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control_advanced.py
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control_advanced.py
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from corrfitter import CorrFitter
from data_manipulations import standard_load,sn_minimize_postload_3pt
from extract_3pt_info import *
from make_data import make_data,import_corfit_file
from make_data_db import make_data_db
#from make_init import make_init_from_fit_file_3pt
from make_init import make_adv_init_from_fit_file_3pt
from make_models import make_models
from make_models_3pt import make_models_3pt,make_models_advanced
from make_prior import make_prior
from make_prior_3pt import make_prior_3pt
#from make_prior_advanced import make_prior_advanced
from make_prior_advanced import truncate_prior_states,truncate_prior_states_2pt
from make_bootstrap import make_bootstrap
from manipulate_dataset import *
from print_results import fmt_reduced_chi2
from print_results import print_fit
from print_results import print_error_budget
from save_data import save_data
from save_fit import save_init_from_fit
from save_prior import save_prior_from_fit
from make_plot import make_plot
from make_plot import make_plot_corr_neg
from make_plot import make_plot_1plus1
#from plot_corr_double_log_folded import plot_corr_double_log_folded
from plot_corr_adv_dl_folded import plot_corr_adv_dl_folded
#from plot_corr_effective_mass import plot_corr_effective_mass
from plot_corr_effective_mass_check import plot_corr_effective_mass_check
from plot_corr_normalized import plot_corr_normalized
#from plot_corr_3pt import plot_corr_3pt
from plot_corr_stacked_3pt import plot_corr_3pt
#from plot_corr_stacked_3pt_clean import plot_corr_3pt
from meta_data import *
from util_files import read_fit_file
import defines as df
import define_prior as dfp
import define_prior_3pt as dfp3
import define_prior_advanced as dfpa
import gvar as gv
import gvar.dataset as gvd
import matplotlib.pyplot as plt
import numpy as np
import util_funcs as utf
import argparse
import hashlib
import sys
import matplotlib as mpl
mpl.use('TkAgg')
parser = argparse.ArgumentParser(description='fit 3-point correlators') # description of what?
parser.add_argument('-r','--reset',dest='override_init',action='store_true')
parser.add_argument('-p','--plot',dest='override_plot',action='store_true')
parser.add_argument('-d','--dump',dest='dump_gvar',action='store_true')
parser.add_argument('-D','--dump-by-name',dest='dump_gvar_name',action='store_const',const=None)
parser.add_argument('-l','--load',dest='load_gvar',action='store_true')
parser.add_argument('-L','--load-by-name',dest='load_gvar_name',action='store_const',const=None)
argsin = parser.parse_known_args(sys.argv[1:]) ## in namespace
argsin = vars(argsin[0]) ## pull out of namespace
print argsin
if df.do_irrep == "8":
irrepStr = '8p'
elif df.do_irrep == "8'":
irrepStr = '8m'
elif df.do_irrep == "16":
irrepStr = '16p'
## 8+ representation
taglist = list() # for gvar.dump hash key
filekey = 'a' ## -- standard choice, no filters
#filekey = 'm' ## -- munich filter
#filekey = 'n' ## -- standard choice, no filters
#print "Using munich filter"
#print "*** USING -1^t FILTER ***"
#taglist.append(('l32v6.mes2pt','mes'))
taglist.append(('l32v6.bar2pt.'+irrepStr,'bar2pt'))
if not(df.do_irrep == "16"):
taglist.append(('l32v6.bar3pt.'+irrepStr+'.axax.t06.p00','axax','t6'))
