def get_results(Fit):
Vnn = collections.OrderedDict()
p = gv.load(Fit['filename'])
for i in range(len(Fit['twists'])):
Fit['M_D_tw{0}'.format(Fit['twists'][i])] = gv.sqrt(
p['dE:{0}'.format(Fit['Dtw{0}'.format(Fit['twists'][i])])][0]**2 -
Fit['momenta'][i]**2)
Fit['E_D_tw{0}'.format(Fit['twists'][i])] = p['dE:{0}'.format(
Fit['Dtw{0}'.format(Fit['twists'][i])])][0]
for mass in Fit['masses']:
Fit['M_G_m{0}'.format(mass)] = p['dE:{0}'.format(
Fit['Gm{0}'.format(mass)])][0]
Fit['M_NG_m{0}'.format(mass)] = p['dE:{0}'.format(
Fit['NGm{0}'.format(mass)])][0]
for twist in Fit['twists']:
if 'SVnn_m{0}_tw{1}'.format(mass, twist) in p:
Fit['Sm{0}_tw{1}'.format(mass, twist)] = 2 * 2 * gv.sqrt(
Fit['E_D_tw{0}'.format(twist)] *
Fit['M_G_m{0}'.format(mass)]) * p['SVnn_m{0}_tw{1}'.format(
mass, twist)][0][0]
if 'VVnn_m{0}_tw{1}'.format(mass, twist) in p:
Fit['Vm{0}_tw{1}'.format(mass, twist)] = 2 * 2 * gv.sqrt(
Fit['E_D_tw{0}'.format(twist)] *
Fit['M_G_m{0}'.format(mass)]) * p['VVnn_m{0}_tw{1}'.format(
mass, twist)][0][0]
return ()
def make_prior(models,prior_dict=None,nst=-1,ost=-1,do_amp_prior=True):
prior = gv.BufferDict()
for model in models:
skey=model.all_datatags[0]
if prior_dict is None: ## -- take default from defines
if (nst > -1 and ost > -1):
dprior = utf.get_prior_dict(df.define_prior,
df.define_prior['nkey'],df.define_prior['okey'],nst,ost,do_amp_prior=do_amp_prior)
else:
dprior = utf.get_prior_dict(df.define_prior,
df.define_prior['nkey'],df.define_prior['okey'],df.num_nst,df.num_ost,
do_amp_prior=do_amp_prior)
for pkey in dprior[skey]:
prior[pkey]=dprior[skey][pkey]
## -- logarithmize the logarithmic coefficients
for pkey in prior:
## -- don't do logs unless they are in the current model, else double counting
bkey = utf.get_basekey(pkey)
#if not(pkey[3:] in model.a+model.b+model.dE) and\
# not(pkey[4:] in model.a+model.b+model.dE):
if not(bkey[1] in model.a+model.b+model.dE):
continue
if bkey[0] == 'log':
negcheck = filter(lambda x: x[1].mean < 0,enumerate(prior[pkey]))
if len(negcheck) > 0:
raise ValueError("Prior indices ",list(np.array(negcheck)[:,0]),
" have negative values, key ",pkey)
prior[pkey] = gv.log(prior[pkey])
elif bkey[0] == 'sqrt':
negcheck = filter(lambda x: x[1].mean < 0,enumerate(prior[pkey]))
if len(negcheck) > 0:
raise ValueError("Prior indices ",list(np.array(negcheck)[:,0]),
" have negative values, key ",pkey)
prior[pkey] = gv.sqrt(prior[pkey])
else: ## -- allow passing of new prior, for chained operations
if (nst > -1 and ost > -1):
dprior = utf.get_prior_dict(prior_dict,
prior_dict['nkey'],prior_dict['okey'],nst,ost)
else:
dprior = utf.get_prior_dict(prior_dict,
prior_dict['nkey'],prior_dict['okey'],df.num_nst,df.num_ost)
for pkey in dprior[skey]:
prior[pkey]=dprior[skey][pkey]
for pkey in prior:
bkey = utf.get_basekey(pkey)
#if not(pkey[3:] in model.a+model.b+model.dE) and\
# not(pkey[4:] in model.a+model.b+model.dE):
if not(bkey[1] in model.a+model.b+model.dE):
continue
if bkey[0] == 'log':
prior[pkey] = gv.log(prior[pkey])
elif bkey[0] == 'sqrt':
prior[pkey] = gv.sqrt(prior[pkey])
return prior
def make_prior_3pt(models,prior_dict=None,nst=-1,ost=-1,n3st=-1,o3st=-1,do_amp_prior=True):
prior = gv.BufferDict()
for model in models:
skey=model.all_datatags[0]
if prior_dict is None: ## -- take default from defines
if (nst > -1 and ost > -1):
dprior = utf.get_prior_dict(df.define_prior_3pt,
df.define_prior_3pt['nkey'],df.define_prior_3pt['okey'],nst,ost,
df.define_prior_3pt['vkey'],n3st,o3st,
do_v_symm=df.do_v_symmetric,do_amp_prior=df.do_amp_prior)
else:
dprior = utf.get_prior_dict(df.define_prior_3pt,
df.define_prior_3pt['nkey'],df.define_prior_3pt['okey'],df.num_nst,df.num_ost,
df.define_prior_3pt['vkey'],df.num_nst_3pt,df.num_ost_3pt,
do_v_symm=df.do_v_symmetric,do_amp_prior=df.do_amp_prior)
for pkey in dprior[skey]:
prior[pkey]=dprior[skey][pkey]
## -- logarithmize the logarithmic coefficients
for pkey in prior:
