hfre = mp.re(hf)
Sval = bix - x * bt2 * hfre
# Get U
c1 = mp.mpc(1.0 + a)
c2 = mp.mpc(-b)
c3 = mp.mpc(1.0)
c4 = mp.mpc(2.0 + a)
Uval = mp.appellf1(c1, c2, c3, c4, e, -e)
Ure = mp.re(Uval)
# Get P & Q
Pval = mp.hyp2f1(a + 1.0, 1.0 - b, a + 2.0,
e) # 2F1, Gauss' hypergeometric function
Pre = mp.re(Pval)
Qval = mp.hyp2f1(a + 1.0, 2.0 - b, a + 2.0,
e) # 2F1, Gauss' hypergeometric function
Qre = mp.re(Qval)
# Get T
Tval = ((e**(1.0 + a)) / (1.0 + a)) * (3.0 * Pre + 2.0 * Qre - Ure)
Tval = Tval + 4.0 * Sval
# Get Rval (ratio)
Rval = 0.25 * (1.0 - e * e) * (
(1.0 - e)**(1.0 - b)) * (e**(1.0 - a)) * Tval
return Rval
def e_ratio(a,b,e,x): # Get S bt2 = mp.beta(a,b-1.0) # Beta function bix = mp.betainc(a,b+1.0,0.0,e) # Incomplete Beta function hf = mp.hyp2f1(1.0,a,a+b-1.0,-1.0) # 2F1, Gauss' hypergeometric function hfre = mp.re(hf) Sval = bix - x*bt2*hfre # Get U c1 = mp.mpc(1.0 + a) c2 = mp.mpc(-b) c3 = mp.mpc(1.0) c4 = mp.mpc(2.0 + a) Uval = mp.appellf1(c1,c2,c3,c4,e,-e) Ure = mp.re(Uval) # Get P & Q Pval = mp.hyp2f1(a+1.0,1.0-b,a+2.0,e) # 2F1, Gauss' hypergeometric function Pre = mp.re(Pval) Qval = mp.hyp2f1(a+1.0,2.0-b,a+2.0,e) # 2F1, Gauss' hypergeometric function Qre = mp.re(Qval) # Get T Tval = ( (e**(1.0+a)) / (1.0+a) )*( 3.0*Pre + 2.0*Qre - Ure ) Tval = Tval + 4.0*Sval # Get Rval (ratio) Rval = 0.25*(1.0-e*e)*( (1.0-e)**(1.0-b) )*( e**(1.0-a) )*Tval return Rval
def log_prob_m_bursty_rep(max_m, k_burst, mean_burst, kR_on, kR_off):
"""
log_prob_m_bursty_rep(max_m, k_burst, mean_burst, kR_on, kR_off)
Computes log prob of observing m mRNA for all m s.t. 0 <= m <= m_max,
given model parameters. The computation uses eq 76 in SI
of Shahrezai & Swain 2008. The implementation uses mpmath to compute
2F1 for max_m and max_m+1, then uses the minimal solution of the
(+00) recursion relation run backwards to efficiently compute the rest.
"""
# first compute alpha, beta, gamma, the parameters in the 2F1 gen fcn
# recall the gen fcn is 2F1(alpha, beta, gamma, mean_burst*(z-1))
rate_sum = k_burst + kR_off + kR_on
sqrt_discrim = np.sqrt((rate_sum)**2 - 4 * k_burst * kR_off)
alpha = (rate_sum + sqrt_discrim) / 2.0
beta = (rate_sum - sqrt_discrim) / 2.0
gamma = kR_on + kR_off
# now compute a, b, c, z, the parameters in the recursion relation,
# in terms of alpha, beta, gamma, & mean_burst
a = alpha
b = gamma - beta
c = 1.0 + alpha - beta
z = (1.0 + mean_burst)**(-1)
# next set up recursive calculation of 2F1
m = np.arange(0, max_m + 1)
# note 1+a-c is strictly > 0 so we needn't worry about signs in gammafcn
lgamma_numer = gammaln(1 + a - c + m)
lgamma_denom = gammaln(1 + a + b - c + m)
twoFone = np.zeros_like(np.asfarray(m))
