def gen_statistical_uncertainties(wdir, dirname, iset, iF2, iFL, iF3, data,
lum):
"""
ATTENTION: sigtot is in 1/GeV^2
lum must be in GeV^2
"""
import lhapdf
stf = lhapdf.mkPDF(dirname, iset)
global par
par = AUX()
for bin in data:
N = bin['sigtot'] * lum
ps = (bin['xmax'] - bin['xmin']) * (bin['Q2max'] - bin['Q2min'])
bin['<value>'] = N / ps / lum
bin['stat_u'] = np.sqrt(N) / ps / lum
bin['xmid'] = 0.5 * (bin['xmax'] + bin['xmin'])
bin['Q2mid'] = 0.5 * (bin['Q2max'] + bin['Q2min'])
x = bin['<x>']
Q2 = bin['<Q2>']
F2 = stf.xfxQ2(iF2, x, Q2)
FL = stf.xfxQ2(iFL, x, Q2)
F3 = 0
rs = bin['rs']
sign = bin['sign']
bin['obs'] = 'dsig/dxdQ2'
bin['value'] = get_sigma_dxdQ2(rs, x, Q2, F2, FL, F3, sign)
bin['x'] = x
bin['Q2'] = Q2
bin['y'] = Q2 / x / (rs**2 - par.M2)
return data
def get_red_sigtot(wdir, dirname, iset, iF2, iFL, iF3, data, current):
import lhapdf
global par
par = AUX()
stf = lhapdf.mkPDF(dirname, iset)
for bin in data:
xmin = bin['xmin']
xmax = bin['xmax']
q2min = bin['Q2min']
q2max = bin['Q2max']
sign = bin['sign']
rs = bin['rs']
bin['sigtot'] = red_integrate(stf, sign, rs, xmin, xmax, q2min, q2max,
iF2, iFL, iF3, 0, current)
bin['<x>'] = integrate(stf, sign, rs, xmin, xmax, q2min, q2max, iF2,
iFL, iF3, 1) / bin['sigtot']
bin['<Q2>'] = integrate(stf, sign, rs, xmin, xmax, q2min, q2max, iF2,
iFL, iF3, 2) / bin['sigtot']
return data
F = conf['aux'].p2n(F)
return _get_FX(x, z, Q2, pT, target, hadron, F, D)
elif target == 'd':
return 0.5 * (get_FX(x, z, Q2, pT, 'p', hadron) +
get_FX(x, z, Q2, pT, 'n', hadron))
if __name__ == '__main__':
from qcdlib.ff1 import FF as FF1
from qcdlib.pdf1 import PDF as PDF1
conf['aux'] = AUX()
conf['Htildepi'] = FF1('pi')
conf['Htildek'] = FF1('k')
conf['transversity'] = PDF1()
x = 0.25
z = 0.5
Q2 = 2.4
pT = 0.3
print get_FX(x, z, Q2, pT, 'p', 'pi+')
print get_FX(x, z, Q2, pT, 'p', 'pi-')
print get_FX(x, z, Q2, pT, 'p', 'pi0')
print get_FX(x, z, Q2, pT, 'n', 'pi+')
print get_FX(x, z, Q2, pT, 'n', 'pi-')
def all_errors(wdir, tar, est, value, lum):
conf['aux'] = AUX()
data = pd.read_excel('%s/sim/all-%s-%s.xlsx' % (wdir, tar, est),
index=False)
data = data.to_dict(orient='list')
target = data['target'][0]
l = len(data['value'])
X = np.array(data['X'])
Q2 = np.array(data['Q2'])
Xup = np.array(data['Xup'])
Xdo = np.array(data['Xdo'])
Q2up = np.array(data['Q2up'])
Q2do = np.array(data['Q2do'])
dx = Xup - Xdo
dQ2 = Q2up - Q2do
bins = dx * dQ2
pole = data['pole'][0]
polh = data['polh'][0]
RS = data['RS'][0]
S = RS**2
M2 = conf['aux'].M2
if tar == 'd': M2 = 4 * M2
#--luminosity
lum = convert_lum(lum)
GF = conf['aux'].GF
#--get structure functions
resman = RESMAN(parallel=False, datasets=False)
parman = resman.parman
resman.setup_idis()
resman.setup_pidis()
pidis = resman.pidis_thy
idis = resman.idis_thy
pidis.data[tar]['g1'] = np.zeros(pidis.X.size)
pidis.data[tar]['g2'] = np.zeros(pidis.X.size)
idis.data[tar]['F2'] = np.zeros(idis.X.size)
idis.data[tar]['FL'] = np.zeros(idis.X.size)
idis = resman.idis_thy
