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runfix3_x1.py
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runfix3_x1.py
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#!/usr/bin/env python
import pickle
import cPickle
import numpy
import pystan
import sivel
f = open('fix3.pkl','rb')
(fit,_) = pickle.load(f)
gamma0 = numpy.median(fit['gamma'],axis=0)
gamma1 = numpy.median(fit['rho1'],axis=0)
gamma0_cov = numpy.cov(fit['gamma'],rowvar=False)
gamma1_cov = numpy.cov(fit['rho1'],rowvar=False)
_, gamma0_ev = numpy.linalg.eigh(gamma0_cov)
# print gamma0_ev[:,0]
ev_median = numpy.median(fit['ev']*numpy.sign(fit['ev'][:,4][:,None]),axis=0)
ev_median = ev_median/numpy.linalg.norm(ev_median)
evsig_median = numpy.median(fit['ev_sig'])
gamma0_eval = numpy.array(fit['gamma'])
for i in xrange(gamma0_eval.shape[0]):
for j in xrange(5):
gamma0_eval[i,j]=numpy.dot(fit['gamma'][i,:], gamma0_ev[:,j])
# gamma0_min = numpy.min(gamma0_eval,axis=0)-0.05
# gamma0_max = numpy.max(gamma0_eval,axis=0)+0.05
# for i in xrange(5):
# plt.hist(gamma0_eval[:,i])
# plt.show()
_, gamma1_ev = numpy.linalg.eigh(gamma1_cov)
gamma1_eval = numpy.array(fit['rho1'])
for i in xrange(gamma1_eval.shape[0]):
for j in xrange(5):
gamma1_eval[i,j]=numpy.dot(fit['rho1'][i,:], gamma1_ev[:,j])
gamma0median = numpy.median(gamma0_eval,axis=0)
gamma1median = numpy.median(gamma1_eval,axis=0)
gamma1_min = (numpy.min(gamma1_eval,axis=0)-gamma1median)*3+gamma1median
gamma1_max = (numpy.max(gamma1_eval,axis=0)-gamma1median)*3+gamma1median
# two color parameter model
pkl_file = open('gege_data.pkl', 'r')
data = pickle.load(pkl_file)
pkl_file.close()
sivel, sivel_err, x1, x1_err, _, _, _ = sivel.sivel(data)
use = numpy.isfinite(sivel)
# The ordering is 'Ca','Si','U','B','V','R','I'
EW_obs = data['obs'][:,0:2]
mag_obs = data['obs'][:,2:]
EW_cov = data['cov'][:,0:2,0:2]
mag_cov = data['cov'][:,2:,2:]
sivel=sivel[use]
sivel_err = sivel_err[use]
x1=x1[use]
x1_err = x1_err[use]
EW_obs=EW_obs[use]
mag_obs=mag_obs[use]
EW_cov= EW_cov[use]
mag_cov=mag_cov[use]
nsne, nmags = mag_obs.shape
# # renormalize the data
EW_mn = EW_obs.mean(axis=0)
EW_renorm = (EW_obs - EW_mn)
mag_mn = mag_obs.mean(axis=0)
mag_renorm = mag_obs-mag_mn
sivel_mn = sivel.mean()
sivel_renorm = sivel-sivel_mn
data = {'D': nsne, 'N_mags': 5, 'N_EWs': 2, 'mag_obs': mag_renorm, 'EW_obs': EW_renorm, 'EW_cov': EW_cov, 'mag_cov':mag_cov, \
'sivel_obs': sivel_renorm, 'sivel_err': sivel_err,'x1_obs': x1, 'x1_err':x1_err, \
'rho1_ev':gamma1_ev, 'rho1_min':gamma1_min, 'rho1_max': gamma1_max }
# pystan.misc.stan_rdump(data, 'data.R')
# wefew
Delta_simplex = numpy.zeros(nsne-1)
# Delta_simplex = numpy.zeros(nsne)+1./nsne
# k_simplex = numpy.zeros(nsne)
R_simplex = ((-1.)**numpy.arange(nsne)*.25 + .5)*2./nsne
R_simplex = R_simplex/R_simplex.sum()
numpy.random.seed(100)
ruv = []
for _ in range(8):
temp = numpy.random.uniform(-1,1,5)
ruv.append(temp/numpy.linalg.norm(temp))
init = [{'EW' : EW_renorm, \
'sivel': sivel_renorm,\
'x1': x1,\
'c_raw' : numpy.zeros(5), \
'alpha_raw' : numpy.zeros(5), \
'beta_raw' : numpy.zeros(5), \
'eta_raw' : numpy.zeros(5), \
'zeta' : numpy.zeros(5), \
'ev_sig': evsig_median, \
'ev': ev_median,\
# 'L_sigma_raw': numpy.zeros(5)+0.03*100, \
'gamma01': gamma0[0],\
'gamma02': gamma0[1],\
'gamma03': gamma0[2],\
'gamma04': gamma0[3],\
'gamma05': gamma0[4],\
'mag_int_raw': numpy.mean(mag_renorm,axis=1), \
# 'L_Omega': numpy.identity(5), \
'Delta_unit':R_simplex, \
'Delta_scale': 15./4, \
'k_unit': R_simplex, \
'R_unit': R_simplex, \
'rho11': gamma1median[0],\
'rho12': gamma1median[1],\
'rho13': gamma1median[2],\
'rho14': gamma1median[3],\
'rho15': gamma1median[4]\
} \
for _ in range(8)]
sm = pystan.StanModel(file='fix3_x1.stan')
control = {'stepsize':1}
fit = sm.sampling(data=data, iter=5000, chains=8,control=control,init=init, thin=1)
output = open('fix3_x1.pkl','wb')
pickle.dump((fit.extract(),fit.get_sampler_params()), output, protocol=2)
output.close()
print fit