# initialize recursion with starting values...
twoFone[-1] = hyp2f1(a + m[-1], b, 1 + a + b - c + m[-1], 1 - z)
twoFone[-2] = hyp2f1(a + m[-2], b, 1 + a + b - c + m[-2], 1 - z)
# ...adjusted by gamma prefactors
twoFone[-2:] *= np.exp(lgamma_numer[-2:] - lgamma_denom[-2:])
# now run the recursion backwards (i.e., decreasing n)
# python indexing rules make the indexing here horribly confusing,
# haven't yet figured out a better/more transparent way
for i, k in enumerate(np.arange(m[-1] - 1, m[0], -1)):
apk = a + k
prefac_k = 2 * apk - c + (b - apk) * z
prefac_kplus1 = apk * (z - 1)
denom = c - apk
twoFone[-(3 + i)] = -(prefac_k * twoFone[-(2 + i)] +
prefac_kplus1 * twoFone[-(1 + i)]) / denom
# when recursion is finished, cancel off prefactor of gammas
logtwoFone = np.log(twoFone) + lgamma_denom - lgamma_numer
# now compute prefactors in P(m), combine, done
gamma_prefac = (gammaln(alpha + m) - gammaln(alpha) + gammaln(beta + m) -
gammaln(beta) - gammaln(gamma + m) + gammaln(gamma) -
gammaln(1 + m))
bern_p = mean_burst / (1 + mean_burst)
burst_prefac = m * np.log(bern_p) + alpha * np.log(1 - bern_p)
return gamma_prefac + burst_prefac + logtwoFone
def viavel(beta, c, alpha, m, n):
if c == 0.:
return 2*alpha*beta/(2*n*m+1)
if c == 1.:
return -1.
if m == 0.:
return 1
k = 2*n*m-1
q = 2*alpha*beta
return (1-c)*float(hyp2f1(1,1,k+2,c))-q*(-k-2+(k+1)*float(hyp2f1(1,1,k+3,c)))/(k+2) - 1
def Ptrue(Qp, I, R, S, Q, b, replacement=True):
"""
Returns the probability of testing with replacement,
in which tested individuals can be retested on the same day.
Args
----
Qp (int): number of positive tests.
I (int): number of infecteds.
R (int): number of recovered.
S (int): number of suceptibles.
Q (int): number of tests.
b (float): biased-testing factor.
replacement (boolean): testing with/without replacement.
"""
if replacement:
f0 = (I + R) / (S + I + R)
Ptrue = binomial(Q, Qp) * power(f0 * exp(b), Qp) * power(
1 - f0, Q - Qp)
Ptrue /= power(1 + (exp(b) - 1) * f0, Q)
else:
product = [(I + R - k) / (S - Q + Qp - k) for k in range(Qp)]
Ptrue = binomial(Q, Qp) * fprod(product) * exp(Qp*b) * \
1/hyp2f1( -I - R, -Q, S - Q + 1, exp(b) )
return Ptrue
def test_hyp2f1_strange_points():
pts = [(2, -1, -1, 0.7), (2, -2, -2, 0.7)]
kw = dict(eliminate=True)
dataset = [p + (float(mpmath.hyp2f1(*p, **kw)),) for p in pts]
dataset = np.array(dataset, dtype=np.float_)
FuncData(sc.hyp2f1, dataset, (0, 1, 2, 3), 4, rtol=1e-10).check()
def G(eta_i, eta_f, k_i, k_f, l):
"""something dumb helper"""
temp1 = abs((k_f - k_i) / (k_f + k_i))**(1j * eta_i + 1j * eta_f)
temp2 = hyp2f1(l + 1 - 1j * eta_f, l + 1 - 1j * eta_i, 2. * l + 2,
-4. * k_i * k_f / (k_i - k_f)**2)
return (temp1 * temp2).real
def hyp_2F1(a, b, c, z):
"""
http://docs.sympy.org/0.7.1/modules/mpmath/functions/hypergeometric.html
"""
import mpmath as mp
return mp.hyp2f1(a, b, c, z)
def B_01(Non, Noff, alpha):
Ntot = Non + Noff
gam = (1 + 2 * Noff) * power(alpha, (0.5 + Ntot)) * gamma(0.5 + Ntot)
delta = ((2 * power((1 + alpha), Ntot)) * gamma(1 + Ntot) *
hyp2f1(0.5 + Noff, 1 + Ntot, 1.5 + Noff, (-1 / alpha)))
c1_c2 = sqrt(pi) / (2 * atan(1 / sqrt(alpha)))
return gam / (c1_c2 * delta)
def SI_number_of_positive_signs(N, nPlus, nc, ncPlus):
"""
Parameters
----------
N: int
Total number of signs.
nPlus: int
Number of positive signs.
nc: int
Number of runs.
ncPlus: int
Number of runs with positive signs.
Returns
-------
float
Neg. log-probability observing nPlus signs with +1, given N signs, nc runs, and ncPlus runs with signs +1.
