def plotalltausfits(filenames, outpath, title=None, xlim=None, ylims=None):
import numpy as np
from readfits import read_corr
ylim0, ylim1, ylim2 = [None, None, None]
if ylims is not None: ylim0, ylim1, ylim2 = ylims
meanr, tau0, cov_tau0 = read_corr(filenames[0])
meanr, tau2, cov_tau2 = read_corr(filenames[1])
meanr, tau5, cov_tau5 = read_corr(filenames[2])
sig_tau0 = np.sqrt(np.diag(cov_tau0))
sig_tau2 = np.sqrt(np.diag(cov_tau2))
sig_tau5 = np.sqrt(np.diag(cov_tau5))
plt.clf()
pretty_rho2(meanr,
tau0,
sig_tau0,
tau2,
sig_tau2,
tau5,
sig_tau5,
tauleg=True,
title=title,
xlim=xlim,
ylim=ylim1)
print("Printing file: ", outpath + 'tau_all_rsrs.png')
plt.savefig(outpath + 'tau_all_rsgal.png')
def plotallrhosfits(filenames, outpath, title=None, xlim=None, ylims=None):
import numpy as np
from readfits import read_corr
ylim0, ylim1 = [None, None]
if ylims is not None: ylim0, ylim1 = ylims
meanr, rho0, cov_rho0 = read_corr(filenames[0])
meanr, rho1, cov_rho1 = read_corr(filenames[1])
meanr, rho2, cov_rho2 = read_corr(filenames[2])
meanr, rho3, cov_rho3 = read_corr(filenames[3])
meanr, rho4, cov_rho4 = read_corr(filenames[4])
meanr, rho5, cov_rho5 = read_corr(filenames[5])
sig_rho0 = np.sqrt(np.diag(cov_rho0))
sig_rho1 = np.sqrt(np.diag(cov_rho1))
sig_rho2 = np.sqrt(np.diag(cov_rho2))
sig_rho3 = np.sqrt(np.diag(cov_rho3))
sig_rho4 = np.sqrt(np.diag(cov_rho4))
sig_rho5 = np.sqrt(np.diag(cov_rho5))
plt.clf()
pretty_rho1(meanr,
rho1,
sig_rho1,
rho3,
sig_rho3,
rho4,
sig_rho4,
title=title,
xlim=xlim,
ylim=ylim0)
print("Printing file: ", outpath + 'rho1_all_rsrs.png')
plt.savefig(outpath + 'rho1_all_rsrs.png')
plt.clf()
pretty_rho2(meanr,
rho0,
sig_rho0,
rho2,
sig_rho2,
rho5,
sig_rho5,
title=title,
xlim=xlim,
ylim=ylim1)
print("Printing file: ", outpath + 'rho2_all_rsrs.png')
plt.savefig(outpath + 'rho2_all_rsrs.png')
def writexipbias(samples,rhosfilenames,xim=False, plots=False,nameterms='terms_dxi.png',namedxip='dxi.png',namecovmat='covmat_pars.png',filename='dxip.fits'):
from readjson import read_rhos
from maxlikelihood import bestparameters
from plot_stats import pretty_rho
from readfits import read_corr
from astropy.io import fits
import numpy as np
#plot covariance matrix of parameters alpha, beta and eta.
if plots:
par_matcov = np.cov(samples)
corr=corrmatrix(par_matcov)
print(par_matcov)
print(corr)
cov_vmin=np.min(corr)
plt.imshow(corr,cmap='viridis'+'_r', interpolation='nearest',
aspect='auto', origin='lower', vmin=cov_vmin, vmax=1.)
plt.colorbar()
plt.title(r'$\alpha \mid \beta \mid \eta $')
plt.savefig(namecovmat, dpi=500)
print(namecovmat, 'Printed!')
