def mres(pion_gv, etas_gv):
prior = dict()
prior['mres'] = gv.gvar(0.0, 1.0)
x = np.arange(len(pion_gv))
c51.scatter_plot(x, pion_gv)
trange = dict()
T = len(pion_gv) - 2
trange['tmin'] = [2, 2]
trange['tmax'] = [T, T]
pionfit = c51.fitscript(trange,
pion_gv,
prior,
c51.mres_fitfcn,
result_flag='off')
print "Pion"
c51.stability_plot(pionfit, 'mres', 'pion')
c51.scatter_plot(x, etas_gv)
etasfit = c51.fitscript(trange,
etas_gv,
prior,
c51.mres_fitfcn,
result_flag='off')
print "Etas"
c51.stability_plot(etasfit, 'mres', 'kaon')
#print kaonfit['post']
return 0
def decay(pion_ss_ps_gv, kaon_ss_ps_gv):
prior = dict()
prior['Z0_s'] = gv.gvar(0.025, 0.01)
prior['Z1_s'] = gv.gvar(0.025, 0.035)
prior['Z2_s'] = gv.gvar(0.025, 0.035)
prior['Z0_p'] = gv.gvar(0.27, 0.15)
prior['Z1_p'] = gv.gvar(0.27, 0.35)
prior['Z2_p'] = gv.gvar(0.27, 0.35)
prior['E0'] = gv.gvar(0.23, 0.2)
prior['E1'] = gv.gvar(0.0, 1.0)
prior['E2'] = gv.gvar(0.0, 1.0)
trange = dict()
trange['tmin'] = [6, 6]
trange['tmax'] = [20, 20]
T = len(pion_ss_ps_gv)
fitfcn = c51.fit_function(T=T, nstates=2)
fit = c51.fitscript(trange,
pion_ss_ps_gv,
prior,
fitfcn.twopt_fitfcn_ss_ps,
sets=2,
result_flag='off')
print "pion"
c51.stability_plot(fit, 'Z0_p')
c51.stability_plot(fit, 'E0')
ml = 0.0158
ms = 0.0902
mres_pi = gv.gvar(0.0009633, 0.0000065)
Z0_p = fit['post'][0]['Z0_p']
E0 = fit['post'][0]['E0']
fpi = Z0_p * np.sqrt(2.) * (2. * ml + 2. * mres_pi) / E0**(3. / 2.)
print 'fpi:', fpi
print "kaon"
prior['Z0_s'] = gv.gvar(0.02, 0.01)
prior['Z1_s'] = gv.gvar(0.02, 0.03)
prior['Z2_s'] = gv.gvar(0.02, 0.03)
prior['Z0_p'] = gv.gvar(0.2, 0.1)
prior['Z1_p'] = gv.gvar(0.2, 0.3)
prior['Z2_p'] = gv.gvar(0.2, 0.3)
prior['E0'] = gv.gvar(0.404, 0.2)
prior['E1'] = gv.gvar(0.0, 1.0)
prior['E2'] = gv.gvar(0.0, 1.0)
fit = c51.fitscript(trange,
kaon_ss_ps_gv,
prior,
fitfcn.twopt_fitfcn_ss_ps,
sets=2,
result_flag='off')
c51.stability_plot(fit, 'Z0_p')
c51.stability_plot(fit, 'E0')
mres_kaon = gv.gvar(0.0006685, 0.0000044)
Z0_p = fit['post'][0]['Z0_p']
E0 = fit['post'][0]['E0']
fk = Z0_p * np.sqrt(2.) * (ml + ms + mres_pi + mres_kaon) / E0**(3. / 2.)
print 'fk:', fk
fkfpi = fk / fpi
print 'fk/fpi:', fkfpi
def decay(pion_ss_ps_gv, kaon_ss_ps_gv):
prior = dict()
prior['Z0_s'] = gv.gvar(0.025, 0.01)
prior['Z1_s'] = gv.gvar(0.025, 0.035)
prior['Z2_s'] = gv.gvar(0.025, 0.035)
prior['Z0_p'] = gv.gvar(0.27, 0.15)
prior['Z1_p'] = gv.gvar(0.27, 0.35)
prior['Z2_p'] = gv.gvar(0.27, 0.35)
prior['E0'] = gv.gvar(0.23, 0.2)
prior['E1'] = gv.gvar(0.0, 1.0)
prior['E2'] = gv.gvar(0.0, 1.0)
trange = dict()
trange['tmin'] = [6,6]
trange['tmax'] = [20,20]
T = len(pion_ss_ps_gv)
fitfcn = c51.fit_function(T=T, nstates=2)
fit = c51.fitscript(trange, pion_ss_ps_gv, prior, fitfcn.twopt_fitfcn_ss_ps, sets=2, result_flag='off')
print "pion"
c51.stability_plot(fit, 'Z0_p')
c51.stability_plot(fit, 'E0')
ml = 0.0158
ms = 0.0902
mres_pi = gv.gvar(0.0009633, 0.0000065)
Z0_p = fit['post'][0]['Z0_p']
E0 = fit['post'][0]['E0']
fpi = Z0_p*np.sqrt(2.)*(2.*ml+2.*mres_pi)/E0**(3./2.)
