fix fit tests

Author: Jammy2211

autoarray/fit/fit_dataset.py

@@ -86,7 +86,7 @@ def chi_squared(self) -> float:
         """
         Returns the chi-squared terms of the model data's fit to an dataset, by summing the chi-squared-map.
         """
-        return fit_util.chi_squared_from(chi_squared_map=self.chi_squared_map)
+        return fit_util.chi_squared_from(chi_squared_map=self.chi_squared_map.array)
 
     @property
     def noise_normalization(self) -> float:
@@ -95,7 +95,7 @@ def noise_normalization(self) -> float:
 
         [Noise_Term] = sum(log(2*pi*[Noise]**2.0))
         """
-        return fit_util.noise_normalization_from(noise_map=self.noise_map)
+        return fit_util.noise_normalization_from(noise_map=self.noise_map.array)
 
     @property
     def log_likelihood(self) -> float:

autoarray/fit/fit_util.py

@@ -84,7 +84,7 @@ def chi_squared_from(*, chi_squared_map: ty.DataLike) -> float:
     chi_squared_map
         The chi-squared-map of values of the model-data fit to the dataset.
     """
-    return jnp.sum(np.array(chi_squared_map))
+    return jnp.sum(chi_squared_map)
 
 
 def noise_normalization_from(*, noise_map: ty.DataLike) -> float:
@@ -98,7 +98,7 @@ def noise_normalization_from(*, noise_map: ty.DataLike) -> float:
     noise_map
         The masked noise-map of the dataset.
     """
-    return jnp.sum(jnp.log(2 * jnp.pi * np.array(noise_map)**2.0))
+    return jnp.sum(jnp.log(2 * jnp.pi * noise_map**2.0))
 
 
 def normalized_residual_map_complex_from(

test_autoarray/fit/test_fit_imaging.py

@@ -30,7 +30,7 @@ def test__data_and_model_are_identical__no_masking__check_values_are_correct():
 
     assert fit.chi_squared == 0.0
     assert fit.reduced_chi_squared == 0.0
-    assert fit.noise_normalization == np.sum(np.log(2 * np.pi * noise_map**2.0))
+    assert fit.noise_normalization == np.sum(np.log(2 * np.pi * noise_map.array**2.0))
     assert fit.log_likelihood == -0.5 * (fit.chi_squared + fit.noise_normalization)
 
 
@@ -59,7 +59,7 @@ def test__data_and_model_are_different__include_masking__check_values_are_correc
 
     assert fit.chi_squared == 0.25
     assert fit.reduced_chi_squared == 0.25 / 3.0
-    assert fit.noise_normalization == np.sum(np.log(2 * np.pi * noise_map**2.0))
+    assert fit.noise_normalization == np.sum(np.log(2 * np.pi * noise_map.array**2.0))
     assert fit.log_likelihood == -0.5 * (fit.chi_squared + fit.noise_normalization)
 
 
@@ -92,7 +92,7 @@ def test__data_and_model_are_identical__inversion_included__changes_certain_prop
 
     assert fit.chi_squared == 0.0
     assert fit.reduced_chi_squared == 0.0
-    assert fit.noise_normalization == np.sum(np.log(2 * np.pi * noise_map**2.0))
+    assert fit.noise_normalization == np.sum(np.log(2 * np.pi * noise_map.array**2.0))
     assert fit.log_likelihood == -0.5 * (fit.chi_squared + fit.noise_normalization)
 
     assert fit.log_likelihood_with_regularization == -0.5 * (