pidis = resman.pidis_thy
idis._update()
pidis._update()
g1 = lambda x, q2: pidis.get_stf(x, q2, stf='g1', tar=tar)
g2 = lambda x, q2: pidis.get_stf(x, q2, stf='g2', tar=tar)
F2 = lambda x, q2: idis.get_stf(x, q2, stf='F2', tar=tar)
FL = lambda x, q2: idis.get_stf(x, q2, stf='FL', tar=tar)
rho2 = lambda x, q2: 1 + 4 * x**2 * M2 / q2
gamma2 = lambda x, q2: 4 * x**2 * M2 / q2
F1 = lambda x, q2: (gamma2(x, q2) * F2(x, q2) - FL(x, q2)) / (2 * x)
y = lambda x, q2: (q2 / 2 / x) / ((S - M2) / 2)
alpha = lambda q2: conf['eweak'].get_alpha(q2)
C = lambda x, q2: 4 * np.pi * alpha(q2)**2 * S / q2
T1 = lambda x, q2: x * F1(x, q2)
T2 = lambda x, q2: (1 - y(x, q2) - y(x, q2)**2 / 4 * gamma2(x, q2)) * F2(
x, q2) / y(x, q2)
T3 = lambda x, q2: x * (2 - y(x, q2) - y(x, q2)**2 / 2 * gamma2(x, q2)
) * g1(x, q2)
T4 = lambda x, q2: x * y(x, q2) * gamma2(x, q2) * g2(x, q2)
_ddsigR = lambda x, q2: C(x, q2) * (T1(x, q2) + T2(x, q2) - T3(x, q2) + T4(
x, q2))
_ddsigL = lambda x, q2: C(x, q2) * (T1(x, q2) + T2(x, q2) + T3(x, q2) - T4(
x, q2))
#--integrate over bin
z1, w1 = np.polynomial.legendre.leggauss(3)
z2, w2 = np.polynomial.legendre.leggauss(3)
ddsigR = np.zeros((len(X), len(z1), len(z2)))
ddsigL = np.zeros((len(X), len(z1), len(z2)))
for i in range(len(X)):
_x = 0.5 * ((Xup[i] - Xdo[i]) * z1 + Xup[i] + Xdo[i])
_q2 = 0.5 * ((Q2up[i] - Q2do[i]) * z2 + Q2up[i] + Q2do[i])
xjac = 0.5 * (Xup[i] - Xdo[i])
q2jac = 0.5 * (Q2up[i] - Q2do[i])
for j in range(len(_x)):
for k in range(len(_q2)):
ddsigR[i][j][k] = _ddsigR(_x[j], _q2[k]) * xjac * q2jac
ddsigL[i][j][k] = _ddsigL(_x[j], _q2[k]) * xjac * q2jac
#--integrate over Q2
dsigR = np.sum(w2 * ddsigR, axis=2)
dsigL = np.sum(w2 * ddsigL, axis=2)
#--integrate over X
sigR = np.sum(w1 * dsigR, axis=1)
sigL = np.sum(w1 * dsigL, axis=1)
NR = lum * sigR
NL = lum * sigL
#--theoretical asymmetry
A = np.array(value)
#--absolute uncertainty (not divided by sigma)
stat2 = (1 + A**2) / (NR + NL)
stat = np.sqrt(stat2)
#--statistical uncertainties
data['stat_u'] = stat
Y = np.array(data['Q2']) / 2 / np.array(data['X']) / ((S - M2) / 2)
#--add systemic uncertainties beyond polarization
#--optimistic: 1% on lum, 1% on Pe, 2% on Ph, no further errors
if est == 'opt':
data['lum_u'] = A * 0.01
data['pole_u'] = A * 0.01
data['polh_u'] = A * 0.02
data['syst_u'] = A * 0.0
#--moderate: do no have yet
elif est == 'mod':
return
#data['syst_u'] = np.zeros(l)
#for i in range(l):
# if Y[i] <= 0.01: data['syst_u'][i] = Am[i]*0.020
# if Y[i] > 0.01: data['syst_u'][i] = Am[i]*0.025
#--pessimistic: 2% on lum, 2% on Pe, 4% on Ph, no further errors
elif est == 'pes':
data['lum_u'] = A * 0.02
data['pole_u'] = A * 0.02
data['polh_u'] = A * 0.04
data['syst_u'] = A * 0.0
else:
print('est must be opt, mod, or pes')
return
return data['stat_u'], data['pole_u'], data['polh_u'], data['lum_u'], data[
'syst_u']
def chi2_histogram(wdir, reaction, dataset, dpi):
conf['datasets'] = {}
if reaction.lower() == 'jet':
from obslib.jets.reader import READER
conf['datasets']['jet'] = {}
