def preprocess(filename, frac=1.0):
os.system("mkdir -p output/")
c = mc.chain(filename)
c.add_column("s8", values="sigma_8*((omega_m/0.3)**2)")
nsamp = len(c.samples)
nburn = int((1.0 - frac) * nsamp)
if (nburn != 0):
c.burn(nburn)
c.write_columns("output/chain.txt")
import tools.emcee as mc
from chainconsumer import ChainConsumer
from matplotlib import rcParams
rcParams['xtick.major.size'] = 3.5
rcParams['xtick.minor.size'] = 1.7
rcParams['ytick.major.size'] = 3.5
rcParams['ytick.minor.size'] = 1.7
rcParams['xtick.direction']='in'
rcParams['ytick.direction']='in'
print('Loading chains...')
base = '/Users/hattifattener/Documents/y3cosmicshear/chains/mice/'
c1 = mc.chain(base+'baseline/chain_1x2pt_fiducial_sim_MICE_baseline_Sep8th.fits_scales_3x2pt_0.5_8_6_v0.4.ini_lcdm.txt')
c2 = mc.chain('/Volumes/groke/work/chains/y3/simulated/mice/chain_1x2pt_fiducial_sim_MICE_wIA_2021.fits_scales_3x2pt_0.5_8_6_v0.4.ini_lcdm.txt')
c3 = mc.chain('/Volumes/groke/work/chains/y3/simulated/mice/chain_1x2pt_nla_sim_MICE_wIA_2021.fits_scales_3x2pt_0.5_8_6_v0.4.ini_lcdm.txt')
c4 = mc.chain('/Volumes/groke/work/chains/y3/simulated/mice/chain_1x2pt_iasonly_MICE_wIA_2021.fits_scales_3x2pt_0.5_8_6_v0.4.ini_lcdm.txt')
#c1 = mc.chain('chain_1x2pt_fiducial_test_0.40_CLASS_NLA_C1_GAMA_twopoint_new.fits_scales_3x2pt_0.5_8_6_v0.4.ini_lcdm.txt')
c1.add_s8()
c2.add_s8()
c3.add_s8()
#'out_redmagic_high_mock_baseline-gp1-gg1-pp1-multinest-fidcov-ta-y3fid-6rpmin_200rpmax-rsd1-lens0-mag0-phot1.txt'
import tools.emcee as mc
from chainconsumer import ChainConsumer
rcParams['xtick.major.size'] = 3.5
rcParams['xtick.minor.size'] = 1.7
rcParams['ytick.major.size'] = 3.5
rcParams['ytick.minor.size'] = 1.7
rcParams['xtick.direction'] = 'in'
rcParams['ytick.direction'] = 'in'
print('Loading chains...')
base = '/Volumes/groke/work/chains/y3/real/'
#c1 = mc.chain(base+'fiducial/chain_1x2pt_hyperrank_2pt_NG_BLINDED_v0.40cov_xcorrGGL_27072020_SOMPZWZsamples_pit.fits_scales_3x2pt_0.5_8_6_v0.4.ini_lcdm.txt')
c1 = mc.chain(base + '/final_paper_chains/chain_1x2pt_lcdm_SR_maglim.txt')
c2 = mc.chain(base + '/final_paper_chains/chain_1x2agg_ML.txt')
c3 = mc.chain(base + '/final_paper_chains/chain_3x2pt_lcdm_SR_maglim.txt')
#c1 = mc.chain('chain_1x2pt_fiducial_test_0.40_CLASS_NLA_C1_GAMA_twopoint_new.fits_scales_3x2pt_0.5_8_6_v0.4.ini_lcdm.txt')
c1.add_s8(alpha=0.5)
c2.add_s8(alpha=0.5)
c3.add_s8(alpha=0.5)
samp1 = np.array([
c1.samples['cosmological_parameters--omega_m'],
c1.samples['cosmological_parameters--s8'],
c1.samples['intrinsic_alignment_parameters--a1'],
c1.samples['intrinsic_alignment_parameters--a2'],
c1.samples['intrinsic_alignment_parameters--alpha1'],
import tools.emcee as mc
from chainconsumer import ChainConsumer
rcParams['xtick.major.size'] = 3.5
rcParams['xtick.minor.size'] = 1.7
rcParams['ytick.major.size'] = 3.5
rcParams['ytick.minor.size'] = 1.7
rcParams['xtick.direction'] = 'in'
rcParams['ytick.direction'] = 'in'
print('Loading chains...')
base = '/Volumes/groke/work/chains/y3/real/'
#c1 = mc.chain(base+'fiducial/chain_1x2pt_hyperrank_2pt_NG_BLINDED_v0.40cov_xcorrGGL_27072020_SOMPZWZsamples_pit.fits_scales_3x2pt_0.5_8_6_v0.4.ini_lcdm.txt')
c1 = mc.chain(base + 'chain_3x2pt_lcdm.txt')
c2 = mc.chain(
'/Volumes/groke/work/chains/y1/blazek/eta/TATT/all/out_all-3x2pt-NG-TATT-alpha-v3.txt'
)
c1.add_s8()
c2.add_s8()
#'out_redmagic_high_mock_baseline-gp1-gg1-pp1-multinest-fidcov-ta-y3fid-6rpmin_200rpmax-rsd1-lens0-mag0-phot1.txt'
samp1 = np.array([
c1.samples['cosmological_parameters--omega_m'],
c1.samples['cosmological_parameters--s8'],
c1.samples['intrinsic_alignment_parameters--a1'],
c1.samples['intrinsic_alignment_parameters--a2'],
c1.samples['intrinsic_alignment_parameters--alpha1'],
import tools.emcee as mc
from chainconsumer import ChainConsumer
from matplotlib import rcParams
rcParams['xtick.major.size'] = 3.5
rcParams['xtick.minor.size'] = 1.7
rcParams['ytick.major.size'] = 3.5
rcParams['ytick.minor.size'] = 1.7
rcParams['xtick.direction'] = 'in'
rcParams['ytick.direction'] = 'in'
print('Loading chains...')