a = b = n = 0; vara = varb = varn = 0; covab = covan = covbn = 0
bestpar = bestparameters(samples)
par_matcov = np.cov(samples)
if (par_matcov.size==1 ): variances = par_matcov
else: variances = np.diagonal(par_matcov)
covariances = sum( (par_matcov[i,i+1: ].tolist() for i in range(len(samples) - 1)) , [] )
if(len(samples)==3):
a, b, n = bestpar
vara, varb, varn = variances
covab, covan, covbn = covariances
elif(len(samples)==2):
a, b = bestpar
vara, varb = variances
covab = covariances[0]
elif(len(samples)==1):
a = bestpar[0]
vara = variances
else:
print("Warning, test type not defined")
rhonames = args.rhos
meanr, rho0, cov_rho0 = read_corr(rhonames[0])
meanr, rho1, cov_rho1 = read_corr(rhonames[1])
meanr, rho2, cov_rho2 = read_corr(rhonames[2])
meanr, rho3, cov_rho3 = read_corr(rhonames[3])
meanr, rho4, cov_rho4 = read_corr(rhonames[4])
meanr, rho5, cov_rho5 = read_corr(rhonames[5])
sig_rho0 = np.sqrt(np.diag(cov_rho0))
sig_rho1 = np.sqrt(np.diag(cov_rho1))
sig_rho2 = np.sqrt(np.diag(cov_rho2))
sig_rho3 = np.sqrt(np.diag(cov_rho3))
sig_rho4 = np.sqrt(np.diag(cov_rho4))
sig_rho5 = np.sqrt(np.diag(cov_rho5))
#Ploting each term of the bias
if(plots):
xlim = [2., 300.]
#supposing that a,b and n are idependent of rhos(scale independent)
var0 = ((2*a*rho0p)**2)*vara + (a**2)*(sig_rho0**2)
var1 = ((2*b*rho1p)**2)*varb + (b**2)*(sig_rho1**2)
var2 = ((2*n*rho3p)**2)*varn + (n**2)*(sig_rho3**2)
varab = vara*(b**2) + varb*(a**2) + 2*covab*(a*b)
#varab = ((a*b)**2)*( (vara/((a)**2)) + (varb/((b)**2)) + 2*covab/(a*b) )
var3 = 4*( (rho2p**2)*varab + (sig_rho2**2)*((a*b)**2) )
#var3 = 4*((a*b*rho2p)**2)*( varab/((a*b)**2) + (sig_rho2/rho2p)**2 )
varbn = varn*(b**2) + varb*(n**2) + 2*covbn*(b*n)
#varbn = ((n*b)**2)*( (varn/((n)**2)) + (varb/((b)**2)) + 2*covbn/(b*n) )
var4 = 4*( (rho4p**2)*varbn + (sig_rho4**2)*((n*b)**2) )
#var4 = 4*((n*b*rho4p)**2)*(varbn/((b*n)**2) + (sig_rho4/rho4p)**2)
varan = varn*(a**2) + vara*(n**2) + 2*covan*(a*n)
#varan = ((n*a)**2)*( (varn/((n)**2)) + (vara/((a)**2)) + 2*covan/(a*n) )
var5 = 4*( (rho5p**2)*varan + (sig_rho5**2)*((n*a)**2) )
#var5 = 4*((n*a*rho5p)**2)*(varan/((a*n)**2) + (sig_rho5/rho5p)**2)
plt.clf()
lfontsize = 7
if (len(samples)==3):
pretty_rho(meanr, (a**2)*rho0p, np.sqrt(np.diag(cov_rho0)), legend=r'$\alpha^{2} \rho_{0}$',lfontsize=lfontsize, color='red', ylabel='Correlations', xlim=xlim)
pretty_rho(meanr, (b**2)*rho1p, np.sqrt(var1), legend=r'$\beta^{2}\rho_{1}$',lfontsize=lfontsize, color='green', ylabel='Correlations', xlim=xlim)
pretty_rho(meanr, (n**2)*rho3p, np.sqrt(var2), legend=r'$\eta^{2}\rho_{3}$', lfontsize=lfontsize, color='black', ylabel='Correlations', xlim=xlim)
pretty_rho(meanr, (2*a*b)*rho2p, np.sqrt(var3), legend=r'$2\alpha\beta \rho_{2}$',lfontsize=lfontsize, color='yellow', ylabel='Correlations', xlim=xlim)
pretty_rho(meanr, (2*b*n)*rho4p, np.sqrt(var4), legend=r'$2\beta\eta\rho_{4}$',lfontsize=lfontsize, color='blue', ylabel='Correlations', xlim=xlim)