taglist.append(('l32v6.bar3pt.'+irrepStr+'.axax.t-7.p00','axax','t7'))
taglist.append(('l32v6.bar3pt.'+irrepStr+'.ayay.t06.p00','ayay','t6'))
taglist.append(('l32v6.bar3pt.'+irrepStr+'.ayay.t-7.p00','ayay','t7'))
taglist.append(('l32v6.bar3pt.'+irrepStr+'.azaz.t06.p00','azaz','t6'))
taglist.append(('l32v6.bar3pt.'+irrepStr+'.azaz.t-7.p00','azaz','t7'))
else:
## -- both 16+ and 16-
irrepStr = '16p'
taglist.append(('l32v6.bar3pt.'+irrepStr+'.axax.t06.p00','axax','t6','16p'))
taglist.append(('l32v6.bar3pt.'+irrepStr+'.axax.t-7.p00','axax','t7','16p'))
taglist.append(('l32v6.bar3pt.'+irrepStr+'.ayay.t06.p00','ayay','t6','16p'))
taglist.append(('l32v6.bar3pt.'+irrepStr+'.ayay.t-7.p00','ayay','t7','16p'))
taglist.append(('l32v6.bar3pt.'+irrepStr+'.azaz.t06.p00','azaz','t6','16p'))
taglist.append(('l32v6.bar3pt.'+irrepStr+'.azaz.t-7.p00','azaz','t7','16p'))
irrepStr = '16m'
taglist.append(('l32v6.bar3pt.'+irrepStr+'.axax.t06.p00','axax','t6','16m'))
taglist.append(('l32v6.bar3pt.'+irrepStr+'.axax.t-7.p00','axax','t7','16m'))
taglist.append(('l32v6.bar3pt.'+irrepStr+'.ayay.t06.p00','ayay','t6','16m'))
taglist.append(('l32v6.bar3pt.'+irrepStr+'.ayay.t-7.p00','ayay','t7','16m'))
taglist.append(('l32v6.bar3pt.'+irrepStr+'.azaz.t06.p00','azaz','t6','16m'))
taglist.append(('l32v6.bar3pt.'+irrepStr+'.azaz.t-7.p00','azaz','t7','16m'))
## -- consolidated all loading into a single file:
dall = standard_load(taglist,filekey,argsin)
models2 = make_models (data=dall,lkey=df.lkey,use_advanced=True)
#models3 = make_models_3pt(data=dall,lkey=df.lkey3)
models3 = make_models_advanced(data=dall,lkey=df.lkey3)
priors2 = make_prior (models2)
priors3 = make_prior_3pt(models3)
priorsa = df.define_prior_adv
## for writing priors to file
#np.set_printoptions(precision=4,linewidth=100)
#d1 = {}
#d1['en0'] = list()
#d1['enc'] = list()
#d1['enb'] = list()
#d1['ens'] = list()
#d1['enr'] = list()
#d1['eo0'] = list()
#d1['eoc'] = list()
#d1['eob'] = list()
#d1['eos'] = list()
#d1['eor'] = list()
#for i in range(6):
# try:
# d1['en0'].append(gv.mean(gv.exp(priorsa['logEn_'+str(i)][0])))
# d1['enc'].append(gv.mean(gv.exp(priorsa['logEn_'+str(i)])))
# d1['enc'][-1][0] = 0
# d1['enb'].append(gv.mean(gv.exp(priorsa['logEn_'+str(i)])))
# d1['ens'].append(gv.sdev(gv.exp(priorsa['logEn_'+str(i)])))
# d1['enr'].append(gv.sdev(gv.exp(priorsa['logEn_'+str(i)]))\
# /gv.mean(gv.exp(priorsa['logEn_'+str(i)])))
# except KeyError:
# pass
# try:
# d1['eo0'].append(gv.mean(gv.exp(priorsa['logEo_'+str(i)][0])))
# d1['eoc'].append(gv.mean(gv.exp(priorsa['logEo_'+str(i)])))
# d1['eoc'][-1][0] = 0
# d1['eob'].append(gv.mean(gv.exp(priorsa['logEo_'+str(i)])))
# d1['eos'].append(gv.sdev(gv.exp(priorsa['logEo_'+str(i)])))
# d1['eor'].append(gv.sdev(gv.exp(priorsa['logEo_'+str(i)]))\
# /gv.mean(gv.exp(priorsa['logEo_'+str(i)])))
# except KeyError:
# continue
#conv = .197/.15
#f = open('s'+irrepStr+'.aprior','w')
##for i,x,y in enumerate(zip(np.array(d1).T,np.array(d2).T)):
#f.write('#i block taste sumE dE sigE sumE[GeV] dE[GeV] sigE[GeV] sigE/dE \n')
#f.write('#even\n')
#k = 0
#for i,(e0,ec,eb,es,er) in enumerate(zip(np.cumsum(d1['en0']),\
# d1['enc'],d1['enb'],d1['ens'],d1['enr'])):
# for j,(ecx,ebx,esx,erx) in enumerate(zip(e0+np.cumsum(ec),eb,es,er)):
# #f.write( str(i)+' '+str(j)+' '+str(ecx)+' '+str(ebx)+' '+str(esx)+' '+str(erx)+'\n' )
# #f.write('%d %d %d %1.3f %1.3f %1.3f %1.3f \n' % (k, i, j, ecx, ebx, esx, erx))
# f.write('%d %d %d %1.3f %1.3f %1.3f %1.3f %1.3f %1.3f %1.3f\n' %\
# (k, i, j, ecx, ebx, esx, conv*ecx, conv*ebx, conv*esx, erx))
# k+=1
#f.write('#odd\n')
