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
sys.path.insert(0, '/home/dfa/sobreira/alsina/alpha-beta-gamma/code/src')
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
plt.style.use('SVA1StyleSheet.mplstyle')
from readjson import read_rhos, read_taus
from plot_stats import pretty_rho
import numpy as np
outpath = os.path.expanduser(
'/home2/dfa/sobreira/alsina/catalogs/output/alpha-beta-gamma/plots')
try:
if not os.path.exists(outpath):
os.makedirs(outpath)
except OSError:
if not os.path.exists(outpath): raise
#Comparing mod and unmod tau correlations
modtaus = "/home2/dfa/sobreira/alsina/catalogs/output/alpha-beta-gamma/tau_all_galaxy-reserved_irz.json"
unmodtaus = "/home2/dfa/sobreira/alsina/catalogs/output/alpha-beta-gamma/tau_all_galaxy-reserved_unmod_irz.json"
meanr, tau0p, tau2p, tau5p, sig_tau0, sig_tau2, sig_tau5 = read_taus(
modtaus)
meanr_m, tau0p_m, tau2p_m, tau5p_m, sig_tau0_m, sig_tau2_m, sig_tau5_m = read_taus(
unmodtaus)
sqrtn = 1
plt.clf()
pretty_rho(meanr,
tau0p,
sig_tau0,
sqrtn,
legend=r'$\tau_{0}$',
color='blue',
ylim=False,
lfontsize=10)
pretty_rho(meanr_m,
tau0p_m,
sig_tau0_m,
sqrtn,
legend=r'$\tau_0*$',
color='blue',
marker='P',
ylim=False,
lfontsize=10)
pretty_rho(meanr,
tau2p,
sig_tau2,
sqrtn,
legend=r'$\tau_{2}$',
color='green',
ylim=False,
lfontsize=10)
pretty_rho(meanr_m,
tau2p_m,
sig_tau2_m,
sqrtn,
legend=r'$\tau_2*$',
color='green',
marker='P',
ylim=False,
lfontsize=10)
pretty_rho(meanr,
tau5p,
sig_tau5,
sqrtn,
legend=r'$\tau_{5}$',
color='red',
ylim=False,
lfontsize=10)
pretty_rho(meanr_m,
tau5p_m,
sig_tau5_m,
sqrtn,
legend=r'$\tau_5*$',
color='red',
marker='P',
ylim=False,
lfontsize=10,
ylabel=r'$\tau(\theta)$')
plt.xlim([0, 1000])
print("Printing :", outpath + '/taustatsmeanvsnomean1.pdf')
plt.savefig(outpath + '/taustatsmeanvsnomean1.pdf')
#Comparing mod and unmod rhos correlations
modrhoseobs = "/home2/dfa/sobreira/alsina/catalogs/output/alpha-beta-gamma/rho_all_reserved_mod_eobs_magcut_irz.json"
modrhosepiff = "/home2/dfa/sobreira/alsina/catalogs/output/alpha-beta-gamma/rho_all_reserved_unmod_eobs_magcut_irz.json"
meanr_obs, rho0p_obs, rho1p_obs, rho2p_obs, rho3p_obs, rho4p_obs, rho5p_obs, sig_rho0_obs, sig_rho1_obs, sig_rho2_obs, sig_rho3_obs, sig_rho4_obs, sig_rho5_obs = read_rhos(
modrhoseobs)
meanr, rho0p, rho1p, rho2p, rho3p, rho4p, rho5p, sig_rho0, sig_rho1, sig_rho2, sig_rho3, sig_rho4, sig_rho5 = read_rhos(
modrhosepiff)
sqrtn = 1
plt.clf()
pretty_rho(meanr,
rho1p_obs,
sig_rho1_obs,
sqrtn,
legend=r'$\rho_{1}$',
color='blue',
ylim=False,
lfontsize=10)
pretty_rho(meanr,
rho1p,
sig_rho1,
sqrtn,
legend=r'$\rho_1*$',
color='blue',
marker='P',
ylim=False,
lfontsize=10)
pretty_rho(meanr,
rho3p_obs,
sig_rho3_obs,
sqrtn,
legend=r'$\rho_{3}$',
color='green',
ylim=False,
lfontsize=10)
pretty_rho(meanr,
rho3p,
sig_rho3,
sqrtn,
legend=r'$\rho_3*$',
color='green',
marker='P',
ylim=False,
lfontsize=10)
pretty_rho(meanr,
rho4p_obs,
sig_rho4_obs,
sqrtn,
