ms = params['grand_ensemble'][ens]['ms']
# decay parameter file
user_flag = c51.user_list()
f = open('./decay.yml','r')
decay_params = yaml.load(f)
f.close()
decay_params['decay_ward_fit']['ens'] = {"tag": ens, "stream": stream}
# log in sql #
psqlpwd = pwd.passwd()
psql = sql.pysql('cchang5','cchang5',psqlpwd)
### read data ###
x_list = dict()
data = []
# mres
for m in ml:
mp, pp = decay.read_mres_bs(psql,decay_params,m)
if len(data) != 0:
x_list['mp_%s' %str(m)] = len(data[0]) + np.arange(len(mp[0]))
data = np.concatenate((data,mp), axis=1)
else:
x_list['mp_%s' %str(m)] = np.arange(len(mp[0]))
data = mp
x_list['pp_%s' %str(m)] = len(data[0]) + np.arange(len(pp[0]))
data = np.concatenate((data,pp), axis=1)
for s in ms:
mp, pp = decay.read_mres_bs(psql,decay_params,s)
x_list['mp_%s' %str(s)] = len(data[0]) + np.arange(len(mp[0]))
data = np.concatenate((data,mp), axis=1)
x_list['pp_%s' %str(s)] = len(data[0]) + np.arange(len(pp[0]))
data = np.concatenate((data,pp), axis=1)
# mesons
import yaml
# import various analysis scripts
import sql_decay_ward as decay
if __name__ == '__main__':
# read yaml #
f = open('./decay_flow.yml', 'r')
decay_params = yaml.load(f)
f.close()
ensemble = decay_params['decay_ward_fit']['ens']['tag']
# log in sql #
psqlpwd = pwd.passwd()
psql = sql.pysql('cchang5', 'cchang5', psqlpwd)
# read data #
# mres for ml
mpl, ppl = decay.read_mres_bs(psql, decay_params,
decay_params['decay_ward_fit']['ml'])
# ms
mps, pps = decay.read_mres_bs(psql, decay_params,
decay_params['decay_ward_fit']['ms'])
# pion correlator
ss_pion, ps_pion = decay.read_decay_bs(psql, decay_params, 'pion')
ss_kaon, ps_kaon = decay.read_decay_bs(psql, decay_params, 'kaon')
# index
x_mpl = np.arange(len(mpl[0]))
x_ppl = len(x_mpl) + np.arange(len(ppl[0]))
x_mps = len(x_ppl) + len(x_mpl) + np.arange(len(mps[0]))
x_pps = len(x_mps) + len(x_ppl) + len(x_mpl) + np.arange(len(pps[0]))
x_ss_pion = len(x_pps) + len(x_mps) + len(x_ppl) + len(x_mpl) + np.arange(
len(ss_pion[0]))
x_ps_pion = len(x_ss_pion) + len(x_pps) + len(x_mps) + len(x_ppl) + len(
x_mpl) + np.arange(len(ps_pion[0]))
axial_params['axial_fit']['ml'] = params['grand_ensemble']['ml']
axial_params['axial_fit']['ms'] = params['grand_ensemble']['ms']
axial_params['flags']['plot_data'] = False
# gA parameter file
f = open('./fh_flow.yml', 'r')
gA_params = yaml.load(f)
f.close()
gA_params['gA_fit']['ens'] = params['grand_ensemble']['ens']
gA_params['gA_fit']['ml'] = params['grand_ensemble']['ml']
gA_params['flags']['plot_data'] = False
# log in sql #
psqlpwd = pwd.passwd()
psql = sql.pysql('cchang5', 'cchang5', psqlpwd)
# read data #
# mres for ml
lmp, lpp = decay.read_mres_bs(psql, decay_params,
decay_params['decay_ward_fit']['ml'])
smp, spp = decay.read_mres_bs(psql, decay_params,
decay_params['decay_ward_fit']['ms'])
