diff --git a/notebooks/dev/cross_correlation_simulatedata.ipynb b/notebooks/dev/cross_correlation_simulatedata.ipynb index 6578d556..08ae3720 100644 --- a/notebooks/dev/cross_correlation_simulatedata.ipynb +++ b/notebooks/dev/cross_correlation_simulatedata.ipynb @@ -478,7 +478,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.10.8" } }, "nbformat": 4, diff --git a/setup.cfg b/setup.cfg index 0f80d7c1..cd229a6b 100644 --- a/setup.cfg +++ b/setup.cfg @@ -27,7 +27,7 @@ install_requires = syslibrary [options.package_data] -soliket = *.yaml,*.bibtex,clusters/data/*,clusters/data/selFn_equD56/*,lensing/data/*.txt,lensing/data/*.fits,mflike/*.yaml,tests/*.yaml,data/xcorr_simulated/*.txt,data/CosmoPower/CP_paper/CMB/*.pkl,tests/data/test_bandpass/*,cross_correlation/*.yaml +soliket = *.yaml,*.bibtex,clusters/data/*,clusters/data/selFn_equD56/*,lensing/data/*.txt,lensing/data/*.fits,mflike/*.yaml,tests/*.yaml,data/xcorr_simulated/*.txt,data/CosmoPower/CP_paper/CMB/*.pkl,tests/data/test_bandpass/*,cross_correlation/*.yaml,tests/data/*.fits testpaths = "soliket" text_file_format = rst diff --git a/soliket/__init__.py b/soliket/__init__.py index 69770a4b..c61a35f1 100644 --- a/soliket/__init__.py +++ b/soliket/__init__.py @@ -4,7 +4,7 @@ from .ps import PSLikelihood, BinnedPSLikelihood # noqa: F401 from .mflike import MFLike # noqa: F401 from .mflike import TheoryForge_MFLike -from .cross_correlation import GalaxyKappaLikelihood, ShearKappaLikelihood # noqa: F401, E501 +from .cross_correlation import CrossCorrelationLikelihood, GalaxyKappaLikelihood, ShearKappaLikelihood # noqa: F401, E501 from .xcorr import XcorrLikelihood # noqa: F401 from .foreground import Foreground from .bandpass import BandPass @@ -15,4 +15,4 @@ from .clusters import ClusterLikelihood # noqa: F401 except ImportError: print('Skipping cluster likelihood (is pyCCL installed?)') - pass \ No newline at end of file + pass diff --git a/soliket/cross_correlation/CrossCorrelationLikelihood.yaml b/soliket/cross_correlation/CrossCorrelationLikelihood.yaml index 0c431b60..617b38e3 100644 --- a/soliket/cross_correlation/CrossCorrelationLikelihood.yaml +++ b/soliket/cross_correlation/CrossCorrelationLikelihood.yaml @@ -1,60 +1,3 @@ -auto_file: soliket/data/xcorr_simulated/clgg_noiseless.txt -cross_file: soliket/data/xcorr_simulated/clkg_noiseless.txt -dndz_file: soliket/data/xcorr_simulated/dndz.txt - datapath: soliket/tests/data/des_s-act_kappa.toy-sim.sacc.fits -use_tracers: all - -z_nuisance_mode: null -m_nuisance_mode: null -ia_mode: null - +use_spectra: all ncovsims: null - -params: - b1: - prior: - min: 0. - max: 10. - latex: b_1 - s1: - value: 0.4 - latex: s_1 - A_IA: - value: 0.54 - latex: A_{IA} - gs_des_bin1_A_IA: - value: 0.50 - gs_des_bin2_A_IA: - value: 0.52 - gs_des_bin3_A_IA: - value: 0.54 - gs_des_bin4_A_IA: - value: 0.56 - eta_IA: - value: 1.7 - latex: \eta_{IA} - gs_des_bin1_deltaz: - value: -0.02 - gs_des_bin2_deltaz: - value: -0.02 - gs_des_bin3_deltaz: - value: -0.02 - gs_des_bin4_deltaz: - value: -0.02 - gs_des_bin1_m: - value: -0.04 - gs_des_bin2_m: - value: -0.04 - gs_des_bin3_m: - value: -0.04 - gs_des_bin4_m: - value: -0.04 - gs_des_bin1_sigmam: - value: 0.01 - gs_des_bin2_sigmam: - value: 0.01 - gs_des_bin3_sigmam: - value: 0.01 - gs_des_bin4_sigmam: - value: 0.01 diff --git