"""
ncMinus = nc - ncPlus
nMinus = N - nPlus
if nc > 1:
if ncMinus > 1:
h2f1 = -mpmath.log(mpmath.hyp2f1(ncPlus, ncPlus + ncMinus - N, 1 + ncPlus - N, 1))
norm = -log_binomial(N - 1 - ncPlus, ncMinus - 1) + h2f1
else:
norm = -log_binomial(N - 1, ncPlus - 1) - np.log((N - ncPlus) / float(ncPlus))
res = -log_binomial(nPlus - 1, ncPlus - 1) - log_binomial(nMinus - 1, ncMinus - 1) - norm
return res
elif nc == 1:
return 0.
else:
return 0.
def test_hyp2f1_real_some_points():
pts = [
(1,2,3,0),
(1./3, 2./3, 5./6, 27./32),
(1./4, 1./2, 3./4, 80./81),
(2,-2,-3,3),
(2,-3,-2,3),
(2,-1.5,-1.5,3),
(1,2,3,0),
(0.7235, -1, -5, 0.3),
(0.25, 1./3, 2, 0.999),
(0.25, 1./3, 2, -1),
(2,3,5,0.99),
(3./2,-0.5,3,0.99),
(2,2.5,-3.25,0.999),
(-8, 18.016500331508873, 10.805295997850628, 0.90875647507000001),
(-10,900,-10.5,0.99),
(-10,900,10.5,0.99),
(-1,2,1,1.0),
(-1,2,1,-1.0),
(-3,13,5,1.0),
(-3,13,5,-1.0),
]
dataset = [p + (float(mpmath.hyp2f1(*p)),) for p in pts]
dataset = np.array(dataset, dtype=np.float_)
olderr = np.seterr(invalid='ignore')
try:
FuncData(sc.hyp2f1, dataset, (0,1,2,3), 4, rtol=1e-10).check()
finally:
np.seterr(**olderr)
def test_hyp2f1_real_some_points():
pts = [
(1, 2, 3, 0),
(1. / 3, 2. / 3, 5. / 6, 27. / 32),
(1. / 4, 1. / 2, 3. / 4, 80. / 81),
(2, -2, -3, 3),
(2, -3, -2, 3),
(2, -1.5, -1.5, 3),
(1, 2, 3, 0),
(0.7235, -1, -5, 0.3),
(0.25, 1. / 3, 2, 0.999),
(0.25, 1. / 3, 2, -1),
(2, 3, 5, 0.99),
(3. / 2, -0.5, 3, 0.99),
(2, 2.5, -3.25, 0.999),
(-8, 18.016500331508873, 10.805295997850628, 0.90875647507000001),
(-10, 900, -10.5, 0.99),
(-10, 900, 10.5, 0.99),
(-1, 2, 1, 1.0),
(-1, 2, 1, -1.0),
(-3, 13, 5, 1.0),
(-3, 13, 5, -1.0),
]
dataset = [p + (float(mpmath.hyp2f1(*p)), ) for p in pts]
dataset = np.array(dataset, dtype=np.float_)
FuncData(sc.hyp2f1, dataset, (0, 1, 2, 3), 4, rtol=1e-10).check()
def Mellin_DoubleSphericalBesselJ(alpha, nu1, nu2):
import mpmath
from numpy import frompyfunc
hyp2f1 = frompyfunc(lambda *a: complex(mpmath.hyp2f1(*a)), 4, 1)
if 0 < alpha < 1:
def MK(z):
return pi * exp(log(2)*(z-3) + log(alpha)*nu2 + loggamma(0.5*(nu1+nu2+z))
- loggamma(0.5*(3+nu1-nu2-z)) - loggamma(1.5+nu2)) \
* hyp2f1(0.5*(-1-nu1+nu2+z), 0.5*(nu1+nu2+z), 1.5+nu2, alpha**2)
elif alpha > 1:
def MK(z):
return pi * exp(log(2)*(z-3) + log(alpha)*(-nu1-z) + loggamma(0.5*(nu1+nu2+z))
- loggamma(0.5*(3-nu1+nu2-z)) - loggamma(1.5+nu1)) \
* hyp2f1(0.5*(-1+nu1-nu2+z), 0.5*(nu1+nu2+z), 1.5+nu1, alpha**-2)
elif alpha == 1:
def MK(z):
return pi * exp(
log(2) *
(z - 3) + loggamma(2 - z) + loggamma(0.5 * (nu1 + nu2 + z)) -
loggamma(0.5 * (3 + nu1 - nu2 - z)) -
loggamma(0.5 *
(3 - nu1 + nu2 - z)) - loggamma(0.5 *
(4 + nu1 + nu2 - z)))
else:
raise ValueError
return MK
def rho2(r, rho0, rc, gmma):
'''
verifying my integral for rho
'''
a = np.array([
float(
mp.hyp2f1(0.5, 0.5 / gmma, 1 + 0.5 / gmma, -(ri / rc)**(2 * gmma)))
