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185 lines
6.0 KiB
Python
185 lines
6.0 KiB
Python
# SPDX-License-Identifier: MPL-2.0
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import numpy as np
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import pytest
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import aare
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def gaussian(x, A, mu, sigma):
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return A * np.exp(-0.5 * ((x - mu) / sigma) ** 2)
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@pytest.fixture
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def gaussian_data():
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x = np.linspace(-6.0, 6.0, 61)
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y = gaussian(x, 120.0, 0.8, 1.3)
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y_err = np.ones_like(y)
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return x, y, y_err
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def test_gaussian_model_evaluates_and_fits_data():
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x = np.linspace(-5.0, 5.0, 51)
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expected = np.array([20.0, 0.5, 1.2])
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model = aare.Gaussian()
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y = model(x, expected)
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result = model.fit(x, y)
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np.testing.assert_allclose(result["par"], expected, atol=2e-3)
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def test_fit_returns_errors_and_chi2(gaussian_data):
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x, y, y_err = gaussian_data
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model = aare.Gaussian(compute_errors=True)
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result = model.fit(x, y, y_err)
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assert set(result) == {"par", "par_err", "chi2"}
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assert result["par"].shape == (3,)
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assert result["par_err"].shape == (3,)
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assert result["chi2"].shape == (1,)
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np.testing.assert_allclose(result["par"], [120.0, 0.8, 1.3], rtol=1e-4)
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np.testing.assert_allclose(
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result["par_err"], [0.361, 0.00451, 0.00451], rtol=2e-2
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)
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assert result["chi2"][0] == pytest.approx(0.0, abs=1e-3)
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without_errors = aare.fit(aare.Gaussian(), x, y)
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assert set(without_errors) == {"par", "chi2"}
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def test_pol1_weighted_errors_match_analytic_formula():
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x = np.arange(20, dtype=float)
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y = 2.0 + 0.5 * x
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y_err = 1.0 + 0.1 * x
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w = 1.0 / y_err**2
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S, Sx, Sxx = w.sum(), (w * x).sum(), (w * x * x).sum()
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delta = S * Sxx - Sx * Sx
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result = aare.Pol1(compute_errors=True).fit(x, y, y_err)
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np.testing.assert_allclose(result["par"], [2.0, 0.5], rtol=1e-6)
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np.testing.assert_allclose(
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result["par_err"],
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[np.sqrt(Sxx / delta), np.sqrt(S / delta)],
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rtol=1e-6,
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)
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def test_fixed_parameter_reports_zero_error(gaussian_data):
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x, y, y_err = gaussian_data
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model = aare.Gaussian(compute_errors=True)
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model.FixParameter("sigma", 1.5)
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result = model.fit(x, y, y_err)
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assert result["par"][2] == 1.5
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assert result["par_err"][2] == 0.0
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assert result["par"][0] == pytest.approx(111.15, rel=1e-3)
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assert result["chi2"][0] == pytest.approx(1684.25, rel=1e-3)
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model.ReleaseParameter("sigma")
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result = model.fit(x, y, y_err)
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assert result["par"][2] == pytest.approx(1.3, rel=1e-4)
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def test_active_limit_ends_on_the_limit(gaussian_data):
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x, y, _ = gaussian_data
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model = aare.Gaussian()
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model.SetParLimits("mu", -5.0, 0.5)
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result = model.fit(x, y)
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assert result["par"][1] == pytest.approx(0.5, abs=1e-9)
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assert result["par"][0] == pytest.approx(116.88, rel=1e-3)
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assert result["chi2"][0] == pytest.approx(4301.95, rel=1e-3)
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inactive = aare.Gaussian()
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inactive.SetParLimits(1, -5.0, 5.0)
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np.testing.assert_allclose(
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inactive.fit(x, y)["par"], [120.0, 0.8, 1.3], rtol=1e-4
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)
