def pl_cov(t, Si=4.33, fL=1.0/20, approx_ksum=False):
"""
Analytically calculate the covariance matrix for a stochastic signal with a
power spectral density given by pl_psd.
@param t: Time-series timestamps
@param Si: Spectral index of power-law spectrum
@param fL: Low-frequency cut-off
@param approx_ksum: Whether we approximate the infinite sum
@return: Covariance matrix
"""
EulerGamma = 0.5772156649015329
alpha = 0.5*(3.0-Si)
# Make a mesh-grid for the covariance matrix elements
t1, t2 = np.meshgrid(t,t)
x = 2 * np.pi * fL * np.abs(t1 - t2)
del t1
del t2
# The tolerance for which to use the Gamma function expansion
tol = 1e-5
# the exact solutions for alpha = 0, -1 should be acceptable in a small
# interval around them...
if abs(alpha) < 1e-7:
cosx, sinx = np.cos(x), np.sin(x)
power = cosx - x * sinx
sinint, cosint = sl.sici(x)
corr = (fL**-2) / (24 * math.pi**2) * (power + x**2 * cosint)
elif abs(alpha + 1) < 1e-7:
cosx, sinx = np.cos(x), np.sin(x)
power = 6 * cosx - 2 * x * sinx - x**2 * cosx + x**3 * sinx
sinint, cosint = ss.sici(x)
corr = (fL**-4) / (288 * np.pi**2) * (power - x**4 * cosint)
else:
# leading-order expansion of Gamma[-2+2*alpha]*Cos[Pi*alpha] around -0.5
# and 0.5
if abs(alpha - 0.5) < tol:
cf = np.pi/2 + (np.pi - np.pi*EulerGamma) * (alpha - 0.5)
elif abs(alpha + 0.5) < tol:
cf = -np.pi/12 + (-11*np.pi/36 + EulerGamma*math.pi/6) * (alpha + 0.5)
elif abs(alpha + 1.5) < tol:
cf = np.pi/240 + (137*np.pi/7200 - EulerGamma*np.pi/120) * (alpha + 1.5)
else:
cf = ss.gamma(-2+2*alpha) * np.cos(np.pi*alpha)
power = cf * x**(2-2*alpha)
# Mathematica solves Sum[(-1)^n x^(2 n)/((2 n)! (2 n + 2 alpha - 2)),
# {n, 0, Infinity}] as HypergeometricPFQ[{-1+alpha}, {1/2,alpha},
# -(x^2/4)]/(2 alpha - 2) the corresponding scipy.special function is
# hyp1f2 (which returns value and error)
if approx_ksum:
ksum = 1.0 / (2*alpha - 2) - x**2 / (4*alpha) + x**4 / (24 * (2 + 2*alpha))
else:
ksum = ss.hyp1f2(alpha-1,0.5,alpha,-0.25*x**2)[0]/(2*alpha-2)
del x
corr = -(fL**(-2+2*alpha)) * (power + ksum)
return corr
def pl_cov(t, Si=4.33, fL=1.0 / 20, approx_ksum=False):
"""
Analytically calculate the covariance matrix for a stochastic signal with a
power spectral density given by pl_psd.
@param t: Time-series timestamps
@param Si: Spectral index of power-law spectrum
@param fL: Low-frequency cut-off
@param approx_ksum: Whether we approximate the infinite sum
@return: Covariance matrix
"""
EulerGamma = 0.5772156649015329
alpha = 0.5 * (3.0 - Si)
# Make a mesh-grid for the covariance matrix elements
t1, t2 = np.meshgrid(t, t)
x = 2 * np.pi * fL * np.abs(t1 - t2)