## -- don't do logs unless they are in the current model, else double counting
bkey = utf.get_basekey(pkey)
if not(bkey[1] in model.a+model.b+model.dEa+model.dEb):
continue
if bkey[0] == 'log':
#print pkey,prior[pkey]
prior[pkey] = gv.log(prior[pkey])
elif bkey[0] == 'sqrt':
prior[pkey] = gv.sqrt(prior[pkey])
else: ## -- allow passing of new prior, for chained operations
if (nst > -1 and ost > -1):
dprior = utf.get_prior_dict(prior_dict,
prior_dict['nkey'],prior_dict['okey'],nst,ost,prior_dict['vkey'],n3st,o3st,df.do_v_symmetric)
else:
dprior = utf.get_prior_dict(prior_dict,
prior_dict['nkey'],prior_dict['okey'],df.num_nst,df.num_ost,
prior_dict['vkey'],df.num_nst_3pt,df.num_ost_3pt,df.do_v_symmetric)
for pkey in dprior[skey]:
prior[pkey]=dprior[skey][pkey]
for pkey in prior:
bkey = utf.get_basekey(pkey)
if not(bkey[1] in model.a+model.b+model.dEa+model.dEb):
continue
if bkey[0] == 'log':
prior[pkey] = gv.log(prior[pkey])
elif bkey[0] == 'sqrt':
prior[pkey] = gv.sqrt(prior[pkey])
return prior
def safesqrt(x, verbose=False):
"""
Takes in gvar x, returns sqrt(x). If x not suitable for sqrt, returns sqrt(1.0(9)).
"""
try:
sqrtx = sqrt(x)
except ZeroDivisionError:
if verbose:
print(
'CorrBayes.safesqrt WARNING: invalid argument for sqrt - replacing with sqrt(1.0(9))'
)
return sqrt(gv.gvar(1.0, 0.9))
else:
return sqrtx
def get_results(Fit,thpts):
p = gv.load(Fit['filename'],method='pickle')
if 'Hsfilename' in Fit:
pHs = gv.load(Fit['Hsfilename'],method='pickle')
# We should only need goldstone masses and energies here
Fit['M_parent_m{0}'.format(Fit['m_c'])] = p['dE:{0}'.format(Fit['parent-Tag'].format(Fit['m_s'],Fit['m_c']))][0]
for mass in Fit['masses']:
Fit['M_parent_m{0}'.format(mass)] = p['dE:{0}'.format(Fit['parent-Tag'].format(Fit['m_s'],mass))][0]
if Fit['conf'] == 'F':
print('F',p['dE:{0}'.format('meson-G5T.m{0}_m{1}'.format(Fit['m_s'],mass))][0]-p['dE:{0}'.format(Fit['parent-Tag'].format(Fit['m_s'],mass))][0])
if Fit['conf'] == 'SF':
print('SF',p['dE:{0}'.format('meson2G5T.m{0}_m{1}'.format(Fit['m_s'],mass))][0]-p['dE:{0}'.format(Fit['parent-Tag'].format(Fit['m_s'],mass))][0])
if Fit['conf'] == 'UF':
print('UF',p['dE:{0}'.format('Bs_G5T-G5T_m{1}'.format(Fit['m_s'],mass))][0]-p['dE:{0}'.format(Fit['parent-Tag'].format(Fit['m_s'],mass))][0])
if 'Hsfilename' in Fit:
mass2 = mass
if mass == '0.45':
mass2 = '0.450'
Fit['MHs_parent_m{0}'.format(mass)] = pHs['dE:{0}'.format(Fit['Hsparent-Tag'].format(Fit['m_s'],mass2))][0]
Fit['M_daughter'] = p['dE:{0}'.format(Fit['daughter-Tag'][0])][0]
for t,twist in enumerate(Fit['twists']):
#Fit is the actual measured value, theory is obtained from the momentum
Fit['E_daughter_tw{0}_fit'.format(twist)] = p['dE:{0}'.format(Fit['daughter-Tag'][t])][0]
Fit['E_daughter_tw{0}_theory'.format(twist)] = gv.sqrt(Fit['M_daughter']**2+Fit['momenta'][t]**2)
for m, mass in enumerate(Fit['masses']):
for thpt in thpts[Fit['conf']]:
if twist != '0' or thpt != 'T': #This second 2 is should be in the data remember for BK
Fit['{0}Vnn_m{1}_tw{2}'.format(thpt,mass,twist)] = p['{0}Vnn_m{1}_tw{2}'.format(thpt,mass,twist)][0][0]
Fit['{0}_m{1}_tw{2}'.format(thpt,mass,twist)] = 2 * 2 * Fit['Zdisc'][m] * gv.sqrt(Fit['M_parent_m{0}'.format(mass)]*Fit['E_daughter_tw{0}_theory'.format(twist)]) * Fit['{0}Vnn_m{1}_tw{2}'.format(thpt,mass,twist)]
#check zdisc is correctly implemented here
return()
def effective_amp(c):
"""
Returns effective amplitude
a_{eff}(t) = sqrt( C(t) * exp( m_{eff}(t) * t ) )
"""
m = effective_mass(c)
return [sqrt(c[t] * exp(m[t] * t)) for t in range(len(c) - 1)]
def line_shift(velocity, line, wavenumber=False, relativistic=True):
"""Return the line shift (in nm or in cm-1) given its relativistic velocity
beta = v / c
gamma = sqrt((1 + beta) / (1 - beta))
lambda - lambda_0 = lambda_0 * (gamma - 1)
:param velocity: Line velocity in km.s-1
:param line: Wavelength/wavenumber of the line. Must be in cm-1 if
wavenumber is True, must be in nm otherwise.
