def interpolate_Zs(dat1, dat2, mu):
"Nasty kludge to calculate step-scale denominator."
# NOTE this is incorrect since p, tw not defined...
result = dat1.__class__(dat1.mres, dat1.p, dat1.tw, None)
result.fourquark_Zs = {}
result.fourquark_sigmaJK = {}
for s in "gg", "gq", "qg", "qq":
# Zs
p1 = (dat1.mu, dat1.fourquark_Zs[s])
p2 = (dat2.mu, dat2.fourquark_Zs[s])
m, b = fits.line_fit_2pt(p1, p2)
result.fourquark_Zs[s] = m * mu + b
# sigmas
p1 = (dat1.mu, dat1.fourquark_sigmaJK[s])
p2 = (dat2.mu, dat2.fourquark_sigmaJK[s])
m, b = fits.line_fit_2pt(p1, p2)
result.fourquark_sigmaJK[s] = m * mu + b
return result
def main():
print "Un-pickling data...",
pickle_root = '/Users/atlytle/Dropbox/pycode/soton/pickle/SUSY_BK'
with open(pickle_root+'/IWf_008_pickle', 'r') as f:
data008 = pickle.load(f)
with open(pickle_root+'/IWf_006_pickle', 'r') as f:
data006 = pickle.load(f)
with open(pickle_root+'/IWf_004_pickle', 'r') as f:
data004 = pickle.load(f)
with open(pickle_root+'/IWc_02_pickle', 'r') as f:
data02 = pickle.load(f)
with open(pickle_root+'/IWc_01_pickle', 'r') as f:
data01 = pickle.load(f)
with open(pickle_root+'/IWc_005_pickle', 'r') as f:
data005 = pickle.load(f)
with open(pickle_root+'/IWc_chiral_pickle', 'r') as f:
data0c = pickle.load(f)
with open(pickle_root+'/IWf_chiral_pickle', 'r') as f:
data0f = pickle.load(f)
print "complete."
print "_________________24^3 Iwasaki NE data________________"
for x in -3, -2:
print '---------------------------------------------'
print '(ap)^2:', data02[x].apSq, ' mu^2:', (data02[x].mu)**2, '\n'
for d in data02[x], data01[x], data005[x]:
print '--- am = ', d.m, '---\n'
print 'Lambda_A:', d.Lambda_A, ' Lambda_V:', d.Lambda_V, '\n'
print 'Z^{-1} in (g, g) scheme:'
print d.Zinv, '\n+/-\n', d.Zinv_sigmaJK
print '\n\n'
print '--- Chiral result ---'
print data0c[x].Zinv, '\n+/-\n', data0c[x].Zinv_sigmaJK, '\n'
print 'Set forbidden elements to zero and invert:\n'
# Chiral Zs.
mask_invert = lambda Zinv: inv(Zinv*m.chiral_mask)
for x in -3, -2:
print '(ap)^2:', data02[x].apSq, ' mu^2:', (data02[x].mu)**2
Z = mask_invert(data0c[x].Zinv)
print 'Z chiral:\n', Z, '\n'
#JKsamples = map(mask_invert, data0c[x].Zinv_JK)
#print 'sigma:\n', m.JKsigma(Z, JKsamples)
#print data0c[x].Zinv_sigmaJK
# Interpolation results.
print 'Interpolation result at 3 GeV (linear interpolation):'
p1 = (data0c[-3].mu, inv(data0c[-3].Zinv*m.chiral_mask))
p2 = (data0c[-2].mu, inv(data0c[-2].Zinv*m.chiral_mask))
a, b = fits.line_fit_2pt(p1, p2) # y = a*x+b.