#k = 0
#for i,(e0,ec,eb,es,er) in enumerate(zip(np.cumsum(d1['eo0']),\
# d1['eoc'],d1['eob'],d1['eos'],d1['eor'])):
# for j,(ecx,ebx,esx,erx) in enumerate(zip(e0+np.cumsum(ec),eb,es,er)):
# #f.write( str(i)+' '+str(j)+' '+str(ecx)+' '+str(ebx)+' '+str(esx)+' '+str(erx)+'\n' )
# f.write('%d %d %d %1.3f %1.3f %1.3f %1.3f %1.3f %1.3f %1.3f \n' %\
# (k, i, j, ecx, ebx, esx, conv*ecx, conv*ebx, conv*esx, erx))
# k+=1
#f.close()
priorsa2 = truncate_prior_states_2pt(df.define_prior_adv,df.num_nst,df.num_ost)
priorsa = truncate_prior_states(df.define_prior_adv,
df.num_nst,df.num_ost,df.num_nst_3pt,df.num_ost_3pt)
models = list()
for model in models2:
models.append(model)
for model in models3:
models.append(model)
priors = gv.BufferDict()
for key in priorsa2:
priors[key] = priorsa2[key]
#for key in priors3:
# if key in priors2:
# continue
# priors[key] = priors3[key]
priors = priorsa
#if df.do_init2:
# init2={}
# if argsin['override_init']:
# init2 = make_init_from_fit_file_3pt(models2,'fit_dict')
# else:
# for key in df.define_init:
# if key[-1] == 'n':
# init2[key] = df.define_init[key][:df.num_nst]
# elif key[-1] == 'o':
# init2[key] = df.define_init[key][:df.num_ost]
#else:
# init2=None
## -- temporary fix
if df.do_init3:
#init3={}
#if argsin['override_init']:
init3 = make_adv_init_from_fit_file_3pt(models3,'fit_adv_'+irrepStr+'_3pt',\
fresh_overlap=True,fresh_amplitude=True)
else:
init3=None
pass
## -- temporary
init2=None
#init3=None
#print init3
fitter2 = CorrFitter(models=models2,maxit=df.maxit)
fitter3 = CorrFitter(models=models,maxit=df.maxit)
#print models
#raise ValueError('test')
print
print 'prior: '
for key in sorted(priors):
print key,priors[key]
#print
#print 'init : ',init3
#for key in init3:
# print key,init3[key]
print
if df.do_2pt:
print "starting 2pt fit..."
fit2 = fitter2.lsqfit(data=dall,prior=priorsa2,p0=init2,svdcut=df.svdcut)
## -- fits take a long time, so print prematurely
print_fit(fit2,priorsa2)
if df.do_3pt:
print "starting 3pt fit..."
#print 'init',init3
#print 'prior',priors
fit3 = fitter3.lsqfit(data=dall,prior=priors,p0=init3,svdcut=df.svdcut)
else:
print "Ignoring 3pt fit!"
fit3=None
#print 'transformed ',fit3.transformed_p
#print
#print 'params ',fit3.p
#print
#print 'fit0 ',models[0].datatag,models[0].fitfcn(fit3.p)
#print
#print 'fit44 ',models[0].datatag
#test = models[0].testfitfcn(fit3.transformed_p)
#print "final:"
#print list(np.transpose([-fit3.p['logc4o_0']*fit3.p['k4o_0']*((-1)**t)*gv.exp(-(48-t)*fit3.p['logEo_0']) for t in range(2,10)])[0])
#print
#print list(np.transpose([gv.exp(fit3.p['logc4n_0'])*fit3.p['k4n_0']*gv.exp(-t*gv.exp(fit3.p['logEn_0']))+gv.exp(fit3.p['logc4o_0'])*fit3.p['k4o_0']*((-1)**t)*gv.exp(-t*gv.exp(fit3.p['logEo_0'])) for t in range(2,10)])[0])
#print
#print list(np.transpose([-fit3.p['logc4n_0']*fit3.p['k4n_0']*gv.exp(-t*fit3.p['logEn_0'])+fit3.p['logc4o_0']*fit3.p['k4o_0']*((-1)**t)*gv.exp(-t*fit3.p['logEo_0']) for t in range(2,10)])[0])
#print
#print 'fit44 ',models[4].datatag
#print models[4].fitfcn(fit3.p)
#print
#print [gv.exp(fit3.p['logc4n_0'])*fit3.p['k4n_0']*gv.exp(-t*fit3.p['logEn_0'])+gv.exp(fit3.p['logc4o_0'])*fit3.p['k4o_0']*((-1)**t)*gv.exp(-t*fit3.p['logEo_0']) for t in range(2,10)]