print 'fpi:', fpi
print "kaon"
prior['Z0_s'] = gv.gvar(0.02, 0.01)
prior['Z1_s'] = gv.gvar(0.02, 0.03)
prior['Z2_s'] = gv.gvar(0.02, 0.03)
prior['Z0_p'] = gv.gvar(0.2, 0.1)
prior['Z1_p'] = gv.gvar(0.2, 0.3)
prior['Z2_p'] = gv.gvar(0.2, 0.3)
prior['E0'] = gv.gvar(0.404, 0.2)
prior['E1'] = gv.gvar(0.0, 1.0)
prior['E2'] = gv.gvar(0.0, 1.0)
fit = c51.fitscript(trange, kaon_ss_ps_gv, prior, fitfcn.twopt_fitfcn_ss_ps, sets=2, result_flag='off')
c51.stability_plot(fit, 'Z0_p')
c51.stability_plot(fit, 'E0')
mres_kaon = gv.gvar(0.0006685, 0.0000044)
Z0_p = fit['post'][0]['Z0_p']
E0 = fit['post'][0]['E0']
fk = Z0_p*np.sqrt(2.)*(ml+ms+mres_pi+mres_kaon)/E0**(3./2.)
print 'fk:', fk
fkfpi = fk/fpi
print 'fk/fpi:', fkfpi
def mres(pion_gv, etas_gv):
prior = dict()
prior['mres'] = gv.gvar(0.0, 1.0)
x = np.arange(len(pion_gv))
c51.scatter_plot(x,pion_gv)
trange = dict()
T = len(pion_gv)-2
trange['tmin'] = [2, 2]
trange['tmax'] = [T, T]
pionfit = c51.fitscript(trange, pion_gv, prior, c51.mres_fitfcn, result_flag='off')
print "Pion"
c51.stability_plot(pionfit,'mres','pion')
c51.scatter_plot(x,etas_gv)
etasfit = c51.fitscript(trange, etas_gv, prior, c51.mres_fitfcn, result_flag='off')
print "Etas"
c51.stability_plot(etasfit,'mres','kaon')
#print kaonfit['post']
return 0
#Plot effective mass
T = len(data) * 0.5
meff = c51.effective_mass(data, 1)
x = np.arange(len(meff))
ylim = c51.find_yrange(meff, 1, 10)
#ylim = [0.47, 0.57]
xr = [1, 15]
c51.scatter_plot(x, meff, 'effective mass', xlim=[xr[0], xr[1]], ylim=ylim)
#ylim = c51.find_yrange(meff, 65, 79)
c51.scatter_plot(x,
meff,
'effective mass ps',
xlim=[T + xr[0], T + xr[1]],
ylim=ylim)
#Fit
inputs = c51.read_yaml('temp.yml')
prior = c51.dict_of_tuple_to_gvar(inputs['prior'])
trange = inputs['trange']
fitfcn = c51.fit_function(T, nstates=2)
fit = c51.fitscript(trange,
data,
prior,
fitfcn.twopt_fitfcn_ss_ps,
sets=2,
result_flag='on')
c51.stability_plot(fit, 'E0')
c51.stability_plot(fit, 'Z0_s')
c51.stability_plot(fit, 'Z0_p')
plt.show()
#datapath = 'prot_w5p6_n94'
filename = 'l2464f211b600m0102m0509m635a_avg.h5'
datapath = 'l2464f211b600m0102m0509m635/wf1p0_m51p2_l58_a51p5_smrw5p0_n75/spectrum/ml0p0126_ms0p0693/pion/corr'
data_ss = c51.read_data(filename, datapath, 0, 0)
data_ps = c51.read_data(filename, datapath, 3, 0)
data = np.concatenate((data_ss, data_ps), axis=1)
data = c51.make_gvars(data)
#data = data_ps
#Plot effective mass
T = len(data)*0.5
meff = c51.effective_mass(data, 1)
x = np.arange(len(meff))
ylim = c51.find_yrange(meff, 1, 10)
#ylim = [0.47, 0.57]
xr = [1,15]
c51.scatter_plot(x, meff, 'effective mass', xlim=[xr[0],xr[1]], ylim=ylim)
#ylim = c51.find_yrange(meff, 65, 79)
c51.scatter_plot(x, meff, 'effective mass ps', xlim=[T+xr[0],T+xr[1]], ylim=ylim)
#Fit
inputs = c51.read_yaml('temp.yml')
prior = c51.dict_of_tuple_to_gvar(inputs['prior'])
trange = inputs['trange']
fitfcn = c51.fit_function(T, nstates=2)
fit = c51.fitscript(trange, data, prior, fitfcn.twopt_fitfcn_ss_ps, sets=2, result_flag='on')
c51.stability_plot(fit, 'E0')
c51.stability_plot(fit, 'Z0_s')
plt.show()