base = '/Users/hattifattener/Documents/y3cosmicshear/chains/'
c0 = mc.chain(
'/Volumes/groke/work/chains/y3/real/maglim/unblinding/chain_1x2pt_lcdm_SR_maglim.txt'
)
c1 = mc.chain(
'/Volumes/groke/work/chains/y3/real/final_paper_chains/tests_1x2pt/maglim/chain_TA_1x2pt_lcdm_maglim.txt'
)
c2 = mc.chain(
'/Volumes/groke/work/chains/y3/real/final_paper_chains/tests_1x2pt/maglim/chain_amplitudes_1x2pt_lcdm_maglim.txt'
)
c3 = mc.chain(
'/Volumes/groke/work/chains/y3/real/final_paper_chains/tests_1x2pt/maglim/chain_TATT_noz_1x2pt_lcdm.txt'
)
#c1 = mc.chain('chain_1x2pt_fiducial_test_0.40_CLASS_NLA_C1_GAMA_twopoint_new.fits_scales_3x2pt_0.5_8_6_v0.4.ini_lcdm.txt')
c0.add_s8()
c1.add_s8()
c2.add_s8()
models = {}
def get_bic(chain):
chi2 = chain.samples['data_vector--2pt_chi2'].min() # min chi2
k = chain.npar # no of free parameters
npts = 227 # apparently
# import pdb ; pdb.set_trace()
return k * np.log10(npts) + chi2
def show_result(name, m):
print('%s: %3.3f - %3.3f = %3.3f' %
(name, m['TATT'], m[name], m['TATT'] - m[name]))
models['noIA'] = get_bic(mc.chain(base + '/../chain_noia_1x2pt_lcdm.txt'))
models['NLA_noz'] = get_bic(mc.chain(base + '/chain_NLA_noz_1x2pt_lcdm.txt'))
models['NLA'] = get_bic(mc.chain(base + '/chain_NLA_1x2pt_lcdm.txt'))
models['TA'] = get_bic(mc.chain(base + '/chain_TA_1x2pt_lcdm.txt'))
models['TATT_noz'] = get_bic(mc.chain(base + '/chain_TATT_noz_1x2pt_lcdm.txt'))
models['TATT'] = get_bic(mc.chain(base + '../chain_1x2pt_lcdm.txt'))
show_result('noIA', models)
show_result('NLA_noz', models)
show_result('NLA', models)
show_result('TA', models)
show_result('TATT_noz', models)
import tools.emcee as mc
from chainconsumer import ChainConsumer
rcParams['xtick.major.size'] = 3.5
rcParams['xtick.minor.size'] = 1.7
rcParams['ytick.major.size'] = 3.5
rcParams['ytick.minor.size'] = 1.7
rcParams['xtick.direction']='in'
rcParams['ytick.direction']='in'
#rcParams['text.usetex']=False
print('Loading chains...')
base = '/Volumes/groke/work/chains/y3/real/external/'
#c1 = mc.chain(base+'fiducial/chain_1x2pt_hyperrank_2pt_NG_BLINDED_v0.40cov_xcorrGGL_27072020_SOMPZWZsamples_pit.fits_scales_3x2pt_0.5_8_6_v0.4.ini_lcdm.txt')
c1 = mc.chain('chains/external/chain_s_b_wcdm.txt')
c2 = mc.chain('chains/external/chain_p-lensing_wcdm.txt')
c3 = mc.chain('chains/data/chain_1x2pt_wcdm_022421_covupdate.v2.txt')
#c4 = mc.chain(base+'')
c1.add_s8()
c2.add_s8()
c3.add_s8()
#c4.add_s8()
#'out_redmagic_high_mock_baseline-gp1-gg1-pp1-multinest-fidcov-ta-y3fid-6rpmin_200rpmax-rsd1-lens0-mag0-phot1.txt'
import numpy as np
import os
import pylab as plt
plt.switch_backend('pdf')
plt.style.use('y1a1')
import tools.emcee as mc
from chainconsumer import ChainConsumer
print('Loading chains...')
base = '/Users/hattifattener/Documents/y3cosmicshear/chains/ias_fid/priors1/'
c0 = mc.chain(
base +
'chain_1x2pt_cosmology_2pt_NG_BLINDED_v0.40cov_xcorrGGL27072020_1000samples_055ramp_all_2609_meanzisreal10.fits_sim_noiseless_SIMULATED_NOIA.fits_scales_3x2pt_0.5_8_6_v0.4.ini_lcdm.txt'
)
c1 = mc.chain(
base +
'../final_priors/mtatt_dnla/nopz/chain_1x2pt_fiducial_nopz_2pt_NG_BLINDED_v0.40cov_xcorrGGL27072020_1000samples_055ramp_all_2609_meanzisreal10.fits_sim_noiseless_SIMULATED_NLA2.fits_scales_3x2pt_0.5_8_6_v0.4.ini_lcdm.txt'
)
c2 = mc.chain(
base +
'../final_priors/mnla_dnla/nopz/chain_1x2pt_fiducial_nopz_2pt_NG_BLINDED_v0.40cov_xcorrGGL27072020_1000samples_055ramp_all_2609_meanzisreal10.fits_sim_noiseless_SIMULATED_NLA.fits_scales_3x2pt_0.5_8_6_v0.4.ini_lcdm.txt'
)
c3 = mc.chain(
base +
'../final_priors/mtatt_dnla/pz/chain_1x2pt_fiducial_2pt_NG_BLINDED_v0.40cov_xcorrGGL27072020_1000samples_055ramp_all_2609_meanzisreal10.fits_sim_noiseless_SIMULATED_NLA2.fits_scales_3x2pt_0.5_8_6_v0.4.ini_lcdm.txt'
)
c4 = mc.chain(
import numpy as np
import os
import pylab as plt
plt.switch_backend('pdf')
plt.style.use('y1a1')
import tools.emcee as mc
from chainconsumer import ChainConsumer
print('Loading chains...')