:param wavenumber: (Optional) If True the result is returned in cm-1,
else it is returned in nm.
"""
if wavenumber:
line = cm12nm(line)
beta = velocity / orb.constants.LIGHT_VEL_KMS
if relativistic:
gamma = gvar.sqrt((1. + beta) / (1. - beta))
shift = line * (gamma - 1.)
else:
shift = line * beta
if wavenumber:
shift = nm2cm1(line + shift) - nm2cm1(line)
return shift
def make_beta_delta_BsEtas(Fits,t_0,Nijk,Npow,addrho,p,fpf0same,Del,MH_s):
#an = make_an_BsEtas(n,Nijk,addrho,p,tag,Fit,alat,mass,amh,f0fpsame)
z0 = make_z(0,t_0,MH_s,Metasphys).mean
zHsstar = make_z(((make_MHsstar(MH_s))**2).mean,t_0,MH_s,Metasphys).mean
Fit = Fits[0]
mass = Fit['masses'][0]
fit = Fit['conf']
fp0 = make_fp_BsEtas(Nijk,Npow,addrho,p,Fit,0,0,z0,mass,fpf0same,0)
fpHsstar = make_an_BsEtas(0,Nijk,addrho,p,'p',Fit,0,mass,0,fpf0same)
f00 = make_f0_BsEtas(Nijk,Npow,addrho,p,Fit,0,0,z0,mass,fpf0same,0)
t_plus = (MH_s + Metasphys)**2
zprime = (-1) / (2* (t_plus + gv.sqrt( t_plus * (t_plus - t_0) ) ) )
f0prime = (f00/p['MHs0_{0}_m{1}'.format(fit,mass)]**2)
fpprime = (fp0/p['MHsstar_{0}_m{1}'.format(fit,mass)]**2)
for n in range(1,Npow):
f0prime += zprime * n * make_an_BsEtas(n,Nijk,addrho,p,'0',Fit,0,mass,0,fpf0same) * z0**(n-1)
fpprime += zprime * n * make_an_BsEtas(n,Nijk,addrho,p,'p',Fit,0,mass,0,fpf0same) * ( z0**(n-1) - (-1)**(n-Npow) * z0**(Npow-1))
fpHsstar += make_an_BsEtas(n,Nijk,addrho,p,'p',Fit,0,mass,0,fpf0same) * ( zHsstar**(n) - (n/Npow) * (-1)**(n-Npow) * zHsstar**(Npow))
delta = 1 - ((MH_s**2-Metasphys**2)/fp0) * (fpprime-f0prime)
invbeta = ((MH_s**2-Metasphys**2)/fp0) * f0prime
alpha = 1 - (fp0/fpHsstar)
if MH_s == MBsphys.mean:
print('delta at MBs = ',delta)
print('alpha at MBs = ',alpha)
print('beta at MBs = ',1/invbeta)
if MH_s == MDsphys.mean:
print('delta at MDs = ',delta)
print('alpha at MDs = ',alpha)
print('beta at MDs = ',1/invbeta)
return(alpha,delta,invbeta)
def apply_fn_op(key,val):
bkey = get_basekey(key)
if bkey[0] is None:
return val
else:
if bkey[0] == 'log':
return gv.log(val)
elif bkey[0] == 'sqrt':
return gv.sqrt(val)
else:
raise KeyError("Unknown function operation:",bkey[0])
def get_results(Fit):
Vnn = collections.OrderedDict()
p = gv.load(Fit['filename'])
for i in range(len(Fit['twists'])):
Fit['M_KG_tw{0}'.format(Fit['twists'][i])] = gv.sqrt(
p['dE:{0}'.format(Fit['KGtw{0}'.format(Fit['twists'][i])])][0]**2 -
Fit['momenta'][i]**2)
Fit['E_KG_tw{0}'.format(Fit['twists'][i])] = p['dE:{0}'.format(
Fit['KGtw{0}'.format(Fit['twists'][i])])][0]
Fit['M_KNG_tw{0}'.format(
Fit['twists'][i])] = gv.sqrt(p['dE:{0}'.format(Fit[
'KNGtw{0}'.format(Fit['twists'][i])])][0]**2 -
Fit['momenta'][i]**2)
Fit['E_KNG_tw{0}'.format(Fit['twists'][i])] = p['dE:{0}'.format(
Fit['KNGtw{0}'.format(Fit['twists'][i])])][0]
for mass in Fit['masses']:
Fit['M_BG_m{0}'.format(mass)] = p['dE:{0}'.format(
Fit['BGm{0}'.format(mass)])][0]
Fit['M_BGo_m{0}'.format(mass)] = p['dE:o{0}'.format(
Fit['BGm{0}'.format(mass)])][0]