a/soliket/cross_correlation/GalaxyKappaLikelihood.yaml b/soliket/cross_correlation/GalaxyKappaLikelihood.yaml new file mode 100644 index 00000000..2379167c --- /dev/null +++ b/soliket/cross_correlation/GalaxyKappaLikelihood.yaml @@ -0,0 +1,9 @@ +params: + b1: + prior: + min: 0. + max: 10. + latex: b_1 + s1: + value: 0.4 + latex: s_1 diff --git a/soliket/cross_correlation/ShearKappaLikelihood.yaml b/soliket/cross_correlation/ShearKappaLikelihood.yaml new file mode 100644 index 00000000..56ccdc62 --- /dev/null +++ b/soliket/cross_correlation/ShearKappaLikelihood.yaml @@ -0,0 +1,43 @@ +z_nuisance_mode: null +m_nuisance_mode: null +ia_mode: null + +params: + A_IA: + value: 0.54 + latex: A_{IA} + gs_des_bin1_A_IA: + value: 0.50 + gs_des_bin2_A_IA: + value: 0.52 + gs_des_bin3_A_IA: + value: 0.54 + gs_des_bin4_A_IA: + value: 0.56 + eta_IA: + value: 1.7 + latex: \eta_{IA} + gs_des_bin1_deltaz: + value: -0.02 + gs_des_bin2_deltaz: + value: -0.02 + gs_des_bin3_deltaz: + value: -0.02 + gs_des_bin4_deltaz: + value: -0.02 + gs_des_bin1_m: + value: -0.04 + gs_des_bin2_m: + value: -0.04 + gs_des_bin3_m: + value: -0.04 + gs_des_bin4_m: + value: -0.04 + gs_des_bin1_sigmam: + value: 0.01 + gs_des_bin2_sigmam: + value: 0.01 + gs_des_bin3_sigmam: + value: 0.01 + gs_des_bin4_sigmam: + value: 0.01 diff --git a/soliket/cross_correlation/__init__.py b/soliket/cross_correlation/__init__.py index 5983588e..1bc79b29 100644 --- a/soliket/cross_correlation/__init__.py +++ b/soliket/cross_correlation/__init__.py @@ -1 +1 @@ -from .cross_correlation import CrossCorrelationLikelihood, GalaxyKappaLikelihood, ShearKappaLikelihood # noqa: F401, E501 \ No newline at end of file +from .cross_correlation import CrossCorrelationLikelihood, GalaxyKappaLikelihood, ShearKappaLikelihood # noqa: F401, E501 diff --git a/soliket/cross_correlation/cross_correlation.py b/soliket/cross_correlation/cross_correlation.py index a7586eb2..16cfa567 100644 --- a/soliket/cross_correlation/cross_correlation.py +++ b/soliket/cross_correlation/cross_correlation.py @@ -19,18 +19,36 @@ class CrossCorrelationLikelihood(GaussianLikelihood): """ def initialize(self): - if self.datapath is None: - self.dndz = np.loadtxt(self.dndz_file) - - x, y, dy = self._get_data() - cov = np.diag(dy**2) - self.data = GaussianData("CrossCorrelation", x, y, cov, self.ncovsims) - else: - self._get_sacc_data() + self._get_sacc_data() + self._check_tracers() def get_requirements(self): return {"CCL": {"kmax": 10, "nonlinear": True}} + def _check_tracers(self): + + # check correct tracers + for tracer_comb in self.sacc_data.get_tracer_combinations(): + + if (self.sacc_data.tracers[tracer_comb[0]].quantity == + self.sacc_data.tracers[tracer_comb[1]].quantity): + raise LoggedError(self.log, + 'You have tried to use {0} to calculate an \ + autocorrelation, but it is a cross-correlation \ + likelihood. Please check your tracer selection in the \ + ini file.'.format(self.__class__.__name__)) + + for tracer in tracer_comb: + if self.sacc_data.tracers[tracer].quantity not in self._allowable_tracers: + raise LoggedError(self.log, + 'You have tried to use a {0} tracer in \ + {1}, which only allows {2}. Please check your \ + tracer selection in the ini file.\ + '.format(self.sacc_data.tracers[tracer].quantity, + self.__class__.