del t1
del t2
# The tolerance for which to use the Gamma function expansion
tol = 1e-5
# the exact solutions for alpha = 0, -1 should be acceptable in a small
# interval around them...
if abs(alpha) < 1e-7:
cosx, sinx = np.cos(x), np.sin(x)
power = cosx - x * sinx
sinint, cosint = sl.sici(x)
corr = (fL**-2) / (24 * math.pi**2) * (power + x**2 * cosint)
elif abs(alpha + 1) < 1e-7:
cosx, sinx = np.cos(x), np.sin(x)
power = 6 * cosx - 2 * x * sinx - x**2 * cosx + x**3 * sinx
sinint, cosint = ss.sici(x)
corr = (fL**-4) / (288 * np.pi**2) * (power - x**4 * cosint)
else:
# leading-order expansion of Gamma[-2+2*alpha]*Cos[Pi*alpha] around -0.5
# and 0.5
if abs(alpha - 0.5) < tol:
cf = np.pi / 2 + (np.pi - np.pi * EulerGamma) * (alpha - 0.5)
elif abs(alpha + 0.5) < tol:
cf = -np.pi / 12 + (-11 * np.pi / 36 +
EulerGamma * math.pi / 6) * (alpha + 0.5)
elif abs(alpha + 1.5) < tol:
cf = np.pi / 240 + (137 * np.pi / 7200 -
EulerGamma * np.pi / 120) * (alpha + 1.5)
else:
cf = ss.gamma(-2 + 2 * alpha) * np.cos(np.pi * alpha)
power = cf * x**(2 - 2 * alpha)
# Mathematica solves Sum[(-1)^n x^(2 n)/((2 n)! (2 n + 2 alpha - 2)),
# {n, 0, Infinity}] as HypergeometricPFQ[{-1+alpha}, {1/2,alpha},
# -(x^2/4)]/(2 alpha - 2) the corresponding scipy.special function is
# hyp1f2 (which returns value and error)
if approx_ksum:
ksum = 1.0 / (2 * alpha -
2) - x**2 / (4 * alpha) + x**4 / (24 *
(2 + 2 * alpha))
else:
ksum = ss.hyp1f2(alpha - 1, 0.5, alpha,
-0.25 * x**2)[0] / (2 * alpha - 2)
del x
corr = -(fL**(-2 + 2 * alpha)) * (power + ksum)
return corr
def Cgw_sec(model, alpha=-2.0/3.0, fL=1.0/500, approx_ksum=False, inc_cor=True):
""" Compute the residual covariance matrix for an hc = 1 x (f year)^alpha GW background.
Result is in units of (100 ns)^2.
Modified from Michele Vallisneri's mc3pta (https://github.com/vallis/mc3pta)
@param: list of libstempo pulsar objects
(as returned by readRealisations)
@param: the H&D correlation matrix
@param: the TOAs
@param: the GWB spectral index
@param: the low-frequency cut-off
@param: approx_ksum
"""
psrobs = model[6]
alphaab = model[5]
times_f = model[0]
day = 86400.0 # seconds, sidereal (?)
year = 3.15581498e7 # seconds, sidereal (?)
EulerGamma = 0.5772156649015329
npsrs = alphaab.shape[0]
t1, t2 = np.meshgrid(times_f,times_f)
# t1, t2 are in units of days; fL in units of 1/year (sidereal for both?)
# so typical values here are 10^-6 to 10^-3
x = 2 * np.pi * (day/year) * fL * np.abs(t1 - t2)
del t1
del t2
# note that the gamma is singular for all half-integer alpha < 1.5
#
# for -1 < alpha < 0, the x exponent ranges from 4 to 2 (it's 3.33 for alpha = -2/3)
# so for the lower alpha values it will be comparable in value to the x**2 term of ksum
#
# possible alpha limits for a search could be [-0.95,-0.55] in which case the sign of `power`
# is always positive, and the x exponent ranges from ~ 3 to 4... no problem with cancellation
# The tolerance for which to use the Gamma function expansion
tol = 1e-5
# the exact solutions for alpha = 0, -1 should be acceptable in a small interval around them...
if abs(alpha) < 1e-7:
cosx, sinx = np.cos(x), np.sin(x)
power = cosx - x * sinx
sinint, cosint = sl.sici(x)
corr = (year**2 * fL**-2) / (24 * math.pi**2) * (power + x**2 * cosint)
elif abs(alpha + 1) < 1e-7:
cosx, sinx = np.cos(x), np.sin(x)
power = 6 * cosx - 2 * x * sinx - x**2 * cosx + x**3 * sinx
sinint, cosint = ss.sici(x)
corr = (year**2 * fL**-4) / (288 * np.pi**2) * (power - x**4 * cosint)
else:
# leading-order expansion of Gamma[-2+2*alpha]*Cos[Pi*alpha] around -0.5 and 0.5
if abs(alpha - 0.5) < tol:
cf = np.pi/2 + (np.pi - np.pi*EulerGamma) * (alpha - 0.5)
elif abs(alpha + 0.5) < tol:
cf = -np.pi/12 + (-11*np.pi/36 + EulerGamma*math.pi/6) * (alpha + 0.5)
elif abs(alpha + 1.5) < tol:
cf = np.pi/240 + (137*np.pi/7200 - EulerGamma*np.pi/120) * (alpha + 1.5)
else:
cf = ss.gamma(-2+2*alpha) * np.cos(np.pi*alpha)
power = cf * x**(2-2*alpha)