# pion correlator
ss_pion, ps_pion = decay.read_decay_bs(psql, decay_params, 'pion')
ss_kaon, ps_kaon = decay.read_decay_bs(psql, decay_params, 'kaon')
# axial correlator
ll = axial.read_axial(psql, axial_params, 'axial_ll')
ls = axial.read_axial(psql, axial_params, 'axial_ls')
# gA parameter file
gAboot0 = gA.read_gA_bs(psql, gA_params, twopt=True)
# concatenate and build grand covariance matrix #
# index
x_lmp = np.arange(len(lmp[0]))
x_lpp = np.arange(len(lpp[0])) + len(x_lmp)
x_smp = np.arange(len(smp[0])) + len(x_lmp) + len(x_lpp)
decay_params = yaml.load(f)
f.close()
decay_params['decay_ward_fit']['ens'] = params['grand_ensemble']['ens']
decay_params['decay_ward_fit']['ml'] = params['grand_ensemble']['ml']
# gA parameter file
f = open('./fhprotonmaster.yml.%s' %(user_flag),'r')
gA_params = yaml.load(f)
f.close()
gA_params['gA_fit']['ens'] = params['grand_ensemble']['ens']
gA_params['gA_fit']['ml'] = params['grand_ensemble']['ml']
# log in sql #
psqlpwd = pwd.passwd()
psql = sql.pysql('cchang5','cchang5',psqlpwd)
# read data #
# mres for ml
mp, pp = decay.read_mres_bs(psql,decay_params,decay_params['decay_ward_fit']['ml'])
# pion correlator
ss_pion, ps_pion = decay.read_decay_bs(psql,decay_params,'pion')
# gA parameter file
gAboot0 = gA.read_gA_bs(psql,gA_params)
# concatenate and build grand covariance matrix #
# index
x_mp = np.arange(len(mp[0]))
x_pp = np.arange(len(pp[0]))+len(x_mp)
x_ss_pion = np.arange(len(ss_pion[0]))+len(x_mp)+len(x_pp)
x_ps_pion = np.arange(len(ps_pion[0]))+len(x_mp)+len(x_pp)+len(x_ss_pion)
x_gAboot0 = np.arange(len(gAboot0[0]))+len(x_mp)+len(x_pp)+len(x_ss_pion)+len(x_ps_pion)
print "len mp:", np.shape(mp)
print "len pp:", np.shape(pp)
print "len ss_pion:", np.shape(ss_pion)
print "len ps_pion:", np.shape(ps_pion)
ms = params['grand_ensemble'][ens]['ms']
# decay parameter file
user_flag = c51.user_list()
f = open('./sqlmaster.yml.%s' %(user_flag),'r')
decay_params = yaml.load(f)
f.close()
decay_params['decay_ward_fit']['ens'] = {"tag": ens, "stream": stream}
# log in sql #
psqlpwd = pwd.passwd()
psql = sql.pysql('cchang5','cchang5',psqlpwd)
### read data ###
x_list = dict()
data = []
# mres
for m in ml:
mp, pp = decay.read_mres_bs(psql,decay_params,m)
if len(data) != 0:
x_list['mp_%s' %str(m)] = len(data[0]) + np.arange(len(mp[0]))
data = np.concatenate((data,mp), axis=1)
else:
x_list['mp_%s' %str(m)] = np.arange(len(mp[0]))
data = mp
x_list['pp_%s' %str(m)] = len(data[0]) + np.arange(len(pp[0]))
data = np.concatenate((data,pp), axis=1)
for s in ms:
mp, pp = decay.read_mres_bs(psql,decay_params,s)
x_list['mp_%s' %str(s)] = len(data[0]) + np.arange(len(mp[0]))
data = np.concatenate((data,mp), axis=1)
x_list['pp_%s' %str(s)] = len(data[0]) + np.arange(len(pp[0]))
data = np.concatenate((data,pp), axis=1)
# mesons
