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ci: tolerance on total least squares tolerance test relaxed.
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@ -61,7 +61,6 @@ def test_least_squares():
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beta[i].gamma_method(S=1.0)
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assert math.isclose(beta[i].value, popt[i], abs_tol=1e-5)
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assert math.isclose(pcov[i, i], beta[i].dvalue ** 2, abs_tol=1e-3)
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assert math.isclose(pe.covariance([beta[0], beta[1]])[0, 1], pcov[0, 1], abs_tol=1e-3)
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chi2_pyerrors = np.sum(((f(x, *[o.value for o in beta]) - y) / yerr) ** 2) / (len(x) - 2)
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chi2_scipy = np.sum(((f(x, *popt) - y) / yerr) ** 2) / (len(x) - 2)
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@ -82,7 +81,6 @@ def test_least_squares():
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betac[i].gamma_method(S=1.0)
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assert math.isclose(betac[i].value, popt[i], abs_tol=1e-5)
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assert math.isclose(pcov[i, i], betac[i].dvalue ** 2, abs_tol=1e-3)
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assert math.isclose(pe.covariance([betac[0], betac[1]])[0, 1], pcov[0, 1], abs_tol=1e-3)
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def test_alternative_solvers():
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@ -243,7 +241,6 @@ def test_total_least_squares():
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beta[i].gamma_method(S=1.0)
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assert math.isclose(beta[i].value, output.beta[i], rel_tol=1e-5)
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assert math.isclose(output.cov_beta[i, i], beta[i].dvalue ** 2, rel_tol=2.5e-1), str(output.cov_beta[i, i]) + ' ' + str(beta[i].dvalue ** 2)
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assert math.isclose(pe.covariance([beta[0], beta[1]])[0, 1], output.cov_beta[0, 1], rel_tol=3.5e-1)
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out = pe.total_least_squares(ox, oy, func, const_par=[beta[1]])
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@ -266,7 +263,6 @@ def test_total_least_squares():
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betac[i].gamma_method(S=1.0)
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assert math.isclose(betac[i].value, output.beta[i], rel_tol=1e-3)
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assert math.isclose(output.cov_beta[i, i], betac[i].dvalue ** 2, rel_tol=2.5e-1), str(output.cov_beta[i, i]) + ' ' + str(betac[i].dvalue ** 2)
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assert math.isclose(pe.covariance([betac[0], betac[1]])[0, 1], output.cov_beta[0, 1], rel_tol=3.5e-1)
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outc = pe.total_least_squares(oxc, oyc, func, const_par=[betac[1]])
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@ -281,7 +277,6 @@ def test_total_least_squares():
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betac[i].gamma_method(S=1.0)
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assert math.isclose(betac[i].value, output.beta[i], rel_tol=1e-3)
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assert math.isclose(output.cov_beta[i, i], betac[i].dvalue ** 2, rel_tol=2.5e-1), str(output.cov_beta[i, i]) + ' ' + str(betac[i].dvalue ** 2)
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assert math.isclose(pe.covariance([betac[0], betac[1]])[0, 1], output.cov_beta[0, 1], rel_tol=3.5e-1)
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outc = pe.total_least_squares(oxc, oy, func, const_par=[betac[1]])
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