legend=r'$\rho_{4}$',
color='red',
ylim=False,
lfontsize=10)
pretty_rho(meanr,
rho4p,
sig_rho4,
sqrtn,
legend=r'$\rho_4*$',
color='red',
marker='P',
ylim=False,
lfontsize=10)
plt.xlim([0, 400])
print("Printing :", outpath + '/statsmeanvsnomean1.pdf')
plt.savefig(outpath + '/statsmeanvsnomean1.pdf')
plt.clf()
pretty_rho(meanr,
rho2p_obs,
sig_rho2_obs,
sqrtn,
legend=r'$\rho_{2}$',
color='blue',
ylim=False,
lfontsize=10)
pretty_rho(meanr,
rho2p,
sig_rho2,
sqrtn,
legend=r'$\rho_2*$',
color='blue',
marker='P',
ylim=False,
lfontsize=10)
pretty_rho(meanr,
rho5p_obs,
sig_rho5_obs,
sqrtn,
legend=r'$\rho_{5}$',
color='green',
ylim=False,
lfontsize=10)
pretty_rho(meanr,
rho5p,
sig_rho5,
sqrtn,
legend=r'$\rho_5*$',
color='green',
marker='P',
ylim=False,
lfontsize=10)
plt.xlim([0, 400])
print("Printing :", outpath + '/statsmeanvsnomean2.pdf')
plt.savefig(outpath + '/statsmeanvsnomean2.pdf')
plt.clf()
pretty_rho(meanr,
rho0p_obs,
sig_rho0_obs,
sqrtn,
legend=r'$\rho_{0}$',
color='blue',
ylim=False,
lfontsize=10)
pretty_rho(meanr,
rho0p,
sig_rho0,
sqrtn,
legend=r'$\rho_0*$',
color='blue',
marker='P',
ylim=False,
lfontsize=10)
plt.xlim([0, 400])
print("Printing :", outpath + '/statsmeanvsnomean0.pdf')
plt.savefig(outpath + '/statsmeanvsnomean0.pdf')
#Reading a ploting reserved stars correlations
modrhoseobs = "/home2/dfa/sobreira/alsina/catalogs/output/alpha-beta-gamma/rho_all_reserved_mod_eobs_magcut_irz.json"
modrhosepiff = "/home2/dfa/sobreira/alsina/catalogs/output/alpha-beta-gamma/rho_all_reserved_mod_epiff_magcut_irz.json"
meanr_obs, rho0p_obs, rho1p_obs, rho2p_obs, rho3p_obs, rho4p_obs, rho5p_obs, sig_rho0_obs, sig_rho1_obs, sig_rho2_obs, sig_rho3_obs, sig_rho4_obs, sig_rho5_obs = read_rhos(
modrhoseobs)
meanr, rho0p, rho1p, rho2p, rho3p, rho4p, rho5p, sig_rho0, sig_rho1, sig_rho2, sig_rho3, sig_rho4, sig_rho5 = read_rhos(
modrhosepiff)
sqrtn = 1
plt.clf()
pretty_rho(meanr,
rho1p_obs,
sig_rho1_obs,
sqrtn,
legend=r'$\rho_{1}(e_{obs})$',
color='blue')
pretty_rho(meanr,
rho1p,
sig_rho1,
sqrtn,
legend=r'$\rho_1(e_{piff})$',
color='green',
marker='P')
print("Printing :", outpath + '/deltarho1_modeobs.pdf')
plt.savefig(outpath + '/deltarho1_modeobs.pdf')
plt.clf()
pretty_rho(meanr,
rho2p + rho1p,
np.sqrt(sig_rho1**2 + sig_rho2**2),
sqrtn,
legend=r'$\rho_2(e_{piff})+\rho_1(e_{piff})$',
color='blue')
pretty_rho(meanr,
rho2p_obs,
sig_rho2_obs,
sqrtn,
legend=r'$\rho_2(e_{obs})$',
color='green',
marker='P')
print("Printing :", outpath + '/r2+r1modepiff.pdf')
plt.savefig(outpath + '/r2+r1modepiff.pdf')
def main():
import sys
args = parse_args()
sys.path.insert(0, args.srcpath)
import numpy as np
from readjson import read_xi, read_dxip
from plot_stats import pretty_rho
#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
meanr_xip, xip_obs, sig_xip = read_xi(args.xipobs)
meanr_dxip, dxip, sig_dxip = read_dxip(args.dxip)
plt.clf()
pretty_rho(meanr_xip, xip_obs, sig_xip, legend=r"$\xi_{+}^{obs}$",lfontsize=10, color='blue', ylabel='Correlations', ylim=False)