for ri in r
])
b = -r**(2 * gmma) / (2 + 4 * gmma) * np.array([
float(
mp.hyp2f1(1.5, (2 * gmma + 1) /
(2 * gmma), 2 + 0.5 / gmma, -(ri / rc)**(2 * gmma)))
for ri in r
]) * (2 * gmma / rc**(2 * gmma))
return rho0 * (a + b)
def tauMinus(n1, n2, l):
if l == 0:
return 0.0
tmp = 2**(2. * l) / factorial(2. * l - 1)
tmp *= (gamma(n1 + l + 1) * gamma(n2 + l) / gamma(n1 - l) /
gamma(n2 - l + 1))**0.5
tmp *= (n1 * n2)**(l + 1)
tmp /= (n1 + n2)**(n1 + n2)
tmp *= (n1 - n2)**(n1 - 2 - l)
tmp *= (n2 - n1)**(n2 - l)
tmp2 = hyp2f1(l + 1 - n1, l - n2, 2 * l, -4. * n1 * n2 / (n1 - n2)**2)
tmp2 -= ((n1 - n2) /
(n1 + n2))**2 * hyp2f1(l - 1 - n1, l - n2, 2 * l, -4. * n1 * n2 /
(n1 - n2)**2)
return tmp * tmp2
def test_hyp2f1_strange_points():
pts = [
(2,-1,-1,3),
(2,-2,-2,3),
]
dataset = [p + (float(mpmath.hyp2f1(*p)),) for p in pts]
dataset = np.array(dataset, dtype=np.float_)
FuncData(sc.hyp2f1, dataset, (0,1,2,3), 4, rtol=1e-10).check()
def test_hyp2f1_strange_points():
pts = [
(2, -1, -1, 3),
(2, -2, -2, 3),
]
dataset = [p + (float(mpmath.hyp2f1(*p)), ) for p in pts]
dataset = np.array(dataset, dtype=np.float_)
FuncData(sc.hyp2f1, dataset, (0, 1, 2, 3), 4, rtol=1e-10).check()
def zk(z, k):
"""
modified dispersion function for Kappa distribution.
(Mace and Hellberg, 1995)
"""
i = mp.mpc(0, 1)
coeff = i * (k + 0.5) * (k-0.5) / (mp.sqrt(k**3) * (k+1))
return coeff * hyp2f1(1, 2*k+2, k+2, (1-z/(i * mp.sqrt(k)))/2)
def DeltaHypergeometric(x1, x2):
""" Difference of squared hypergeometric functions evaluated at x1 and x2, respectively.
@param x1 : First argument.
@param x2 : Second argument.
@return : | F(-x1+1, -x2, 1, -4 x1 x2 /(x1-x2)^2) |^2 - | F(-x2+1, -x1, 1, -4 x1 x2 /(x1-x2)^2) |^2
"""
# x variable:
y = -4. * x1 * x2 / (x1 - x2)**2
f1 = (hyp2f1(-x1 + 1, -x2, 1., y))**2
f2 = (hyp2f1(-x2 + 1, -x1, 1., y))**2
Delta = f1 - f2
return Delta
def _student_t_cdf(df, t, dps=None):
if dps is None:
dps = mpmath.mp.dps
with mpmath.workdps(dps):
df, t = mpmath.mpf(df), mpmath.mpf(t)
fac = mpmath.hyp2f1(0.5, 0.5*(df + 1), 1.5, -t**2/df)
fac *= t*mpmath.gamma(0.5*(df + 1))
fac /= mpmath.sqrt(mpmath.pi*df)*mpmath.gamma(0.5*df)
return 0.5 + fac
def _student_t_cdf(df, t, dps=None):
if dps is None:
dps = mpmath.mp.dps
with mpmath.workdps(dps):
df, t = mpmath.mpf(df), mpmath.mpf(t)
fac = mpmath.hyp2f1(0.5, 0.5*(df + 1), 1.5, -t**2/df)
fac *= t*mpmath.gamma(0.5*(df + 1))
fac /= mpmath.sqrt(mpmath.pi*df)*mpmath.gamma(0.5*df)
return 0.5 + fac
def test_hyp2f1_strange_points():
pts = [
(2, -1, -1, 0.7),
(2, -2, -2, 0.7),
]
kw = dict(eliminate=True)
dataset = [p + (float(mpmath.hyp2f1(*p, **kw)), ) for p in pts]
dataset = np.array(dataset, dtype=np.float_)
FuncData(sc.hyp2f1, dataset, (0, 1, 2, 3), 4, rtol=1e-10).check()
def test_hyp2f1_complex(self):