pretty_rho(meanr, (2*n*a)*rho5p, np.sqrt(var5), legend=r'$2\eta\alpha\rho_{5}$', lfontsize=lfontsize, color='gray', ylabel='Correlations', xlim=xlim)
print('Printing', nameterms)
plt.savefig(nameterms, dpi=200)
if (len(samples)==2):
pretty_rho(meanr, (a**2)*rho0p, np.sqrt(var0), legend=r'$\alpha^{2} \rho_{0}$',lfontsize=lfontsize, color='red', ylabel='Correlations', xlim=xlim)
pretty_rho(meanr, (b**2)*rho1p, np.sqrt(var1), legend=r'$\beta^{2}\rho_{1}$',lfontsize=lfontsize, color='green', ylabel='Correlations', xlim=xlim)
pretty_rho(meanr, (2*a*b)*rho2p, np.sqrt(var3), legend=r'$2\alpha\beta \rho_{2}$',lfontsize=lfontsize, color='yellow', ylabel='Correlations', xlim=xlim)
print('Printing', nameterms)
plt.savefig(nameterms, dpi=200)
if (len(samples)==1):
pretty_rho(meanr, (a**2)*rho0p, np.sqrt(var0), legend=r'$\alpha^{2} \rho_{0}$',lfontsize=lfontsize, color='red', ylabel='Correlations', xlim=xlim)
print('Printing', nameterms)
plt.savefig(nameterms, dpi=200)
#supposing that a,b and n are idependent of rhos(scale independent)
dxip = (a**2)*rho0p + (b**2)*rho1p + (n**2)*rho3p + (2*a*b)*rho2p + (2*b*n)*rho4p + (2*n*a)*rho5p
f1 = 2*(a*rho0p + b*rho2p + n*rho5p)
f2 = 2*(b*rho1p + a*rho2p + n*rho4p)
f3 = 2*(n*rho3p + b*rho4p + a*rho5p)
f4 = a**2 ; f5 = b**2; f6 = 2*a*b
f7 = n**2 ; f8 = 2*b*n; f9 = 2*n*a
covmat_dxip = np.diag( (f1**2)*vara + (f2**2)*varb + (f3**2)*varn + + 2*(f1*f2*covab + f1*f3*covan + f2*f3*covbn) ) \
+ (f4**2)*(cov_rho0) + (f5**2)*(cov_rho1) + (f6**2)*(cov_rho2) + (f7**2)*(cov_rho3) +(f8**2)*(cov_rho4) + (f9**2)*(cov_rho5)
if(plots):
plt.clf()
pretty_rho(meanr, dxip, np.sqrt(np.diag(covmat_dxip)) , legend=r"$\delta \xi_{+}$", ylabel=r"$\delta \xi_{+}$", xlim=xlim)
print('Printing', dxipname)
plt.savefig(dxipname, dpi=150)
nrows = len(dxip)
hdu = fits.PrimaryHDU()
hdul = fits.HDUList([hdu])
covmathdu = fits.ImageHDU(covmat_dxip, name='COVMAT')
hdul.insert(1, covmathdu)
angarray = meanr
valuearray = np.array(dxip)
bin1array = np.array([ -999]*nrows)
bin2array = np.array([ -999]*nrows)
angbinarray = np.arange(nrows)
array_list = [bin1array, bin2array, angbinarray, valuearray, angarray ]
for array, name in zip(array_list, names): outdata[name] = array
corrhdu = fits.BinTableHDU(outdata, name=nam)
hdul.insert(2, corrhdu)
if xim:
hdul.writeto(filename + 'm.fits', clobber=True)
else:
hdul.writeto(filename + 'p.fits', clobber=True)
def main():
import sys
args = parse_args()
sys.path.insert(0, args.srcpath)
from readfits import read_corr
from plot_stats import plotallrhosfits, plotallrhoscorrmatfits, plotalltausfits, plotalltauscorrmatfits
import numpy as np
#Make directory where the ouput data will be
outpath = os.path.expanduser(args.outpath)
try:
if not os.path.exists(outpath):
os.makedirs(outpath)
except OSError:
if not os.path.exists(outpath): raise
plotspath = os.path.expanduser(args.plotspath)
try:
if not os.path.exists(plotspath):
os.makedirs(plotspath)
except OSError:
if not os.path.exists(outpath): raise
if (args.plots):
xlim = [2., 300.]
