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How to use assert_between method in Testify

Best Python code snippet using Testify_python

test_stacking_models.py

Source:test_stacking_models.py

...89        fit_2gaussian(vels, twogauss_spectrum)90    actual_params = [0.5, 0.0, 0.05, 0.5, 0.3]91    for fit, act, err in zip(parvals, actual_params, parerrs):92        npt.assert_allclose(act, fit, atol=2 * err)93def assert_between(act, val, low_err, up_err):94    assert (act >= val - low_err) & (act <= val + up_err)95def test_hwhm_model():96    vels = np.linspace(-1, 1, 100)97    model, gauss, lor = \98        gauss_with_wings_noise(vels, 1., 0.0, 0.1, 0.0, return_models=True)99    noisy_model = \100        gauss_with_wings_noise(vels, 1., 0.0, 0.1, 0.01)101    parvals, parerrs, param_names, hwhm_gauss = \102        fit_hwhm(vels, noisy_model, sigma_noise=0.01,103                 niters=100)104    parvals_clean, parerrs_clean = \105        fit_hwhm(vels, model, sigma_noise=0.01,106                 niters=100)[:2]107    f_wings = np.sum((model - gauss)) / np.sum(model)108    sigma_wings = np.sqrt(np.sum([(m - g) * v**2 for m, g, v in109                                  zip(model, gauss, vels)]) /110                          np.sum((model - gauss)))111    asymm = 0.0112    kappa = 0.0113    actual_params = [0.1, 0.0, f_wings, sigma_wings, asymm, kappa]114    # Compare the clean parameter estimates115    for act, val, low, up in zip(actual_params, parvals_clean, *parerrs_clean):116        assert_between(act, val, low, up)117    # Compare the noisy parameter estimates118    for act, val, low, up in zip(actual_params, parvals, *parerrs):119        assert_between(act, val, low, up)120def test_hwhm_model_wgauss():121    vels = np.linspace(-1, 1, 100)122    model, gauss = \123        gauss_with_noise(vels, 1., 0.0, 0.1, 0.0, return_models=True)124    noisy_model = \125        gauss_with_noise(vels, 1., 0.0, 0.1, 0.01)126    parvals, parerrs, param_names, hwhm_gauss = \127        fit_hwhm(vels, noisy_model, sigma_noise=0.01,128                 niters=100)129    parvals_clean, parerrs_clean = \130        fit_hwhm(vels, model, sigma_noise=0.01,131                 niters=100)[:2]132    f_wings = 0.0133    sigma_wings = 0.0134    asymm = 0.0135    kappa = 0.0136    actual_params = [0.1, 0.0, f_wings, sigma_wings, asymm, kappa]137    names = ['sigma', 'vcen', 'fwing', 'sig_wing', 'asymm', 'kappa']138    # Compare the clean parameter estimates139    for name, act, val, low, up in zip(names, actual_params, parvals_clean,140                                       *parerrs_clean):141        if name == 'sig_wing':142            continue143        assert_between(act, val, low, up)144    # Compare the noisy parameter estimates145    for name, act, val, low, up in zip(names, actual_params, parvals,146                                       *parerrs):147        if name == 'sig_wing':148            continue149        assert_between(act, val, low, up)150def test_hwhm_model_asymmwings():151    vels = np.linspace(-1, 1, 100)152    model, gauss, lor = \153        gauss_with_wings_noise(vels, 1., 0.0, 0.1, 0.0, return_models=True,154                               asymm=-0.85)155    noisy_model = \156        gauss_with_wings_noise(vels, 1., 0.0, 0.1, 0.01,157                               asymm=-0.85)158    parvals, parerrs, param_names, hwhm_gauss = \159        fit_hwhm(vels, noisy_model, sigma_noise=0.01,160                 niters=100)161    parvals_clean, parerrs_clean = \162        fit_hwhm(vels, model, sigma_noise=0.01,163                 niters=100)[:2]164    high_mask = vels > 0.0 + 0.1 * np.sqrt(2 * np.log(2))165    low_mask = vels < 0.0 - 0.1 * np.sqrt(2 * np.log(2))166    # peak_high_mask = np.logical_and(vels > 0.,167    #                                 vels < 0.0 + 0.1 * np.sqrt(2 * np.log(2)))168    # peak_low_mask = np.logical_and(vels < 0.,169    #                                vels > 0.0 - 0.1 * np.sqrt(2 * np.log(2)))170    f_wings = (np.sum(model[low_mask] - gauss[low_mask]) +171               np.sum(model[high_mask] - gauss[high_mask])) / np.sum(model)172    sigma_wings = np.NaN173    pos_mask = vels > 0174    neg_mask = vels < 0175    asymm = np.sum(model[neg_mask] - model[pos_mask]) / np.sum(model)176    kappa = 0.0177    actual_params = [0.1, 0.0, f_wings, sigma_wings, asymm, kappa]178    names = ['sigma', 'vcen', 'fwing', 'sig_wing', 'asymm', 'kappa']179    # Not comparing the sigma_wings b/c we aren't using it in any analysis180    # and the definition doesn't seem well-suited to substantial asymmetry181    # Compare the clean parameter estimates182    for name, act, val, low, up in zip(names, actual_params, parvals_clean,183                                       *parerrs_clean):184        if name == 'sig_wing':185            continue186        assert_between(act, val, low, up)187    # Compare the noisy parameter estimates188    for name, act, val, low, up in zip(names, actual_params, parvals,189                                       *parerrs):190        if name == 'sig_wing':191            continue192        assert_between(act, val, low, up)193def test_symm_asymm_fwing():194    '''195    Use above asymmetric setup.196    '''197    vels = np.linspace(-1, 1, 100)198    model, gauss, lor = \199        gauss_with_wings_noise(vels, 1., 0.0, 0.1, 0.0, return_models=True,200                               asymm=-0.85)201    noisy_model = \202        gauss_with_wings_noise(vels, 1., 0.0, 0.1, 0.01,203                               asymm=-0.85)204    s_model = model205    n_model = model[::-1]206    tot_model = n_model + s_model207    params_clean = find_linewing_asymm(vels, n_model, s_model)208    s_model_noisy = noisy_model209    n_model_noisy = noisy_model[::-1]210    params, low_lim, up_lim = \211        find_linewing_asymm(vels, n_model_noisy, s_model_noisy,212                            niters=100,213                            sigma_noise_n=0.01,214                            sigma_noise_s=0.01)215    high_mask = vels > 0.0 + 0.1 * np.sqrt(2 * np.log(2))216    low_mask = vels < 0.0 - 0.1 * np.sqrt(2 * np.log(2))217    # We added the model twice, so multiple gauss by 2218    f_wings = (np.sum(tot_model[low_mask] - 2 * gauss[low_mask]) +219               np.sum(tot_model[high_mask] - 2 * gauss[high_mask])) / \220        np.sum(tot_model)221    blue_excess = np.sum(s_model[low_mask] - n_model[low_mask])222    red_excess = np.sum(n_model[high_mask] - s_model[high_mask])223    f_asymm = (blue_excess + red_excess) / np.sum(tot_model)224    f_symm = f_wings - f_asymm225    npt.assert_allclose(f_wings, params_clean[0], atol=0.005)226    assert_between(f_wings, params[0], low_lim[0], up_lim[0])227    npt.assert_allclose(f_symm, params_clean[1], atol=0.005)228    assert_between(f_symm, params[1], low_lim[1], up_lim[1])229    npt.assert_allclose(f_asymm, params_clean[2], atol=0.005)230    # Errors seem to be a bit more asymmetric for f_asymm. Allow231    # a larger range232    avg_sig = 0.5 * ((params[2] - low_lim[2]) + (up_lim[2] - params[2]))233    assert_between(f_asymm, params[2], low_lim[2] - 0.5 * avg_sig,...