# Scipy's hyp2f1 seems to have performance and accuracy problems
assert_mpmath_equal(
lambda a, b, c, x: sc.hyp2f1(a.real, b.real, c.real, x),
_exception_to_nan(
lambda a, b, c, x: mpmath.hyp2f1(a, b, c, x, **HYPERKW)),
[Arg(-1e2, 1e2),
Arg(-1e2, 1e2),
Arg(-1e2, 1e2),
ComplexArg()],
n=10)
def test_hyp2f1_real_some():
dataset = []
for a in [-10, -5, -1.8, 1.8, 5, 10]:
for b in [-2.5, -1, 1, 7.4]:
for c in [-9, -1.8, 5, 20.4]:
for z in [-10, -1.01, -0.99, 0, 0.6, 0.95, 1.5, 10]:
try:
v = float(mpmath.hyp2f1(a, b, c, z))
except:
continue
dataset.append((a, b, c, z, v))
dataset = np.array(dataset, dtype=np.float_)
FuncData(sc.hyp2f1, dataset, (0, 1, 2, 3), 4, rtol=1e-9).check()
def test_hyp2f1_real_some():
dataset = []
for a in [-10, -5, -1.8, 1.8, 5, 10]:
for b in [-2.5, -1, 1, 7.4]:
for c in [-9, -1.8, 5, 20.4]:
for z in [-10, -1.01, -0.99, 0, 0.6, 0.95, 1.5, 10]:
try:
v = float(mpmath.hyp2f1(a, b, c, z))
except:
continue
dataset.append((a, b, c, z, v))
dataset = np.array(dataset, dtype=np.float_)
FuncData(sc.hyp2f1, dataset, (0, 1, 2, 3), 4, rtol=1e-9).check()
def test_hyp2f1_some_points_2():
# Taken from mpmath unit tests -- this point failed for mpmath 0.13 but
# was fixed in their SVN since then
pts = [
(112, 51./10, -9./10, -0.99999),
## Mpmath currently (0.13) fails also for these:
#(10,-900,10.5,0.99),
#(10,-900,-10.5,0.99),
]
dataset = [p + (float(mpmath.hyp2f1(*p)),) for p in pts]
dataset = np.array(dataset, dtype=np.float_)
FuncData(sc.hyp2f1, dataset, (0,1,2,3), 4, rtol=1e-10).check()
def test_hyp2f1_some_points_2():
# Taken from mpmath unit tests -- this point failed for mpmath 0.13 but
# was fixed in their SVN since then
pts = [(112, (51, 10), (-9, 10), -0.99999), (10, -900, 10.5, 0.99), (10, -900, -10.5, 0.99)]
def fev(x):
if isinstance(x, tuple):
return float(x[0]) / x[1]
else:
return x
dataset = [tuple(map(fev, p)) + (float(mpmath.hyp2f1(*p)),) for p in pts]
dataset = np.array(dataset, dtype=np.float_)
FuncData(sc.hyp2f1, dataset, (0, 1, 2, 3), 4, rtol=1e-10).check()
def sf(x, df):
"""
Survival function of Student's t distribution.
"""
if df <= 0:
raise ValueError('df must be greater than 0')
with mpmath.extradps(5):
half = mpmath.mp.one/2
x = mpmath.mpf(x)
df = mpmath.mpf(df)
h = (df + 1) / 2
p1 = x * mpmath.gamma(h)
p2 = mpmath.hyp2f1(half, h, 3*half, -x**2/df)
return half - p1*p2/mpmath.sqrt(mpmath.pi*df)/mpmath.gamma(df/2)
def Mellin_DoubleSphericalBesselJ(alpha, l1, l2):
import mpmath
from numpy import frompyfunc
hyp2f1 = frompyfunc(lambda *a: complex(mpmath.hyp2f1(*a)), 4, 1)
if 0 < alpha <= 1:
def UK(z):
return pi * exp(log(2)*(z-3) + log(alpha)*l2 + loggamma(0.5*(l1+l2+z))
- loggamma(0.5*(3+l1-l2-z)) - loggamma(1.5+l2)) \
* hyp2f1(0.5*(-1-l1+l2+z), 0.5*(l1+l2+z), 1.5+l2, alpha**2)
elif alpha > 1:
def UK(z):
return pi * exp(log(2)*(z-3) + log(alpha)*(-l1-z) + loggamma(0.5*(l1+l2+z))