SMOM_result = a*3+b
print SMOM_result, '\n'
print 'MSbar result at 3 GeV:'
print np.dot(SMOM_to_MSbar, SMOM_result[:3,:3]), '\n'
print "_________________32^3 Iwasaki NE data________________"
for x in -4, -3:
print '---------------------------------------------'
print '(ap)^2:', data008[x].apSq, ' mu^2:', (data008[x].mu)**2, '\n'
for d in data008[x], data006[x], data004[x]:
print '--- am = ', d.m, '---\n'
print 'Lambda_A:', d.Lambda_A, ' Lambda_V:', d.Lambda_V, '\n'
print 'Z^{-1} in (g, g) scheme:'
print d.Zinv, '\n+/-\n', d.Zinv_sigmaJK
print '\n\n'
print '--- Chiral result ---'
print data0f[x].Zinv, '\n+/-\n', data0f[x].Zinv_sigmaJK
print 'Set forbidden elements to zero and invert:\n'
# Chiral Zs.
for x in -4, -3:
print '(ap)^2:', data008[x].apSq, ' mu^2:', (data008[x].mu)**2
print 'Z chiral:\n', inv(data0f[x].Zinv*m.chiral_mask), '\n'
# Interpolation results.
print 'Interpolation result at 3 GeV (linear interpolation):'
p1 = (data0f[-4].mu, inv(data0f[-4].Zinv*m.chiral_mask))
p2 = (data0f[-3].mu, inv(data0f[-3].Zinv*m.chiral_mask))
a, b = fits.line_fit_2pt(p1, p2) # y = a*x+b.
SMOM_result = a*3+b
print SMOM_result, '\n'
print 'MSbar result at 3 GeV:'
print np.dot(SMOM_to_MSbar, SMOM_result[:3,:3]), '\n'
def main():
print "Loading 24^3 IW exceptional data...",
pickle_root = '/Users/atlytle/Dropbox/pycode/soton/pickle/'\
'IW_exceptional_24cube/'
with open(pickle_root +'IW_exceptional_24cube_03.pkl', 'r') as f:
data_03 = pickle.load(f)
with open(pickle_root +'IW_exceptional_24cube_02.pkl', 'r') as f:
data_02 = pickle.load(f)
with open(pickle_root +'IW_exceptional_24cube_01.pkl', 'r') as f:
data_01 = pickle.load(f)
with open(pickle_root +'IW_exceptional_24cube_005.pkl', 'r') as f:
data_005 = pickle.load(f)
print "complete."
print "Loading 32^3 IW exceptional data...",
root = '/Users/atlytle/Dropbox/pycode/soton/pickle/IW_exceptional'
with open(root+'/IWf_exceptional_004.pkl', 'r') as f:
data004 = pickle.load(f)
with open(root+'/IWf_exceptional_006.pkl', 'r') as f:
data006 = pickle.load(f)
with open(root+'/IWf_exceptional_008.pkl', 'r') as f:
data008 = pickle.load(f)
print "complete."
print "Performing pole subtractions...",
map(pole_fits.pole_subtract_Data2, data_005, data_01, data_02)
map(pole_fits.pole_subtract_Data2, data004, data006, data008)
print "complete."
data_0c = map(fits.line_fit_Data_e, data_005, data_01, data_02)
data_0 = map(fits.line_fit_Data_e, data004, data006, data008)
print "______________24^3 IW Exceptional data________________"
for x in -2, -1,:
print '-------------------------------------------'
print '(ap)^2:', data_02[x].apSq, ' mu^2:', (data_02[x].mu)**2, '\n'
for d in data_02[x], data_01[x], data_005[x]:
print '--- am = ', d.m, '---\n'
print 'Lambda_A:', d.Lambda_A, ' Lambda_V:', d.Lambda_V, '\n'
print 'Z^{-1} in RI scheme (naive):\n'
print d.Zinv, '\n+/-\n', d.Zinv_sigmaJK, '\n'
print 'Z^{-1} in RI scheme after subtraction:\n'
print d.Zinv_sub, '\n+/-\n', d.Zinv_sub_sigma, '\n'
print 'Set forbidden elements to zero and invert:\n'
# Chiral Zs.
for x in -2, -1:
print '(ap)^2:', data_02[x].apSq, ' mu^2:', (data_02[x].mu)**2
print 'Z chiral:\n', inv(data_0c[x].Zinv_sub*chiral_mask), '\n'
# Interpolation results.
print 'Interpolation result at 3 GeV (linear interpolation):'
p1 = (data_0c[-2].mu, inv(data_0c[-2].Zinv_sub*chiral_mask))
p2 = (data_0c[-1].mu, inv(data_0c[-1].Zinv_sub*chiral_mask))
a, b = fits.line_fit_2pt(p1, p2) # y = a*x+b.
