refactor: Added type hints and docstrings to helper functions. Removed unused helper functions

Author: pabloitu

tests/test_plots.py

@@ -291,166 +291,166 @@ def savefig(self, ax, name):
 #         gc.collect()
 #
 #
-# class TestPlotDistributionTests(TestPlots):
-#
-#     def setUp(self):
-#         self.result_obs_scalar = MagicMock()
-#         self.result_obs_scalar.test_distribution = numpy.random.normal(0, 1, 1000)
-#         self.result_obs_scalar.observed_statistic = numpy.random.rand(1)[0]
-#
-#         self.result_obs_array = MagicMock()
-#         self.result_obs_array.test_distribution = numpy.random.normal(0, 1, 1000)
-#         self.result_obs_array.observed_statistic = numpy.random.normal(0, 1, 100)
-#
-#         self.result_nan = MagicMock()
-#         self.result_nan.test_distribution = numpy.random.normal(0, 1, 1000)
-#         self.result_nan.observed_statistic = -numpy.inf
-#
-#         # Example data for testing
-#         n_test = os.path.join(
-#             self.artifacts, "example_csep2_forecasts", "Results", "catalog_n_test.json"
-#         )
-#         s_test = os.path.join(
-#             self.artifacts, "example_csep2_forecasts", "Results", "catalog_s_test.json"
-#         )
-#         m_test = os.path.join(
-#             self.artifacts, "example_csep2_forecasts", "Results", "catalog_m_test.json"
-#         )
-#         l_test = os.path.join(
-#             self.artifacts, "example_csep2_forecasts", "Results", "catalog_l_test.json"
-#         )
-#
-#         with open(n_test, "r") as fp:
-#             self.n_test = CatalogNumberTestResult.from_dict(json.load(fp))
-#         with open(s_test, "r") as fp:
-#             self.s_test = CatalogSpatialTestResult.from_dict(json.load(fp))
-#         with open(m_test, "r") as fp:
-#             self.m_test = CatalogMagnitudeTestResult.from_dict(json.load(fp))
-#         with open(l_test, "r") as fp:
-#             self.l_test = CatalogPseudolikelihoodTestResult.from_dict(json.load(fp))
-#
-#     def test_plot_dist_test_with_scalar_observation_default(self):
-#         ax = plot_distribution_test(
-#             evaluation_result=self.result_obs_scalar,
-#             show=show_plots,
-#         )
-#
-#         # Check if a vertical line was drawn for the scalar observation
-#         lines = [line for line in ax.get_lines() if line.get_linestyle() == "--"]
-#         self.assertEqual(len(lines), 1)  # Expect one vertical line
-#         self.assertEqual(lines[0].get_xdata()[0], self.result_obs_scalar.observed_statistic)
-#
-#     def test_plot_dist_test_with_scalar_observation_w_labels(self):
-#         ax = plot_distribution_test(
-#             evaluation_result=self.result_obs_scalar,
-#             xlabel="Test X Label",
-#             ylabel="Test Y Label",
-#             title="Test Title",
-#             show=show_plots,
-#         )
-#
-#         # Check if a vertical line was drawn for the scalar observation
-#         lines = [line for line in ax.get_lines() if line.get_linestyle() == "--"]
-#         self.assertEqual(len(lines), 1)  # Expect one vertical line
-#         self.assertEqual(lines[0].get_xdata()[0], self.result_obs_scalar.observed_statistic)
-#
-#     def test_plot_dist_test_with_array_observation(self):
-#         ax = plot_distribution_test(
-#             evaluation_result=self.result_obs_array,
-#             alpha=0.5,
-#             show=show_plots,
-#         )
-#         bars = ax.patches
-#         self.assertTrue(
-#             all(bar.get_alpha() == 0.5 for bar in bars),
-#             "Alpha transparency not set correctly for bars",
-#         )
-#
-#     def test_plot_dist_test_with_percentile_shading(self):
-#         ax = plot_distribution_test(
-#             evaluation_result=self.result_obs_scalar,
-#             percentile=60,
-#             show=show_plots,
-#         )
-#         expected_red = (1.0, 0.0, 0.0)
-#         red_patches = []
-#         for patch_ in ax.patches:
-#             facecolor = patch_.get_facecolor()[:3]  # Get RGB, ignore alpha
-#             if all(abs(facecolor[i] - expected_red[i]) < 0.01 for i in range(3)):
-#                 red_patches.append(patch_)
-#         self.assertGreater(