Fit['M_BNG_m{0}'.format(mass)] = p['dE:{0}'.format(
Fit['BNGm{0}'.format(mass)])][0]
Fit['M_BNGo_m{0}'.format(mass)] = p['dE:o{0}'.format(
Fit['BNGm{0}'.format(mass)])][0]
#print(gv.evalcorr([Fit['M_NG_m{0}'.format(mass)],Fit['M_G_m{0}'.format(mass)]]))
for twist in Fit['twists']:
if 'SVnn_m{0}_tw{1}'.format(mass, twist) in p:
Fit['Sm{0}_tw{1}'.format(mass, twist)] = 2 * 2 * gv.sqrt(
Fit['E_KG_tw{0}'.format(twist)] *
Fit['M_BG_m{0}'.format(mass)]) * p[
'SVnn_m{0}_tw{1}'.format(mass, twist)][0][0]
Fit['Vm{0}_tw{1}'.format(mass, twist)] = 2 * 2 * gv.sqrt(
Fit['E_KG_tw{0}'.format(twist)] *
Fit['M_BG_m{0}'.format(mass)]) * p[
'VVnn_m{0}_tw{1}'.format(mass, twist)][0][0]
Fit['Tm{0}_tw{1}'.format(mass, twist)] = 2 * 2 * gv.sqrt(
Fit['E_KG_tw{0}'.format(twist)] * Fit['M_BG_m{0}'.format(
mass)]) * p['TVnn_m{0}_tw{1}'.format(
mass, twist)][0][0] ### Is this correct???
return ()
def apod2width(apod):
"""Return the width of the gaussian window for a given apodization level.
:param apod: Apodization level (must be >= 1.)
The apodization level is the broadening factor of the line (an
apodization level of 2 mean that the line fwhm will be 2 times
wider).
"""
if apod < 1.:
raise Exception('Apodization level (broadening factor) must be > 1')
return apod - 1. + (gvar.erf(math.pi / 2. * gvar.sqrt(apod - 1.)) *
orb.constants.FWHM_SINC_COEFF)
def gaussian1d(x,h,a,dx,fwhm):
"""Return a 1D gaussian given a set of parameters.
:param x: Array giving the positions where the gaussian is evaluated
:param h: Height
:param a: Amplitude
:param dx: Position of the center
:param fwhm: FWHM, :math:`\\text{FWHM} = \\text{Width} \\times 2 \\sqrt{2 \\ln 2}`
"""
if not isinstance(x, np.ndarray):
x = np.array(x)
w = fwhm / (2. * gvar.sqrt(2. * gvar.log(2.)))
return h + a * gvar.exp(-(x - dx)**2. / (2. * w**2.))
def gaussian1d_complex(x,h,a,dx,fwhm):
"""Return a 1D gaussian given a set of parameters.
:param x: Array giving the positions where the gaussian is evaluated
:param h: Height
:param a: Amplitude
:param dx: Position of the center
:param fwhm: FWHM, :math:`\\text{FWHM} = \\text{Width} \\times 2 \\sqrt{2 \\ln 2}`
"""
if not isinstance(x, np.ndarray):
x = np.array(x)
w = fwhm / (2. * gvar.sqrt(2. * gvar.log(2.)))
b = (x - dx) / (np.sqrt(2) * w)
return (h + a * gvar.exp(-b**2.), h + a * 2 / np.sqrt(np.pi) * special.dawsn(b))
def amp_superav(c):
"""
Takes superaverage of correlator c, then effective amplitude,
then applies a correction factor sqrt( 2 / 1 + e^{-m_{eff}(t)} ).
This is because taking the superaverage shifts the effective amplitude of a correlator,
this correction cancels that shift.
"""
c = superav(c)
m = effective_mass(c)
return [
sqrt(c[t] * exp(m[t] * t) * 2 / (1 + exp(-m[t])))
for t in range(len(c) - 1)
]
def sincgauss1d_complex(x, h, a, dx, fwhm, sigma):
"""The "complex" version of the sincgauss (dawson definition).
This is the real sinc*gauss function when ones wants to fit both the real
part and the imaginary part of the spectrum.