pretty_rho(meanr_dxip, dxip, sig_dxip, legend=r"$\delta \xi_{+}$",lfontsize=10, color='red', ylabel='Correlations', ylim=False)
fname = 'plots/xiobs_vs_xibias_abe2.png'
print('Printing:', outpath +fname)
plt.savefig(outpath +fname)
meanr_dxip, dxip, sig_dxip = read_dxip('/home2/dfa/sobreira/alsina/catalogs/output/alpha-beta-gamma/dxip-alpha-beta.json')
plt.clf()
pretty_rho(meanr_xip, xip_obs, sig_xip, legend=r"$\xi_{+}^{obs}$",lfontsize=10, color='blue', ylabel='Correlations', ylim=False)
pretty_rho(meanr_dxip, dxip, sig_dxip, legend=r"$\delta \xi_{+}$",lfontsize=10, color='red', ylabel='Correlations', ylim=False)
fname = 'plots/xiobs_vs_xibias_ab2.png'
print('Printing:', outpath +fname)
plt.savefig(outpath +fname)
meanr_dxip, dxip, sig_dxip = read_dxip('/home2/dfa/sobreira/alsina/catalogs/output/alpha-beta-gamma/dxip-alpha.json')
plt.clf()
pretty_rho(meanr_xip, xip_obs, sig_xip, legend=r"$\xi_{+}^{obs}$",lfontsize=10, color='blue', ylabel='Correlations', ylim=False)
pretty_rho(meanr_dxip, dxip, sig_dxip, legend=r"$\delta \xi_{+}$",lfontsize=10, color='red', ylabel='Correlations', ylim=False)
fname = 'plots/xiobs_vs_xibias_a2.png'
print('Printing:', outpath +fname)
plt.savefig(outpath +fname)
def main():
import sys
sys.path.insert(0, '/home/dfa/sobreira/alsina/alpha-beta-gamma/code/src')
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
plt.style.use('SVA1StyleSheet.mplstyle')
from readjson import read_rhos, read_taus
from plot_stats import pretty_rho
import numpy as np
outpath = os.path.expanduser(
'/home2/dfa/sobreira/alsina/catalogs/output/alpha-beta-gamma/plots')
try:
if not os.path.exists(outpath):
os.makedirs(outpath)
except OSError:
if not os.path.exists(outpath): raise
tausp1 = "/home2/dfa/sobreira/alsina/catalogs/output/alpha-beta-gamma/tau_all_galaxy-reserved_mod_patch1_irz.json"
tausp2 = "/home2/dfa/sobreira/alsina/catalogs/output/alpha-beta-gamma/tau_all_galaxy-reserved_mod_patch2_irz.json"
tausp3 = "/home2/dfa/sobreira/alsina/catalogs/output/alpha-beta-gamma/tau_all_galaxy-reserved_mod_patch3_irz.json"
tausp4 = "/home2/dfa/sobreira/alsina/catalogs/output/alpha-beta-gamma/tau_all_galaxy-reserved_mod_patch4_irz.json"
meanr1, tau0p1, tau2p1, tau5p1, sig_tau01, sig_tau21, sig_tau51 = read_taus(
tausp1)
meanr2, tau0p2, tau2p2, tau5p2, sig_tau02, sig_tau22, sig_tau52 = read_taus(
tausp2)
meanr3, tau0p3, tau2p3, tau5p3, sig_tau03, sig_tau23, sig_tau53 = read_taus(
tausp3)
meanr4, tau0p4, tau2p4, tau5p4, sig_tau04, sig_tau24, sig_tau54 = read_taus(
tausp4)
sqrtn = 1
plt.clf()
pretty_rho(meanr1,
tau0p1,
sig_tau01,
sqrtn,
legend='P++',
lfontsize=10,
color='blue',
ylabel=r'$\tau_{0}$',
ylim=False)
pretty_rho(meanr2,
tau0p2,
sig_tau02,
sqrtn,
legend='P-+',
lfontsize=10,
color='red',
ylabel=r'$\tau_{0}$',
ylim=False)
pretty_rho(meanr3,
tau0p3,
sig_tau03,
sqrtn,
legend='P--',
lfontsize=10,
color='green',
ylabel=r'$\tau_{0}$',
ylim=False)
pretty_rho(meanr4,
tau0p4,
sig_tau04,
