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first attempt of trying pytest for both binned and unbinned cluster likelihood
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,89 +1,148 @@ | ||
| import numpy as np | ||
| import copy | ||
| import pytest | ||
|
|
||
| from cobaya.model import get_model | ||
|
|
||
| fiducial_params = { | ||
| "ombh2": 0.02225, | ||
| "omch2": 0.1198, | ||
| "H0": 67.3, | ||
| "tau": 0.06, | ||
| "As": 2.2e-9, | ||
| "ns": 0.96, | ||
| "mnu": 0.0, | ||
| "nnu": 3.046, | ||
| "omnuh2": 0., | ||
|
|
||
| params = { | ||
| 'h': 0.68, | ||
| 'n_s': 0.965, | ||
| 'Omega_b': 0.049, | ||
| 'Omega_c': 0.261, | ||
| 'sigma8': 0.81, | ||
| 'm_nu': 0., | ||
| 'tenToA0': 1.9e-05, | ||
| 'B0': 0.08, | ||
| 'scatter_sz': 0., | ||
| 'bias_sz': 1., | ||
| 'C0': 2. | ||
| } | ||
|
|
||
| info_unbinned = { | ||
| "params": fiducial_params, | ||
| "likelihood": {"soliket.UnbinnedClusterLikelihood": | ||
| {"stop_at_error": True, | ||
| "theorypred":{"choose_theory":'camb'}}}, | ||
| "theory": { | ||
| "camb": { | ||
| "extra_args": { | ||
| "accurate_massive_neutrino_transfers": True, | ||
| "num_massive_neutrinos": 1, | ||
| "redshifts": np.linspace(0, 2, 41), | ||
| "nonlinear": False, | ||
| "kmax": 10.0, | ||
| "dark_energy_model": "ppf" | ||
| }, | ||
| "ignore_obsolete": True | ||
| }, | ||
| path = './clusters/data/advact/DR5CosmoSims/sim-kit_NemoCCL_A10tSZ_DR5White_ACT-DR5_tenToA0Tuned/NemoCCL_A10tSZ_DR5White_ACT-DR5_tenToA0Tuned/' | ||
|
|
||
| lkl_common = { | ||
| 'verbose': True, | ||
| 'stop_at_error': True, | ||
| 'data': { | ||
| 'data_path': path, | ||
| 'cat_file': 'NemoCCL_A10tSZ_DR5White_ACT-DR5_tenToA0Tuned_mass.fits', | ||
| 'Q_file': 'selFn/QFit.fits', | ||
| 'tile_file': 'selFn/tileAreas.txt', | ||
| 'rms_file': 'selFn/RMSTab.fits' | ||
| }, | ||
| 'theorypred': { | ||
| 'choose_theory': 'CCL', | ||
| 'massfunc_mode': 'ccl', | ||
| 'compl_mode': 'erf_diff', | ||
| 'md_hmf': '200c', | ||
| 'md_ym': '200c' | ||
| }, | ||
| 'YM': { | ||
| 'Mpivot': 4.25e14 | ||
| }, | ||
| 'selfunc': { | ||
| 'SNRcut': 5., | ||
| 'method': 'SNRbased', | ||
| 'whichQ': 'injection', | ||
| 'resolution': 'downsample', | ||
| 'dwnsmpl_bins': 2, | ||
| 'save_dwsmpld': False, | ||
| }, | ||
| 'binning': { | ||
| 'z': { | ||
| 'zmin': 0., | ||
| 'zmax': 2., | ||
| 'dz': 0.1 | ||
| }, | ||
| 'q': { | ||
| 'log10qmin': 0.6, | ||
| 'log10qmax': 2.0, | ||
| 'dlog10q': 0.25 | ||
| }, | ||
| 'M': { | ||
| 'Mmin': 5e13, | ||
| 'Mmax': 1e16, | ||
| 'dlogM': 0.01 | ||
| }, | ||
| 'exclude_zbin': 2, | ||
| } | ||
| } | ||
|
|
||
| info_binned = copy.copy(info_unbinned) | ||
| info_binned['likelihood'] = {"soliket.BinnedClusterLikelihood": | ||
| {"stop_at_error": True, | ||
| "datapath": './soliket/tests/data/toy_cashc.txt'}} | ||
| ccl_baseline = { | ||
| 'transfer_function': 'boltzmann_camb', | ||
| 'matter_pk': 'halofit', | ||
| 'baryons_pk': 'nobaryons', | ||
| 'md_hmf': '200c' | ||
| } | ||
|
|
||
|
|
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| def test_clusters_unbinned_model(): | ||
|
|
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| model_fiducial = get_model(info_unbinned) | ||
|
|
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| info_binned = { | ||
| 'params': params, | ||
| 'likelihood': {'soliket.BinnedClusterLikelihood': lkl_common}, | ||
| 'theory': {'soliket.clusters.CCL': ccl_baseline} | ||
| } | ||
|