# Mathematica solves Sum[(-1)^n x^(2 n)/((2 n)! (2 n + 2 alpha - 2)), {n, 0, Infinity}]
# as HypergeometricPFQ[{-1+alpha}, {1/2,alpha}, -(x^2/4)]/(2 alpha - 2)
# the corresponding scipy.special function is hyp1f2 (which returns value and error)
# TO DO, for speed: could replace with the first few terms of the sum!
if approx_ksum:
ksum = 1.0 / (2*alpha - 2) - x**2 / (4*alpha) + x**4 / (24 * (2 + 2*alpha))
else:
ksum = ss.hyp1f2(alpha-1,0.5,alpha,-0.25*x**2)[0]/(2*alpha-2)
del x
# this form follows from Eq. (A31) of Lee, Jenet, and Price ApJ 684:1304 (2008)
corr = -(year**2 * fL**(-2+2*alpha)) / (12 * np.pi**2) * (power + ksum)
if inc_cor:
# multiply by alphaab; there must be a more numpythonic way to do it
# npsrs psrobs
inda, indb = 0, 0
for a in range(npsrs):
for b in range(npsrs):
corr[inda:inda+psrobs[a], indb:indb+psrobs[b]] *= alphaab[a, b]
indb += psrobs[b]
indb = 0
inda += psrobs[a]
return corr
def Cred_sec(toas, alpha=-2.0/3.0, fL=1.0/20, approx_ksum=False):
day = 86400.0
year = 3.15581498e7
EulerGamma = 0.5772156649015329
psrobs = [len(toas)]
alphaab = np.array([[1.0]])
times_f = toas / day
npsrs = alphaab.shape[0]
t1, t2 = np.meshgrid(times_f,times_f)
# t1, t2 are in units of days; fL in units of 1/year (sidereal for both?)
# so typical values here are 10^-6 to 10^-3
x = 2 * np.pi * (day/year) * fL * np.abs(t1 - t2)
del t1
del t2
# note that the gamma is singular for all half-integer alpha < 1.5
#
# for -1 < alpha < 0, the x exponent ranges from 4 to 2 (it's 3.33 for alpha = -2/3)
# so for the lower alpha values it will be comparable in value to the x**2 term of ksum
#
# possible alpha limits for a search could be [-0.95,-0.55] in which case the sign of `power`
# is always positive, and the x exponent ranges from ~ 3 to 4... no problem with cancellation
# The tolerance for which to use the Gamma function expansion
tol = 1e-5
# the exact solutions for alpha = 0, -1 should be acceptable in a small interval around them...
if abs(alpha) < 1e-7:
cosx, sinx = np.cos(x), np.sin(x)
power = cosx - x * sinx
sinint, cosint = sl.sici(x)
corr = (year**2 * fL**-2) / (24 * math.pi**2) * (power + x**2 * cosint)
elif abs(alpha + 1) < 1e-7:
cosx, sinx = np.cos(x), np.sin(x)
power = 6 * cosx - 2 * x * sinx - x**2 * cosx + x**3 * sinx
sinint, cosint = ss.sici(x)
corr = (year**2 * fL**-4) / (288 * np.pi**2) * (power - x**4 * cosint)
else:
# leading-order expansion of Gamma[-2+2*alpha]*Cos[Pi*alpha] around -0.5 and 0.5
if abs(alpha - 0.5) < tol:
cf = np.pi/2 + (np.pi - np.pi*EulerGamma) * (alpha - 0.5)
elif abs(alpha + 0.5) < tol:
cf = -np.pi/12 + (-11*np.pi/36 + EulerGamma*math.pi/6) * (alpha + 0.5)
elif abs(alpha + 1.5) < tol:
cf = np.pi/240 + (137*np.pi/7200 - EulerGamma*np.pi/120) * (alpha + 1.5)
else:
cf = ss.gamma(-2+2*alpha) * np.cos(np.pi*alpha)
power = cf * x**(2-2*alpha)