-#             len(red_patches),
-#             0,
-#             "Expected some patches to be colored red for percentile shading",
-#         )
-#
-#     def test_plot_dist_test_with_annotation(self):
-#         annotation_text = "Test Annotation"
-#         ax = plot_distribution_test(
-#             evaluation_result=self.result_obs_scalar,
-#             xlabel="Test X Label",
-#             ylabel="Test Y Label",
-#             title="Test Title",
-#             annotation_text=annotation_text,
-#             annotation_xy=(0.5, 0.5),
-#             annotation_fontsize=12,
-#             show=show_plots,
-#         )
-#         annotations = ax.texts
-#         self.assertEqual(len(annotations), 1)
-#         self.assertEqual(annotations[0].get_text(), annotation_text)
-#
-#     def test_plot_dist_test_xlim(self):
-#         xlim = (-5, 5)
-#         ax = plot_distribution_test(
-#             evaluation_result=self.result_obs_scalar,
-#             percentile=95,
-#             xlim=xlim,
-#             show=show_plots,
-#         )
-#         self.savefig(ax, "plot_dist_test_xlims.png")
-#         self.assertEqual(ax.get_xlim(), xlim)
-#
-#     def test_plot_dist_test_autoxlim_nan(self):
-#
-#         ax = plot_distribution_test(
-#             evaluation_result=self.result_nan,
-#             percentile=95,
-#             show=show_plots,
-#         )
-#         self.savefig(ax, "plot_dist_test_xlims_inf.png")
-#
-#     def test_plot_n_test(self):
-#         ax = plot_distribution_test(
-#             self.n_test,
-#             show=show_plots,
-#         )
-#         self.savefig(ax, "plot_n_test.png")
-#
-#     def test_plot_m_test(self):
-#         ax = plot_distribution_test(
-#             self.m_test,
-#             show=show_plots,
-#         )
-#         self.savefig(ax, "plot_m_test.png")
-#
-#     def test_plot_s_test(self):
-#         ax = plot_distribution_test(
-#             self.s_test,
-#             show=show_plots,
-#         )
-#         self.savefig(ax, "plot_s_test.png")
-#
-#     def test_plot_l_test(self):
-#         ax = plot_distribution_test(
-#             self.l_test,
-#             show=show_plots,
-#         )
-#         self.savefig(ax, "plot_l_test.png")
-#
-#     def tearDown(self):
-#         plt.close("all")
-#         gc.collect()
+class TestPlotDistributionTests(TestPlots):
+
+    def setUp(self):
+        self.result_obs_scalar = MagicMock()
+        self.result_obs_scalar.test_distribution = numpy.random.normal(0, 1, 1000)
+        self.result_obs_scalar.observed_statistic = numpy.random.rand(1)[0]
+
+        self.result_obs_array = MagicMock()
+        self.result_obs_array.test_distribution = numpy.random.normal(0, 1, 1000)
+        self.result_obs_array.observed_statistic = numpy.random.normal(0, 1, 100)
+
+        self.result_nan = MagicMock()
+        self.result_nan.test_distribution = numpy.random.normal(0, 1, 1000)
+        self.result_nan.observed_statistic = -numpy.inf
+
+        # Example data for testing
+        n_test = os.path.join(
+            self.artifacts, "example_csep2_forecasts", "Results", "catalog_n_test.json"
+        )
+        s_test = os.path.join(
+            self.artifacts, "example_csep2_forecasts", "Results", "catalog_s_test.json"
+        )
+        m_test = os.path.join(
+            self.artifacts, "example_csep2_forecasts", "Results", "catalog_m_test.json"
+        )
+        l_test = os.path.join(
+            self.artifacts, "example_csep2_forecasts", "Results", "catalog_l_test.json"
+        )
+
+        with open(n_test, "r") as fp:
+            self.n_test = CatalogNumberTestResult.from_dict(json.load(fp))
+        with open(s_test, "r") as fp:
+            self.s_test = CatalogSpatialTestResult.from_dict(json.load(fp))
+        with open(m_test, "r") as fp:
+            self.m_test = CatalogMagnitudeTestResult.from_dict(json.load(fp))
+        with open(l_test, "r") as fp:
+            self.l_test = CatalogPseudolikelihoodTestResult.from_dict(json.load(fp))
+
+    def test_plot_dist_test_with_scalar_observation_default(self):
+        ax = plot_distribution_test(
+            evaluation_result=self.result_obs_scalar,
