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