__name__, + self._allowable_tracers)) + + def _get_nz(self, z, tracer, tracer_name, **params_values): if self.z_nuisance_mode == 'deltaz': @@ -46,11 +64,12 @@ def _get_sacc_data(self, **params_values): self.sacc_data = sacc.Sacc.load_fits(self.datapath) - if self.use_tracers == 'all': + if self.use_spectra == 'all': pass else: - raise LoggedError('Tracer selection not implemented yet!') - # self.sacc_data.keep_selection(tracers=self.use_tracers.split(',')) + for tracer_comb in self.sacc_data.get_tracer_combinations(): + if tracer_comb not in self.use_spectra: + self.sacc_data.remove_selection(tracers=tracer_comb) self.x = self._construct_ell_bins() self.y = self.sacc_data.mean @@ -88,6 +107,16 @@ def _get_data(self, **params_values): return x, y, dy + def get_binning(self, tracer_comb): + + bpw_idx = self.sacc_data.indices(tracers=tracer_comb) + bpw = self.sacc_data.get_bandpower_windows(bpw_idx) + ells_theory = bpw.values + ells_theory = np.asarray(ells_theory, dtype=int) + w_bins = bpw.weight.T + + return ells_theory, w_bins + def logp(self, **params_values): theory = self._get_theory(**params_values) return self.data.loglike(theory) @@ -97,31 +126,51 @@ class GalaxyKappaLikelihood(CrossCorrelationLikelihood): r""" Likelihood for cross-correlations of galaxy and CMB lensing data. """ + _allowable_tracers = ['cmb_convergence', 'galaxy_density'] + def _get_theory(self, **params_values): + cosmo = self.provider.get_CCL()["cosmo"] + tracer_comb = self.sacc_data.get_tracer_combinations() + + for tracer in np.unique(tracer_comb): + + if self.sacc_data.tracers[tracer].quantity == "cmb_convergence": + cmbk_tracer = tracer + elif self.sacc_data.tracers[tracer].quantity == "galaxy_density": + gal_tracer = tracer + + z_gal_tracer = self.sacc_data.tracers[gal_tracer].z + nz_gal_tracer = self.sacc_data.tracers[gal_tracer].nz + + # this should use the bias theory! tracer_g = ccl.NumberCountsTracer(cosmo, has_rsd=False, - dndz=self.dndz.T, - bias=(self.dndz[:, 0], + dndz=(z_gal_tracer, nz_gal_tracer), + bias=(z_gal_tracer, params_values["b1"] * - np.ones(len(self.dndz[:, 0]))), - mag_bias=(self.dndz[:, 0], + np.ones(len(z_gal_tracer))), + mag_bias=(z_gal_tracer, params_values["s1"] * - np.ones(len(self.dndz[:, 0]))) + np.ones(len(z_gal_tracer))) ) tracer_k = ccl.CMBLensingTracer(cosmo, z_source=1060) - cl_gg = ccl.cells.angular_cl(cosmo, tracer_g, tracer_g, self.ell_auto) # + 1e-7 - cl_kg = ccl.cells.angular_cl(cosmo, tracer_k, tracer_g, self.ell_cross) + ells_theory_gk, w_bins_gk = self.get_binning((gal_tracer, cmbk_tracer)) + + cl_gk_unbinned = ccl.cells.angular_cl(cosmo, tracer_k, tracer_g, ells_theory_gk) - return np.concatenate([cl_gg, cl_kg]) + cl_gk_binned = np.dot(w_bins_gk, cl_gk_unbinned) + + return cl_gk_binned class ShearKappaLikelihood(CrossCorrelationLikelihood): r""" Likelihood for cross-correlations of galaxy weak lensing shear and CMB lensing data. """ + _allowable_tracers = ["cmb_convergence", "galaxy_shear"] def _get_theory(self, **params_values): @@ -131,14 +180,6 @@ def _get_theory(self, **params_values): for tracer_comb in self.sacc_data.get_tracer_combinations(): - if (self.sacc_data.tracers[tracer_comb[0]].quantity - == self.sacc_data.tracers[tracer_comb[1]].quantity): - self.log.warning('You requested auto-correlation in '\ - 'ShearKappaLikelihood but it is only implemented for '\ - 'cross-correlations and resulting bias