base = '/Users/hattifattener/Documents/y3cosmicshear/chains/ias_fid/'
c2 = mc.chain(base+'nom/chain_1x2pt_hyperrank_2pt_SOMPZWZsamples_pit_goodlowz_shifted_1000samples_ramped.055_PITfix_sim_noiseless_GCharSmail_SIMULATED.fits_scales_3x2pt_0.5_8_6_v0.4.ini_lcdm.txt')
c3 = mc.chain(base+'nom/chain_1x2pt_hyperrank_2pt_SOMPZWZsamples_pit_goodlowz_shifted_1000samples_ramped.055_PITfix_sim_noiseless_GCharSmail_SIMULATED_NLA.fits_scales_3x2pt_0.5_8_6_v0.4.ini_lcdm.txt')
#c1 = mc.chain('chain_1x2pt_fiducial_test_0.40_CLASS_NLA_C1_GAMA_twopoint_new.fits_scales_3x2pt_0.5_8_6_v0.4.ini_lcdm.txt')
#c1.add_s8()
c2.add_s8()
c3.add_s8()
#'out_redmagic_high_mock_baseline-gp1-gg1-pp1-multinest-fidcov-ta-y3fid-6rpmin_200rpmax-rsd1-lens0-mag0-phot1.txt'
#samp1 = np.array([c1.samples['cosmological_parameters--omega_m'], c1.samples['cosmological_parameters--s8'], c1.samples['intrinsic_alignment_parameters--a1'], c1.samples['intrinsic_alignment_parameters--alpha1']])
samp2 = np.array([c2.samples['cosmological_parameters--omega_m'], c2.samples['cosmological_parameters--s8'], c2.samples['intrinsic_alignment_parameters--a1'], c2.samples['intrinsic_alignment_parameters--alpha1'], c2.samples['intrinsic_alignment_parameters--a2'], c2.samples['intrinsic_alignment_parameters--alpha2'], c2.samples['intrinsic_alignment_parameters--bias_ta']])
samp3 = np.array([c3.samples['cosmological_parameters--omega_m'], c3.samples['cosmological_parameters--s8'], c3.samples['intrinsic_alignment_parameters--a1'], c3.samples['intrinsic_alignment_parameters--alpha1']])
import tools.emcee as mc
from chainconsumer import ChainConsumer
rcParams['xtick.major.size'] = 3.5
rcParams['xtick.minor.size'] = 1.7
rcParams['ytick.major.size'] = 3.5
rcParams['ytick.minor.size'] = 1.7
rcParams['xtick.direction']='in'
rcParams['ytick.direction']='in'
print('Loading chains...')
base = '/Volumes/groke/work/chains/y3/real/'
c1 = mc.chain(base+'chain_1x2pt_omegabh2_wcdm.txt')
c0 = mc.chain(base+'chain_1x2pt_wcdm.txt')
#c1 = mc.chain('chain_1x2pt_fiducial_test_0.40_CLASS_NLA_C1_GAMA_twopoint_new.fits_scales_3x2pt_0.5_8_6_v0.4.ini_lcdm.txt')
c0.add_s8()
c1.add_s8()
#'out_redmagic_high_mock_baseline-gp1-gg1-pp1-multinest-fidcov-ta-y3fid-6rpmin_200rpmax-rsd1-lens0-mag0-phot1.txt'
samp0 = np.array([c0.samples['cosmological_parameters--omega_m'], c0.samples['cosmological_parameters--s8'], c0.samples['cosmological_parameters--w'], c0.samples['cosmological_parameters--omega_b']*c0.samples['cosmological_parameters--h0']**2 ])
samp1 = np.array([c1.samples['cosmological_parameters--omega_m'], c1.samples['cosmological_parameters--s8'], c1.samples['cosmological_parameters--w'], c1.samples['cosmological_parameters--omega_b']*c1.samples['cosmological_parameters--h0']**2 ])
import numpy as np
import os
import pylab as plt
plt.switch_backend('pdf')
plt.style.use('y1a1')
import tools.emcee as mc
from chainconsumer import ChainConsumer
print('Loading chains...')
#c1 = mc.chain('chain_1x2pt_fiducial_test_0.40_CLASS_C1_DEEP2_twopoint_new.fits_scales_3x2pt_0.5_8_6_v0.4.ini_lcdm.txt')
c2 = mc.chain('chain_1x2pt_fiducial_test_0.40_CLASS_NLA_C1_GAMA_twopoint_new.fits_scales_3x2pt_0.5_8_6_v0.4.ini_lcdm_btatest.txt')
c3 = mc.chain('chain_1x2pt_fiducial_v0.40_fiducial.fits_scales_1x2pt_v0.40fid_0.5.ini_lcdm.txt')
c1 = mc.chain('chain_1x2pt_fiducial_test_0.40_CLASS_NLA_C1_GAMA_twopoint_new.fits_scales_3x2pt_0.5_8_6_v0.4.ini_lcdm.txt')
c1.add_s8()
c2.add_s8()
c3.add_s8()
#'out_redmagic_high_mock_baseline-gp1-gg1-pp1-multinest-fidcov-ta-y3fid-6rpmin_200rpmax-rsd1-lens0-mag0-phot1.txt'
samp1 = np.array([c1.samples['cosmological_parameters--s8'], c1.samples['cosmological_parameters--omega_m'], c1.samples['intrinsic_alignment_parameters--a1'], c1.samples['intrinsic_alignment_parameters--a2'], c1.samples['intrinsic_alignment_parameters--alpha1'], c1.samples['intrinsic_alignment_parameters--alpha2'], c1.samples['intrinsic_alignment_parameters--bias_ta']])
samp2 = np.array([c2.samples['cosmological_parameters--s8'], c2.samples['cosmological_parameters--omega_m'], c2.samples['intrinsic_alignment_parameters--a1'], c2.samples['intrinsic_alignment_parameters--a2'], c2.samples['intrinsic_alignment_parameters--alpha1'], c2.samples['intrinsic_alignment_parameters--alpha2'], c2.samples['intrinsic_alignment_parameters--bias_ta']])
samp3 = np.array([c3.samples['cosmological_parameters--s8'], c3.samples['cosmological_parameters--omega_m'], c3.samples['intrinsic_alignment_parameters--a1'], c3.samples['intrinsic_alignment_parameters--a2'], c3.samples['intrinsic_alignment_parameters--alpha1'], c3.samples['intrinsic_alignment_parameters--alpha2'], c3.samples['intrinsic_alignment_parameters--bias_ta']])
#import pdb ; pdb.set_trace()
'bias_parameters--b_5': r'$b^{(5)}_{g,l}$'
}
colours = [
'purple', 'purple', 'purple', 'royalblue', 'royalblue', 'royalblue',
'purple', 'purple', 'purple'
]
ls = ['--', ':', '-'] * 3
alpha = [0., 0., 0.3] * 3
panel_numbers = {}
npar = None
for j, path in enumerate(files):
chain = mc.chain(path)
chain.add_column("s8", values="sigma_8*((omega_m/0.3)**0.5)")
vals = arr.add_col(np.array(chain.samples), 'weight', chain.weight)
names = chain.samples.dtype.names
npar = len(names)
nrows = 1 + int(npar / 5)
ipanel = 0
for i, name in enumerate(names):
label = parameter_labels[name]
if label not in panel_numbers.keys():
ipanel += 1
panel_numbers[label] = ipanel
from matplotlib import rcParams
import tools.emcee as mc
from chainconsumer import ChainConsumer
rcParams['xtick.major.size'] = 3.5
rcParams['xtick.minor.size'] = 1.7
rcParams['ytick.major.size'] = 3.5
rcParams['ytick.minor.size'] = 1.7
rcParams['xtick.direction'] = 'in'
rcParams['ytick.direction'] = 'in'
print('Loading chains...')