conf['datasets']['jet']['xlsx'] = {}
conf['datasets']['jet']['xlsx'][
dataset] = 'jets/expdata/%s.xlsx' % dataset
## please notice the inconsistence of names, 'jet' and 'jets'
## so please do not remove this part
elif reaction.lower() == 'pjet':
from obslib.pjets.reader import READER
conf['datasets']['pjet'] = {}
conf['datasets']['pjet']['xlsx'] = {}
conf['datasets']['pjet']['xlsx'][
dataset] = 'pjets/expdata/%s.xlsx' % dataset
## please notice the inconsistence of names, 'pjet' and 'pjets'
## so please do not remove this part
elif reaction.lower() == 'idis':
from obslib.idis.reader import READER
elif reaction.lower() == 'sidis':
from obslib.sidis.reader import READER
elif reaction.lower() == 'pidis':
from obslib.pidis.reader import READER
elif reaction.lower() == 'psidis':
from obslib.psidis.reader import READER
elif reaction.lower() == 'dy':
from obslib.dy.reader import READER
elif reaction.lower() == 'sia':
from obslib.sia.reader import READER
conf['aux'] = AUX()
if reaction not in conf['datasets']:
conf['datasets'][reaction] = {}
conf['datasets'][reaction]['xlsx'] = {}
conf['datasets'][reaction]['xlsx'][dataset] = '%s/expdata/%s.xlsx' % (
reaction, dataset)
data_table = READER().load_data_sets(reaction)
## plot histogram of relative 'chi2' for 'dataset' in 'reaction'
load_config('%s/input.py' % wdir)
istep = core.get_istep()
collaboration = data_table[dataset]['col'][0]
print('\nplotting relative chi2 histogram for %d in %s from %s' %
(dataset, reaction, wdir))
replicas = core.get_replicas(wdir)
relative_chi2 = []
for replica in replicas:
# print replica['chi2'][reaction][dataset]
relative_chi2.append(replica['chi2'][reaction][dataset]['chi2'] /
float(replica['chi2'][reaction][dataset]['npts']))
n_rows, n_columns = 1, 1
figure, ax = py.subplots(n_rows, n_columns)
ax.hist(relative_chi2,
len(relative_chi2),
histtype='bar',
color='b',
label=r'$\mathrm{%s,~%s}$' %
(reaction.upper(), collaboration.upper()))
ax.legend(fontsize=20, loc='upper right')
# ax.set_ylabel(r'\boldmath$\mathrm{n}$', size = 24)
# ax.yaxis.set_label_coords(-0.17, 2.0)
ax.set_xlabel(r'\boldmath$\chi^2/\mathrm{n}$', size=24)
# ax.xaxis.set_label_coords(0.90, -0.12)
py.tight_layout()
py.savefig('%s/gallery/chi2-histogram-%s-%s-%d.png' %
(wdir, reaction, collaboration, istep),
dpi=dpi)
def pvdis_errors(wdir, idxs, tar, lum='100:fb-1'):
for idx in idxs:
conf['aux'] = AUX()
conf['eweak'] = EWEAK()
data = pd.read_excel('%s/sim/%s%s.xlsx' % (wdir, idx, tar),
index=False)
data = data.to_dict(orient='list')
target = data['target'][0]
l = len(data['value'])
X = np.array(data['X'])
Q2 = np.array(data['Q2'])
Xup = np.array(data['Xup'])
Xdo = np.array(data['Xdo'])
Q2up = np.array(data['Q2up'])
Q2do = np.array(data['Q2do'])
RS = data['RS'][0]
S = RS**2
M2 = conf['aux'].M2
if target == 'd': M2 = 4 * M2
#--luminosity
lum = convert_lum(lum)
dx = Xup - Xdo
dQ2 = Q2up - Q2do
bins = dx * dQ2
GF = conf['aux'].GF
if target == 'p': tabname = 'JAM4EIC_p'
