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test: adapt existing suites to unconditional core, drop dead detector tests

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unzhakov
2026-09-30 03:08:13 +03:00
parent 7dc2ec79d1
commit 8172ba2541
6 changed files with 103 additions and 320 deletions
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tests/fitter/test_fit_engine.py
+24 -24
View File
@@ -9,7 +9,7 @@ from fitter.model import SpectrumModel
class TestFitConfig:
"""Test FitConfig defaults."""
def test_defaults(self):
def test_defaults(self) -> None:
config = FitConfig()
assert config.method == "trf"
assert config.max_nfev == 1000
@@ -19,11 +19,13 @@ class TestFitConfig:
class TestSpectrumFitter:
"""Test SpectrumFitter."""
def _make_fitter(self):
def _make_fitter(self) -> "SpectrumFitter":
"""Create a simple fitter with synthetic data."""
# Create a simple linear model
def linear_model(energies_keV, norm, bg):
def linear_model(
energies_keV: np.ndarray, norm: float, bg: float
) -> np.ndarray:
return norm * (1.0 - energies_keV / 100.0) + bg
# Synthetic data
@@ -33,7 +35,9 @@ class TestSpectrumFitter:
# Mock model
class MockModel:
def evaluate(self, energies_keV, norm, bg):
def evaluate(
self, energies_keV: np.ndarray, norm: float, bg: float
) -> np.ndarray:
return linear_model(energies_keV, norm, bg)
model = MockModel()
@@ -42,7 +46,7 @@ class TestSpectrumFitter:
)
return fitter
def test_fit_basic(self):
def test_fit_basic(self) -> None:
fitter = self._make_fitter()
result = fitter.fit(x0=[10.0, 2.0], param_names=["norm", "bg"])
@@ -53,7 +57,7 @@ class TestSpectrumFitter:
assert result.chi2_per_dof > 0
assert len(result.parameters) == 2
def test_fit_with_bounds(self):
def test_fit_with_bounds(self) -> None:
fitter = self._make_fitter()
result = fitter.fit(
x0=[10.0, 2.0],
@@ -61,36 +65,36 @@ class TestSpectrumFitter:
)
assert result.success
def test_fit_returns_residuals(self):
def test_fit_returns_residuals(self) -> None:
fitter = self._make_fitter()
result = fitter.fit(x0=[10.0, 2.0])
assert len(result.residuals) == 200
assert len(result.model_values) == 200
def test_parameters_property(self):
def test_parameters_property(self) -> None:
fitter = self._make_fitter()
fitter.fit(x0=[10.0, 2.0])
params = fitter.parameters
assert len(params) == 2
def test_covariance_property(self):
def test_covariance_property(self) -> None:
fitter = self._make_fitter()
fitter.fit(x0=[10.0, 2.0])
cov = fitter.covariance
assert cov.shape == (2, 2)
def test_chi2_per_dof(self):
def test_chi2_per_dof(self) -> None:
fitter = self._make_fitter()
result = fitter.fit(x0=[10.0, 2.0])
assert result.chi2_per_dof == result.chi2 / result.n_free
def test_p_value(self):
def test_p_value(self) -> None:
fitter = self._make_fitter()
result = fitter.fit(x0=[10.0, 2.0])
assert 0.0 <= result.p_value <= 1.0
def test_parameters_with_errors(self):
def test_parameters_with_errors(self) -> None:
fitter = self._make_fitter()
result = fitter.fit(x0=[10.0, 2.0])
params = result.parameters_with_errors
@@ -100,7 +104,7 @@ class TestSpectrumFitter:
assert isinstance(err, float)
assert err >= 0
def test_correlation_matrix(self):
def test_correlation_matrix(self) -> None:
fitter = self._make_fitter()
result = fitter.fit(x0=[10.0, 2.0])
corr = result.correlation_matrix
@@ -108,7 +112,7 @@ class TestSpectrumFitter:
# Diagonal should be 1
np.testing.assert_allclose(np.diag(corr), 1.0)
def test_summary(self):
def test_summary(self) -> None:
fitter = self._make_fitter()
result = fitter.fit(x0=[10.0, 2.0], param_names=["norm", "bg"])
summary = result.summary(param_names=["norm", "bg"])
@@ -116,7 +120,7 @@ class TestSpectrumFitter:
assert "bg" in summary
assert "Fit Results" in summary
def test_profile_likelihood(self):
def test_profile_likelihood(self) -> None:
fitter = self._make_fitter()
result = fitter.fit(x0=[10.0, 2.0])
param_vals, chi2_vals, norm_vals = result.profile_likelihood(0, n_points=10)
@@ -125,7 +129,7 @@ class TestSpectrumFitter:
assert len(norm_vals) == 10
assert np.min(norm_vals) >= 0 # Normalized χ² should be >= 0
def test_confidence_interval(self):
def test_confidence_interval(self) -> None:
fitter = self._make_fitter()
result = fitter.fit(x0=[10.0, 2.0])
ci = result.confidence_interval(0, confidence=0.95)
@@ -136,7 +140,7 @@ class TestSpectrumFitter:
class TestSpectrumFitterWithRealModel:
"""Test SpectrumFitter with a real SpectrumModel."""