test_pca.py

Source:test_pca.py

...26    slice_used = slice(0, tester.n_eigs)27    npt.assert_allclose(tester.eigvals[slice_used],28                        computed_data['pca_val'][slice_used])29    fit_values = computed_data["pca_fit_vals"].reshape(-1)[0]30    assert_between(fit_values["index"], tester.index_error_range[0],31                   tester.index_error_range[1])32    assert_between(fit_values["gamma"], tester.gamma_error_range[0],33                   tester.gamma_error_range[1])34    assert_between(fit_values["intercept"].value,35                   tester.intercept_error_range[0].value,36                   tester.intercept_error_range[1].value)37    assert_between(fit_values["sonic_length"].value,38                   tester.sonic_length()[1][0].value,39                   tester.sonic_length()[1][1].value)40@pytest.mark.skipif("not EMCEE_INSTALLED")41def test_PCA_method_w_bayes():42    tester = PCA(dataset1["cube"])43    tester.run(mean_sub=True, n_eigs=50,44               spatial_method='contour',45               spectral_method='walk-down',46               fit_method='bayes', brunt_beamcorrect=False)47    slice_used = slice(0, tester.n_eigs)48    npt.assert_allclose(tester.eigvals[slice_used],49                        computed_data['pca_val'][slice_used])50    fit_values = computed_data["pca_fit_vals"].reshape(-1)[0]51    assert_between(fit_values["index_bayes"], tester.index_error_range[0],52                   tester.index_error_range[1])53    assert_between(fit_values["gamma_bayes"], tester.gamma_error_range[0],54                   tester.gamma_error_range[1])55    assert_between(fit_values["intercept_bayes"].value,56                   tester.intercept_error_range[0].value,57                   tester.intercept_error_range[1].value)58    assert_between(fit_values["sonic_length_bayes"].value,59                   tester.sonic_length()[1][0].value,60                   tester.sonic_length()[1][1].value)61@pytest.mark.parametrize(("method", "min_eigval"),62                         [("proportion", 0.99), ("value", 0.001)])63def test_PCA_auto_n_eigs(method, min_eigval):64    tester = PCA(dataset1["cube"])65    tester.run(mean_sub=True, n_eigs='auto', min_eigval=min_eigval,66               eigen_cut_method=method, decomp_only=True)67    fit_values = computed_data["pca_fit_vals"].reshape(-1)[0]68    assert tester.n_eigs == fit_values["n_eigs_" + method]69def test_PCA_distance():70    tester_dist = \71        PCA_Distance(dataset1["cube"],72                     dataset2["cube"]).distance_metric()...

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