#Make directory where the ouput data will be
plotallrhosfits(args.rhos, outpath=plotspath, xlim=xlim)
plotallrhoscorrmatfits(args.rhos, outpath=plotspath)
#plotalltausfits(args.taus, outpath=plotspath, xlim=xlim)
#plotalltauscorrmatfits(args.taus, outpath=plotspath)
rhonames = args.rhos
meanr, rho0p, cov_rho0p = read_corr(rhonames[0]); meanr, rho0m, cov_rho0m = read_corr(rhonames[6])
meanr, rho1p, cov_rho1p = read_corr(rhonames[1]); meanr, rho1m, cov_rho1m = read_corr(rhonames[7])
meanr, rho2p, cov_rho2p = read_corr(rhonames[2]); meanr, rho2m, cov_rho2m = read_corr(rhonames[8])
meanr, rho3p, cov_rho3p = read_corr(rhonames[3]); meanr, rho3m, cov_rho3m = read_corr(rhonames[9])
meanr, rho4p, cov_rho4p = read_corr(rhonames[4]); meanr, rho4m, cov_rho4m = read_corr(rhonames[10])
meanr, rho5p, cov_rho5p = read_corr(rhonames[5]); meanr, rho5m, cov_rho5m = read_corr(rhonames[11])
rhosp = [rho0p, rho1p, rho2p, rho3p, rho4p, rho5p]
covrhosp = [cov_rho0p, cov_rho1p, cov_rho2p, cov_rho3p, cov_rho4p, cov_rho5p]
rhosm = [rho0m, rho1m, rho2m, rho3m, rho4m, rho5m]
covrhosm = [cov_rho0m, cov_rho1m, cov_rho2m, cov_rho3m, cov_rho4m, cov_rho5m]
taunames = args.tau
meanr, tau0p, cov_tau0p = read_corr(taunames[0]); meanr, tau0m, cov_tau0m = read_corr(taunames[3])
meanr, tau2p, cov_tau2p = read_corr(taunames[1]); meanr, tau2m, cov_tau2m = read_corr(taunames[4])
meanr, tau5p, cov_tau5p = read_corr(taunames[2]); meanr, tau5m, cov_tau5m = read_corr(taunames[5])
tausp = [tau0p, tau2p, tau5p]
covtausp = [cov_tau0p, cov_tau2p, cov_tau5p]
tausm = [tau0m, tau2m, tau5m]
covtausm = [cov_tau0m, cov_tau2m, cov_tau5m]
data = {}
data['rhosp'] = rhosp; data['rhosm'] = rhosm
data['covrhosp'] = covrhosp; data['covrhosm'] = covrhosm
data['tausp'] = tausp; data['tausm'] = tausm
data['covtausp'] = covtausp; data['covtausm'] = covtausm
#Finding best alpha beta gamma
nwalkers, nsteps = 100, 1000
moderr = False
nsig = 1
eq = 'All'
i_guess0 = [ -0.01, 1, -1 ] #fiducial values
if not (args.abe or args.ab or args.a): args.abe = True
## ALPHA-BETA-ETA
if(args.abe):
print("### Runing alpha, beta and eta test ### ")
gflag, bflag = True, True
i_guess = i_guess0
namemc = plotspath + 'mcmc_alpha-beta-eta_eq_' + str(eq) + '_join_.png'
namecont = plotspath +'contours_alpha-beta-eta_eq_' + str(eq) + '_join_.png'
nameterms = plotspath +'termsdxip_alpha-beta-eta_eq_' + str(eq) + '_join_.png'
namecovmat = plotspath +'covmatrix_alpha-beta-eta_eq_' + str(eq) + '_join_.png'
namedxip = plotspath +'xobias_abe_' + str(eq) + '_join_.png'
filename = outspath +'abe_dxip_join.fits'
samples = RUNtest(args, data, nwalkers, nsteps, i_guess, gflag, bflag, eq, moderr, nsig, namemc, namecont)
writexipbias(samples, args.rhos, args.plots, args.xim, nameterms, namedxip, namecovmat, filename )
## ALPHA-BETA
if(args.ab):
print("### Runing alpha and beta test ### ")
gflag, bflag = False, True
i_guess = i_guess0[:2] #fiducial values
namemc = plotspath + 'mcmc_alpha-beta_eq_' + str(eq) + '_join_.png'
namecont = plotspath + 'contours_alpha-beta_eq_' + str(eq) + '_join_.png'
nameterms = plotspath + 'termsdxip_alpha-beta_eq_' + str(eq) + '_join_.png'
namecovmat = plotspath +'covmatrix_alpha-beta_eq_' + str(eq) + '_join_.png'
namedxip = plotspath +'xibias_ab_' + str(eq) + '_join_.png'
filename = outspath +'ab_dxip_join.fits'
samples = RUNtest(args, data, nwalkers, nsteps, i_guess, gflag, bflag, eq, moderr, nsig, namemc, namecont)
writexipbias(samples, args.rhos, args.plots, args.xim, nameterms, namedxip, namecovmat, filename )
## ALPHA
if(args.a):