+            show=show_plots,
+        )
+
+        # Check if a vertical line was drawn for the scalar observation
+        lines = [line for line in ax.get_lines() if line.get_linestyle() == "--"]
+        self.assertEqual(len(lines), 1)  # Expect one vertical line
+        self.assertEqual(lines[0].get_xdata()[0], self.result_obs_scalar.observed_statistic)
+
+    def test_plot_dist_test_with_scalar_observation_w_labels(self):
+        ax = plot_distribution_test(
+            evaluation_result=self.result_obs_scalar,
+            xlabel="Test X Label",
+            ylabel="Test Y Label",
+            title="Test Title",
+            show=show_plots,
+        )
+
+        # Check if a vertical line was drawn for the scalar observation
+        lines = [line for line in ax.get_lines() if line.get_linestyle() == "--"]
+        self.assertEqual(len(lines), 1)  # Expect one vertical line
+        self.assertEqual(lines[0].get_xdata()[0], self.result_obs_scalar.observed_statistic)
+
+    def test_plot_dist_test_with_array_observation(self):
+        ax = plot_distribution_test(
+            evaluation_result=self.result_obs_array,
+            alpha=0.5,
+            show=show_plots,
+        )
+        bars = ax.patches
+        self.assertTrue(
+            all(bar.get_alpha() == 0.5 for bar in bars),
+            "Alpha transparency not set correctly for bars",
+        )
+
+    def test_plot_dist_test_with_percentile_shading(self):
+        ax = plot_distribution_test(
+            evaluation_result=self.result_obs_scalar,
+            percentile=60,
+            show=show_plots,
+        )
+        expected_red = (1.0, 0.0, 0.0)
+        red_patches = []
+        for patch_ in ax.patches:
+            facecolor = patch_.get_facecolor()[:3]  # Get RGB, ignore alpha
+            if all(abs(facecolor[i] - expected_red[i]) < 0.01 for i in range(3)):
+                red_patches.append(patch_)
+        self.assertGreater(
+            len(red_patches),
+            0,
+            "Expected some patches to be colored red for percentile shading",
+        )
+
+    def test_plot_dist_test_with_annotation(self):
+        annotation_text = "Test Annotation"
+        ax = plot_distribution_test(
+            evaluation_result=self.result_obs_scalar,
+            xlabel="Test X Label",
+            ylabel="Test Y Label",
+            title="Test Title",
+            annotation_text=annotation_text,
+            annotation_xy=(0.5, 0.5),
+            annotation_fontsize=12,
+            show=show_plots,
+        )
+        annotations = ax.texts
+        self.assertEqual(len(annotations), 1)
+        self.assertEqual(annotations[0].get_text(), annotation_text)
+
+    def test_plot_dist_test_xlim(self):
+        xlim = (-5, 5)
+        ax = plot_distribution_test(
+            evaluation_result=self.result_obs_scalar,
+            percentile=95,
+            xlim=xlim,
+            show=show_plots,
+        )
+        self.savefig(ax, "plot_dist_test_xlims.png")
+        self.assertEqual(ax.get_xlim(), xlim)
+
+    def test_plot_dist_test_autoxlim_nan(self):
+
+        ax = plot_distribution_test(
+            evaluation_result=self.result_nan,
+            percentile=95,
+            show=show_plots,
+        )
+        self.savefig(ax, "plot_dist_test_xlims_inf.png")
+
+    def test_plot_n_test(self):
+        ax = plot_distribution_test(
+            self.n_test,
+            show=show_plots,
+        )
+        self.savefig(ax, "plot_n_test.png")
+
+    def test_plot_m_test(self):
+        ax = plot_distribution_test(
+            self.m_test,
+            show=show_plots,
+        )
+        self.savefig(ax, "plot_m_test.png")
+
+    def test_plot_s_test(self):
+        ax = plot_distribution_test(
+            self.s_test,
+            show=show_plots,
+        )
+        self.savefig(ax, "plot_s_test.png")
+
+    def test_plot_l_test(self):
+        ax = plot_distribution_test(
+            self.l_test,
+            show=show_plots,
+        )
+        self.savefig(ax, "plot_l_test.png")
+
+    def tearDown(self):
+        plt.close("all")
+        gc.collect()
 
 
 class TestPlotCalibrationTest(TestPlots):