|
||
| info_unbinned = { | ||
| 'params': params, | ||
| 'likelihood': {'soliket.UnbinnedClusterLikelihood': lkl_common}, | ||
| 'theory': {'soliket.clusters.CCL': ccl_baseline} | ||
| } | ||
|
|
||
|
|
||
| def test_clusters_unbinned_model(): | ||
| def test_clusters_import(): | ||
|
|
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| model_fiducial = get_model(info_unbinned) | ||
| from soliket.clusters import BinnedClusterLikelihood | ||
| from soliket.clusters import UnbinnedClusterLikelihood | ||
|
|
||
|
|
||
| def test_clusters_unbinned_loglike(): | ||
| def test_clusters_model(): | ||
|
|
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| model_fiducial = get_model(info_unbinned) | ||
| binned_model = get_model(info_binned) | ||
| unbinned_model = get_model(info_unbinned) | ||
|
|
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| lnl = model_fiducial.loglikes({})[0] | ||
|
|
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| print('lnl: ',lnl) | ||
| # exit(0) | ||
| def test_clusters_loglike(): | ||
|
|
||
| # assert np.isclose(lnl, -885.678) | ||
| binned_model = get_model(info_binned) | ||
| unbinned_model = get_model(info_unbinned) | ||
|
|
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| binned_lnl = binned_model.loglikes({})[0] | ||
| unbinned_lnl = unbinned_model.loglikes({})[0] | ||
|
|
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| def test_clusters_unbinned_n_expected(): | ||
| assert np.isfinite(binned_lnl) | ||
| assert np.isfinite(unbinned_lnl) | ||
|
|
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| model_fiducial = get_model(info_unbinned) | ||
|
|
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| lnl = model_fiducial.loglikes({})[0] | ||
| def test_clusters_prediction(): | ||
|
|
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| like = model_fiducial.likelihood["soliket.UnbinnedClusterLikelihood"] | ||
| binned_model = get_model(info_binned) | ||
| unbinned_model = get_model(info_unbinned) | ||
|
|
||
| print('like._get_n_expected():',like._get_n_expected()) | ||
| print('like._get_nz_expected():',like._get_nz_expected()) | ||
| binned_model.loglikes({})[0] | ||
| unbinned_model.loglikes({})[0] | ||
|
|
||
| assert np.isfinite(lnl) | ||
| assert like._get_n_expected() > 40 | ||
| binned_like = binned_model.likelihood['soliket.BinnedClusterLikelihood'] | ||
| unbinned_like = unbinned_model.likelihood['soliket.UnbinnedClusterLikelihood'] | ||
|
|
||
| binned_pk_intp = binned_like.theory.get_Pk_interpolator() | ||
| unbinned_pk_intp = unbinned_like.theory.get_Pk_interpolator() | ||
| SZparams = { | ||
| 'tenToA0': 1.9e-05, | ||
| 'B0': 0.08, | ||
| 'C0': 2., | ||
| 'scatter_sz': 0., | ||
| 'bias_sz': 1. | ||
| } | ||
|
|
||
| def test_clusters_binned_model(): | ||
| Nzq = binned_like._get_theory(binned_pk_intp, **SZparams) | ||
| Ntot = unbinned_like._get_n_expected(unbinned_pk_intp, **SZparams) | ||
|
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| assert np.isclose(Nzq.sum(), Ntot) | ||
|
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| model_fiducial = get_model(info_binned) | ||
|
|
||
| # for debugging purposes: | ||
| # test_clusters_unbinned_loglike() | ||
| # test_clusters_unbinned_model() | ||
| test_clusters_unbinned_n_expected() | ||
| # test_clusters_import() | ||
| # test_clusters_model() | ||
| # test_clusters_loglike() | ||
| # test_clusters_prediction() |