Python chain 예제들

예제 #1

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")

예제 #2

파일: mice.py 프로젝트: ssamuroff/y3cosmicshear

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'

예제 #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 + '/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'],

예제 #4

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'],

예제 #5

파일: ias_all_maglim2.py 프로젝트: ssamuroff/y3cosmicshear

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()

예제 #6

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)

예제 #7

파일: external_wcdm.py 프로젝트: ssamuroff/y3cosmicshear

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'

예제 #8

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(

예제 #9

파일: iatest_fixedm.py 프로젝트: ssamuroff/y3cosmicshear

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']])

예제 #10

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 ])

예제 #11

파일: deep.py 프로젝트: ssamuroff/y3cosmicshear

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()

예제 #12

    '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

예제 #13

파일: data_nla.py 프로젝트: ssamuroff/y3cosmicshear

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']

예제 #14

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'],

예제 #15

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']

예제 #16

파일: ias_wcdm.py 프로젝트: ssamuroff/y3cosmicshear

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([

예제 #17

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()

예제 #18

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'],

예제 #19

파일: iasperbin.py 프로젝트: ssamuroff/y3cosmicshear

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'],

예제 #20

파일: nutest.py 프로젝트: ssamuroff/y3cosmicshear

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'],

예제 #21

파일: external_lcdm.py 프로젝트: ssamuroff/y3cosmicshear

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'

예제 #22

파일: external_all_singlepanel.py 프로젝트: ssamuroff/y3cosmicshear

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)

예제 #23

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']])

예제 #24

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()

예제 #25

파일: make_big_cornerplot2.py 프로젝트: ssamuroff/y3cosmicshear

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',

예제 #26

파일: cs2_key_noSR.py 프로젝트: ssamuroff/y3cosmicshear

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'],

예제 #27

파일: cs2_tatt2_optimised.py 프로젝트: ssamuroff/y3cosmicshear

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']
])

예제 #28

파일: cs1_key.py 프로젝트: ssamuroff/y3cosmicshear

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']])

예제 #29

파일: external_lensing.py 프로젝트: ssamuroff/y3cosmicshear

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')

예제 #30

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')