Merge pull request #996 from rhayes777/feature/commented_tests

feature/commented tests

autofit/non_linear/result.py

@@ -17,7 +17,6 @@
 
 
 class Placeholder:
-
     def __getattr__(self, item):
         """
         Placeholders return None to represent the missing result's value
@@ -49,6 +48,9 @@ def summary(self):
 
 
 class AbstractResult(ABC):
+    """
+    @DynamicAttrs
+    """
 
     def __init__(self, samples_summary, paths):
         """
@@ -65,17 +67,6 @@ def __init__(self, samples_summary, paths):
         self._samples_summary = samples_summary
         self.paths = paths
 
-    def __getattr__(self, name):
-        """
-        The Result object may be overwritten and extended by projects wrapping autofit.
-
-        lint errpors commonly occur because the Result object does not contain the attribute that is being accessed.
-
-        This method is used to disable these errors.
-        """
-        warnings.warn('No member "%s" contained Result.' % name)
-        return super().__getattribute__(name)
-
     @property
     def samples_summary(self):
         return self._samples_summary
@@ -122,7 +113,6 @@ def log_likelihood(self):
 
     @property
     def instance(self):
-
         try:
             return self.samples_summary.instance
         except AttributeError as e:
@@ -204,11 +194,11 @@ def model_bounded(self, b: float) -> AbstractPriorModel:
 class Result(AbstractResult):
     def __init__(
         self,
-        samples_summary : SamplesSummary,
-        paths : Optional[AbstractPaths] = None,
+        samples_summary: SamplesSummary,
+        paths: Optional[AbstractPaths] = None,
         samples: Optional[Samples] = None,
-        search_internal : Optional[object] = None,
-        analysis : Optional[Analysis] = None
+        search_internal: Optional[object] = None,
+        analysis: Optional[Analysis] = None,
     ):
         """
         The result of a non-linear search.
@@ -255,10 +245,7 @@ def __init__(
         analysis
             The `Analysis` object that was used to perform the model-fit from which this result is inferred.
         """
-        super().__init__(
-            samples_summary=samples_summary,
-            paths=paths
-        )
+        super().__init__(samples_summary=samples_summary, paths=paths)
 
         self._samples = samples
         self._search_internal = search_internal
@@ -269,8 +256,6 @@ def __init__(
 
         self.child_results = None
 
-
-
     def dict(self) -> dict:
         """
         Human-readable dictionary representation of the results

test_autofit/interpolator/test_covariance.py

@@ -45,25 +45,26 @@ def test_covariance_matrix(interpolator):
 # def test_interpolate(interpolator):
 #     assert isinstance(interpolator[interpolator.t == 0.5].gaussian.centre, float)
 
+
 #
 # def test_interpolate_other_field(interpolator):
 #     assert isinstance(
 #         interpolator[interpolator.gaussian.centre == 0.5].gaussian.centre,
 #         float,
 #     )
-#
-#
-# def test_linear_analysis_for_value(interpolator):
-#     analysis = interpolator._analysis_for_value(interpolator.t == 0.5)
-#     assert (analysis.x == np.array([0, 1, 2])).all()
-#     assert (analysis.y == np.array([0, 0, 0, 1, 1, 1, 2, 2, 2])).all()
-#
-#
-# def test_model(interpolator):
-#     model = interpolator.model()
-#     assert model.prior_count == 6
-#
-#
+
+
+def test_linear_analysis_for_value(interpolator):
+    analysis = interpolator._analysis_for_value(interpolator.t == 0.5)
+    assert (analysis.x == np.array([0, 1, 2])).all()
+    assert (analysis.y == np.array([0, 0, 0, 1, 1, 1, 2, 2, 2])).all()
+
+
+def test_model(interpolator):
+    model = interpolator.model()
+    assert model.prior_count == 6
+
+
 # def test_single_variable():
 #     samples_list = [
 #         af.SamplesPDF(