# Mathematica solves Sum[(-1)^n x^(2 n)/((2 n)! (2 n + 2 alpha - 2)), {n, 0, Infinity}]
# as HypergeometricPFQ[{-1+alpha}, {1/2,alpha}, -(x^2/4)]/(2 alpha - 2)
# the corresponding scipy.special function is hyp1f2 (which returns value and error)
# TO DO, for speed: could replace with the first few terms of the sum!
if approx_ksum:
ksum = 1.0 / (2*alpha - 2) - x**2 / (4*alpha) + x**4 / (24 * (2 + 2*alpha))
else:
ksum = ss.hyp1f2(alpha-1,0.5,alpha,-0.25*x**2)[0]/(2*alpha-2)
del x
# this form follows from Eq. (A31) of Lee, Jenet, and Price ApJ 684:1304 (2008)
corr = -(year**2 * fL**(-2+2*alpha)) / (12 * np.pi**2) * (power + ksum)
return corr
def Cgw_sec(model,
alpha=-2.0 / 3.0,
fL=1.0 / 500,
approx_ksum=False,
inc_cor=True):
""" Compute the residual covariance matrix for an hc = 1 x (f year)^alpha GW background.
Result is in units of (100 ns)^2.
Modified from Michele Vallisneri's mc3pta (https://github.com/vallis/mc3pta)
@param: list of libstempo pulsar objects
(as returned by readRealisations)
@param: the H&D correlation matrix
@param: the TOAs
@param: the GWB spectral index
@param: the low-frequency cut-off
@param: approx_ksum
"""
psrobs = model[6]
alphaab = model[5]
times_f = model[0]
day = 86400.0 # seconds, sidereal (?)
year = 3.15581498e7 # seconds, sidereal (?)
EulerGamma = 0.5772156649015329
npsrs = alphaab.shape[0]
t1, t2 = np.meshgrid(times_f, times_f)
# t1, t2 are in units of days; fL in units of 1/year (sidereal for both?)
# so typical values here are 10^-6 to 10^-3
x = 2 * np.pi * (day / year) * fL * np.abs(t1 - t2)
del t1
del t2
# note that the gamma is singular for all half-integer alpha < 1.5
#
# for -1 < alpha < 0, the x exponent ranges from 4 to 2 (it's 3.33 for alpha = -2/3)
# so for the lower alpha values it will be comparable in value to the x**2 term of ksum
#
# possible alpha limits for a search could be [-0.95,-0.55] in which case the sign of `power`
# is always positive, and the x exponent ranges from ~ 3 to 4... no problem with cancellation
# The tolerance for which to use the Gamma function expansion
tol = 1e-5
# the exact solutions for alpha = 0, -1 should be acceptable in a small interval around them...
if abs(alpha) < 1e-7:
cosx, sinx = np.cos(x), np.sin(x)
power = cosx - x * sinx
sinint, cosint = sl.sici(x)
corr = (year**2 * fL**-2) / (24 * math.pi**2) * (power + x**2 * cosint)
elif abs(alpha + 1) < 1e-7:
cosx, sinx = np.cos(x), np.sin(x)
power = 6 * cosx - 2 * x * sinx - x**2 * cosx + x**3 * sinx
sinint, cosint = ss.sici(x)
corr = (year**2 * fL**-4) / (288 * np.pi**2) * (power - x**4 * cosint)
else:
# leading-order expansion of Gamma[-2+2*alpha]*Cos[Pi*alpha] around -0.5 and 0.5
if abs(alpha - 0.5) < tol:
cf = np.pi / 2 + (np.pi - np.pi * EulerGamma) * (alpha - 0.5)
elif abs(alpha + 0.5) < tol:
cf = -np.pi / 12 + (-11 * np.pi / 36 +
EulerGamma * math.pi / 6) * (alpha + 0.5)
elif abs(alpha + 1.5) < tol:
cf = np.pi / 240 + (137 * np.pi / 7200 -
EulerGamma * np.pi / 120) * (alpha + 1.5)
else:
cf = ss.gamma(-2 + 2 * alpha) * np.cos(np.pi * alpha)
power = cf * x**(2 - 2 * alpha)