calculations '\ - 'will be incorrect. Please check your tracer '\ - 'combinations in the sacc file.') - if self.sacc_data.tracers[tracer_comb[0]].quantity == "cmb_convergence": tracer1 = ccl.CMBLensingTracer(cosmo, z_source=1060) diff --git a/soliket/data/xcorr_simulated/clgg_noiseless.txt b/soliket/data/xcorr_simulated/clgg_noiseless.txt deleted file mode 100644 index 091c9423..00000000 --- a/soliket/data/xcorr_simulated/clgg_noiseless.txt +++ /dev/null @@ -1,3 +0,0 @@ -3.550000000000000000e+01 7.650000000000000000e+01 1.265000000000000000e+02 1.765000000000000000e+02 2.265000000000000000e+02 2.765000000000000000e+02 3.265000000000000000e+02 3.765000000000000000e+02 4.265000000000000000e+02 4.765000000000000000e+02 5.265000000000000000e+02 5.760000000000000000e+02 -2.171386555992278359e-07 2.074818774537804928e-07 1.760569576742365607e-07 1.556208570083821835e-07 1.434590789832146224e-07 1.347070083607898911e-07 1.277731731363569596e-07 1.224832434049751676e-07 1.186677876068538790e-07 1.159080212760721562e-07 1.137746016890491462e-07 1.120196152233489823e-07 -2.171386555992278359e-08 2.074818774537804994e-08 1.760569576742365674e-08 1.556208570083821835e-08 1.434590789832146224e-08 1.347070083607899044e-08 1.277731731363569629e-08 1.224832434049751775e-08 1.186677876068538790e-08 1.159080212760721661e-08 1.137746016890491529e-08 1.120196152233489823e-08 diff --git a/soliket/data/xcorr_simulated/clkg_noiseless.txt b/soliket/data/xcorr_simulated/clkg_noiseless.txt deleted file mode 100644 index c15dfe54..00000000 --- a/soliket/data/xcorr_simulated/clkg_noiseless.txt +++ /dev/null @@ -1,3 +0,0 @@ -3.550000000000000000e+01 7.650000000000000000e+01 1.265000000000000000e+02 1.765000000000000000e+02 2.265000000000000000e+02 2.765000000000000000e+02 3.265000000000000000e+02 3.765000000000000000e+02 4.265000000000000000e+02 4.765000000000000000e+02 5.265000000000000000e+02 5.760000000000000000e+02 -1.424982171873059867e-07 1.234732647730792034e-07 8.611508401982061149e-08 6.248438515784781822e-08 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' TFORM2 = 'D ' TTYPE3 = 'max ' TFORM3 = 'D ' SACCTYPE= 'window ' SACCCLSS= 'TopHat ' EXTNAME = 'window:TopHat' END XTENSION= 'BINTABLE' / binary table extension BITPIX = 8 / array data type NAXIS = 2 / number of array dimensions NAXIS1 = 24 / length of dimension 1 NAXIS2 = 0 / length of dimension 2 PCOUNT = 0 / number of group parameters GCOUNT = 1 / number of groups TFIELDS = 3 / number of table fields TTYPE1 = 'id ' TFORM1 = 'D ' TTYPE2 = 'min ' TFORM2 = 'D ' TTYPE3 = 'max ' TFORM3 = 'D ' SACCTYPE= 'window ' SACCCLSS= 'LogTopHat' EXTNAME = 'window:LogTopHat' END XTENSION= 'BINTABLE' / binary table extension BITPIX = 8 / array data type NAXIS = 2 / number of array dimensions NAXIS1 = 160 / length of dimension 1 NAXIS2 = 3072 / length of dimension 2 PCOUNT = 0 / number of group parameters GCOUNT = 1 / number of groups TFIELDS = 2 / number of table fields TTYPE1 = 'values ' TFORM1 = 'K ' TTYPE2 = 'weight ' TFORM2 = '19D ' TDIM2 = '(19) ' SACCTYPE= 'window ' SACCCLSS= 'Bandpower' SACCNAME= 140207322073120 EXTNAME = 'window:Bandpower' END ????????? ? +? ? ? ??????????????????? ?!?"?#?$?%?&?'?(?)?*?+?,?-?.?/?0?1?2?3?4?5?6?7?8?9?:?;????@?A?B?C?D?E?F?G?H?I?J?K?L?M?N?O?P?Q?R?S?T?U?V?W?X?Y?Z?[?\?]?^?_?`?a?b?c?d?e?f?g?h?i?j?k?l?m?n?o?p?q?r?s?t?u?v?w?x?y?z?