- loggamma(0.5*(3-l1+l2-z)) - loggamma(1.5+l1)) \
* hyp2f1(0.5*(-1+l1-l2+z), 0.5*(l1+l2+z), 1.5+l1, alpha**-2)
else:
raise ValueError
return UK
def hypergeometric(z):
#u_3^((2))(x) = (-z)^(b-c)(1-z)^(c-a-b)_2F_1(1-b,c-b;a+1-b;z^(-1))
#u_6^((4))(x) = z^(b-c)(1-z)^(c-a-b)_2F_1(c-b,1-b;c+1-a-b;1-z^(-1))
#u_2^((3))(x) = z^(-a)_2F_1(a,a+1-c;a+b+1-c;1-z^(-1))
#u_3(x) = z^(-a)_2F_1(a,a+1-c;a+1-b;z^(-1))
#ii**(-a)*hyp2f1(a,1+a-c,a+1-b,ii**(-1))
#u_4(x) = z^(-b)_2F_1(b+1-c,b;b+1-a;z^(-1))
x = []
for ii in z:
x.append(((1 + sigma_m0 * ii) / (1 - sigma_m0)))
#print(x)
r_list = []
for ii in x:
r_list.append(hyp2f1(a, b, c, ii**(-1)))
#print(ii**(b-c)*(1-ii)**(c-a-b))
#print(x)
return r_list
def fun1(t, ei, ef):
kf = np.sqrt(2 * m * ef)
lf = (z1 + 1) * z2 * aa * np.sqrt(m / 2 / ef)
ki = np.sqrt(2 * m * ei)
li = z1 * z2 * aa * np.sqrt(m / 2 / ei)
x = 2 * (1 - t) / (ki / kf + kf / ki - 2 * t)
a = 1 + li * 1j
b = -lf * 1j
c = 1
res = mp.hyp2f1(a, b, c, x)
#checkRes = (1-x)**(c-a-b) * mp.hyp2f1(c-a, c-b, c, x)
#diff = np.sqrt((res.imag - checkRes.imag)**2 +
# (res.real - checkRes.real)**2)
# if (diff > 1e-9):
# print("Error: " + str(diff))
return (res.real * res.real + res.imag * res.imag) / (
ki * ki + kf * kf - 2 * ki * kf * t) / (ki * ki + kf * kf -
2 * ki * kf * t)
def sigma(n, rho):
inner0 = n / 2 * mm.log(1 - rho**2)
inner1 = mm.log(mm.hyp3f2(3 / 2, n / 2, n / 2, 1 / 2, n / 2 + 1, rho**2))
inner2 = -mm.log(n)
inner = mm.mp.e**(inner0 + inner1 + inner2)
outer0 = n / 2 * np.log(1 - rho**2) + np.log(abs(rho))
outer1 = 2 * sc.gammaln(n / 2 + 1 / 2)
outer2 = mm.log(mm.hyp2f1(n / 2 + 1 / 2, n / 2 + 1 / 2, n / 2 + 1, rho**2))
outer3 = -sc.gammaln(n / 2)
outer4 = -sc.gammaln(n / 2 + 1)
outer = outer0 + outer1 + outer2 + outer3 + outer4
outer = 2 * outer
return float(mm.sqrt(inner - mm.mp.e**(outer)))
def test_hyp2f1_real_some():
dataset = []
for a in [-10, -5, -1.8, 1.8, 5, 10]:
for b in [-2.5, -1, 1, 7.4]:
for c in [-9, -1.8, 5, 20.4]:
for z in [-10, -1.01, -0.99, 0, 0.6, 0.95, 1.5, 10]:
try:
v = float(mpmath.hyp2f1(a, b, c, z))
except:
continue
dataset.append((a, b, c, z, v))
dataset = np.array(dataset, dtype=np.float_)
olderr = np.seterr(invalid='ignore')
try:
FuncData(sc.hyp2f1, dataset, (0,1,2,3), 4, rtol=1e-9,
ignore_inf_sign=True).check()
finally:
np.seterr(**olderr)
def test_hyp2f1_some_points_2():
# Taken from mpmath unit tests -- this point failed for mpmath 0.13 but
# was fixed in their SVN since then
pts = [
(112, (51, 10), (-9, 10), -0.99999),
(10, -900, 10.5, 0.99),
(10, -900, -10.5, 0.99),
]
def fev(x):
if isinstance(x, tuple):
return float(x[0]) / x[1]
else:
return x
dataset = [tuple(map(fev, p)) + (float(mpmath.hyp2f1(*p)), ) for p in pts]
dataset = np.array(dataset, dtype=np.float_)
FuncData(sc.hyp2f1, dataset, (0, 1, 2, 3), 4, rtol=1e-10).check()
