def test_bicep_keck_2015_classy(modules):
info_theory = {"classy": {"extra_args": cmb_precision["classy"]}}
# extra tolerance for CLASS
chi2_classy = deepcopy(chi2)
chi2_classy["tolerance"] *= 2
body_of_test(modules, test_point, lik_info, info_theory, chi2_classy,
extra_model={"primordial": "SFSR_t"})
def test_planck_2015_l_camb(modules):
best_fit = params_lensing
info_likelihood = lik_info_lensing
info_theory = {"camb": {"extra_args": cmb_precision["camb"]}}
best_fit_derived = derived_lensing
body_of_test(modules, best_fit, info_likelihood, info_theory,
chi2_lensing, best_fit_derived)
def test_planck_2015_p_camb(modules):
best_fit = params_lowTEB_highTTTEEE
info_likelihood = lik_info_lowTEB_highTTTEEE
info_theory = {"camb": {"extra_args": cmb_precision["camb"]}}
best_fit_derived = derived_lowTEB_highTTTEEE
body_of_test(modules, best_fit, info_likelihood, info_theory,
chi2_lowTEB_highTTTEEE, best_fit_derived)
def test_sn_jla_classy(modules):
best_fit = deepcopy(params_lowTEB_highTTTEEE)
best_fit.update(best_fit_sn)
lik = "sn_jla"
info_likelihood = {lik: {}}
info_theory = {"classy": None}
body_of_test(modules, best_fit, info_likelihood, info_theory, chi2_sn_jla)
def test_planck_2015_l_classy(modules):
best_fit = params_lensing
info_likelihood = lik_info_lensing
info_theory = {"classy": {"extra_args": cmb_precision["classy"]}}
best_fit_derived = deepcopy(derived_lensing)
for p in classy_unknown:
best_fit_derived.pop(p, None)
body_of_test(modules, best_fit, info_likelihood, info_theory,
chi2_lensing, best_fit_derived)
def test_cosmo_des_y1_clustering_classy(modules):
like = "des_y1_clustering"
info_likelihood = {like: {}}
best_fit_clustering = deepcopy(best_fit)
best_fit_clustering.update(test_params_clustering)
info_theory = {"classy": {"extra_args": classy_extra}}
body_of_test(modules, best_fit_clustering, info_likelihood, info_theory, {
like: ref_chi2["clustering"],
"tolerance": tolerance
})
def test_cosmo_des_y1_shear_classy(modules):
like = "des_y1_shear"
info_likelihood = {like: {}}
best_fit_shear = deepcopy(best_fit)
best_fit_shear.update(test_params_shear)
info_theory = {"classy": {"extra_args": classy_extra}}
body_of_test(modules, best_fit_shear, info_likelihood, info_theory, {
like: ref_chi2["shear"],
"tolerance": tolerance
})
def test_planck_2015_l2_camb(modules):
best_fit = params_lensing
lik_name = "planck_2015_lensing_cmblikes"
clik_name = "planck_2015_lensing"
info_likelihood = {lik_name: lik_info_lensing[clik_name]}
chi2_lensing_cmblikes = deepcopy(chi2_lensing)
chi2_lensing_cmblikes[lik_name] = chi2_lensing[clik_name]
info_theory = {"camb": {"extra_args": cmb_precision["camb"]}}
best_fit_derived = derived_lensing
body_of_test(modules, best_fit, info_likelihood, info_theory,
chi2_lensing_cmblikes, best_fit_derived)
def test_cosmo_des_y1_joint_camb(modules):
like = "des_y1_joint"
info_likelihood = {like: {}}
best_fit_joint = deepcopy(best_fit)
best_fit_joint.update(test_params_shear)
best_fit_joint.update(test_params_clustering)
info_theory = {"camb": {"extra_args": camb_extra}}
body_of_test(modules, best_fit_joint, info_likelihood, info_theory, {
like: ref_chi2["joint"],
"tolerance": tolerance
})
def test_planck_2015_p_classy(modules):
best_fit = params_lowTEB_highTTTEEE
info_likelihood = lik_info_lowTEB_highTTTEEE
info_theory = {"classy": {"extra_args": cmb_precision["classy"]}}
best_fit_derived = deepcopy(derived_lowTEB_highTTTEEE)
for p in classy_unknown:
best_fit_derived.pop(p, None)
chi2_lowTEB_highTTTEEE_classy = deepcopy(chi2_lowTEB_highTTTEEE)
chi2_lowTEB_highTTTEEE_classy["tolerance"] += classy_extra_tolerance
body_of_test(modules, best_fit, info_likelihood, info_theory,
chi2_lowTEB_highTTTEEE_classy, best_fit_derived)
def test_planck_2015_l2_classy(modules):
best_fit = params_lensing
lik_name = "planck_2015_lensing_cmblikes"
clik_name = "planck_2015_lensing"
info_likelihood = {lik_name: lik_info_lensing[clik_name]}
chi2_lensing_cmblikes = deepcopy(chi2_lensing)
chi2_lensing_cmblikes[lik_name] = chi2_lensing[clik_name]
info_theory = {"classy": {"extra_args": cmb_precision["classy"]}}
best_fit_derived = deepcopy(derived_lensing)
for p in classy_unknown:
best_fit_derived.pop(p, None)
body_of_test(modules, best_fit, info_likelihood, info_theory,
chi2_lensing_cmblikes, best_fit_derived)
def test_sdss_dr7_mgs_classy(modules):
lik = "sdss_dr7_mgs"
info_likelihood = {lik: {}}
info_theory = {"classy": None}
body_of_test(modules, best_fit, info_likelihood, info_theory,
chi2_sdss_dr7_mgs)
def test_sn_jla_lite_slow_camb(modules):
lik = "sn_jla_lite"
info_likelihood = {lik: {"marginalize": True, "precompute_covmats": False}}
info_theory = {"camb": None}
body_of_test(modules, best_fit, info_likelihood, info_theory,
chi2_sn_jla_lite)
def test_sn_pantheon_camb(modules):
lik = "sn_pantheon"
info_likelihood = {lik: {}}
info_theory = {"camb": None}
body_of_test(modules, best_fit, info_likelihood, info_theory,
chi2_sn_pantheon)
def test_bicep_keck_2015_camb(modules):
info_theory = {"camb": {"extra_args": cmb_precision["camb"]}}
body_of_test(modules, test_point, lik_info, info_theory, chi2,
extra_model={"primordial": "SFSR_t"})
def test_sn_jla_lite_camb(modules):
lik = "sn_jla_lite"
info_likelihood = {lik: {"marginalize": True}}
info_theory = {"camb": None}
body_of_test(modules, best_fit, info_likelihood, info_theory,
chi2_sn_jla_lite)
def test_sdss_dr12_consensus_bao_camb(modules):
lik = "sdss_dr12_consensus_bao"
info_likelihood = {lik: {}}
info_theory = {"camb": None}
body_of_test(modules, best_fit, info_likelihood, info_theory,
chi2_sdss_dr12_consensus_bao)
def test_sixdf_2011_bao_classy(modules):
lik = "sixdf_2011_bao"
info_likelihood = {lik: {}}
info_theory = {"classy": None}
body_of_test(modules, best_fit, info_likelihood, info_theory,
chi2_sixdf_2011_bao)
def test_sdss_dr12_consensus_final_classy(modules):
lik = "sdss_dr12_consensus_final"
info_likelihood = {lik: {}}
info_theory = {"classy": None}
chi2_classy = deepcopy(chi2_sdss_dr12_consensus_final)
body_of_test(modules, best_fit, info_likelihood, info_theory, chi2_classy)
