[STYLE] Lint formatting of lifestyle factors

src/ageml/ui.py

@@ -21,7 +21,7 @@
 
 import ageml.messages as messages
 from ageml.visualizer import Visualizer
-from ageml.utils import *
+from ageml.utils import create_directory, feature_extractor, significant_markers, convert, log
 from ageml.modelling import AgeML
 from ageml.processing import find_correlations
 
@@ -259,9 +259,8 @@ def load_data(self, required=None):
                         non_shared_subjects = [s for s in dfs[i].index.to_list()
                                                if s not in dfs[j].index.to_list()]
                         if non_shared_subjects.__len__() != 0:
-                            warn_message = (
-                            "Subjects in dataframe %s not in dataframe %s: %s" % (labels[i], labels[j], non_shared_subjects)
-                            )
+                            warn_message = ("Subjects in dataframe %s not in dataframe %s: %s"
+                                            % (labels[i], labels[j], non_shared_subjects))
                             print(warn_message)
                             warnings.warn(warn_message, category=UserWarning)
                             self.subjects_missing_data = (

src/ageml/utils.py

@@ -49,6 +49,7 @@ def feature_extractor(df):
 
     return X, y, feature_names
 
+
 def significant_markers(bon, fdr):
     """Returns markers for significant features.
 
@@ -67,6 +68,7 @@ def significant_markers(bon, fdr):
             markers.append("")
     return markers
 
+
 def log(func):
     """Decorator function to log stdout to log.txt."""
 

src/ageml/visualizer.py

@@ -221,4 +221,4 @@ def deltas_by_groups(self, deltas, labels):
         plt.xlabel("Gruop")
         plt.ylabel("Delta")
         plt.savefig(os.path.join(self.path_for_fig, "clinical_groups_box_plot.svg"))
-        plt.close()
+        plt.close()

tests/test_ageml/test_processing.py

@@ -16,6 +16,7 @@ def test_find_correlations():
     assert np.array_equal(order, order_expected) is True
     assert np.allclose(p_values, p_values_expected, atol=1e-7) is True
 
+
 @pytest.mark.parametrize('X, Y, exception_msg', [
     (np.array([[2, 4, np.nan], [4, 8, -12], [6, 12, -18], [8, 16, -24]]), np.array([1, 2, 3, 4]), "NaN entrie(s) found in X."),
     (np.array([[2, 4, -6], [4, 8, -12], [6, 12, -18], [8, 16, -24]]), np.array([1, 2, 3, np.nan]), "NaN entrie(s) found in Y.")

tests/test_ageml/test_ui.py

@@ -349,6 +349,7 @@ def test_run_age(dummy_interface, features):
         ]
     )
 
+
 def test_run_lifestyle(dummy_interface, ages, factors):
     # Run the lifestyle pipeline
     ages_path = create_csv(ages, dummy_interface.dir_path)
@@ -371,6 +372,7 @@ def test_run_lifestyle(dummy_interface, ages, factors):
     log_path = os.path.join(dummy_interface.dir_path, "log.txt")
     assert os.path.exists(log_path)
 
+
 def test_run_clinical(dummy_interface, ages, clinical):
     # Run the clinical pipeline
     ages_path = create_csv(ages, dummy_interface.dir_path)

tests/test_ageml/test_visualizer.py

@@ -90,6 +90,7 @@ def test_age_bias_correction(dummy_viz, np_test_data, dummy_ml):
     # Cleanup
     shutil.rmtree(os.path.dirname(svg_path))
 
+
 def test_factors_vs_deltas(dummy_viz):
     # Create dummy data
     corrs = [[0.5, 0.6, 0.7, 0.8, 0.9]]
@@ -104,6 +105,7 @@ def test_factors_vs_deltas(dummy_viz):
     # Cleanup
     shutil.rmtree(os.path.dirname(svg_path))
 
+
 def test_deltas_by_groups(dummy_viz, np_test_data, dummy_ml):
     # Separate data in X and Y
     X = np_test_data[:, :3]