## -- save fit as an initial value dictionary
if df.do_irrep == "16":
irrepStr = '16'
if df.do_2pt:
save_init_from_fit(fit2,'fit_adv_'+irrepStr+'_2pt.py')
if df.do_3pt:
save_init_from_fit(fit3,'fit_adv_'+irrepStr+'_3pt.py',df.do_v_symmetric)
#fit3 = fitter3.lsqfit(data=dall,prior=priors,svdcut=df.svdcut)
print fmt_reduced_chi2(fit3)
#save_data('./test.fit.out',fit,dall)
## -- print
#if df.do_2pt:
# print_fit(fit2,priors2)
print_fit(fit3,priors,df.do_v_symmetric)
### -- save fit as an initial value dictionary
#if df.do_irrep == "16":
# irrepStr = '16'
#if df.do_2pt:
# save_init_from_fit(fit2,'fit_dict'+irrepStr+'_2pt.py')
#if df.do_3pt:
# save_init_from_fit(fit3,'fit_dict'+irrepStr+'_3pt.py',df.do_v_symmetric)
## -- test routines
#import util_plots as utp
#for model in models:
# try:
# fn = utp.create_fit_func_3pt(model,fit3)
# test1 = model.fitfcn(fit3.transformed_p)
# test2 = fn(model.tfit)
# print model.datatag
# print test1
# print test2
# except AttributeError:
# ## -- 2pt
# pass
## -- plot
if df.do_plot or argsin['override_plot']:
#plot_corr_effective_mass_check(models2,dall,None,**df.fitargs)
#plot_corr_effective_mass(models2,dall,None,**df.fitargs)
#plot_corr_double_log(models2,dall,fit3,**df.fitargs)
#plot_corr_adv_dl_folded(models2,dall,fit3,req=None,**df.fitargs)
#plot_corr_adv_dl_folded(models2,dall,fit3,req=[[0],list()],**df.fitargs)
#plot_corr_adv_dl_folded(models2,dall,fit3,req=[[1],list()],**df.fitargs)
#plot_corr_adv_dl_folded(models2,dall,fit3,req=[list(),[0]],**df.fitargs)
#plot_corr_adv_dl_folded(models2,dall,fit3,req=[list(),[1]],**df.fitargs)
#plot_corr_adv_dl_folded(models2,dall,fit3,req=[list(),[0,1]],**df.fitargs)
#plot_corr_normalized(models2,dall,fit3,**df.fitargs)
if df.do_3pt:
#plot_corr_3pt(models3,dall,fit3,**df.fitargs)
pass
if df.do_plot_terminal:
plt.show()
#if df.do_sn_minimize:
# ## -- shortcut
# cvec6,kvec6,_ = sn_minimize_postload_3pt(dall,6,'aiai')
# cvec7,kvec7,_ = sn_minimize_postload_3pt(dall,7,'aiai')
# print "source vectors:"
# print cvec6
# print cvec7
# print "sink vectors:"
# print kvec6
# print kvec7
# clist = list()
# klist = list()
# for key in dall:
# ## -- deconstruct key based on current conventions
# tkey = 't'.join(key.split('t')[:-1])
# if len(tkey) == 0:
# continue
# k = int(tkey[-1])
# if not(k in klist):
# klist.append(k)
# c = int(tkey[-2])
# if not(c in clist):
# clist.append(c)
# call6 = list()
# call7 = list()
# for i,c in zip(range(len(clist)),sorted(clist)):
# call6.append(list(np.zeros(len(klist))))
# call7.append(list(np.zeros(len(klist))))
# for j,k in zip(range(len(klist)),sorted(klist)):
# call6[i][j] = dall['aiais'+str(c)+str(k)+'t6']
# call7[i][j] = dall['aiais'+str(c)+str(k)+'t7']
# cdia6 = diagonalize_correlator(call6,cvec6,kvec6)
# cdia7 = diagonalize_correlator(call7,cvec6,kvec6)
# #ddia = {}
# for i,c in zip(range(len(clist)),sorted(clist)):
# for j,k in zip(range(len(klist)),sorted(klist)):
# dall['aiais'+str(c)+str(k)+'t6'] = cdia6[i][j]
# dall['aiais'+str(c)+str(k)+'t7'] = cdia7[i][j]
# #kwargs = df.fitargs
# for key in dall:
# tkey = 't'.join(key.split('t')[:-1])
# if len(tkey) > 0:
# df.fitargs[tkey]["p3_save_name"] = \
# "s3-s8p-l3248-coul-"+tkey+".pdf"
# df.fitargs[tkey]["y_scale"] = [-.2,.6]
# df.fitargs[tkey]["yaxistitle"] = r"$\beta v_{i}^{T}C_{ij}w_{j}(t,T)$"
# plot_corr_3pt(models3,dall,fit3,**df.fitargs)
# plt.show()