Updated Test file

test/test_Simulation_functions.py

@@ -40,6 +40,7 @@ def test_neg_dist_apparent_G():
 # ----------------------------------------------------------------------------------------------------------------------
 @pytest.fixture
 def initialized_class_object():
+
     # Generate random data for columns X and Y
     np.random.seed(42)  # Set seed for reproducibility
     isochrone_data = {
@@ -160,7 +161,7 @@ def test_add_binary_fraction(initialized_class_object):
     # Find indices where elements in second_array are different from first_array
     non_matching_indices = np.where(obj.abs_mag_incl_plx_binarity != original_abs_mag_incl_plx)[0]
     # Calculate the difference between corresponding elements in the two arrays
-    differences = obj.abs_mag_incl_plx_binarity - original_abs_mag_incl_plx
+    differences = obj.abs_mag_incl_plx_binarity  - original_abs_mag_incl_plx
     # Filter differences corresponding to the non-matching indices
     non_matching_differences = differences[non_matching_indices]
     # Check if the absolute differences are close to 0.753
@@ -216,6 +217,7 @@ def test_add_field_unallowed_vals(initialized_class_object):
 
 
 def test_add_field_contamination_sampling(initialized_class_object):
+
     contamination_frac = 0.9
     obj = initialized_class_object
     obj.set_CMD_type(1)
@@ -231,6 +233,7 @@ def test_add_field_contamination_sampling(initialized_class_object):
 
 
 def test_add_field_contamination_conversion(initialized_class_object):
+
     contamination_frac = 0.7
     obj = initialized_class_object
     obj.set_CMD_type(1)
@@ -246,6 +249,7 @@ def test_add_field_contamination_conversion(initialized_class_object):
 
 
 def test_add_field_contamination_merging(initialized_class_object):
+
     contamination_frac = 0.7
     obj = initialized_class_object
 
@@ -295,7 +299,7 @@ def test_plot_verification_returns_figure_and_axes(initialized_class_object):
     # Verify the return types
     assert isinstance(fig, plt.Figure)
     assert isinstance(axes, np.ndarray)
-    assert axes.shape == (6,)  # Assuming 2x3 subplots
+    assert axes.shape == (6, )  # Assuming 2x3 subplots
 
 
 def test_plot_verification_plots_correct_data(initialized_class_object):
@@ -323,16 +327,14 @@ def test_plot_verification_plots_correct_data(initialized_class_object):
     x_plx_uncertainty = obj.cax
     y_plx_uncertainty = obj.abs_mag_incl_plx
     plx_uncertainty_scatter = ax_plx_uncertainty.collections[0]  # Assuming scatter plot is the only collection
-    assert np.array_equal(plx_uncertainty_scatter.get_offsets(),
-                          np.column_stack((x_plx_uncertainty, y_plx_uncertainty)))
+    assert np.array_equal(plx_uncertainty_scatter.get_offsets(), np.column_stack((x_plx_uncertainty, y_plx_uncertainty)))
 
     # Test binary subplot
     ax_bin_uncertainty = axes[2]
     x_bin_uncertainty = obj.cax
     y_bin_uncertainty = obj.abs_mag_incl_plx_binarity
     bin_uncertainty_scatter = ax_bin_uncertainty.collections[0]  # Assuming scatter plot is the only collection
-    assert np.array_equal(bin_uncertainty_scatter.get_offsets(),
-                          np.column_stack((x_bin_uncertainty, y_bin_uncertainty)))
+    assert np.array_equal(bin_uncertainty_scatter.get_offsets(), np.column_stack((x_bin_uncertainty, y_bin_uncertainty)))
 
     # Test Av subplot
     ax_Av_uncertainty = axes[3]
@@ -365,4 +367,9 @@ def test_RSS():
     e1 = 0.1
     e2 = 0.2
     expected_result = np.sqrt(e1 ** 2 + e2 ** 2)
-    assert np.isclose(RSS(e1, e2), expected_result)
+    assert np.isclose(RSS(e1, e2), expected_result)
+
+
+
+
+