def test_hyp2f1_real_some():
dataset = []
for a in [-10, -5, -1.8, 1.8, 5, 10]:
for b in [-2.5, -1, 1, 7.4]:
for c in [-9, -1.8, 5, 20.4]:
for z in [-10, -1.01, -0.99, 0, 0.6, 0.95, 1.5, 10]:
try:
v = float(mpmath.hyp2f1(a, b, c, z))
except:
continue
dataset.append((a, b, c, z, v))
dataset = np.array(dataset, dtype=np.float_)
olderr = np.seterr(invalid='ignore')
try:
FuncData(sc.hyp2f1,
dataset, (0, 1, 2, 3),
4,
rtol=1e-9,
ignore_inf_sign=True).check()
finally:
np.seterr(**olderr)
def fun1(t):
a = 1 + 2j
b = 3j
c = 3 + 2j
x = 0.2
rez = mp.hyp2f1(a, b, c, x)
# print(rez)
ef = send_ef
ei = send_ei
kki = math.sqrt(2*m*ei)
kkf = math.sqrt(2*m*ef)
#print(kki, kkf, t)
x = 2*(1-t)/(ki(ei)/kf(ef)+kf(ef)/ki(ei)-2*t)
# llf = (z1+1)*z2*aa*math.sqrt(m/2/ef)
# lli = z1*z2*aa*math.sqrt(m/2/ei)
a = 1 + 2j
b = 3j
c = 3 + 2j
x = 0.2
# print(rez)
per = kki*kki+kkf*kkf
return 1e-300*(rez.real*rez.real+rez.imag*rez.imag)/(per-2*kki*kkf*t)/(per-2*kki*kkf*t)
def test_hyp2f1_real_some_points():
pts = [
(1, 2, 3, 0),
(1.0 / 3, 2.0 / 3, 5.0 / 6, 27.0 / 32),
(1.0 / 4, 1.0 / 2, 3.0 / 4, 80.0 / 81),
(2, -2, -3, 3),
(2, -3, -2, 3),
(2, -1.5, -1.5, 3),
(1, 2, 3, 0),
(0.7235, -1, -5, 0.3),
(0.25, 1.0 / 3, 2, 0.999),
(0.25, 1.0 / 3, 2, -1),
(2, 3, 5, 0.99),
(3.0 / 2, -0.5, 3, 0.99),
(2, 2.5, -3.25, 0.999),
(-8, 18.016500331508873, 10.805295997850628, 0.90875647507000001),
(-10, 900, -10.5, 0.99),
(-10, 900, 10.5, 0.99),
]
dataset = [p + (float(mpmath.hyp2f1(*p)),) for p in pts]
dataset = np.array(dataset, dtype=np.float_)
FuncData(sc.hyp2f1, dataset, (0, 1, 2, 3), 4, rtol=1e-10).check()
def mp_hyp2f1(a, b, c, z):
"""Return mpmath hyp2f1 calculated on same branch as scipy hyp2f1.
For most values of a,b,c mpmath returns the x - 0j branch of hyp2f1 on the
branch cut x=(1,inf) whereas scipy's hyp2f1 calculates the x + 0j branch.
Thus, to generate the right comparison values on the branch cut, we
evaluate mpmath.hyp2f1 at x + 1e-15*j.
The exception to this occurs when c-a=-m in which case both mpmath and
scipy calculate the x + 0j branch on the branch cut. When this happens
mpmath.hyp2f1 will be evaluated at the original z point.
"""
on_branch_cut = z.real > 1.0 and abs(z.imag) < 1.0e-15
cond1 = abs(c - a - round(c - a)) < 1.0e-15 and round(c - a) <= 0
cond2 = abs(c - b - round(c - b)) < 1.0e-15 and round(c - b) <= 0
# Make sure imaginary part is *exactly* zero
if on_branch_cut:
z = z.real + 0.0j
if on_branch_cut and not (cond1 or cond2):
z_mpmath = z.real + 1.0e-15j
else:
z_mpmath = z
return complex(mpmath.hyp2f1(a, b, c, z_mpmath))
def Perr(Qtildep, Q, Qp, FNR, FPR):
"""
Returns observed error-prone probability distribution.
Args
----
Qtildep (int): number of positive tests.
Qp (int): number of positive tests.
Q (int): number of tests.
FNR (float): false-negative fraction.
FPR (float): false-positive fraction.