base = '/Users/hattifattener/Documents/y3cosmicshear/chains/data/nla/'
c1 = mc.chain(base + 'chain_NLA_1x2pt_lcdm.txt')
c2 = mc.chain(base + 'chain_1x2pt_nla_alex.txt')
#c1 = mc.chain('chain_1x2pt_fiducial_test_0.40_CLASS_NLA_C1_GAMA_twopoint_new.fits_scales_3x2pt_0.5_8_6_v0.4.ini_lcdm.txt')
c1.add_s8()
c2.add_s8()
#c3.add_s8()
#'out_redmagic_high_mock_baseline-gp1-gg1-pp1-multinest-fidcov-ta-y3fid-6rpmin_200rpmax-rsd1-lens0-mag0-phot1.txt'
samp1 = np.array([
c1.samples['cosmological_parameters--omega_m'],
c1.samples['cosmological_parameters--s8'],
c1.samples['intrinsic_alignment_parameters--a1'],
c1.samples['intrinsic_alignment_parameters--alpha1']
import tools.emcee as mc
from chainconsumer import ChainConsumer
rcParams['xtick.major.size'] = 3.5
rcParams['xtick.minor.size'] = 1.7
rcParams['ytick.major.size'] = 3.5
rcParams['ytick.minor.size'] = 1.7
rcParams['xtick.direction'] = 'in'
rcParams['ytick.direction'] = 'in'
print('Loading chains...')
base = '/Volumes/groke/work/chains/y3/real/'
#c1 = mc.chain(base+'fiducial/chain_1x2pt_hyperrank_2pt_NG_BLINDED_v0.40cov_xcorrGGL_27072020_SOMPZWZsamples_pit.fits_scales_3x2pt_0.5_8_6_v0.4.ini_lcdm.txt')
c1 = mc.chain(base + '/final_paper_chains/chain_3x2lcdm_0321_IAeff.txt')
c1.add_s8(alpha=0.5)
#'out_redmagic_high_mock_baseline-gp1-gg1-pp1-multinest-fidcov-ta-y3fid-6rpmin_200rpmax-rsd1-lens0-mag0-phot1.txt'
samp1 = np.array([
c1.samples['intrinsic_alignment_parameters--aeff1_1'],
c1.samples['intrinsic_alignment_parameters--aeff2_1']
])
samp2 = np.array([
c1.samples['intrinsic_alignment_parameters--aeff1_2'],
c1.samples['intrinsic_alignment_parameters--aeff2_2']
])
samp3 = np.array([
c1.samples['intrinsic_alignment_parameters--aeff1_3'],
import tools.emcee as mc
from chainconsumer import ChainConsumer
rcParams['xtick.major.size'] = 3.5
rcParams['xtick.minor.size'] = 1.7
rcParams['ytick.major.size'] = 3.5
rcParams['ytick.minor.size'] = 1.7
rcParams['xtick.direction'] = 'in'
rcParams['ytick.direction'] = 'in'
print('Loading chains...')
base = '/Volumes/groke/work/chains/y3/real/'
#c1 = mc.chain(base+'fiducial/chain_1x2pt_hyperrank_2pt_NG_BLINDED_v0.40cov_xcorrGGL_27072020_SOMPZWZsamples_pit.fits_scales_3x2pt_0.5_8_6_v0.4.ini_lcdm.txt')
c1 = mc.chain(base + 'chain_1x2pt_lcdm.txt')
c2 = mc.chain(base + 'chain_1x2pt_wcdm.txt')
c1.add_s8()
c2.add_s8()
#'out_redmagic_high_mock_baseline-gp1-gg1-pp1-multinest-fidcov-ta-y3fid-6rpmin_200rpmax-rsd1-lens0-mag0-phot1.txt'
samp1 = np.array([
c1.samples['cosmological_parameters--omega_m'],
c1.samples['cosmological_parameters--s8'],
c1.samples['intrinsic_alignment_parameters--a1'],
c1.samples['intrinsic_alignment_parameters--a2'],
c1.samples['intrinsic_alignment_parameters--alpha1'],
c1.samples['intrinsic_alignment_parameters--alpha2'],
c1.samples['intrinsic_alignment_parameters--bias_ta']
from matplotlib import rcParams
import tools.emcee as mc
from chainconsumer import ChainConsumer
rcParams['xtick.major.size'] = 3.5
rcParams['xtick.minor.size'] = 1.7
rcParams['ytick.major.size'] = 3.5
rcParams['ytick.minor.size'] = 1.7
rcParams['xtick.direction'] = 'in'
rcParams['ytick.direction'] = 'in'
print('Loading chains...')
