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refactor: maintainability issues in tests fixed.
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07d947d5b1
commit
338bf8906a
3 changed files with 7 additions and 8 deletions
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@ -249,9 +249,9 @@ def test_matrix_corr():
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def test_corr_none_entries():
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a = pe.pseudo_Obs(1.0, 0.1, 'a')
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l = np.asarray([[a, a], [a, a]])
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la = np.asarray([[a, a], [a, a]])
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n = np.asarray([[None, None], [None, None]])
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x = [l, n]
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x = [la, n]
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matr = pe.Corr(x)
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matr.projected(np.asarray([1.0, 0.0]))
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@ -194,7 +194,7 @@ def test_linear_fit_guesses():
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lin_func = lambda a, x: a[0] + a[1] * x
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with pytest.raises(Exception):
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pe.least_squares(xvals, yvals, lin_func)
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[o.gamma_method() for o in yvals];
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[o.gamma_method() for o in yvals]
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with pytest.raises(Exception):
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pe.least_squares(xvals, yvals, lin_func, initial_guess=[5])
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@ -414,7 +414,7 @@ def test_fit_vs_jackknife():
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for i, cov in enumerate([cov1, cov2, cov3]):
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dat = pe.misc.gen_correlated_data(np.arange(1, 4), cov, 'test', 0.5, samples=samples)
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[o.gamma_method(S=0) for o in dat];
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[o.gamma_method(S=0) for o in dat]
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func = lambda a, x: a[0] + a[1] * x
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fr = pe.least_squares(np.arange(1, 4), dat, func)
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fr.gamma_method(S=0)
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@ -448,8 +448,7 @@ def test_correlated_fit_vs_jackknife():
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x_val = np.arange(1, 6, 2)
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for i, cov in enumerate([cov1, cov2, cov3]):
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dat = pe.misc.gen_correlated_data(x_val + x_val ** 2 + np.random.normal(0.0, 0.1, 3), cov, 'test', 0.5, samples=samples)
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[o.gamma_method(S=0) for o in dat];
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dat
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[o.gamma_method(S=0) for o in dat]
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func = lambda a, x: a[0] * x + a[1] * x ** 2
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fr = pe.least_squares(x_val, dat, func, correlated_fit=True, silent=True)
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[o.gamma_method(S=0) for o in fr]
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@ -258,8 +258,8 @@ def test_complex_matrix_inverse():
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inverse_matrix = np.linalg.inv(matrix)
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inverse_obs_matrix = pe.linalg.inv(obs_matrix)
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for (n, m), entry in np.ndenumerate(inverse_matrix):
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assert np.isclose(inverse_matrix[n, m].real, inverse_obs_matrix[n, m].real.value)
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assert np.isclose(inverse_matrix[n, m].imag, inverse_obs_matrix[n, m].imag.value)
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assert np.isclose(inverse_matrix[n, m].real, inverse_obs_matrix[n, m].real.value)
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assert np.isclose(inverse_matrix[n, m].imag, inverse_obs_matrix[n, m].imag.value)
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def test_matrix_functions():
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