if target == 'd': tabname = 'JAM4EIC_d'
stf = lhapdf.mkPDF(tabname, 0)
#--integrate over bin
#--get structure functions
#F2g = lambda x,q2: stf.xfxQ2(900,x,q2)
#FLg = lambda x,q2: stf.xfxQ2(901,x,q2)
#F2gZ = lambda x,q2: stf.xfxQ2(902,x,q2)
#FLgZ = lambda x,q2: stf.xfxQ2(903,x,q2)
#F3gZ = lambda x,q2: stf.xfxQ2(904,x,q2)
#rho2 = lambda x,q2: 1 + 4*x**2*M2/q2
#y=lambda x,q2: (q2/2/x)/((S-M2)/2)
#YP = lambda x,q2: y(x,q2)**2*(rho2(x,q2)+1)/2 - 2*y(x,q2) +2
#YM = lambda x,q2: 1-(1-y(x,q2))**2
#sin2w = lambda q2: conf['eweak'].get_sin2w(q2)
#alpha = lambda q2: conf['eweak'].get_alpha(q2)
#gA = -0.5
#gV = lambda q2: -0.5 + 2*sin2w(q2)
#C = lambda q2: GF*q2/(2*np.sqrt(2)*np.pi*alpha(q2))
#C1 = lambda x,q2: 2*np.pi*alpha(q2)**2/(x*y(x,q2)*q2)
#T1g = lambda x,q2: YP(x,q2)*F2g(x,q2) - y(x,q2)**2*FLg(x,q2)
#T1gZ = lambda x,q2: YP(x,q2)*F2gZ(x,q2) - y(x,q2)**2*FLgZ(x,q2)
#T2 = lambda x,q2: x*YM(x,q2)*F3gZ(x,q2)
#_ddsigR = lambda x,q2: C1(x,q2)*(T1g(x,q2) + C(q2)*(gV(q2)-gA)*(T1gZ(x,q2) - T2(x,q2)))
#_ddsigL = lambda x,q2: C1(x,q2)*(T1g(x,q2) + C(q2)*(gV(q2)+gA)*(T1gZ(x,q2) + T2(x,q2)))
#z1,w1 = np.polynomial.legendre.leggauss(5)
#z2,w2 = np.polynomial.legendre.leggauss(5)
#ddsigR = np.zeros((len(X),len(z1),len(z2)))
#ddsigL = np.zeros((len(X),len(z1),len(z2)))
#for i in range(len(X)):
# _x = 0.5*((Xup[i] -Xdo[i])*z1 + Xup[i] + Xdo[i])
# _q2 = 0.5*((Q2up[i]-Q2do[i])*z2 + Q2up[i] + Q2do[i])
# xjac = 0.5*(Xup[i]-Xdo[i])
# q2jac = 0.5*(Q2up[i]-Q2do[i])
# for j in range(len(_x)):
# for k in range(len(_q2)):
# ddsigR[i][j][k] = _ddsigR(_x[j],_q2[k])*xjac*q2jac
# ddsigL[i][j][k] = _ddsigL(_x[j],_q2[k])*xjac*q2jac
##--integrate over Q2
#dsigR = np.sum(w2*ddsigR,axis=2)
#dsigL = np.sum(w2*ddsigL,axis=2)
##--integrate over X
#sigR = np.sum(w1*dsigR,axis=1)
#sigL = np.sum(w1*dsigL,axis=1)
#--multiply by bin
#--get structure functions
F2g = np.array([stf.xfxQ2(900, X[i], Q2[i]) for i in range(l)])
FLg = np.array([stf.xfxQ2(901, X[i], Q2[i]) for i in range(l)])
F2gZ = np.array([stf.xfxQ2(902, X[i], Q2[i]) for i in range(l)])
FLgZ = np.array([stf.xfxQ2(903, X[i], Q2[i]) for i in range(l)])
F3gZ = np.array([stf.xfxQ2(904, X[i], Q2[i]) for i in range(l)])
rho2 = 1 + 4 * X**2 * M2 / Q2
y = (Q2 / 2 / X) / ((S - M2) / 2)
YP = y**2 * (rho2 + 1) / 2 - 2 * y + 2
YM = 1 - (1 - y)**2
sin2w = np.array([conf['eweak'].get_sin2w(q2) for q2 in Q2])
alpha = np.array([conf['eweak'].get_alpha(q2) for q2 in Q2])
gA = -0.5
gV = -0.5 + 2 * sin2w
C = GF * Q2 / (2 * np.sqrt(2) * np.pi * alpha)
C1 = 2 * np.pi * alpha**2 / (X * y * Q2) * bins
T1g = YP * F2g - y**2 * FLg
T1gZ = YP * F2gZ - y**2 * FLgZ
T2 = X * YM * F3gZ
sigR = C1 * (T1g + C * (gV - gA) * (T1gZ - T2))
sigL = C1 * (T1g + C * (gV + gA) * (T1gZ + T2))
NR = lum * sigR
NL = lum * sigL
C = 4 / (NR + NL)**4
T1 = NL**2 * NR
T2 = NR**2 * NL
stat2 = C * (T1 + T2)
stat = np.sqrt(stat2)
#--add syst errors and save new excel sheet
data['stat_u'] = stat
print stat
df = pd.DataFrame(data)
df.to_excel('%s/sim/%s%s.xlsx' % (wdir, idx, tar), index=False)