base = '/Volumes/groke/work/chains/y3/real/'
c0 = mc.chain(base + 'chain_noia_3x2pt_lcdm.txt')
c1 = mc.chain(base + 'chain_3x2pt_lcdm.txt')
#c1 = mc.chain('chain_1x2pt_fiducial_test_0.40_CLASS_NLA_C1_GAMA_twopoint_new.fits_scales_3x2pt_0.5_8_6_v0.4.ini_lcdm.txt')
c0.add_s8()
c1.add_s8()
#'out_redmagic_high_mock_baseline-gp1-gg1-pp1-multinest-fidcov-ta-y3fid-6rpmin_200rpmax-rsd1-lens0-mag0-phot1.txt'
samp0 = np.array([
c0.samples['cosmological_parameters--omega_m'],
c0.samples['cosmological_parameters--s8'],
c0.samples['cosmological_parameters--sigma_8'],
])
samp1 = np.array([
import numpy as np
import os
import pylab as plt
plt.switch_backend('pdf')
plt.style.use('y1a1')
import tools.emcee as mc
from chainconsumer import ChainConsumer
print('Loading chains...')
base = '/Users/hattifattener/Documents/y3cosmicshear/chains/DEEP2/'
c1 = mc.chain(
base +
'chain_1x2pt_fiducial_test_0.40_CLASS_C1_DEEP2_twopoint_new.fits_scales_3x2pt_0.5_8_6_v0.4.ini_lcdm_btatest.txt'
)
c2 = mc.chain(
base +
'chain_1x2pt_fiducial_test_0.40_CLASS_C1_DEEP2_twopoint_new.fits_scales_3x2pt_0.5_8_6_v0.4_nobin44.ini_lcdm.txt'
)
c3 = mc.chain(
base +
'chain_1x2pt_fiducial_v0.40_fiducial.fits_scales_1x2pt_v0.40fid_0.5.ini_lcdm.txt'
)
#c1 = mc.chain('chain_1x2pt_fiducial_test_0.40_CLASS_NLA_C1_GAMA_twopoint_new.fits_scales_3x2pt_0.5_8_6_v0.4.ini_lcdm.txt')
c1.add_s8()
c2.add_s8()
c3.add_s8()
import tools.emcee as mc
from chainconsumer import ChainConsumer
rcParams['xtick.major.size'] = 3.5
rcParams['xtick.minor.size'] = 1.7
rcParams['ytick.major.size'] = 3.5
rcParams['ytick.minor.size'] = 1.7
rcParams['xtick.direction'] = 'in'
rcParams['ytick.direction'] = 'in'
print('Loading chains...')
base = '/Users/hattifattener/Documents/y3cosmicshear/chains/scale_cuts/final/'
#c1 = mc.chain(base+'fiducial/chain_1x2pt_hyperrank_2pt_NG_BLINDED_v0.40cov_xcorrGGL_27072020_SOMPZWZsamples_pit.fits_scales_3x2pt_0.5_8_6_v0.4.ini_lcdm.txt')
c1 = mc.chain(
base +
'chain_SR1x2pt_fiducial_sim_2pt_NG_BLINDED_v0.40cov_xcorrGGL27072020_1000samples_055ramp_all_2609_meanzisreal10.fits_sim_noiseless.fits_scales_3x2pt_0.5_8_6_v0.4.ini_lcdm.txt'
)
c2 = mc.chain(
base +
'chain_SR1x2pt_fiducial_sim_final_nl_bias_baryon.fits_scales_3x2pt_0.5_8_6_v0.4.ini_lcdm.txt'
)
#c1 = mc.chain('chain_1x2pt_fiducial_test_0.40_CLASS_NLA_C1_GAMA_twopoint_new.fits_scales_3x2pt_0.5_8_6_v0.4.ini_lcdm.txt')
c1.add_s8()
c2.add_s8()
#'out_redmagic_high_mock_baseline-gp1-gg1-pp1-multinest-fidcov-ta-y3fid-6rpmin_200rpmax-rsd1-lens0-mag0-phot1.txt'
samp1 = np.array([
c1.samples['cosmological_parameters--omega_m'],
from matplotlib import rcParams
import tools.emcee as mc
from chainconsumer import ChainConsumer
rcParams['xtick.major.size'] = 3.5
rcParams['xtick.minor.size'] = 1.7
rcParams['ytick.major.size'] = 3.5
rcParams['ytick.minor.size'] = 1.7
rcParams['xtick.direction'] = 'in'
rcParams['ytick.direction'] = 'in'
print('Loading chains...')
base = '/Volumes/groke/work/chains/y3/real/'
c1 = mc.chain(base + 'chain_1x2pt_nla_perbin.txt')
c2 = mc.chain(base + 'chain_2x2pt_nla_perbin.txt')
#c1 = mc.chain('chain_1x2pt_fiducial_test_0.40_CLASS_NLA_C1_GAMA_twopoint_new.fits_scales_3x2pt_0.5_8_6_v0.4.ini_lcdm.txt')
c1.add_s8()
c2.add_s8()
#'out_redmagic_high_mock_baseline-gp1-gg1-pp1-multinest-fidcov-ta-y3fid-6rpmin_200rpmax-rsd1-lens0-mag0-phot1.txt'
samp1 = np.array([
c1.samples['cosmological_parameters--s8'],
c1.samples['intrinsic_alignment_parameters--a1bin1'],
c1.samples['intrinsic_alignment_parameters--a1bin2'],
c1.samples['intrinsic_alignment_parameters--a1bin3'],
from matplotlib import rcParams
import tools.emcee as mc
from chainconsumer import ChainConsumer
rcParams['xtick.major.size'] = 3.5
rcParams['xtick.minor.size'] = 1.7
rcParams['ytick.major.size'] = 3.5
rcParams['ytick.minor.size'] = 1.7
rcParams['xtick.direction'] = 'in'
rcParams['ytick.direction'] = 'in'
print('Loading chains...')