print("### Runing alpha test ### ")
gflag, bflag = False, False
i_guess = i_guess0[:1] #fiducial values
namemc = plotspath +'mcmc_alpha_eq_' + str(eq) + '_join_.png'
namecont = plotspath +'contours_alpha_eq_' + str(eq) + '_join_.png'
nameterms = plotspath +'termsdxip_alpha_eq_' + str(eq) + '_join_.png'
namecovmat = plotspath +'covmatrix_alpha-beta-eta_eq_' + str(eq) + '_join_.png'
namedxip = plotspath +'xibias_a_' + str(eq) + '_join_.png'
filename = outspath +'a_dxip_join.fits'
samples = RUNtest(args, data, nwalkers, nsteps, i_guess, gflag, bflag, eq, moderr, nsig, namemc, namecont)
writexipbias(samples, args.rhos, args.plots, nameterms, namedxip, namecovmat, filename )
def main():
import sys
args = parse_args()
sys.path.insert(0, args.srcpath)
from readfits import read_corr
from plot_stats import plotallrhosfits, plotallrhoscorrmatfits, plotalltausfits, plotalltauscorrmatfits
import numpy as np
#Make directory where the ouput data will be
outpath = os.path.expanduser(args.outpath)
try:
if not os.path.exists(outpath):
os.makedirs(outpath)
except OSError:
if not os.path.exists(outpath): raise
plotspath = os.path.expanduser(args.plotspath)
try:
if not os.path.exists(plotspath):
os.makedirs(plotspath)
except OSError:
if not os.path.exists(outpath): raise
if (args.xim):
args.rhos = \
['/home/dfa/sobreira/alsina/alpha-beta-gamma/code/correlations/RHO0M.fits',
'/home/dfa/sobreira/alsina/alpha-beta-gamma/code/correlations/RHO1M.fits',
'/home/dfa/sobreira/alsina/alpha-beta-gamma/code/correlations/RHO2M.fits',
'/home/dfa/sobreira/alsina/alpha-beta-gamma/code/correlations/RHO3M.fits',
'/home/dfa/sobreira/alsina/alpha-beta-gamma/code/correlations/RHO4M.fits',
'/home/dfa/sobreira/alsina/alpha-beta-gamma/code/correlations/RHO5M.fits']
args.taus = \
['/home/dfa/sobreira/alsina/alpha-beta-gamma/code/correlations/TAU0M.fits',
'/home/dfa/sobreira/alsina/alpha-beta-gamma/code/correlations/TAU2M.fits',
'/home/dfa/sobreira/alsina/alpha-beta-gamma/code/correlations/TAU5M.fits']
if (args.plots):
xlim = [2., 300.]
ylims = [[1.e-11, 1.e-7], [3.e-8, 3.e-4]]
rhostitle = ''
plotallrhosfits(args.rhos,
outpath=plotspath,
title=rhostitle,
xlim=xlim,
ylims=ylims)
plotallrhoscorrmatfits(args.rhos, outpath=plotspath)
plotalltausfits(args.taus, outpath=plotspath, xlim=xlim)
plotalltauscorrmatfits(args.taus, outpath=plotspath)
rhonames = args.rhos
meanr, rho0, cov_rho0 = read_corr(rhonames[0], maxscale=args.maxscale)
meanr, rho1, cov_rho1 = read_corr(rhonames[1], maxscale=args.maxscale)
meanr, rho2, cov_rho2 = read_corr(rhonames[2], maxscale=args.maxscale)
meanr, rho3, cov_rho3 = read_corr(rhonames[3], maxscale=args.maxscale)
meanr, rho4, cov_rho4 = read_corr(rhonames[4], maxscale=args.maxscale)
meanr, rho5, cov_rho5 = read_corr(rhonames[5], maxscale=args.maxscale)
rhos = [rho0, rho1, rho2, rho3, rho4, rho5]
covrhos = [cov_rho0, cov_rho1, cov_rho2, cov_rho3, cov_rho4, cov_rho5]
taunames = args.taus
meanr, tau0, cov_tau0 = read_corr(taunames[0], maxscale=args.maxscale)
meanr, tau2, cov_tau2 = read_corr(taunames[1], maxscale=args.maxscale)
meanr, tau5, cov_tau5 = read_corr(taunames[2], maxscale=args.maxscale)
taus = [tau0, tau2, tau5]
covtaus = [cov_tau0, cov_tau2, cov_tau5]
data = {}
data['rhos'] = rhos
data['covrhos'] = covrhos
data['taus'] = taus
data['covtaus'] = covtaus
#for i in taus: print i.size
#for i in covrhos: print i.shape
#Finding best alpha beta gamma
nwalkers, nsteps = 100, 1000
moderr = False
nsig = 1
eq = 2
#'All'
i_guess0 = [-0.01, 1, -1] #fiducial values
if not (args.abe or args.ab or args.a): args.abe = True