:param x: 1D array of float64 giving the positions where the
function is evaluated
:param h: Height
:param a: Amplitude
:param dx: Position of the center
:param fwhm: FWHM of the sinc
:param sigma: Sigma of the gaussian.
"""
if np.size(sigma) > 1:
if np.any(sigma != sigma[0]):
raise Exception('Only one value of sigma can be passed')
else:
sigma = sigma[0]
sigma = np.fabs(sigma)
fwhm = np.fabs(fwhm)
broadening_ratio = np.fabs(sigma / fwhm)
max_broadening_ratio = gvar.mean(broadening_ratio) + gvar.sdev(
broadening_ratio)
if broadening_ratio < 1e-2:
return sinc1d_complex(x, h, a, dx, fwhm)
if np.isclose(gvar.mean(sigma), 0.):
return sinc1d_complex(x, h, a, dx, fwhm)
if max_broadening_ratio > 7:
return gaussian1d_complex(x, h, a, dx,
sigma * (2. * gvar.sqrt(2. * gvar.log(2.))))
width = gvar.fabs(fwhm) / orb.constants.FWHM_SINC_COEFF
width /= np.pi ###
a_ = sigma / np.sqrt(2) / width
b_ = ((x - dx) / np.sqrt(2) / sigma)
if b_.dtype == np.float128: b_ = b_.astype(float)
sg1c = orb.cgvar.sincgauss1d_complex(a_, b_)
return (h + a * sg1c[0], h + a * sg1c[1])
def sincgauss1d(x, h, a, dx, fwhm, sigma):
"""Return a 1D sinc convoluted with a gaussian of parameter sigma.
If sigma == 0 returns a pure sinc.
:param x: 1D array of float64 giving the positions where the
sinc is evaluated
:param h: Height
:param a: Amplitude
:param dx: Position of the center
:param fwhm: FWHM of the sinc
:param sigma: Sigma of the gaussian.
"""
if np.size(sigma) > 1:
if np.any(sigma != sigma[0]):
raise Exception('Only one value of sigma can be passed')
else:
sigma = sigma[0]
sigma = np.fabs(sigma)
fwhm = np.fabs(fwhm)
broadening_ratio = np.fabs(sigma / fwhm)
max_broadening_ratio = gvar.mean(broadening_ratio) + gvar.sdev(
broadening_ratio)
if broadening_ratio < 1e-2:
return sinc1d(x, h, a, dx, fwhm)
if np.isclose(gvar.mean(sigma), 0.):
return sinc1d(x, h, a, dx, fwhm)
if max_broadening_ratio > 7:
return gaussian1d(x, h, a, dx,
sigma * (2. * gvar.sqrt(2. * gvar.log(2.))))
width = gvar.fabs(fwhm) / orb.constants.FWHM_SINC_COEFF
width /= np.pi ###
a_ = sigma / math.sqrt(2) / width
b_ = (x - dx) / math.sqrt(2) / sigma
return h + a * orb.cgvar.sincgauss1d(a_, b_)
def line_shift(velocity, line, wavenumber=False):
"""Return the line shift given its velocity in nm or in cm-1.
beta = v / c
gamma = sqrt((1 + beta) / (1 - beta))
lambda - lambda_0 = lambda_0 * (gamma - 1)
:param velocity: Line velocity in km.s-1
:param line: Wavelength/wavenumber of the line. Must be in cm-1 if
wavenumber is True, must be in nm otherwise.
:param wavenumber: (Optional) If True the result is returned in cm-1,
else it is returned in nm.
"""
beta = velocity / orb.constants.LIGHT_VEL_KMS
gamma = gvar.sqrt((1. + beta) / (1. - beta))
if wavenumber:
shift = line * (1. / gamma - 1.)
else:
shift = line * (gamma - 1.)
return shift
def get_sigma_str(key,fit,prior,j,do_unicode=True):
## -- list the sigma away from prior
#
try:
## -- requesting log, log prior
## or requesting linear, linear prior
sig=int(np.abs(np.trunc(\
(fit.p[key][j].mean-prior[key][j].mean)\
/(prior[key][j]).sdev)))
except KeyError:
## -- requested linear, have log or sqrt prior
try:
sig=int(np.abs(np.trunc(\
(gv.log(fit.p[key][j].mean)-prior['log('+key+')'][j].mean)\
/(prior['log('+key+')'][j]).sdev)))
except KeyError:
sig=int(np.abs(np.trunc(\
(gv.sqrt(fit.p[key][j].mean)-prior['sqrt('+key+')'][j].mean)\
/(prior['sqrt('+key+')'][j]).sdev)))
except TypeError:
## -- Vnn, Vno, etc...
#print key,j
sig=int(np.abs(np.trunc(\
(fit.p[key][j[0]][j[1]].mean-prior[key][j[0]][j[1]].mean)\
/(prior[key][j[0]][j[1]]).sdev)))
if do_unicode:
if sig > 0:
sigstr=str(sig)+u'\u03C3' # unicode for sigma (cannot be saved to string)
else:
sigstr=' '
else:
if sig > 0:
sigstr=str(sig)+'sig'
else:
sigstr=' '
return sigstr
def gaussian_pdf(x, f=1.):
xmean = gv.mean(x)
xvar = gv.var(x) * f ** 2
return gv.exp(-xmean ** 2 / 2. /xvar) / gv.sqrt(2 * np.pi * xvar)
import gvar as gv import util_funcs as utf ## -- HISQ a=0.15 l3248 physical ## -- prior mass splittings xnpi=gv.sqrt(30) ## -- N pi state suppression factor (EFT gives 1/30.) ## -- PDG inputs ## -- S8' ngrd_s8p=gv.gvar(0.94,0.1) ## -- even ground state (PDG \Delta mass) nn1440_s8p=gv.gvar(1.10,0.1) ## -- even radial state (PDG Nucleon(1440) state) nn1680_s8p=gv.gvar(1.28,0.1) ## -- even radial state (PDG Nucleon(1680) state) on1520_s8p=gv.gvar(1.16,0.1) ## -- odd nucleon state (PDG nucleon(1520) state) on1535_s8p=gv.gvar(1.17,0.1) ## -- odd nucleon state (PDG nucleon(1535) state) on1650_s8p=gv.gvar(1.26,0.1) ## -- odd nucleon state (PDG delta(1675) state) on1675_s8p=gv.gvar(1.28,0.1) ## -- odd nucleon state (PDG delta(1675) state) nd1600_s8p=gv.gvar(1.22,0.1) ## -- odd delta state (PDG delta(1600) state) nd1620_s8p=gv.gvar(1.23,0.1) ## -- odd delta state (PDG delta(1600) state) nd1680_s8p=gv.gvar(1.28,0.1) ## -- odd delta state (PDG delta(1680) state) od1620_s8p=gv.gvar(1.23,0.1) ## -- odd delta state (PDG delta(1620) state) (UNUSED) od1700_s8p=gv.gvar(1.29,0.1) ## -- odd delta state (PDG delta(1700) state) delr_s8p=gv.gvar(0.30,0.1) ## -- radial splitting (PDG radial state) ## -- S8 ngrd_s8=gv.gvar(0.71,0.1) ## -- even ground state (PDG Nucleon mass) ogrd_s8=gv.gvar(0.97,0.1) ## -- odd ground state (N+pi, 1 momentum unit) onpi100t_s8=gv.gvar(1.01,0.1) ## -- odd ground state (N+pi, 1 momentum unit+taste) #onpi100t_s8=gv.gvar(1.03,0.1) ## -- odd ground state (N+pi, 1 momentum unit+taste) onpi110_s8=gv.gvar(1.07,0.1) ## -- odd ground state (N+pi, 2 momentum unit) #onpi110_s8=gv.gvar(1.07,0.03) ## -- odd ground state (N+pi, 2 momentum unit) nn1440_s8=nn1440_s8p ## -- even radial state (PDG Nucleon(1440) state) nn1680_s8=nn1680_s8p ## -- even radial state (PDG Nucleon(1680) state)
def gaussian_pdf(x, f=1.):
xmean = gv.mean(x)
xvar = gv.var(x) * f**2
return gv.exp(-xmean**2 / 2. / xvar) / gv.sqrt(2 * np.pi * xvar)
def effective_mass(c):
"""
Returns effective mass of correlator "c"
m_{eff}(t) = log( C(t) / C(t+1) ).
"""
return [log(sqrt(c[t] / c[t + 1])**2) for t in range(len(c) - 1)]
def print_fit(self,par,outfile):
"""print the energy and amplitude results from a fit"""
fit = self.results
name = self.name
p = fit.p
self.chi2_cal()
#self.chi2_aug_part()
self.Q = conf_int(self.chi2/2, par.ncon, self.dof/2)
nexp = self.child.nexp
name = self.child.name
CdE = exp(p['log('+name+':dE)'])
CE = [sum(CdE[:i+1]) for i in range(nexp)]
a = exp(p['log('+name+':a)'])
if self.name != 'pimom0':
CdEo = exp(p['log('+name+':dEo)'])
CEo = [sum(CdEo[:i+1]) for i in range(nexp)]
ao = exp(p['log('+name+':ao)'])
name = self.parent.name
PdE = exp(p['log('+name+':dE)'])
PE = [sum(PdE[:i+1]) for i in range(nexp)]
b = exp(p['log('+name+':a)'])
if self.name != 'pimom0':
PdEo = exp(p['log('+name+':dEo)'])
PEo = [sum(PdEo[:i+1]) for i in range(nexp)]
bo = exp(p['log('+name+':ao)'])