base = '/Volumes/groke/work/chains/y3/real/'
c1 = mc.chain(base + 'chain_1x2pt_lcdm.txt')
c2 = mc.chain(base + 'chain_TATT_1x2pt_lcdm_nonu.txt')
#c1 = mc.chain('chain_1x2pt_fiducial_test_0.40_CLASS_NLA_C1_GAMA_twopoint_new.fits_scales_3x2pt_0.5_8_6_v0.4.ini_lcdm.txt')
c1.add_s8()
c2.add_s8()
#'out_redmagic_high_mock_baseline-gp1-gg1-pp1-multinest-fidcov-ta-y3fid-6rpmin_200rpmax-rsd1-lens0-mag0-phot1.txt'
samp1 = np.array([
c1.samples['cosmological_parameters--omega_m'],
c1.samples['cosmological_parameters--s8'],
c1.samples['intrinsic_alignment_parameters--a1'],
c1.samples['intrinsic_alignment_parameters--alpha1'],
c1.samples['intrinsic_alignment_parameters--a2'],
plt.style.use('y1a1')
import tools.emcee as mc
from chainconsumer import ChainConsumer
from matplotlib import rcParams
rcParams['xtick.major.size'] = 3.5
rcParams['xtick.minor.size'] = 1.7
rcParams['ytick.major.size'] = 3.5
rcParams['ytick.minor.size'] = 1.7
rcParams['xtick.direction'] = 'in'
rcParams['ytick.direction'] = 'in'
print('Loading chains...')
c1 = mc.chain('/Volumes/groke/work/chains/y3/real/chain_1x2pt_lcdm.txt')
c2 = mc.chain('/Volumes/groke/work/chains/y3/real/external/chain_s_b_lcdm.txt')
c3 = mc.chain(
'/Volumes/groke/work/chains/y3/real/external/chain_1x2pt_s_b_lcdm.txt')
#c1 = mc.chain('chain_1x2pt_fiducial_test_0.40_CLASS_NLA_C1_GAMA_twopoint_new.fits_scales_3x2pt_0.5_8_6_v0.4.ini_lcdm.txt')
c1.add_s8()
c2.add_s8()
c3.add_s8()
c3.samples['cosmological_parameters--omega_m'] += 0.15
c3.samples['cosmological_parameters--sigma_8'] += 0.15
#'out_redmagic_high_mock_baseline-gp1-gg1-pp1-multinest-fidcov-ta-y3fid-6rpmin_200rpmax-rsd1-lens0-mag0-phot1.txt'
from chainconsumer import ChainConsumer
rcParams['xtick.major.size'] = 3.5
rcParams['xtick.minor.size'] = 1.7
rcParams['ytick.major.size'] = 3.5
rcParams['ytick.minor.size'] = 1.7
rcParams['xtick.direction'] = 'in'
rcParams['ytick.direction'] = 'in'
print('Loading chains...')
base = '/Volumes/groke/work/chains/y3/real/'
#import pdb ; pdb.set_trace()
#c1 = mc.chain(base+'fiducial/chain_1x2pt_hyperrank_2pt_NG_BLINDED_v0.40cov_xcorrGGL_27072020_SOMPZWZsamples_pit.fits_scales_3x2pt_0.5_8_6_v0.4.ini_lcdm.txt')
c1 = mc.chain(base + '/final_paper_chains/chain_1x2pt_lcdm_SR_maglim.txt')
c5 = mc.chain(base + '/final_paper_chains/chain_1x2agg_ML.txt')
#final_paper_chains/chain_1x2_0321.txt')
c2 = mc.chain(base + 'final_paper_chains/chain_p-TTTEEE-lowE_lcdm.txt')
c3 = mc.chain(
base +
'final_paper_chains/tests_1x2pt/maglim/chain_1x2pt_s_b_lcdm_SR_maglim.txt')
c4 = mc.chain(base + 'external/chain_s_b_lcdm.txt')
#c1 = mc.chain('chain_1x2pt_fiducial_test_0.40_CLASS_NLA_C1_GAMA_twopoint_new.fits_scales_3x2pt_0.5_8_6_v0.4.ini_lcdm.txt')
c1.add_s8(alpha=0.5)
c2.add_s8(alpha=0.5)
c3.add_s8(alpha=0.5)
c4.add_s8(alpha=0.5)
c5.add_s8(alpha=0.5)
import tools.emcee as mc
from chainconsumer import ChainConsumer
rcParams['xtick.major.size'] = 1.5
rcParams['xtick.minor.size'] = 0.85
rcParams['ytick.major.size'] = 1.5
rcParams['ytick.minor.size'] = 0.85
rcParams['xtick.direction']='in'
rcParams['ytick.direction']='in'
#rcParams['text.usetex']=False
print('Loading chains...')
base = '/Users/hattifattener/Documents/y3cosmicshear/chains/cs2/iaxnz_test/'
#c1 = mc.chain(base+'fiducial/chain_1x2pt_hyperrank_2pt_NG_BLINDED_v0.40cov_xcorrGGL_27072020_SOMPZWZsamples_pit.fits_scales_3x2pt_0.5_8_6_v0.4.ini_lcdm.txt')
c1 = mc.chain(base+'chain_1x2pt_fiducial_sim_2pt_NG_BLINDED_v0.40cov_xcorrGGL27072020_1000samples_055ramp_all_2609_meanzisreal10.fits_sim_noiseless.fits_scales_3x2pt_0.5_8_6_v0.4.ini_lcdm.txt')
c2 = mc.chain(base+'chain_1x2pt_fiducial_v0.40_noiseless_104.fits_scales_3x2pt_0.5_8_6_v0.4.ini_lcdm.txt')
c3 = mc.chain(base+'chain_1x2pt_fiducial_v0.40_noiseless_788.fits_scales_3x2pt_0.5_8_6_v0.4.ini_lcdm.txt')
c4 = mc.chain(base+'chain_1x2pt_fiducial_v0.40_noiseless_328.fits_scales_3x2pt_0.5_8_6_v0.4.ini_lcdm.txt')
c1.add_s8()
c2.add_s8()
c3.add_s8()
c4.add_s8()
#'out_redmagic_high_mock_baseline-gp1-gg1-pp1-multinest-fidcov-ta-y3fid-6rpmin_200rpmax-rsd1-lens0-mag0-phot1.txt'
samp1 = np.array([c1.samples['cosmological_parameters--omega_m'], c1.samples['cosmological_parameters--s8'], c1.samples['intrinsic_alignment_parameters--a1'], c1.samples['intrinsic_alignment_parameters--a2'],c1.samples['intrinsic_alignment_parameters--alpha1'],c1.samples['intrinsic_alignment_parameters--alpha2'],c1.samples['intrinsic_alignment_parameters--bias_ta']])
import tools.emcee as mc
from chainconsumer import ChainConsumer
rcParams['xtick.major.size'] = 3.5
rcParams['xtick.minor.size'] = 1.7
rcParams['ytick.major.size'] = 3.5
rcParams['ytick.minor.size'] = 1.7
rcParams['xtick.direction']='in'
rcParams['ytick.direction']='in'
print('Loading chains...')