sqrtn,
legend='P+-',
lfontsize=10,
color='black',
ylabel=r'$\tau_{0}$',
ylim=False)
print("Printing :", outpath + '/taus0_quadrants.png')
plt.savefig(outpath + '/taus0_quadrants.png')
plt.clf()
pretty_rho(meanr1,
tau2p1,
sig_tau21,
sqrtn,
legend='P++',
lfontsize=10,
color='blue',
ylabel=r'$\tau_{2}$',
ylim=False)
pretty_rho(meanr2,
tau2p2,
sig_tau22,
sqrtn,
legend='P-+',
lfontsize=10,
color='red',
ylabel=r'$\tau_{2}$',
ylim=False)
pretty_rho(meanr3,
tau2p3,
sig_tau23,
sqrtn,
legend='P--',
lfontsize=10,
color='green',
ylabel=r'$\tau_{2}$',
ylim=False)
pretty_rho(meanr4,
tau2p4,
sig_tau24,
sqrtn,
legend='P+-',
lfontsize=10,
color='black',
ylabel=r'$\tau_{2}$',
ylim=False)
print("Printing :", outpath + '/taus2_quadrants.png')
plt.savefig(outpath + '/taus2_quadrants.png')
plt.clf()
pretty_rho(meanr1,
tau5p1,
sig_tau51,
sqrtn,
legend='P++',
lfontsize=10,
color='blue',
ylabel=r'$\tau_{5}$',
ylim=False)
pretty_rho(meanr2,
tau5p2,
sig_tau52,
sqrtn,
legend='P-+',
lfontsize=10,
color='red',
ylabel=r'$\tau_{5}$',
ylim=False)
pretty_rho(meanr3,
tau5p3,
sig_tau53,
sqrtn,
legend='P--',
lfontsize=10,
color='green',
ylabel=r'$\tau_{5}$',
ylim=False)
pretty_rho(meanr4,
tau5p4,
sig_tau54,
sqrtn,
legend='P+-',
lfontsize=10,
color='black',
ylabel=r'$\tau_{5}$',
ylim=False)
print("Printing :", outpath + '/taus5_quadrants.png')
plt.savefig(outpath + '/taus5_quadrants.png')
def plot_correlations(outpath):
from plot_stats import pretty_rho
import numpy as np
ylims = [[ - 0.1, 0.1],[ - 30, 30],[ -600, 600] ]
outputnames = ['rho0.png', 'rho1.png', 'rho2.png', 'rho3.png', 'rho4.png', 'rho5.png']
colors = ['black', 'green', 'blue', 'red', 'gray', 'pink']
min_sep_list = [0.05, 0.1, 0.2, 0.3, 0.4]
#min_sep_list = [0.5, 0.6, 0.7, 0.8, 0.9, 1.0]
for min_sep in enumeratemin_sep_list:
meanr, rho0p, rho1p, rho2p, rho3p, rho4p, rho5p, sig_rho0, sig_rho1, sig_rho2, sig_rho3, sig_rho4, sig_rho5 =read_rhos(os.path.join(outpath, "rho_%f.json"%(min_sep)))
plt.clf()
plt.figure(0)
pretty_rho(meanr, rho0p, sig_rho0, legend=str(min_sep), lfontsize=24,
color='black', marker='o',
ylabel=r'$\rho_{0}(\theta)$',title=r'$\rho_{0}(\theta)$',
xlim=None, ylim=None)
plt.clf()
plt.figure(1)
pretty_rho(meanr, rho1p, sig_rho1, legend=str(min_sep), lfontsize=24,
color='black', marker='o',
ylabel=r'$\rho_{1}(\theta)$',title=r'$\rho_{1}(\theta)$',
xlim=None, ylim=None)
plt.clf()
plt.figure(2)
pretty_rho(meanr, rho2p, sig_rho2, legend=str(min_sep), lfontsize=24,
color='black', marker='o',
ylabel=r'$\rho_{2}(\theta)$',title=r'$\rho_{2}(\theta)$',
xlim=None, ylim=None)
plt.clf()
plt.figure(3)
pretty_rho(meanr, rho3p, sig_rho3, legend=str(min_sep), lfontsize=24,
color='black', marker='o',
ylabel=r'$\rho_{3}(\theta)$',title=r'$\rho_{3}(\theta)$',
xlim=None, ylim=None)
plt.clf()
plt.figure(4)
pretty_rho(meanr, rho4p, sig_rho4, legend=str(min_sep), lfontsize=24,
color='black', marker='o',
ylabel=r'$\rho_{4}(\theta)$',title=r'$\rho_{4}(\theta)$',
xlim=None, ylim=None)
