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

tests/fitter/test_fit_engine.py

@@ -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)