def getxipbias(samples, rhosfilename, plotname=None, plots=False):
from readjson import read_rhos
from maxlikelihood import bestparameters
from plot_stats import pretty_rho
import numpy as np
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 not defined")
meanr, rho0p, rho1p, rho2p, rho3p, rho4p, rho5p,\
sig_rho0, sig_rho1, sig_rho2, sig_rho3, sig_rho4,\
sig_rho5 = read_rhos(rhosfilename)
#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
var_dxip = (f1**2)*vara + (f2**2)*varb + (f3**2)*varn + \
(f4**2)*(sig_rho0**2) + (f5**2)*(sig_rho1**2) + \
(f6**2)*(sig_rho2**2) + (f7**2)*(sig_rho3**2) + \
(f8**2)*(sig_rho4**2) + (f9**2)*(sig_rho5**2) + 2*(f1*f2*covab + f1*f3*covan + f2*f3*covbn)
return meanr, dxip, var_dxip
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 plotallrhos(filename, outpath, title=None, xlim=None, ylims=None):
from readjson import read_rhos
ylim0, ylim1, ylim2 = [None, None, None]
if ylims is not None: ylim0, ylim1, ylim2 = ylims
meanr, rho0p, rho1p, rho2p, rho3p, rho4p, rho5p, sig_rho0, sig_rho1, sig_rho2, sig_rho3, sig_rho4, sig_rho5 = read_rhos(
filename)
plt.clf()
pretty_rho1(meanr,
rho1p,
sig_rho1,
rho3p,
sig_rho3,
rho4p,
sig_rho4,
title=title,
xlim=xlim,
ylim=ylim1)
print("Printing file: ", outpath + 'rho1_all_rsrs.png')
plt.savefig(outpath + 'rho1_all_rsrs.png')
plt.clf()
pretty_rho2(meanr,
rho0p,
sig_rho0,
rho2p,
sig_rho2,
rho5p,
sig_rho5,
title=title,
xlim=xlim,
ylim=ylim2)
print("Printing file: ", outpath + 'rho2_all_rsrs.png')
plt.savefig(outpath + 'rho2_all_rsrs.png')
def run_parspatch(outpath, rhosfolder, tausfolder):
from readjson import read_rhos, read_taus
from chi2 import minimizeCHI2
from maxlikelihood import MCMC, percentiles
import numpy as np
nwalkers, nsteps = 100, 1000
eq = 'All'
moderr = False
gflag, bflag = True, True
nsig = 1
i_guess0 = [-0.01, 1, -1] #fiducial values
data = {}
for i in range(1, 5):
rhosp = rhosfolder + findpatchfile(rhosfolder, i)
tausp = tausfolder + findpatchfile(tausfolder, i)
a_c = []
a_l = []
a_r = []
b_c = []
b_l = []
b_r = []
d_c = []
d_l = []
d_r = []
for ibin in range(1, 21):
meanr, rho0p, rho1p, rho2p, rho3p, rho4p, rho5p,\
sig_rho0, sig_rho1, sig_rho2, sig_rho3, sig_rho4,\
sig_rho5 = read_rhos(rhosp, maxbin=ibin)
meanr, tau0p, tau2p, tau5p, sig_tau0, sig_tau2,sig_tau5 =\
read_taus(tausp, maxbin=ibin)
rhos = [rho0p, rho1p, rho2p, rho3p, rho4p, rho5p]
sigrhos = [
sig_rho0, sig_rho1, sig_rho2, sig_rho3, sig_rho4, sig_rho5
]
taus = [tau0p, tau2p, tau5p]
sigtaus = [sig_tau0, sig_tau2, sig_tau5]
data['rhos'] = rhos
data['sigrhos'] = sigrhos
data['taus'] = taus
data['sigtaus'] = sigtaus
fit_pars, chisq = minimizeCHI2(data,
i_guess0,
eq=eq,
gflag=gflag,
bflag=bflag,
moderr=moderr)
samples = MCMC(fit_pars,
data,
nwalkers,
nsteps,
eq=eq,
gflag=gflag,
bflag=bflag,
moderr=moderr,
plot=False)
mcmcpars = percentiles(samples, nsig=nsig)
a_c.append(mcmcpars[0][0])
a_l.append(mcmcpars[0][1])
a_r.append(mcmcpars[0][2])
b_c.append(mcmcpars[1][0])
b_l.append(mcmcpars[1][1])
b_r.append(mcmcpars[1][2])
d_c.append(mcmcpars[2][0])
d_l.append(mcmcpars[2][1])
d_r.append(mcmcpars[2][2])
write_pars(outpath + 'parspatch' + str(i) + '.json', meanr, a_c, a_l,
a_r, b_c, b_l, b_r, d_c, d_l, d_r)
def main():
import sys
args = parse_args()
sys.path.insert(0, args.srcpath)
import numpy as np
import itertools
from readjson import read_rhos, read_taus
import fitsio
from fitsio import FITS, FITSHDR