base = '/Volumes/groke/work/chains/y3/real/'
#c1 = mc.chain(base+'fiducial/chain_1x2pt_hyperrank_2pt_NG_BLINDED_v0.40cov_xcorrGGL_27072020_SOMPZWZsamples_pit.fits_scales_3x2pt_0.5_8_6_v0.4.ini_lcdm.txt')
c1 = mc.chain(base+'/final_paper_chains/chain_1x2pt_lcdm_SR_maglim.txt')
c2 = mc.chain(base+'/final_paper_chains/tests_1x2pt/maglim/chain_NLA_1x2pt_lcdm_maglim.txt')
c3 = mc.chain(base+'/final_paper_chains/tests_1x2pt/maglim/chain_NLA_noz_1x2pt_lcdm_maglim.txt')
c4 = mc.chain(base+'/final_paper_chains/tests_1x2pt/maglim/chain_noia_1x2pt_lcdm_maglim.txt')
#mc.chain('/Volumes/groke/work/chains/y1/blazek/eta/TATT/all/out_all-1x2pt-NG-TATT-alpha-v3.txt')
c5 = mc.chain(base+'/final_paper_chains/chain_1x2agg_ML.txt')
c1.add_s8()
c2.add_s8()
c3.add_s8()
c4.add_s8()
c5.add_s8()
import tools.emcee as mc
from chainconsumer import ChainConsumer
rcParams['xtick.major.size'] = 3.5
rcParams['xtick.minor.size'] = 1.7
rcParams['ytick.major.size'] = 3.5
rcParams['ytick.minor.size'] = 1.7
rcParams['xtick.direction'] = 'in'
rcParams['ytick.direction'] = 'in'
print('Loading chains...')
base = '/Volumes/groke/work/chains/y3/real/'
#c1 = mc.chain(base+'fiducial/chain_1x2pt_hyperrank_2pt_NG_BLINDED_v0.40cov_xcorrGGL_27072020_SOMPZWZsamples_pit.fits_scales_3x2pt_0.5_8_6_v0.4.ini_lcdm.txt')
c1 = mc.chain(base + 'final_paper_chains/chain_1x2pt_lcdm_SR_maglim.txt')
c2 = mc.chain('chains/chain_prior.txt')
#c2 = mc.chain(base+'external/chain_p-TTTEEE-lowE_lcdm.txt')
#c1 = mc.chain('chain_1x2pt_fiducial_test_0.40_CLASS_NLA_C1_GAMA_twopoint_new.fits_scales_3x2pt_0.5_8_6_v0.4.ini_lcdm.txt')
c1.add_s8()
c2.add_s8()
#'out_redmagic_high_mock_baseline-gp1-gg1-pp1-multinest-fidcov-ta-y3fid-6rpmin_200rpmax-rsd1-lens0-mag0-phot1.txt'
#import pdb ; pdb.set_trace()
exclude = [
'like', 'cosmological_parameters--sigma_12', 'data_vector--2pt_chi2',
'shear_calibration_parameters--m1', 'shear_calibration_parameters--m2',
import tools.emcee as mc
from chainconsumer import ChainConsumer
rcParams['xtick.major.size'] = 3.5
rcParams['xtick.minor.size'] = 1.7
rcParams['ytick.major.size'] = 3.5
rcParams['ytick.minor.size'] = 1.7
rcParams['xtick.direction'] = 'in'
rcParams['ytick.direction'] = 'in'
print('Loading chains...')
base = '/Volumes/groke/work/chains/y3/real/'
#c1 = mc.chain(base+'fiducial/chain_1x2pt_hyperrank_2pt_NG_BLINDED_v0.40cov_xcorrGGL_27072020_SOMPZWZsamples_pit.fits_scales_3x2pt_0.5_8_6_v0.4.ini_lcdm.txt')
c1 = mc.chain(base + '/final_paper_chains/chain_1x2pt_lcdm_noSR.txt')
#final_paper_chains/chain_1x2_0321.txt')
c2 = mc.chain(base + 'final_paper_chains/chain_p-TTTEEE-lowE_lcdm.txt')
c3 = mc.chain(base +
'/final_paper_chains/chain_1x2pt_lcdm_noSR_maglim_optimized.txt')
#c1 = mc.chain('chain_1x2pt_fiducial_test_0.40_CLASS_NLA_C1_GAMA_twopoint_new.fits_scales_3x2pt_0.5_8_6_v0.4.ini_lcdm.txt')
c1.add_s8(alpha=0.5)
c2.add_s8(alpha=0.5)
c3.add_s8(alpha=0.5)
#'out_redmagic_high_mock_baseline-gp1-gg1-pp1-multinest-fidcov-ta-y3fid-6rpmin_200rpmax-rsd1-lens0-mag0-phot1.txt'
samp1 = np.array([
c1.samples['cosmological_parameters--omega_m'],
import tools.emcee as mc
from chainconsumer import ChainConsumer
rcParams['xtick.major.size'] = 3.5
rcParams['xtick.minor.size'] = 1.7
rcParams['ytick.major.size'] = 3.5
rcParams['ytick.minor.size'] = 1.7
rcParams['xtick.direction'] = 'in'
rcParams['ytick.direction'] = 'in'
print('Loading chains...')