## ALPHA-BETA-ETA
if (args.abe):
print("### Runing alpha, beta and eta test ### ")
eflag, bflag = True, True
i_guess = i_guess0
namemc = plotspath + 'mcmc_alpha-beta-eta_eq_' + str(eq) + '_.png'
namecont = plotspath + 'contours_alpha-beta-eta_eq_' + str(
eq) + '_.png'
nameterms = plotspath + 'termsdxip_alpha-beta-eta_eq_' + str(
eq) + '_.png'
namecovmat = plotspath + 'covmatrix_alpha-beta-eta_eq_' + str(
eq) + '_.png'
namedxip = plotspath + 'xobias_abe_' + str(eq) + '_.png'
filename = outpath + 'abe_dxi.fits'
samples = RUNtest(data, nwalkers, nsteps, i_guess, eflag, bflag, eq,
args.uwmprior, moderr, nsig, namemc, namecont)
writexipbias(samples, args.rhos, args.plots, args.xim, nameterms,
namedxip, namecovmat, filename)
## ALPHA-BETA
if (args.ab):
print("### Runing alpha and beta test ### ")
eflag, bflag = False, True
i_guess = i_guess0[:2] #fiducial values
namemc = plotspath + 'mcmc_alpha-beta_eq_' + str(eq) + '_.png'
namecont = plotspath + 'contours_alpha-beta_eq_' + str(eq) + '_.png'
nameterms = plotspath + 'termsdxip_alpha-beta_eq_' + str(eq) + '_.png'
namecovmat = plotspath + 'covmatrix_alpha-beta_eq_' + str(eq) + '_.png'
namedxip = plotspath + 'xibias_ab_' + str(eq) + '_.png'
filename = outpath + 'ab_dxi.fits'
samples = RUNtest(data, nwalkers, nsteps, i_guess, eflag, bflag, eq,
args.uwmprior, moderr, nsig, namemc, namecont)
writexipbias(samples, args.rhos, args.plots, args.xim, nameterms,
namedxip, namecovmat, filename)
## ALPHA
if (args.a):
print("### Runing alpha test ### ")
eflag, bflag = False, False
i_guess = i_guess0[:1] #fiducial values
namemc = plotspath + 'mcmc_alpha_eq_' + str(eq) + '_.png'
namecont = plotspath + 'contours_alpha_eq_' + str(eq) + '_.png'
nameterms = plotspath + 'termsdxip_alpha_eq_' + str(eq) + '_.png'
namecovmat = plotspath + 'covmatrix_alpha-beta-eta_eq_' + str(
eq) + '_.png'
namedxip = plotspath + 'xibias_a_' + str(eq) + '_.png'
filename = outpath + 'a_dxi.fits'
samples = RUNtest(data, nwalkers, nsteps, i_guess, eflag, bflag, eq,
args.uwmprior, moderr, nsig, namemc, namecont)
writexipbias(samples, args.rhos, args.plots, args.xim, nameterms,
namedxip, namecovmat, filename)
def main():
import sys
args = parse_args()
sys.path.insert(0, args.srcpath)
from readfits import read_corr
from plot_stats import plotallrhosfits, plotallrhoscorrmatfits, plotalltausfits, plotalltauscorrmatfits, plot_samplesdist
import numpy as np
#Make directory where the ouput data will be
outpath = os.path.expanduser(args.outpath)
try:
if not os.path.exists(outpath):
os.makedirs(outpath)
except OSError:
if not os.path.exists(outpath): raise
plotspath = os.path.expanduser(args.plotspath)
try:
if not os.path.exists(plotspath):
os.makedirs(plotspath)
except OSError:
if not os.path.exists(outpath): raise
if(args.xim):
args.rhos = \
['/home/dfa/sobreira/alsina/alpha-beta-gamma/code/correlations/RHO0M.fits',
'/home/dfa/sobreira/alsina/alpha-beta-gamma/code/correlations/RHO1M.fits',
'/home/dfa/sobreira/alsina/alpha-beta-gamma/code/correlations/RHO2M.fits',
'/home/dfa/sobreira/alsina/alpha-beta-gamma/code/correlations/RHO3M.fits',
'/home/dfa/sobreira/alsina/alpha-beta-gamma/code/correlations/RHO4M.fits',
'/home/dfa/sobreira/alsina/alpha-beta-gamma/code/correlations/RHO5M.fits']
args.taus = \
['/home/dfa/sobreira/alsina/alpha-beta-gamma/code/correlations/TAU0M.fits',
'/home/dfa/sobreira/alsina/alpha-beta-gamma/code/correlations/TAU2M.fits',
'/home/dfa/sobreira/alsina/alpha-beta-gamma/code/correlations/TAU5M.fits']
if (args.plots):
xlim = [2., 300.]