from astropy.io import fits
#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
nwalkers, nsteps = 100, 1000
moderr = False
nsig = 1
eq = 'All'
i_guess0 = [-0.01, 1, -1] #fiducial values
meanr, rho0p, rho1p, rho2p, rho3p, rho4p, rho5p,\
sig_rho0, sig_rho1, sig_rho2, sig_rho3, sig_rho4,\
sig_rho5 =read_rhos(args.rhos, args.maxscale)
rhos = [rho0p, rho1p, rho2p, rho3p, rho4p, rho5p]
sigrhos = [sig_rho0, sig_rho1, sig_rho2, sig_rho3, sig_rho4, sig_rho5]
data = {}
data['rhos'] = rhos
data['sigrhos'] = sigrhos
names = ['BIN1', 'BIN2', 'ANGBIN', 'ANG', 'VALUE']
forms = ['i4', 'i4', 'i4', 'f8', 'f8']
dtype = dict(names=names, formats=forms)
nrows = 20
outdata = np.recarray((nrows, ), dtype=dtype)
nbins = 4
a = [i for i in range(1, nbins + 1)]
bin_pairs = []
for p in itertools.combinations_with_replacement(a, 2):
bin_pairs.append(p)
covmat = np.zeros(shape=(nbins * nrows, nbins * nrows))
covmatbin = None
listofmat = []
for i, j in bin_pairs:
taufilename = findbinfile(args.tausfolder, i, j)
if taufilename is None:
continue
print(i, j, taufilename)
meanr2, tau0p, tau2p, tau5p, sig_tau0, sig_tau2, sig_tau5 \
= read_taus(args.tausfolder + taufilename, args.maxscale)
taus = [tau0p, tau2p, tau5p]
sigtaus = [sig_tau0, sig_tau2, sig_tau5]
data['taus'] = taus
data['sigtaus'] = sigtaus
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
meanr, dxip, vardxip = RUNtest(args, data, nwalkers, nsteps,
i_guess, gflag, bflag, eq, moderr,
nsig)
covmat[nrows * (i - 1):nrows * i,
nrows * (j - 1):nrows * j] = np.diag(vardxip)
angarray = meanr
valuearray = dxip
bin1array = np.array([i] * len(meanr))
bin2array = np.array([j] * len(meanr))
angbinarray = np.array([i for i in range(len(meanr))])
array_list = [
bin1array, bin2array, angbinarray, angarray, valuearray
]
for array, name in zip(array_list, names):
outdata[name] = array
write_fit(outdata, names, outpath + args.filename)
## ALPHA-BETA
if (args.ab):
print("### Runing alpha and beta test ### ")
gflag, bflag = False, True
i_guess = i_guess0[:2] #fiducial values
meanr, dxip, vardxip = RUNtest(args, data, nwalkers, nsteps,
i_guess, gflag, bflag, eq, moderr,
nsig)
covmat[nrows * (i - 1):nrows * i,
nrows * (j - 1):nrows * j] = np.diag(vardxip)
angarray = meanr
valuearray = dxip
bin1array = np.array([i] * len(meanr))
bin2array = np.array([j] * len(meanr))
angbinarray = np.array([i for i in range(len(meanr))])
array_list = [
bin1array, bin2array, angbinarray, angarray, valuearray
]
for array, name in zip(array_list, names):
outdata[name] = array
write_fit(outdata, names, outpath + args.filename)
## ALPHA
if (args.a):
print("### Runing alpha test ### ")
gflag, bflag = False, False
i_guess = i_guess0[:1] #fiducial values
meanr, dxip, vardxip = RUNtest(args, data, nwalkers, nsteps,
i_guess, gflag, bflag, eq, moderr,
nsig)
covmat[nrows * (i - 1):nrows * i,
nrows * (j - 1):nrows * j] = np.diag(vardxip)
angarray = meanr
valuearray = dxip
bin1array = np.array([i] * len(meanr))
bin2array = np.array([j] * len(meanr))
angbinarray = np.array([i for i in range(len(meanr))])
array_list = [
bin1array, bin2array, angbinarray, angarray, valuearray
]
for array, name in zip(array_list, names):
outdata[name] = array
write_fit(outdata, names, outpath + args.filename)
hdulist = fits.open(outpath + args.filename)
hdulist[1].name = 'xip'
covmathdu = fits.ImageHDU(covmat, name='COVMAT')
hdulist.insert(1, covmathdu)
hdulist.writeto(outpath + args.filename, clobber=True)
