From 25f4f5a0c2e8227538bef0abf13f6294fb3f9fb5 Mon Sep 17 00:00:00 2001 From: rottenstea Date: Thu, 22 Feb 2024 09:38:45 +0100 Subject: [PATCH] Updated Test file --- test/test_Simulation_functions.py | 21 ++++++++++++++------- 1 file changed, 14 insertions(+), 7 deletions(-) diff --git a/test/test_Simulation_functions.py b/test/test_Simulation_functions.py index 9a3beaf..eb3e7df 100644 --- a/test/test_Simulation_functions.py +++ b/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) \ No newline at end of file + assert np.isclose(RSS(e1, e2), expected_result) + + + + +