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def test_user_start_value(gaussian_data):
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x, y, _ = gaussian_data
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model = aare.Gaussian()
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model.SetParameter("mu", 0.0)
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np.testing.assert_allclose(model.fit(x, y)["par"], [120.0, 0.8, 1.3], rtol=1e-4)
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def test_max_calls_failure_returns_zeros(gaussian_data):
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x, y, y_err = gaussian_data
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result = aare.Gaussian(max_calls=3, compute_errors=True).fit(x, y, y_err)
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assert np.all(result["par"] == 0.0)
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assert np.all(result["par_err"] == 0.0)
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assert np.all(result["chi2"] == 0.0)
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def test_strategy_is_accepted_and_ignored(gaussian_data):
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x, y, _ = gaussian_data
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a = aare.Gaussian(strategy=0).fit(x, y)
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b = aare.Gaussian(strategy=1).fit(x, y)
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np.testing.assert_array_equal(a["par"], b["par"])
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def test_invalid_parameter_access_raises():
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model = aare.Gaussian()
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assert model.par_names == ["A", "mu", "sigma"]
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assert model.n_par == 3
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with pytest.raises(RuntimeError):
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model.SetParameter("nope", 1.0)
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with pytest.raises(IndexError):
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model.SetParameter(3, 1.0)
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with pytest.raises(RuntimeError):
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model.SetParLimits("mu", 1.0, 1.0)
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@pytest.mark.parametrize("cls", [aare.RisingScurve, aare.FallingScurve])
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def test_scurve_fit(cls):
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truth = np.array([10.0, 0.1, 50.0, 3.0, 1000.0, 2.0])
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x = np.linspace(0.0, 100.0, 100)
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model = cls(max_calls=500, compute_errors=True)
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y = model(x, truth)
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result = model.fit(x, y, np.ones_like(y))
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np.testing.assert_allclose(result["par"], truth, rtol=1e-3)
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assert np.all(result["par_err"] > 0.0)
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@pytest.mark.parametrize("with_errors", [False, True])
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def test_fit_3d_matches_fit_1d(with_errors):
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x = np.linspace(-6.0, 6.0, 61)
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rows, cols = 4, 3
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y = np.empty((rows, cols, x.size))
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for r in range(rows):
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for c in range(cols):
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y[r, c] = gaussian(x, 80.0 + 10.0 * r, -0.6 + 0.3 * c, 0.9 + 0.1 * r)
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y_err = np.ones_like(y) if with_errors else None
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model = aare.Gaussian(compute_errors=with_errors)
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cube = model.fit(x, y, y_err, n_threads=3)
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assert cube["par"].shape == (rows, cols, 3)
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assert cube["chi2"].shape == (rows, cols)
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for r in range(rows):
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for c in range(cols):
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single = model.fit(x, y[r, c], None if y_err is None else y_err[r, c])
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np.testing.assert_allclose(cube["par"][r, c], single["par"], rtol=1e-10)
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assert cube["chi2"][r, c] == pytest.approx(single["chi2"][0], abs=1e-10)
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if with_errors:
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np.testing.assert_allclose(
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cube["par_err"][r, c], single["par_err"], rtol=1e-10
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)
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np.testing.assert_allclose(cube["par"][3, 2], [110.0, 0.0, 1.2], rtol=1e-4, atol=1e-6)
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def test_minuit2_backend_matches_builtin_solver(gaussian_data):
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fit_minuit2 = getattr(aare.experimental, "fit_minuit2", None)
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if fit_minuit2 is None:
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pytest.skip("aare was built without AARE_MINUIT2")
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x, y, y_err = gaussian_data
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model = aare.Gaussian(compute_errors=True)
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model.SetParLimits("mu", -5.0, 0.5)
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lm = model.fit(x, y, y_err)
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mn = fit_minuit2(model, x, y, y_err)
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np.testing.assert_allclose(lm["par"], mn["par"], rtol=1e-3)
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np.testing.assert_allclose(lm["chi2"], mn["chi2"], rtol=1e-3)
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