def test_fit_with_real_model(self):
def test_fit_with_real_model(self) -> None:
from beta_spectrum import BetaSpectrum, SpectrumConfig
config = SpectrumConfig(
@@ -144,16 +148,12 @@ class TestSpectrumFitterWithRealModel:
Z_daughter=44,
A_number=99,
endpoint_MeV=0.294,
detector_model="gaussian",
detector_sigma_a_keV=0.5,
detector_n_channels=200,
)
spectrum = BetaSpectrum.from_config(config)
detector = BetaSpectrum.create_detector_from_config(config)
model = SpectrumModel(spectrum, detector)
model = SpectrumModel(spectrum)
# Generate synthetic data from model
energies_keV = detector.channel_energies
# Generate synthetic data from the unconvolved model on a keV grid
energies_keV = np.linspace(1.0, 293.0, 200)
true_norm = 5.0
true_bg = 0.1
counts = model.evaluate(energies_keV, true_norm, true_bg)
tests/fitter/test_model.py
+15 -36
View File
@@ -10,8 +10,8 @@ from fitter.model import SpectrumModel
class TestSpectrumModel:
"""Test SpectrumModel."""
def _make_model(self, with_detector: bool = False):
"""Create a test SpectrumModel."""
def _make_model(self) -> tuple[SpectrumModel, SpectrumConfig]:
"""Create a test SpectrumModel (no detector response)."""
config = SpectrumConfig(
Z_parent=43,
Z_daughter=44,
@@ -20,24 +20,10 @@ class TestSpectrumModel:
)
spectrum = BetaSpectrum.from_config(config)
detector = None
if with_detector:
detector = BetaSpectrum.create_detector_from_config(
SpectrumConfig(
Z_parent=43,
Z_daughter=44,
A_number=99,
endpoint_MeV=0.294,
detector_model="gaussian",
detector_sigma_a_keV=1.0,
detector_n_channels=100,
)
)
model = SpectrumModel(spectrum, detector)
model = SpectrumModel(spectrum)
return model, config
def test_evaluate_unconvolved(self):
def test_evaluate_unconvolved(self) -> None:
model, config = self._make_model()
energies_keV = np.linspace(0.01, 0.29, 50)
values = model.evaluate_unconvolved(energies_keV)
@@ -45,44 +31,34 @@ class TestSpectrumModel:
assert np.all(np.isfinite(values))
assert np.all(values >= 0)
def test_evaluate_no_detector(self):
def test_evaluate_no_detector(self) -> None:
model, config = self._make_model()
energies_keV = np.linspace(0.01, 0.29, 50)
values = model.evaluate(energies_keV, normalization=1.0, background=0.0)
assert len(values) == 50
assert np.all(np.isfinite(values))
def test_evaluate_with_normalization(self):
def test_evaluate_with_normalization(self) -> None:
model, config = self._make_model()
energies_keV = np.linspace(0.01, 0.29, 50)
values1 = model.evaluate(energies_keV, normalization=1.0, background=0.0)
values2 = model.evaluate(energies_keV, normalization=2.0, background=0.0)
np.testing.assert_allclose(values2, values1 * 2.0)
def test_evaluate_with_background(self):
def test_evaluate_with_background(self) -> None:
model, config = self._make_model()
energies_keV = np.linspace(0.01, 0.29, 50)
values1 = model.evaluate(energies_keV, normalization=1.0, background=0.0)
values2 = model.evaluate(energies_keV, normalization=1.0, background=5.0)
np.testing.assert_allclose(values2, values1 + 5.0)
def test_convolve_requires_detector(self):
def test_convolve_requires_detector(self) -> None:
model, _ = self._make_model()
energies_keV = np.linspace(0.01, 0.29, 50)
with pytest.raises(RuntimeError):
model.convolve(energies_keV)
def test_convolve_with_detector(self):
model, _ = self._make_model(with_detector=True)
energies_keV = model.detector_response.channel_energies
values = model.convolve(energies_keV)
assert len(values) == model.detector_response.n_channels
assert np.all(np.isfinite(values))
# Convolution can produce small negative values due to numerical effects
# — check that most values are non-negative
assert np.sum(values >= 0) > len(values) * 0.9
def test_default_params(self):
def test_default_params(self) -> None:
from exp_data.spectrum import ExpSpectrum
energies = np.linspace(0, 100, 200)
@@ -93,7 +69,7 @@ class TestSpectrumModel:
assert norm > 0
assert bg >= 0
def test_default_bounds(self):
def test_default_bounds(self) -> None:
lower, upper = SpectrumModel.default_bounds(100)
assert len(lower) == 2
assert len(upper) == 2
@@ -104,8 +80,11 @@ class TestSpectrumModel:
class TestSpectrumModelKeVToW:
"""Test energy unit conversion in SpectrumModel."""
def test_keV_to_W(self):
model = SpectrumModel(None) # type: ignore
def test_keV_to_W(self) -> None:
config = SpectrumConfig(
Z_parent=43, Z_daughter=44, A_number=99, endpoint_MeV=0.294
)
model = SpectrumModel(BetaSpectrum.from_config(config))
# 0 keV → W = 1.0
W = model._keV_to_W(np.array([0.0]))
assert np.isclose(W[0], 1.0)