# Mathematica solves Sum[(-1)^n x^(2 n)/((2 n)! (2 n + 2 alpha - 2)), {n, 0, Infinity}]
# as HypergeometricPFQ[{-1+alpha}, {1/2,alpha}, -(x^2/4)]/(2 alpha - 2)
# the corresponding scipy.special function is hyp1f2 (which returns value and error)
# TO DO, for speed: could replace with the first few terms of the sum!
if approx_ksum:
ksum = 1.0 / (2 * alpha -
2) - x**2 / (4 * alpha) + x**4 / (24 *
(2 + 2 * alpha))
else:
ksum = ss.hyp1f2(alpha - 1, 0.5, alpha,
-0.25 * x**2)[0] / (2 * alpha - 2)
del x
# this form follows from Eq. (A31) of Lee, Jenet, and Price ApJ 684:1304 (2008)
corr = -(year**2 * fL**(-2 + 2 * alpha)) / (12 * np.pi**2) * (power +
ksum)
if inc_cor:
# multiply by alphaab; there must be a more numpythonic way to do it
# npsrs psrobs
inda, indb = 0, 0
for a in range(npsrs):
for b in range(npsrs):
corr[inda:inda + psrobs[a],
indb:indb + psrobs[b]] *= alphaab[a, b]
indb += psrobs[b]
indb = 0
inda += psrobs[a]
return corr
def Cgw_sec(toas, alpha=-2.0 / 3.0, fL=1.0 / 20, approx_ksum=False):
day = 86400.0
year = 3.15581498e7
EulerGamma = 0.5772156649015329
psrobs = [len(toas)]
alphaab = np.array([[1.0]])
times_f = toas / day
npsrs = alphaab.shape[0]
t1, t2 = np.meshgrid(times_f, times_f)
# t1, t2 are in units of days; fL in units of 1/year (sidereal for both?)
# so typical values here are 10^-6 to 10^-3
x = 2 * np.pi * (day / year) * fL * np.abs(t1 - t2)
del t1
del t2
# note that the gamma is singular for all half-integer alpha < 1.5
#
# for -1 < alpha < 0, the x exponent ranges from 4 to 2 (it's 3.33 for alpha = -2/3)
# so for the lower alpha values it will be comparable in value to the x**2 term of ksum
#
# possible alpha limits for a search could be [-0.95,-0.55] in which case the sign of `power`
# is always positive, and the x exponent ranges from ~ 3 to 4... no problem with cancellation
# The tolerance for which to use the Gamma function expansion
tol = 1e-5
# the exact solutions for alpha = 0, -1 should be acceptable in a small interval around them...
if abs(alpha) < 1e-7:
cosx, sinx = np.cos(x), np.sin(x)
power = cosx - x * sinx
sinint, cosint = sl.sici(x)
corr = (year**2 * fL**-2) / (24 * math.pi**2) * (power + x**2 * cosint)
elif abs(alpha + 1) < 1e-7:
cosx, sinx = np.cos(x), np.sin(x)
power = 6 * cosx - 2 * x * sinx - x**2 * cosx + x**3 * sinx
sinint, cosint = ss.sici(x)
corr = (year**2 * fL**-4) / (288 * np.pi**2) * (power - x**4 * cosint)
else:
# leading-order expansion of Gamma[-2+2*alpha]*Cos[Pi*alpha] around -0.5 and 0.5
if abs(alpha - 0.5) < tol:
cf = np.pi / 2 + (np.pi - np.pi * EulerGamma) * (alpha - 0.5)
elif abs(alpha + 0.5) < tol:
cf = -np.pi / 12 + (-11 * np.pi / 36 +
EulerGamma * math.pi / 6) * (alpha + 0.5)
elif abs(alpha + 1.5) < tol:
cf = np.pi / 240 + (137 * np.pi / 7200 -
EulerGamma * np.pi / 120) * (alpha + 1.5)
else:
cf = ss.gamma(-2 + 2 * alpha) * np.cos(np.pi * alpha)
power = cf * x**(2 - 2 * alpha)