{?|?}?~??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ? +? ? ? ??????????????????? ?!?"?#?$?%?&?'?(?)?*?+?,?-?.?/?0?1?2?3?4?5?6?7?8?9?:?;?<?=?>???@?A?B?C?D?E?F?G?H?I?J?K?L?M?N?O?P?Q?R?S?T?U?V?W?X?Y?Z?[?\?]?^?_?`?a?b?c?d?e?f?g?h?i?j?k?l?m?n?o?p?q?r?s?t?u?v?w?x?y?z?{?|?}?~??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ? +? ? ? ??????????????????? 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" # $ % & ' ( ) * + , - . / 0 1 2 3 4 5 6 7 8 9 : ; < = > ? @ A B C D E F G H I J K L M N O P Q R S T U V W X Y Z [ \ ] ^ _ ` a b c d e f g h i j k l m n o p q r s t u v w x y z { | } ~  XTENSION= 'BINTABLE' / binary table extension BITPIX = 8 / array data type NAXIS = 2 / number of array dimensions NAXIS1 = 16 / length of dimension 1 NAXIS2 = 3000 / length of dimension 2 PCOUNT = 0 / number of group parameters GCOUNT = 1 / number of groups TFIELDS = 2 / number of table fields TTYPE1 = 'ell ' TFORM1 = 'K ' TTYPE2 = 'beam ' TFORM2 = 'D ' SACCTYPE= 'tracer ' SACCCLSS= 'Map ' SACCNAME= 'ck_actdr4' SACCQTTY= 'cmb_convergence' EXTNAME = 'tracer:Map:ck_actdr4:beam' MAP_UNIT= 'none ' SPIN = 0 END ??,?چ?>?\-? +D??5L:?>g& ? +?z` ?݋ ?2' ?-:d?G#?R%?L ?5[??g[?ð>?7?V?VS?bl?3d??ϗ?Қ?)yl?>]w?B% ?6N +!?"?#?ׯ[$?aM %?} &?Д8;'?ܠ(?ˆ)?,3*?7$W+?vV/,?-?=.?6/?Gp0?R1?E2?i1.3?%J 4?j15?m>6?7?t8?+59?9Y:?;?k0???~@?zAA?v|sB?rVC?n:GD?iE?ev"F?`^G?\p H?WAI?S)DJ?NmcK?I +.L?D xM??ֺ2[N?: +@O?5P?0xQ?+}R?&>]S? T?0U?hV?3;W? 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"b1": 1, - "s1": 0.4, + # "b1": 1, + # "s1": 0.4, }, "theory": {"camb": None, "ccl": {"external": CCL, "nonlinear": False}}, "debug": False, @@ -42,28 +40,29 @@ def test_galaxykappa_model(request): from soliket.cross_correlation import GalaxyKappaLikelihood + info["params"]["b1"] = 2. + info["params"]["s1"] = 0.4 + info["likelihood"] = { "GalaxyKappaLikelihood": {"external": GalaxyKappaLikelihood, - "datapath": None, - 'cross_file': os.path.join(request.config.rootdir, - cross_file), - 'auto_file': os.path.join(request.config.rootdir, - auto_file), - 'dndz_file': os.path.join(request.config.rootdir, - dndz_file)} - } + "datapath": os.path.join(request.config.rootdir, + gkappa_sacc_file)}} model = get_model(info) # noqa F841 -# @pytest.mark.xfail(reason="data file not in repo") def test_shearkappa_model(request): from soliket.cross_correlation import ShearKappaLikelihood + # clear out the galaxykappa params if they've been added + info["params"].pop("b1", None) + info["params"].pop("s1", None) + info["likelihood"] = {"ShearKappaLikelihood": {"external": ShearKappaLikelihood, - "datapath": os.path.join(request.config.rootdir, sacc_file)}} + "datapath": os.path.join(request.config.rootdir, + gammakappa_sacc_file)}} model = get_model(info) # noqa F841 @@ -72,23 +71,22 @@ def test_galaxykappa_like(request): from soliket.cross_correlation import GalaxyKappaLikelihood + info["params"]["b1"] = 2. + info["params"]["s1"] = 0.4 + info["likelihood"] = { "GalaxyKappaLikelihood": {"external": GalaxyKappaLikelihood, - "datapath": None, - 'cross_file': os.path.join(request.config.rootdir, - cross_file), - 'auto_file': os.path.join(request.config.rootdir, - auto_file), - 'dndz_file': os.path.join(request.config.rootdir, - dndz_file)} - } + "datapath": os.path.join(request.config.rootdir, + gkappa_sacc_file), + "use_spectra": [('gc_cmass', 