print(covmat)
def main():
import os
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 chi2 import minimizeCHI2
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
meanr, rho0p, rho1p, rho2p, rho3p, rho4p, rho5p, sig_rho0, sig_rho1, sig_rho2, sig_rho3, sig_rho4, sig_rho5 = read_rhos(
'/home2/dfa/sobreira/alsina/catalogs/output/alpha-beta-gamma/rho_all_reserved_mod_epiff_magcut_irz.json'
)
meanr2, tau0p, tau2p, tau5p, sig_tau0, sig_tau2, sig_tau5 = read_taus(
'/home2/dfa/sobreira/alsina/catalogs/output/alpha-beta-gamma/tau_all_galaxy-reserved_irz.json'
)
meanr3, tau0p_sn, tau2p_sn, tau5p_sn, sig_tau0_sn, sig_tau2_sn, sig_tau5_sn = read_taus(
'/home2/dfa/sobreira/alsina/catalogs/output/alpha-beta-gamma/tau_all_galaxy-reserved_shapenoise_irz.json'
)
plt.clf()
plt.plot(meanr,
sig_tau0**2,
color='blue',
label=r'$var(\tau_{0})$',
marker='o')
plt.plot(meanr,
sig_tau2**2,
color='red',
label=r'$var(\tau_{2})$',
marker='o')
plt.plot(meanr,
sig_tau5**2,
color='green',
label=r'$var(\tau_{5})$',
marker='o')
plt.plot(meanr,
sig_tau0_sn**2,
color='blue',
label=r'$var(\tau_{0sn})$',
marker='P')
plt.plot(meanr,
sig_tau2_sn**2,
color='red',
label=r'$var(\tau_{2sn})$',
marker='P')
plt.plot(meanr,
sig_tau5_sn**2,
color='green',
label=r'$var(\tau_{5sn})$',
marker='P')
plt.legend(loc='upper right', shadow=True, fontsize=7)
plt.tick_params(axis='both', which='major', labelsize=24)
plt.xlim([0.5, 300.])
plt.ylim([10**-25, 10**-9])
plt.xlabel(r'$\theta$ (arcmin)', fontsize=24)
plt.ylabel('Variances', fontsize=24)
plt.xscale('log')
plt.yscale('log', nonposy='clip')
plt.tight_layout()
print("Printing :" + outpath + '/treecorr_vs_notes_variances.pdf')
plt.savefig(outpath + '/treecorr_vs_notes_variances.pdf')
plt.clf()
plt.plot(meanr, (sig_tau0_sn**2) / (sig_tau0**2),
color='blue',
label=r'$var(\tau_{0})/var(\tau_{0sn})$',
marker='o')
plt.plot(meanr, (sig_tau2_sn**2) / (sig_tau2**2),
color='green',
label=r'$var(\tau_{2})/var(\tau_{2sn})$',
marker='o')
plt.plot(meanr, (sig_tau5_sn**2) / (sig_tau5**2),
color='red',
label=r'$var(\tau_{5})/var(\tau_{5sn})$',
marker='o')
plt.legend(loc='best', shadow=True, fontsize=7)
plt.tick_params(axis='both', which='major', labelsize=24)
plt.xlim([0.5, 300.])
plt.ylim([0, 3])
plt.xlabel(r'$\theta$ (arcmin)', fontsize=24)
plt.ylabel('Variances', fontsize=24)
plt.xscale('log')
#plt.yscale('log', nonposy='clip')
plt.tight_layout()
print("Printing :" + outpath + '/ratio_treecorr_vs_notes_variances.pdf')
plt.savefig(outpath + '/ratio_treecorr_vs_notes_variances.pdf')
plt.clf()
plt.plot(meanr,
sig_tau0_sn**2,
color='blue',
label=r'$var(\tau_{0sn})$',
marker='o')
plt.plot(meanr,
sig_tau2_sn**2,
color='red',
label=r'$var(\tau_{2sn})$',
marker='o')
plt.plot(meanr,
sig_tau5_sn**2,
color='green',
label=r'$var(\tau_{5sn})$',
marker='o')
plt.plot(meanr,
sig_rho0**2,
color='black',
label=r'$var(\rho_{0})$',
marker='o')
plt.plot(meanr,
sig_rho1**2,
color='yellow',
label=r'$var(\rho_{1})$',
marker='o')
plt.plot(meanr,
sig_rho2**2,
color='gray',
label=r'$var(\rho_{2})$',
marker='o')
plt.plot(meanr,
sig_rho3**2,
color='magenta',
label=r'$var(\rho_{3})$',
marker='o')
plt.plot(meanr,
sig_rho4**2,
color='pink',
label=r'$var(\rho_{4})$',
marker='o')
plt.plot(meanr,
sig_rho5**2,
color='orange',
label=r'$var(\rho_{5})$',
marker='o')
plt.legend(loc='upper right', shadow=True, fontsize=7)
plt.tick_params(axis='both', which='major', labelsize=24)
plt.xlim([0.5, 300.])