# Mathematica solves Sum[(-1)^n x^(2 n)/((2 n)! (2 n + 2 alpha - 2)), {n, 0, Infinity}]
# as HypergeometricPFQ[{-1+alpha}, {1/2,alpha}, -(x^2/4)]/(2 alpha - 2)
# the corresponding scipy.special function is hyp1f2 (which returns value and error)
# TO DO, for speed: could replace with the first few terms of the sum!
if approx_ksum:
ksum = 1.0 / (2 * alpha -
2) - x**2 / (4 * alpha) + x**4 / (24 *
(2 + 2 * alpha))
else:
ksum = ss.hyp1f2(alpha - 1, 0.5, alpha,
-0.25 * x**2)[0] / (2 * alpha - 2)
del x
# this form follows from Eq. (A31) of Lee, Jenet, and Price ApJ 684:1304 (2008)
corr = -(year**2 * fL**(-2 + 2 * alpha)) / (12 * np.pi**2) * (power +
ksum)
return corr
def Cgw_100ns(alphaab,times_f,alpha=-2/3,fL=1.0/10000,approx_ksum=False):
"""Compute the residual covariance matrix for an hc = 1 x (f year)^alpha GW background.
Result is in units of (100 ns)^2."""
t1, t2 = N.meshgrid(times_f,times_f)
# t1, t2 are in units of days; fL in units of 1/year (sidereal for both?)
# so typical values here are 10^-6 to 10^-3
x = 2 * math.pi * (day/year) * fL * N.abs(t1 - t2)
# note that the gamma is singular for all half-integer alpha < 1.5
#
# for -1 < alpha < 0, the x exponent ranges from 4 to 2 (it's 3.33 for alpha = -2/3)
# so for the lower alpha values it will be comparable in value to the x**2 term of ksum
#
# possible alpha limits for a search could be [-0.95,-0.55] in which case the sign of `power`
# is always positive, and the x exponent ranges from ~ 3 to 4... no problem with cancellation
# the variance, as computed below, is in units of year^2; we convert it
# to units of (100 ns)^2, so the multiplier is ~ 10^28
year100ns = year/1e-7
tol = 1e-5
# the exact solutions for alpha = 0, -1 should be acceptable in a small interval around them...
if abs(alpha) < 1e-7:
cosx, sinx = N.cos(x), N.sin(x)
power = cosx - x * sinx
sinint, cosint = SL.sici(x)
corr = (year100ns**2 * fL**-2) / (24 * math.pi**2) * (power + x**2 * cosint)
elif abs(alpha + 1) < 1e-7:
cosx, sinx = N.cos(x), N.sin(x)
power = 6 * cosx - 2 * x * sinx - x**2 * cosx + x**3 * sinx
sinint, cosint = SL.sici(x)
corr = (year100ns**2 * fL**-4) / (288 * math.pi**2) * (power - x**4 * cosint)
else:
# leading-order expansion of Gamma[-2+2*alpha]*Cos[Pi*alpha] around -0.5 and 0.5
if abs(alpha - 0.5) < tol:
cf = math.pi/2 + (math.pi - math.pi*EulerGamma) * (alpha - 0.5)
elif abs(alpha + 0.5) < tol:
cf = -math.pi/12 + (-11*math.pi/36 + EulerGamma*math.pi/6) * (alpha + 0.5)
elif abs(alpha + 1.5) < tol:
cf = math.pi/240 + (137*math.pi/7200 - EulerGamma*math.pi/120) * (alpha + 1.5)
else:
cf = SS.gamma(-2+2*alpha) * math.cos(math.pi*alpha)
power = cf * x**(2-2*alpha)
# Mathematica solves Sum[(-1)^n x^(2 n)/((2 n)! (2 n + 2 alpha - 2)), {n, 0, Infinity}]
# as HypergeometricPFQ[{-1+alpha}, {1/2,alpha}, -(x^2/4)]/(2 alpha - 2)
# the corresponding scipy.special function is hyp1f2 (which returns value and error)
# TO DO, for speed: could replace with the first few terms of the sum!
if approx_ksum:
ksum = 1.0 / (2*alpha - 2) - x**2 / (4*alpha) + x**4 / (24 * (2 + 2*alpha))
else:
ksum = SS.hyp1f2(alpha-1,0.5,alpha,-0.25*x**2)[0]/(2*alpha-2)
# this form follows from Eq. (A31) of Lee, Jenet, and Price ApJ 684:1304 (2008)
corr = -(year100ns**2 * fL**(-2+2*alpha)) / (12 * math.pi**2) * (power + ksum)
# multiply by alphaab; there must be a more numpythonic way to do it
ps, ts = len(alphaab), len(times_f) / len(alphaab)
for i in range(ps):
for j in range(ps):
corr[i*ts:(i+1)*ts,j*ts:(j+1)*ts] *= alphaab[i,j]
return corr