ylims = [[1.e-11,1.e-7 ],[3.e-8 ,3.e-4 ]]
rhostitle = ''
plotallrhosfits(args.rhos, outpath=plotspath, title=rhostitle, xlim=xlim, ylims=ylims)
plotallrhoscorrmatfits(args.rhos, outpath=plotspath)
plotalltausfits(args.taus, outpath=plotspath, xlim=xlim)
plotalltauscorrmatfits(args.taus, outpath=plotspath)
rhonames = args.rhos
meanr, rho0, cov_rho0 = read_corr(rhonames[0], maxscale=args.maxscale)
meanr, rho1, cov_rho1 = read_corr(rhonames[1], maxscale=args.maxscale)
meanr, rho2, cov_rho2 = read_corr(rhonames[2], maxscale=args.maxscale)
meanr, rho3, cov_rho3 = read_corr(rhonames[3], maxscale=args.maxscale)
meanr, rho4, cov_rho4 = read_corr(rhonames[4], maxscale=args.maxscale)
meanr, rho5, cov_rho5 = read_corr(rhonames[5], maxscale=args.maxscale)
rhos = [rho0, rho1, rho2, rho3, rho4, rho5]
covrhos = [cov_rho0, cov_rho1, cov_rho2, cov_rho3, cov_rho4, cov_rho5]
taunames = args.taus
meanr, tau0, cov_tau0 = read_corr(taunames[0], maxscale=args.maxscale)
meanr, tau2, cov_tau2 = read_corr(taunames[1], maxscale=args.maxscale)
meanr, tau5, cov_tau5 = read_corr(taunames[2], maxscale=args.maxscale)
taus = [tau0, tau2, tau5]
covtaus = [cov_tau0, cov_tau2, cov_tau5]
data = {}
data['rhos'] = rhos
data['covrhos'] = covrhos
data['taus'] = taus
data['covtaus'] = covtaus
#for i in taus: print i.size
#for i in covrhos: print i.shape
#Finding best alpha beta gamma
nwalkers, nsteps = 100, 1000
moderr = False
minimize = True
nsig = 1
eq = args.eq
print("Using equations: ", eq)
i_guess0 = [ -0.01, 1, -1 ] #fiducial values
if not (args.abe or args.ab or args.a): args.abe = True
## ALPHA-BETA-ETA
if(args.abe):
print("### Runing alpha, beta and eta test ### ")
mflags = [True, True, True] ##alpha,beta,eta
namemc = plotspath + 'mcmc_alpha-beta-eta_eq_' + str(eq) + '_.png'
namecont = plotspath +'contours_alpha-beta-eta_eq_' + str(eq) + '_.png'
nameterms = plotspath +'termsdxip_alpha-beta-eta_eq_' + str(eq) + '_.png'
namecovmat = plotspath +'covmatrix_alpha-beta-eta_eq_' + str(eq) + '_.png'
namedxip = plotspath +'xibias_abe_' + str(eq) + '_.png'
filename = outpath +'abe_dxi.fits'
## ALPHA-BETA
if(args.ab):
print("### Runing alpha and beta test ### ")
mflags = [True, True, False] ##alpha,beta,eta
namemc = plotspath + 'mcmc_alpha-beta_eq_' + str(eq) + '_.png'
namecont = plotspath + 'contours_alpha-beta_eq_' + str(eq) + '_.png'
nameterms = plotspath + 'termsdxip_alpha-beta_eq_' + str(eq) + '_.png'
namecovmat = plotspath +'covmatrix_alpha-beta_eq_' + str(eq) + '_.png'
namedxip = plotspath +'xibias_ab_' + str(eq) + '_.png'
filename = outpath +'ab_dxi.fits'
## ALPHA-ETA
if(args.ae):
print("### Runing alpha and eta test ### ")
mflags = [True, False, True] ##alpha,eta,eta