plt.ylim([10**-25, 10**-9])
plt.xlabel(r'$\theta$ (arcmin)', fontsize=24)
plt.ylabel('Variances', fontsize=24)
plt.xscale('log')
plt.yscale('log', nonposy='clip')
plt.tight_layout()
print("Printing :" + outpath + '/all_variances_bybin.pdf')
plt.savefig(outpath + '/all_variances_bybin.pdf')
#Finding best alpha beta gamma
rhos = [rho0p, rho1p, rho2p, rho3p, rho4p, rho5p]
sigrhos = [sig_rho0, sig_rho1, sig_rho2, sig_rho3, sig_rho4, sig_rho5]
taus = [tau0p, tau2p, tau5p]
sigtaus = [sig_tau0, sig_tau2, sig_tau5]
data = {}
data['rhos'] = rhos
data['sigrhos'] = sigrhos
data['taus'] = taus
data['sigtaus'] = sigtaus
gflag, bflag = False, False
i_guess = [0] #fiducial values
alpha0, chisq0 = minimizeCHI2(data,
i_guess,
eq=0,
gflag=gflag,
bflag=bflag)
alpha1, chisq1 = minimizeCHI2(data,
i_guess,
eq=1,
gflag=gflag,
bflag=bflag)
alpha2, chisq2 = minimizeCHI2(data,
i_guess,
eq=2,
gflag=gflag,
bflag=bflag)
print(alpha0, alpha1, alpha2)
res0 = (tau0p - alpha0 * rho0p)**2
res1 = (tau2p - alpha1 * rho2p)**2
res2 = (tau5p - alpha2 * rho5p)**2
plt.clf()
plt.plot(meanr,
sig_tau0**2,
color='blue',
label=r'$var(\tau_{0})$',
marker='o')
plt.plot(meanr,
sig_tau2**2,
color='red',
label=r'$var(\tau_{2})$',
marker='o')
plt.plot(meanr,
sig_tau5**2,
color='green',
label=r'$var(\tau_{5})$',
marker='o')
plt.legend(loc='lower left', shadow=True, fontsize=15)
plt.tick_params(axis='both', which='major', labelsize=24)
plt.xlim([0.5, 300.])
#plt.ylim( [10**-22,10 **-10] )
plt.xlabel(r'$\theta$ (arcmin)', fontsize=24)
plt.ylabel('Variances', fontsize=24)
plt.xscale('log')
plt.yscale('log', nonposy='clip')
plt.tight_layout()
print("Printing variances_bybin.pdf")
plt.savefig(outpath + '/variances_bybin.pdf')
plt.clf()
plt.plot(meanr,
res0,
color='blue',
label=r'$(\tau_{0}-\alpha_{0}\rho_{0})^2$',
marker='o')
plt.plot(meanr,
res1,
color='red',
label=r'$(\tau_{2}-\alpha_{1}\rho_{2})^2$',
marker='o')
plt.plot(meanr,
res2,
color='green',
label=r'$(\tau_{5}-\alpha_{2}\rho_{5})^2$',
marker='o')
plt.legend(loc='lower left', shadow=True, fontsize=15)
plt.tick_params(axis='both', which='major', labelsize=24)
plt.xlim([0.5, 300.])
plt.ylim([10**-22, 10**-10])
plt.xlabel(r'$\theta$ (arcmin)', fontsize=24)
plt.ylabel('Residuals', fontsize=24)
plt.xscale('log')
plt.yscale('log', nonposy='clip')
plt.tight_layout()
print("Printing Residual_chi2_bybin.pdf")
plt.savefig(outpath + '/Residuals_chi2_bybin.pdf')
plt.clf()
plt.plot(meanr,
res0 / sig_tau0**2,
color='blue',
label=r'$\chi_{0}^2$',
marker='o')
plt.plot(meanr,
res1 / sig_tau2**2,
color='red',
label=r'$\chi_{1}^2$',
marker='o')
plt.plot(meanr,
res2 / sig_tau5**2,
color='green',
label=r'$\chi_{2}^2$',
marker='o')
plt.legend(loc='upper left', shadow=True, fontsize=15)
plt.tick_params(axis='both', which='major', labelsize=24)
plt.xlim([0.5, 300.])
plt.ylim([0.01, 50000.])
plt.xlabel(r'$\theta$ (arcmin)', fontsize=24)
plt.ylabel(r'$\chi^{2}$', fontsize=24)
plt.xscale('log')
plt.yscale('log', nonposy='clip')
plt.tight_layout()
print("Printing chi2_bybin.pdf")
plt.savefig(outpath + '/chi2_bybin.pdf')