def pdf_moments(cnt):
"""Return the Gaussian expanded pdf function given the list of central
moments (first one is mean).
"""
N = len(cnt)
if N < 2:
raise ValueError, "At least two moments must be given to" + \
"approximate the pdf."
totp = poly1d(1)
sig = sqrt(cnt[1])
mu = cnt[0]
if N > 2:
Dvals = _hermnorm(N+1)
for k in range(3,N+1):
# Find Ck
Ck = 0.0
for n in range((k-3)/2):
m = k-2*n
if m % 2: # m is odd
momdiff = cnt[m-1]
else:
momdiff = cnt[m-1] - sig*sig*scipy.factorial2(m-1)
Ck += Dvals[k][m] / sig**m * momdiff
# Add to totp
totp = totp + Ck*Dvals[k]
def thisfunc(x):
xn = (x-mu)/sig
return totp(xn)*exp(-xn*xn/2.0)/sqrt(2*pi)/sig
return thisfunc
def pdf_moments(cnt):
"""Return the Gaussian expanded pdf function given the list of central
moments (first one is mean).
"""
N = len(cnt)
if N < 2:
raise ValueError, "At least two moments must be given to" + \
"approximate the pdf."
totp = poly1d(1)
sig = sqrt(cnt[1])
mu = cnt[0]
if N > 2:
Dvals = _hermnorm(N+1)
for k in range(3,N+1):
# Find Ck
Ck = 0.0
for n in range((k-3)/2):
m = k-2*n
if m % 2: # m is odd
momdiff = cnt[m-1]
else:
momdiff = cnt[m-1] - sig*sig*scipy.factorial2(m-1)
Ck += Dvals[k][m] / sig**m * momdiff
# Add to totp
totp = totp + Ck*Dvals[k]
def thisfunc(x):
xn = (x-mu)/sig
return totp(xn)*exp(-xn*xn/2.0)/sqrt(2*pi)/sig
return thisfunc
def pdf_moments_st(cnt):
"""Return the Gaussian expanded pdf function given the list of central
moments (first one is mean).
version of scipy.stats, any changes ?
the scipy.stats version has a bug and returns normal distribution
"""
N = len(cnt)
if N < 2:
raise ValueError("At least two moments must be given to "
"approximate the pdf.")
totp = poly1d(1)
sig = sqrt(cnt[1])
mu = cnt[0]
if N > 2:
Dvals = _hermnorm(N + 1)
for k in range(3, N + 1):
# Find Ck
Ck = 0.0
for n in range((k - 3) / 2):
m = k - 2 * n
if m % 2: # m is odd
momdiff = cnt[m - 1]
else:
momdiff = cnt[m - 1] - sig * sig * scipy.factorial2(m - 1)
Ck += Dvals[k][m] / sig**m * momdiff
# Add to totp
raise SystemError
print(Dvals)
print(Ck)
totp = totp + Ck * Dvals[k]
def thisfunc(x):
xn = (x - mu) / sig
return totp(xn) * exp(-xn * xn / 2.0) / sqrt(2 * np.pi) / sig
return thisfunc, totp
def pdf_moments_st(cnt):
"""Return the Gaussian expanded pdf function given the list of central
moments (first one is mean).
version of scipy.stats, any changes ?
the scipy.stats version has a bug and returns normal distribution
"""
N = len(cnt)
if N < 2:
raise ValueError("At least two moments must be given to" + \
"approximate the pdf.")
totp = poly1d(1)
sig = sqrt(cnt[1])
mu = cnt[0]
if N > 2:
Dvals = _hermnorm(N+1)
for k in range(3,N+1):
# Find Ck
Ck = 0.0
for n in range((k-3)/2):
m = k-2*n
if m % 2: # m is odd
momdiff = cnt[m-1]
else:
momdiff = cnt[m-1] - sig*sig*scipy.factorial2(m-1)
Ck += Dvals[k][m] / sig**m * momdiff
# Add to totp
raise
print(Dvals)
print(Ck)
totp = totp + Ck*Dvals[k]
def thisfunc(x):
xn = (x-mu)/sig
return totp(xn)*exp(-xn*xn/2.0)/sqrt(2*np.pi)/sig
return thisfunc, totp
def dfunc0(self,x): px=x*twopi y=numpy.ones(len(x)) for i in range(1,self.Para.para[1]+1): y+=scipy.factorial2(2*i-1)/scipy.factorial2(2*i)*numpy.sin(px)**(2*i) return self.Para.para[0]*numpy.cos(px)*y
def func0(self,x): px=x*twopi y=numpy.sin(px) for i in range(1,int(self.Para.para[1])+1): y+=scipy.factorial2(2*i-1)/scipy.factorial2(2*i)/(2*i+1)*numpy.sin(px)**(2*i+1) return self.Para.para[0]/twopi*y