"""
Qm = Q - Qp
Perr = power(FNR, Qp) * power(1 - FPR, Qm - Qtildep ) * power(FPR, Qtildep) \
* binomial( Qm, Qtildep ) * hyp2f1( -Qp, -Qtildep, Qm + 1 - Qtildep, \
(FNR-1)*(FPR-1)/(FNR*FPR) )
if isnan(Perr):
Perr = 0
return Perr
def test_hyp2f1_real_random():
dataset = []
npoints = 500
dataset = np.zeros((npoints, 5), np.float_)
np.random.seed(1234)
dataset[:,0] = np.random.pareto(1.5, npoints)
dataset[:,1] = np.random.pareto(1.5, npoints)
dataset[:,2] = np.random.pareto(1.5, npoints)
dataset[:,3] = 2*np.random.rand(npoints) - 1
dataset[:,0] *= (-1)**np.random.randint(2, npoints)
dataset[:,1] *= (-1)**np.random.randint(2, npoints)
dataset[:,2] *= (-1)**np.random.randint(2, npoints)
for ds in dataset:
if mpmath.__version__ < '0.14':
# mpmath < 0.14 fails for c too much smaller than a, b
if abs(ds[:2]).max() > abs(ds[2]):
ds[2] = abs(ds[:2]).max()
ds[4] = float(mpmath.hyp2f1(*tuple(ds[:4])))
FuncData(sc.hyp2f1, dataset, (0,1,2,3), 4, rtol=1e-9).check()
def test_hyp2f1_real_random():
dataset = []
npoints = 500
dataset = np.zeros((npoints, 5), np.float_)
np.random.seed(1234)
dataset[:, 0] = np.random.pareto(1.5, npoints)
dataset[:, 1] = np.random.pareto(1.5, npoints)
dataset[:, 2] = np.random.pareto(1.5, npoints)
dataset[:, 3] = 2 * np.random.rand(npoints) - 1
dataset[:, 0] *= (-1)**np.random.randint(2, npoints)
dataset[:, 1] *= (-1)**np.random.randint(2, npoints)
dataset[:, 2] *= (-1)**np.random.randint(2, npoints)
for ds in dataset:
if mpmath.__version__ < '0.14':
# mpmath < 0.14 fails for c too much smaller than a, b
if abs(ds[:2]).max() > abs(ds[2]):
ds[2] = abs(ds[:2]).max()
ds[4] = float(mpmath.hyp2f1(*tuple(ds[:4])))
FuncData(sc.hyp2f1, dataset, (0, 1, 2, 3), 4, rtol=1e-9).check()
def test_hyp2f1(self):
assert_mpmath_equal(sc.hyp2f1,
_exception_to_nan(lambda a, b, c, x: mpmath.hyp2f1(a, b, c, x, **HYPERKW)),
[Arg(), Arg(), Arg(), Arg()])
def G(eta1, eta2, k1, k2, l):
Gl = abs((k2 - k1) / (k2 + k1))**(1j * eta1 + 1j * eta2) * hyp2f1(
l + 1 - 1j * eta2, l + 1 - 1j * eta1, 2. * l + 2, -4. * k1 * k2 /
(k1 - k2)**2)
return Gl.real
def z(ZZ):
mpmath.mp.dps = 20
return N.exp(-N.log(1728*ZZ) / 5) * mpmath.hyp2f1(31.0/60, 11.0/60, 6.0/5, 1.0/ZZ) /\
mpmath.hyp2f1(19.0/60, -1.0/60, 4.0/5, 1.0/ZZ)
def _mpmath_hyp2f1(self, a, b, c, z):
result = []
for a_, b_, c_, z_ in zip(a, b, c, z):
result.append(np.float64(mpmath.hyp2f1(a_, b_, c_, z_)))
return np.array(result)
def test_hyp2f1_complex(self):
# Scipy's hyp2f1 seems to have performance and accuracy problems
assert_mpmath_equal(lambda a, b, c, x: sc.hyp2f1(a.real, b.real, c.real, x),
_exception_to_nan(lambda a, b, c, x: mpmath.hyp2f1(a, b, c, x, **HYPERKW)),
[Arg(-1e2, 1e2), Arg(-1e2, 1e2), Arg(-1e2, 1e2), ComplexArg()],
n=10)
as yet, since it tries to import the libraries from *this* folder, rather than
installation (which doesn't work because the fortran code isn't installed.)
"""
import inspect
import os
#LOCATION = "/".join(os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))).split("/")[:-1])
import sys
#sys.path.insert(0, LOCATION)
import numpy as np
from halomod import ProjectedCF
from halomod.integrate_corr import projected_corr_gal
from astropy.units import Mpc
from mpmath import gamma,hyp2f1
hyp2f1A = np.frompyfunc(lambda a,b,c,z: float(hyp2f1(a,b,c,z)), 4, 1)
def wprp_pl(rp,r0,g):
return rp*(rp/r0)**-g * gamma(0.5)*gamma((g-1)/2.0)/gamma(g/2.0)
def wprp_pl_lim(rp,r0,g,rmax):
return (1/gamma(g/2)) * (h.rp*h.rlim/r0)**-g * gamma((g-1)/2) * \
(gamma(0.5) * h.rp * h.rlim**g - h.rp**g *h.rlim * gamma(g/2)*\
hyp2f1A(0.5,(g-1)/2,(g+1)/2,h.rp.value**2/h.rlim.value**2))
class TestProjCorr():
def __init__(self):
self.rp = np.logspace(-2,1.2,50)
self.gamma = 1.85 # Values from S1 sample of Beutler+2011
self.r0 = 5.14