# calculating f_0
# DOESN"T WORK FOR D TO K YET!!!!!!!!!
if self.child.name == 'pimom0':
mpi = CE[0]
else:
mpi = gvar(par.pi0,par.pi0_err)
if self.child.name == 'pimom0' or self.child.name == 'pimom2':
m_q = par.m_l
else:
m_q = par.m_s
Epi = CE[0]
mD = PE[0]
v = p['Vnn'][0][0]
self.f_0 = v*sqrt(Epi*mD)*(par.m_c-m_q)/(mD**2-mpi**2)
self.qsq = mpi**2+mD**2-2*mD*Epi
ofile = open(outfile,'a')
if self.dof != 0:
chi2dof = self.chi2/self.dof
else:
chi2dof = 99.9
if chi2dof > 99:
chi2dof = 99.9
pars = "{:s} & {:2d}-{:2d} & {:2d}-{:2d} & {:d}+{:d} & {:4.1f} & {:3d} & {:4.2f} & "
energies = "{:s} & {:<11s} & {:<11s} & "
form = "{:<12s} & {:<12s} \\\\ \n"
ofile.write( pars.format( self.name,self.tmin,self.child.tmax,self.tmax,self.parent.tmax,
nexp,nexp,chi2dof,self.dof,self.Q )
+energies.format( self.child.name,CE[0].fmt(ndecimal=4),a[0].fmt(ndecimal=4) )
+energies.format( self.parent.name,PE[0].fmt(ndecimal=4),b[0].fmt(ndecimal=4) )
+form.format( self.f_0.fmt(ndecimal=4), self.qsq.fmt(ndecimal=4) ) )
def main():
make_params()
for Fit in Fits:
print('Plot for', Fit['filename'])
get_results(Fit)
F_0 = collections.OrderedDict()
F_plus = collections.OrderedDict()
F_T = collections.OrderedDict()
qSq = collections.OrderedDict()
Z = collections.OrderedDict()
Sca = collections.OrderedDict()
Vec = collections.OrderedDict()
Ten = collections.OrderedDict()
for mass in Fit['masses']:
F_0[mass] = collections.OrderedDict()
F_plus[mass] = collections.OrderedDict()
F_T[mass] = collections.OrderedDict()
qSq[mass] = collections.OrderedDict()
Z[mass] = collections.OrderedDict()
Sca[mass] = collections.OrderedDict()
Vec[mass] = collections.OrderedDict()
Ten[mass] = collections.OrderedDict()
Z_v = (float(mass) -
float(Fit['m_s'])) * Fit['Sm{0}_tw0'.format(mass)] / (
(Fit['M_BG_m{0}'.format(mass)] - Fit['M_KG_tw0']) *
Fit['Vm{0}_tw0'.format(mass)])
plt.errorbar((Fit['a']**2).mean,
Z_v.mean,
xerr=(Fit['a']**2).sdev,
yerr=Z_v.sdev,
label=mass)
for twist in Fit['twists']:
if 'Sm{0}_tw{1}'.format(mass, twist) in Fit:
delta = (float(mass) - float(Fit['m_s'])) * (
Fit['M_BG_m{0}'.format(mass)] -
Fit['E_KG_tw{0}'.format(twist)])
qsq = Fit['M_BG_m{0}'.format(mass)]**2 + Fit[
'M_KG_tw{0}'.format(
twist)]**2 - 2 * Fit['M_BG_m{0}'.format(
mass)] * Fit['E_KG_tw{0}'.format(twist)]
t = (Fit['M_BG_m{0}'.format(mass)] +
Fit['M_KG_tw{0}'.format(twist)])**2
z = (gv.sqrt(t - qsq) - gv.sqrt(t)) / (gv.sqrt(t - qsq) +
gv.sqrt(t))
if FitNegQsq == False:
if qsq.mean >= 0:
F0 = (float(mass) - float(Fit['m_s'])) * (
1 / (Fit['M_BG_m{0}'.format(mass)]**2 -
Fit['M_KG_tw{0}'.format(twist)]**2)
) * Fit['Sm{0}_tw{1}'.format(mass, twist)]
FT = Fit['Sm{0}_tw{1}'.format(mass, twist)] * (
Fit['M_BG_m{0}'.format(mass)] +
Fit['M_KG_m{0}'.format(mass)]) / (
2 * Fit['M_BG_m{0}'.format(mass)] *
float(twist)
) # Have we used correct masses?