namemc = plotspath + 'mcmc_alpha-eta_eq_' + str(eq) + '_.png'
namecont = plotspath + 'contours_alpha-eta_eq_' + str(eq) + '_.png'
nameterms = plotspath + 'termsdxip_alpha-eta_eq_' + str(eq) + '_.png'
namecovmat = plotspath +'covmatrix_alpha-eta_eq_' + str(eq) + '_.png'
namedxip = plotspath +'xibias_ae_' + str(eq) + '_.png'
filename = outpath +'ae_dxi.fits'
## BETA-ETA
if(args.be):
print("### Runing beta and eta test ### ")
mflags = [True, False, True] ##beta,eta,eta
namemc = plotspath + 'mcmc_beta-eta_eq_' + str(eq) + '_.png'
namecont = plotspath + 'contours_beta-eta_eq_' + str(eq) + '_.png'
nameterms = plotspath + 'termsdxip_beta-eta_eq_' + str(eq) + '_.png'
namecovmat = plotspath +'covmatrix_beta-eta_eq_' + str(eq) + '_.png'
namedxip = plotspath +'xibias_be_' + str(eq) + '_.png'
filename = outpath +'be_dxi.fits'
## ALPHA
if(args.a):
print("### Runing alpha test ### ")
mflags = [True, False, False] ##alpha,beta,eta
namemc = plotspath +'mcmc_alpha_eq_' + str(eq) + '_.png'
namecont = plotspath +'contours_alpha_eq_' + str(eq) + '_.png'
nameterms = plotspath +'termsdxip_alpha_eq_' + str(eq) + '_.png'
namecovmat = plotspath +'covmatrix_alpha_eq_' + str(eq) + '_.png'
namedxip = plotspath +'xibias_a_' + str(eq) + '_.png'
filename = outpath +'a_dxi.fits'
## Beta
if(args.b):
print("### Runing beta test ### ")
mflags = [False, True, False] ##alpha,beta,eta
namemc = plotspath +'mcmc_beta_eq_' + str(eq) + '_.png'
namecont = plotspath +'contours_beta_eq_' + str(eq) + '_.png'
nameterms = plotspath +'termsdxip_beta_eq_' + str(eq) + '_.png'
namecovmat = plotspath +'covmatrix_beta_eq_' + str(eq) + '_.png'
namedxip = plotspath +'xibias_b_' + str(eq) + '_.png'
filename = outpath +'b_dxi.fits'
## Eta
if(args.e):
print("### Runing eta test ### ")
mflags = [False, False, True] ##alpha,eta,eta
namemc = plotspath +'mcmc_eta_eq_' + str(eq) + '_.png'
namecont = plotspath +'contours_eta_eq_' + str(eq) + '_.png'
nameterms = plotspath +'termsdxip_eta_eq_' + str(eq) + '_.png'
namecovmat = plotspath +'covmatrix_eta_eq_' + str(eq) + '_.png'
namedxip = plotspath +'xibias_e_' + str(eq) + '_.png'
filename = outpath +'e_dxi.fits'
i_guess = np.array(i_guess0)[np.array(mflags)].tolist()
samples, chains = RUNtest(i_guess, data, nwalkers, nsteps, eq=eq,
mflags=mflags, moderr=moderr,
uwmprior=args.uwmprior, minimize= minimize)
#samples= np.c_[[par[int(0.2 * len(par)):] for par in samples]].T
#print("Total samples", [len(i) for i in samples] )
if(args.plots): plot_samplesdist(samples, chains, mflags, nwalkers, nsteps, namemc, namecont )
writexipbias(samples, args.rhos, plots=args.plots, xim=args.xim,
nameterms=nameterms, dxiname=namedxip,
namecovmat=namecovmat, filename=filename )