MOM_result = a*3+b
print MOM_result, '\n'
print 'MSbar result at 3 GeV:'
print np.dot(MOM_to_MSbar, MOM_result), '\n'
# print "______________32^3 IW Exceptional data________________"
# for x in -3, -2:
# print '-------------------------------------------'
# print '(ap)^2:', data008[x].apSq, ' mu^2:', (data008[x].mu)**2, '\n'
# for d in data008[x], data006[x], data004[x]:
# print '--- am = ', d.m, '---\n'
# print 'Lambda_A:', d.Lambda_A, ' Lambda_V:', d.Lambda_V, '\n'
# print 'Z^{-1} in RI scheme (naive):\n'
# print d.Zinv, '\n+/-\n', d.Zinv_sigmaJK, '\n'
# print 'Z^{-1} in RI scheme after subtraction:\n'
# print d.Zinv_sub, '\n+/-\n', d.Zinv_sub_sigma, '\n'
#
# print 'Pole coefficients as determined from linear fit method.'
# print data004[x].polefit_params.a, ' +/-', data004[x].polefit_params.sig_a, '\n\n'
# for d in data_0[x],:
# print '--- Chiral limit ---'
# print d.Zinv_sub, '\n'
#
# for i in range(5):
# for j in range(5):
# print 'i =', i+1, ' j=', j+1
# for d in data008[-2], data006[-2], data004[-2], data_0[-2]:
# print d.Zinv_sub[i,j], ' +/-', d.Zinv_sub_sigma[i,j]
# print ''
#
## print inv(d.Zinv_sub*chiral_mask), '\n'
## # Cobble together bits of Zinv and return Z for each m.
## for x in data004, data006, data008:
## map(m.Zsub, x)
## # Linear extrapolate results.
## data_sub = map(fits.line_fit_Data_e, data004, data006, data008)
## for x in -2,:
## print "Subtracted Z in MOM scheme:"
## print '(ap)^2:', data008[x].apSq, ' mu^2:', (data008[x].mu)**2, '\n'
## print data_sub[x].Zsub, '\n+/-'
## print data_sub[x].Zsub_sigmaJK, '\n'
## print "Subtracted Z in MSbar scheme:"
## print np.dot(MOM_to_MSbar, data_sub[x].Zsub), '\n'
## print 'Interpolation result at 3 GeV (linear interpolation):'
## p1 = (data_sub[-3].mu, data_sub[-3].Zsub)
## p2 = (data_sub[-2].mu, data_sub[-2].Zsub)
## a, b = fits.line_fit_2pt(p1, p2) # y = a*x + b.
## MOM_result = a*3+b
## print MOM_result, '\n+/-'
## print data_sub[-3].Zsub_sigmaJK, '\n' # Conservative error.
#
# print 'Set forbidden elements to zero and invert:\n'
# # Chiral Zs.
# for x in -3, -2:
# print '(ap)^2:', data008[x].apSq, ' mu^2:', (data008[x].mu)**2
# print 'Z chiral:\n', inv(data_0[x].Zinv_sub*chiral_mask), '\n'
# # Interpolation results.
# print 'Interpolation result at 3 GeV (linear interpolation):'
# p1 = (data_0[-3].mu, inv(data_0[-3].Zinv_sub*chiral_mask))
# p2 = (data_0[-2].mu, inv(data_0[-2].Zinv_sub*chiral_mask))
# a, b = fits.line_fit_2pt(p1, p2) # y = a*x+b.
# MOM_result = a*3+b
# print MOM_result, '\n'
# print 'MSbar result at 3 GeV:'
# print np.dot(MOM_to_MSbar, MOM_result), '\n'
return 0