F_0[mass][twist] = F0
F_T[mass][twist] = FT
qSq[mass][twist] = qsq
Z[mass][twist] = z
Sca[mass][twist] = Fit['Sm{0}_tw{1}'.format(
mass, twist)]
Vec[mass][twist] = Fit['Vm{0}_tw{1}'.format(
mass, twist)]
Ten[mass][twist] = Fit['Tm{0}_tw{1}'.format(
mass, twist)]
A = Fit['M_BG_m{0}'.format(mass)] + Fit[
'E_KG_tw{0}'.format(twist)]
B = (Fit['M_BG_m{0}'.format(mass)]**2 -
Fit['M_KG_tw{0}'.format(twist)]**2) * (
Fit['M_BG_m{0}'.format(mass)] -
Fit['E_KG_tw{0}'.format(twist)]) / qsq
if twist != '0':
F_plus[mass][twist] = (1 / (A - B)) * (
Z_v * Fit['Vm{0}_tw{1}'.format(
mass, twist)] - B * F0)
elif FitNegQsq == True:
F0 = (float(mass) - float(Fit['m_s'])) * (
1 / (Fit['M_BG_m{0}'.format(mass)]**2 -
Fit['M_KG_tw{0}'.format(twist)]**2)
) * Fit['Sm{0}_tw{1}'.format(mass, twist)]
FT = Fit['Sm{0}_tw{1}'.format(mass, twist)] * (
Fit['M_BG_m{0}'.format(mass)] +
Fit['M_KG_m{0}'.format(mass)]
) / (2 * Fit['M_BG_m{0}'.format(mass)] * float(twist))
F_0[mass][twist] = F0
F_T[mass][twist] = FT
qSq[mass][twist] = qsq
Z[mass][twist] = z
Sca[mass][twist] = Fit['Sm{0}_tw{1}'.format(
mass, twist)]
Vec[mass][twist] = Fit['Vm{0}_tw{1}'.format(
mass, twist)]
Ten[mass][twist] = Fit['Tm{0}_tw{1}'.format(
mass, twist)]
A = Fit['M_BG_m{0}'.format(mass)] + Fit[
'E_KG_tw{0}'.format(twist)]
B = (Fit['M_BG_m{0}'.format(mass)]**2 -
Fit['M_KG_tw{0}'.format(twist)]**2) * (
Fit['M_BG_m{0}'.format(mass)] -
Fit['E_KG_tw{0}'.format(twist)]) / qsq
if twist != '0':
F_plus[mass][twist] = (1 / (A - B)) * (
Z_v * Fit['Vm{0}_tw{1}'.format(mass, twist)] -
B * F0)
plot_f(Fit, F_0, F_plus, qSq, Z, Sca, Vec)
plt.show()
return ()
def main():
make_params()
for Fit in Fits:
print('Plot for', Fit['filename'])
get_results(Fit)
F_0 = collections.OrderedDict()
F_plus = collections.OrderedDict()
qSq = collections.OrderedDict()
Z = collections.OrderedDict()
Sca = collections.OrderedDict()
Vec = collections.OrderedDict()
for mass in Fit['masses']:
F_0[mass] = []
F_plus[mass] = []
qSq[mass] = []
Z[mass] = []
Sca[mass] = []
Vec[mass] = []
Z_v = (float(mass) -
float(Fit['m_s'])) * Fit['Sm{0}_tw0'.format(mass)] / (
(Fit['M_G_m{0}'.format(mass)] - Fit['M_D_tw0']) *
Fit['Vm{0}_tw0'.format(mass)])
for twist in Fit['twists']:
if 'Sm{0}_tw{1}'.format(mass, twist) in Fit:
delta = (float(mass) - float(Fit['m_s'])) * (
Fit['M_G_m{0}'.format(mass)] -
Fit['E_D_tw{0}'.format(twist)])
qsq = (Fit['M_G_m{0}'.format(mass)]**2 +
Fit['M_D_tw{0}'.format(twist)]**2 -
2 * Fit['M_G_m{0}'.format(mass)] *
Fit['E_D_tw{0}'.format(twist)])
t = (Fit['M_G_m{0}'.format(mass)] +
Fit['M_D_tw{0}'.format(twist)])**2
z = (gv.sqrt(t - qsq) - gv.sqrt(t)) / (gv.sqrt(t - qsq) +
gv.sqrt(t))
if qsq.mean >= 0:
F0 = (float(mass) - float(Fit['m_s'])) * (
1 / (Fit['M_G_m{0}'.format(mass)]**2 -
Fit['M_D_tw{0}'.format(twist)]**2)
) * Fit['Sm{0}_tw{1}'.format(mass, twist)]
F_0[mass].append(F0)
if GeV == True:
qSq[mass].append(qsq / (Fit['a']**2))
else:
qSq[mass].append(qsq)
Z[mass].append(z)
Sca[mass].append(Fit['Sm{0}_tw{1}'.format(mass,
twist)])
Vec[mass].append(Fit['Vm{0}_tw{1}'.format(mass,
twist)])
A = Fit['M_G_m{0}'.format(mass)] + Fit[
'E_D_tw{0}'.format(twist)]
B = (Fit['M_G_m{0}'.format(mass)]**2 -
Fit['M_D_tw{0}'.format(twist)]**2) * (
Fit['M_G_m{0}'.format(mass)] -
Fit['E_D_tw{0}'.format(twist)]) / qsq
if twist != '0':
F_plus[mass].append(
(1 / (A - B)) *
(Z_v * Fit['Vm{0}_tw{1}'.format(mass, twist)] -
B * F0))
plot_f(Fit, F_0, F_plus, qSq, Z, Sca, Vec)
plt.show()
return ()
def make_z(qsq,t_0,M_parent,M_daughter):
t_plus = make_t_plus(M_parent,M_daughter)
z = (gv.sqrt(t_plus - qsq) - gv.sqrt(t_plus - t_0)) / (gv.sqrt(t_plus - qsq) + gv.sqrt(t_plus - t_0))
if z.mean ==0 and z.sdev ==0:
z = gv.gvar(0,1e-16)
return(z)