'ck_actdr4')]}} + model = get_model(info) loglikes, derived = model.loglikes() - assert np.isclose(loglikes[0], 88.2, atol=0.2, rtol=0.0) + + assert np.isclose(loglikes[0], 174.013, atol=0.2, rtol=0.0) -# @pytest.mark.xfail(reason="data file not in repo") def test_shearkappa_like(request): from soliket.cross_correlation import ShearKappaLikelihood @@ -113,9 +111,7 @@ def test_shearkappa_like(request): "As": 2.1e-9, "tau": 0.094, "mnu": 0.0, - "nnu": 3.046, - "s1": 0.4, - "b1": 1.0} + "nnu": 3.046} model = get_model(info) loglikes, derived = model.loglikes() @@ -123,6 +119,58 @@ def test_shearkappa_like(request): assert np.isclose(loglikes, 637.64473666) +def test_shearkappa_tracerselect(request): + + from soliket.cross_correlation import ShearKappaLikelihood + import copy + + rootdir = request.config.rootdir + + test_datapath = os.path.join(rootdir, gammakappa_sacc_file) + + info["likelihood"] = { + "ShearKappaLikelihood": {"external": ShearKappaLikelihood, + "datapath": test_datapath, + 'use_spectra': 'all'} + } + + info_onebin = copy.deepcopy(info) + info_onebin['likelihood']['ShearKappaLikelihood']['use_spectra'] = \ + [('gs_des_bin1', 'ck_act')] + + info_twobin = copy.deepcopy(info) + info_twobin['likelihood']['ShearKappaLikelihood']['use_spectra'] = \ + [ + ('gs_des_bin1', 'ck_act'), + ('gs_des_bin3', 'ck_act'), + ] + + model = get_model(info) + loglikes, derived = model.loglikes() + + lhood = model.likelihood['ShearKappaLikelihood'] + + model_onebin = get_model(info_onebin) + loglikes_onebin, derived_onebin = model_onebin.loglikes() + + lhood_onebin = model_onebin.likelihood['ShearKappaLikelihood'] + + model_twobin = get_model(info_twobin) + loglikes_twobin, derived_twobin = model_twobin.loglikes() + + lhood_twobin = model_twobin.likelihood['ShearKappaLikelihood'] + + n_ell_perbin = len(lhood.data.x) // 4 + + assert n_ell_perbin == len(lhood_onebin.data.x) + assert np.allclose(lhood.data.y[:n_ell_perbin], lhood_onebin.data.y) + + assert 2 * n_ell_perbin == len(lhood_twobin.data.x) + assert np.allclose(np.concatenate([lhood.data.y[:n_ell_perbin], + lhood.data.y[2 * n_ell_perbin:3 * n_ell_perbin]]), + lhood_twobin.data.y) + + def test_shearkappa_hartlap(request): from soliket.cross_correlation import ShearKappaLikelihood @@ -148,9 +196,7 @@ def test_shearkappa_hartlap(request): "As": 2.5e-9, # offset the theory to upweight inv_cov in loglike "tau": 0.094, "mnu": 0.0, - "nnu": 3.046, - "s1": 0.4, - "b1": 1.0} + "nnu": 3.046} model = get_model(info) loglikes, derived = model.loglikes() @@ -170,7 +216,8 @@ def test_shearkappa_deltaz(request): info["likelihood"] = {"ShearKappaLikelihood": {"external": ShearKappaLikelihood, - "datapath": os.path.join(request.config.rootdir, sacc_file), + "datapath": os.path.join(request.config.rootdir, + gammakappa_sacc_file), "z_nuisance_mode": "deltaz"}} model = get_model(info) # noqa F841 @@ -185,7 +232,8 @@ def test_shearkappa_m(request): info["likelihood"] = {"ShearKappaLikelihood": {"external": ShearKappaLikelihood, - "datapath": os.path.join(request.config.rootdir, sacc_file), + "datapath": os.path.join(request.config.rootdir, + gammakappa_sacc_file), "m_nuisance_mode": True}} model = get_model(info) # noqa F841 @@ -200,7 +248,8 @@ def