plt.clf()
plt.figure(5)
pretty_rho(meanr, rho5p, sig_rho5, legend=str(min_sep), lfontsize=24,
color='black', marker='o',
ylabel=r'$\rho_{5}(\theta)$',title=r'$\rho_{5}(\theta)$',
xlim=None, ylim=None)
for i in range(6):
plt.figure(i)
plt.tight_layout()
print("Printing :", outpath + outputnames[t])
plt.savefig(outpath +outputnames[i], dpi=200)
def main():
import sys
args = parse_args()
sys.path.insert(0, args.srcpath)
import numpy as np
from readjson import read_taus
from plot_stats import pretty_rho, plot_tomograpically_bin
#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
section_name = 'galaxy_cl_nbb'
xlabel = r'$\theta$ [arcmin]'
ylabel = r'$\tau$'
nbins = 4
fig, ax = plt.subplots(nbins,
nbins,
figsize=(1.6 * nbins, 1.6 * nbins),
sharey=True,
sharex=True)
bins = [[1, 1], [2, 2], [3, 3], [4, 4]]
lines = []
cols = ['red', 'blue', 'green', 'gray']
for i, j in bins:
fileaux = findbinfile(args.tausbins, i, j)
meanr, tau0p, tau2p, tau5p, sig_tau0p, sig_tau2p, sig_tau5p = read_taus(
args.tausbins + fileaux)
plot_tomograpically_bin(ax,
i,
j,
meanr,
tau0p,
yerr=sig_tau0p,
xlabel=xlabel,
ylabel=ylabel,
nbins=4,
color='blue') #, label=r'$\tau_{0}$')
plot_tomograpically_bin(ax,
i,
j,
meanr,
tau2p,
yerr=sig_tau2p,
xlabel=xlabel,
ylabel=ylabel,
nbins=4,
color='red') #, label=r'$\tau_{2}$')
plot_tomograpically_bin(ax,
i,
j,
meanr,
tau5p,
yerr=sig_tau5p,
xlabel=xlabel,
ylabel=ylabel,
nbins=4,
color='green') #, label=r'$\tau_{05}$')
lines.append(ax[1][1].lines)
fig.legend(lines,
labels=[r'$\tau_{0}$', r'$\tau_{2}$', r'$\tau_{5}$'],
bbox_to_anchor=(1.1, 1.08))
plt.tight_layout()
fname = 'all_taus_tomo.png'
print('Printing:', outpath + fname)
plt.savefig(outpath + fname, dpi=300)
plt.close(fig)
#tau0
plt.clf()
for i, j in bins:
fileaux = findbinfile(args.tausbins, i, j)
meanr, tau0p, tau2p, tau5p, sig_tau0p, sig_tau2p, sig_tau5p = read_taus(
args.tausbins + fileaux)
pretty_rho(meanr,
tau0p,
sig_tau0p,
legend="{},{}".format(i, j),
lfontsize=15,
color=cols[i - 1],
ylabel=r'$\tau_{0}(\theta)$',
ylim=False)
fname = 'taus0_tomo.png'
print('Printing:', outpath + fname)
plt.savefig(outpath + fname, dpi=300)
#tau2
plt.clf()
for i, j in bins:
fileaux = findbinfile(args.tausbins, i, j)
meanr, tau0p, tau2p, tau5p, sig_tau0p, sig_tau2p, sig_tau5p = read_taus(
args.tausbins + fileaux)
pretty_rho(meanr,
tau2p,
sig_tau2p,
legend="{},{}".format(i, j),
lfontsize=15,
color=cols[i - 1],
ylabel=r'$\tau_{2}(\theta)$',
ylim=False)
fname = 'taus2_tomo.png'
print('Printing:', outpath + fname)
plt.savefig(outpath + fname, dpi=300)
#tau5
plt.clf()
for i, j in bins:
fileaux = findbinfile(args.tausbins, i, j)
meanr, tau0p, tau2p, tau5p, sig_tau0p, sig_tau2p, sig_tau5p = read_taus(
args.tausbins + fileaux)
pretty_rho(meanr,
tau5p,
sig_tau5p,
legend="{},{}".format(i, j),
lfontsize=15,
color=cols[i - 1],
ylabel=r'$\tau_{5}(\theta)$',
ylim=False)
fname = 'taus5_tomo.png'
print('Printing:', outpath + fname)
plt.savefig(outpath + fname, dpi=300)