base = '/Volumes/groke/work/chains/y3/real/'
#c1 = mc.chain(base+'fiducial/chain_1x2pt_hyperrank_2pt_NG_BLINDED_v0.40cov_xcorrGGL_27072020_SOMPZWZsamples_pit.fits_scales_3x2pt_0.5_8_6_v0.4.ini_lcdm.txt')
c1 = mc.chain(base +
'/final_paper_chains/chain_1x2pt_lcdm_noSR_maglim_optimized.txt')
c2 = mc.chain(
base +
'/final_paper_chains/chain_1x2pt_nla_lcdm_noSR_maglim_optimized.txt')
c1.add_s8()
c2.add_s8()
#'out_redmagic_high_mock_baseline-gp1-gg1-pp1-multinest-fidcov-ta-y3fid-6rpmin_200rpmax-rsd1-lens0-mag0-phot1.txt'
samp1 = np.array([
c1.samples['cosmological_parameters--omega_m'],
c1.samples['cosmological_parameters--s8'],
c1.samples['intrinsic_alignment_parameters--a1'],
c1.samples['intrinsic_alignment_parameters--a2']
])
import tools.emcee as mc
from chainconsumer import ChainConsumer
rcParams['xtick.major.size'] = 3.5
rcParams['xtick.minor.size'] = 1.7
rcParams['ytick.major.size'] = 3.5
rcParams['ytick.minor.size'] = 1.7
rcParams['xtick.direction']='in'
rcParams['ytick.direction']='in'
print('Loading chains...')
base = '/Volumes/groke/work/chains/y3/real/'
#c1 = mc.chain(base+'fiducial/chain_1x2pt_hyperrank_2pt_NG_BLINDED_v0.40cov_xcorrGGL_27072020_SOMPZWZsamples_pit.fits_scales_3x2pt_0.5_8_6_v0.4.ini_lcdm.txt')
c1 = mc.chain(base+'/final_paper_chains/chain_1x2pt_lcdm_SR_maglim.txt')
#final_paper_chains/chain_1x2_0321.txt')
c2 = mc.chain(base+'external/chain_p-TTTEEE-lowE_lcdm.txt')
c3 = mc.chain(base+'/final_paper_chains/chain_1x2agg_ML.txt')
#c1 = mc.chain('chain_1x2pt_fiducial_test_0.40_CLASS_NLA_C1_GAMA_twopoint_new.fits_scales_3x2pt_0.5_8_6_v0.4.ini_lcdm.txt')
c1.add_s8(alpha=0.5)
c2.add_s8(alpha=0.5)
c3.add_s8(alpha=0.5)
#'out_redmagic_high_mock_baseline-gp1-gg1-pp1-multinest-fidcov-ta-y3fid-6rpmin_200rpmax-rsd1-lens0-mag0-phot1.txt'
samp1 = np.array([c1.samples['cosmological_parameters--omega_m'], c1.samples['cosmological_parameters--s8'], c1.samples['cosmological_parameters--sigma_8']])
import tools.emcee as mc
from chainconsumer import ChainConsumer
from matplotlib import rcParams
rcParams['xtick.major.size'] = 3.5
rcParams['xtick.minor.size'] = 1.7
rcParams['ytick.major.size'] = 3.5
rcParams['ytick.minor.size'] = 1.7
rcParams['xtick.direction'] = 'in'
rcParams['ytick.direction'] = 'in'
print('Loading chains...')
base = '/Users/hattifattener/Documents/y3cosmicshear/chains/'
c0 = mc.chain(
'/Volumes/groke/work/chains/y3/real/final_paper_chains/chain_1x2pt_lcdm_SR_maglim.txt'
)
#chain_1x2pt_lcdm.txt')
c1 = np.genfromtxt(base + '/external/lensing/HSC_Y1_LCDM_post_fid.txt',
names=True)
c2 = mc.chain(
'/Volumes/groke/work/chains/y3/real/final_paper_chains/chain_1x2agg_ML.txt'
)
#mc.chain('/Volumes/groke/work/chains/y1/fiducial/all/out_all-1x2pt-NG.txt')
c3 = mc.chain(
'/Volumes/groke/work/other_peoples_datasets/kids1000/KiDS1000_Cosebis_output_multinest_C.txt'
)
base = '/Volumes/groke/work/chains/y3/real/'
c4 = mc.chain(base + 'external/chain_p-TTTEEE-lowE_lcdm.txt')
import tools.emcee as mc
from chainconsumer import ChainConsumer
from matplotlib import rcParams
rcParams['xtick.major.size'] = 3.5
rcParams['xtick.minor.size'] = 1.7
rcParams['ytick.major.size'] = 3.5
rcParams['ytick.minor.size'] = 1.7
rcParams['xtick.direction'] = 'in'
rcParams['ytick.direction'] = 'in'
print('Loading chains...')
base = '/Users/hattifattener/Documents/y3cosmicshear/chains/ias_fid/'
c1 = mc.chain(
base +
'nopz/mtatt_dnla/chain_1x2pt_fiducial_2pt_SOMPZWZsamples_pit_goodlowz_shifted_1000samples_ramped.055_PITfix_sim_noiseless_GCharSmail_SIMULATED_NLA.fits_scales_3x2pt_0.5_8_6_v0.4.ini_lcdm.txt'
)
c2 = mc.chain(
base +
'nopz/mnla_dnla/chain_1x2pt_fiducial_2pt_SOMPZWZsamples_pit_goodlowz_shifted_1000samples_ramped.055_PITfix_sim_noiseless_GCharSmail_SIMULATED_NLA.fits_scales_3x2pt_0.5_8_6_v0.4.ini_lcdm.txt'
)
c3 = mc.chain(
base +
'fidm/mtatt_dnla/chain_1x2pt_hyperrank_2pt_SOMPZWZsamples_pit_goodlowz_shifted_1000samples_ramped.055_PITfix_sim_noiseless_GCharSmail_SIMULATED_NLA.fits_scales_3x2pt_0.5_8_6_v0.4.ini_lcdm.txt'
)
c4 = mc.chain(
base +
'fidm/mnla_dnla/chain_1x2pt_hyperrank_2pt_SOMPZWZsamples_pit_goodlowz_shifted_1000samples_ramped.055_PITfix_sim_noiseless_GCharSmail_SIMULATED_NLA.fits_scales_3x2pt_0.5_8_6_v0.4.ini_lcdm.txt'
)
#c1 = mc.chain('chain_1x2pt_fiducial_test_0.40_CLASS_NLA_C1_GAMA_twopoint_new.fits_scales_3x2pt_0.5_8_6_v0.4.ini_lcdm.txt')