test_shearkappa_ia_nla_noevo(request): info["likelihood"] = {"ShearKappaLikelihood": {"external": ShearKappaLikelihood, - "datapath": os.path.join(request.config.rootdir, sacc_file), + "datapath": os.path.join(request.config.rootdir, + gammakappa_sacc_file), "ia_mode": 'nla-noevo'}} model = get_model(info) # noqa F841 @@ -215,7 +264,8 @@ def test_shearkappa_ia_nla(request): info["likelihood"] = {"ShearKappaLikelihood": {"external": ShearKappaLikelihood, - "datapath": os.path.join(request.config.rootdir, sacc_file), + "datapath": os.path.join(request.config.rootdir, + gammakappa_sacc_file), "ia_mode": 'nla'}} info["params"]["eta_IA"] = 1.7 @@ -232,7 +282,8 @@ def test_shearkappa_ia_perbin(request): info["likelihood"] = {"ShearKappaLikelihood": {"external": ShearKappaLikelihood, - "datapath": os.path.join(request.config.rootdir, sacc_file), + "datapath": os.path.join(request.config.rootdir, + gammakappa_sacc_file), "ia_mode": 'nla-perbin'}} model = get_model(info) # noqa F841 @@ -247,7 +298,8 @@ def test_shearkappa_hmcode(request): info["likelihood"] = {"ShearKappaLikelihood": {"external": ShearKappaLikelihood, - "datapath": os.path.join(request.config.rootdir, sacc_file)}} + "datapath": os.path.join(request.config.rootdir, + gammakappa_sacc_file)}} info["theory"] = {"camb": {'extra_args': {'halofit_version': 'mead2020_feedback', 'HMCode_logT_AGN': 7.8}}, "ccl": {"external": CCL, "nonlinear": False}} diff --git a/soliket/tests/test_cross_correlation.yaml b/soliket/tests/test_galaxykappa.yaml similarity index 81% rename from soliket/tests/test_cross_correlation.yaml rename to soliket/tests/test_galaxykappa.yaml index be231c1f..3eb02983 100644 --- a/soliket/tests/test_cross_correlation.yaml +++ b/soliket/tests/test_galaxykappa.yaml @@ -27,11 +27,15 @@ params: min: 0. max: 10. latex: b_1 - s1: 0.4 + s1: + value: 0.4 + latex: s_1 sigma8: likelihood: soliket.cross_correlation.GalaxyKappaLikelihood: - datapath: null + datapath: soliket/tests/data/des_s-act_kappa.toy-sim.sacc.fits + use_spectra: all + ncovsims: null theory: camb: extra_args: @@ -42,4 +46,4 @@ stop_at_error: true sampler: evaluate: -output: chains/test_cross_correlation +output: chains/test_galaxykappa \ No newline at end of file diff --git a/soliket/tests/test_runs.py b/soliket/tests/test_runs.py index 1d72bfd4..c589cb18 100644 --- a/soliket/tests/test_runs.py +++ b/soliket/tests/test_runs.py @@ -22,7 +22,8 @@ "lensing", "lensing_lite", "multi", - "cross_correlation", + # "galaxykappa", + # "shearkappa" # "xcorr" ]) def test_evaluate(lhood): @@ -33,9 +34,6 @@ def test_evaluate(lhood): if lhood == "mflike": pytest.skip(reason="don't want to install 300Mb of data!") - if lhood == "cross_correlation": - pytest.skip(reason="cannot locate data files") - info = yaml_load(pkgutil.get_data("soliket", f"tests/test_{lhood}.yaml")) info["force"] = True info['sampler'] = {'evaluate': {}} @@ -51,7 +49,8 @@ def test_evaluate(lhood): "lensing", "lensing_lite", "multi", - "cross_correlation", + # "galaxykappa", + # "shearkappa" # "xcorr" ]) def test_mcmc(lhood): @@ -62,9 +61,6 @@ def test_mcmc(lhood): if lhood == "mflike": pytest.skip(reason="don't want to install 300Mb of data!") - if lhood == "cross_correlation": - pytest.skip(reason="cannot locate data files") - info = yaml_load(pkgutil.get_data("soliket", f"tests/test_{lhood}.yaml")) info["force"] = True info['sampler'] = {'mcmc': {'max_samples': 10, 'max_tries': 1000}}