ran ruff to make imports consistent

.gitignore

@@ -157,4 +157,6 @@ cython_debug/
 #  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
 #  and can be added to the global gitignore or merged into this file.  For a more nuclear
 #  option (not recommended) you can uncomment the following to ignore the entire idea folder.
-.idea/
+.idea/
+
+.ruff_cache/

example.py

@@ -1,11 +1,10 @@
-from lorepy import loreplot
-
-from sklearn.datasets import load_iris
 import matplotlib.pyplot as plt
+import pandas as pd
+from lorepy import loreplot
 from matplotlib.colors import ListedColormap
-from sklearn.svm import SVC
+from sklearn.datasets import load_iris
 from sklearn.ensemble import RandomForestClassifier
-import pandas as pd
+from sklearn.svm import SVC
 
 # Load iris dataset and convert to dataframe
 iris_obj = load_iris()

example_uncertainty.py

@@ -1,10 +1,9 @@
-from lorepy import uncertainty_plot
-
-from sklearn.datasets import load_iris
 import matplotlib.pyplot as plt
+import pandas as pd
+from lorepy import uncertainty_plot
 from matplotlib.colors import ListedColormap
+from sklearn.datasets import load_iris
 from sklearn.svm import SVC
-import pandas as pd
 
 # Load iris dataset and convert to dataframe
 iris_obj = load_iris()

setup.py

@@ -1,4 +1,4 @@
-from setuptools import setup, find_packages
+from setuptools import find_packages, setup
 
 with open("README.md", "r", encoding="utf-8") as fh:
     long_description = fh.read()

src/lorepy/lorepy.py

@@ -1,9 +1,10 @@
+from typing import Optional, Tuple
+
+import matplotlib.pyplot as plt
+import numpy as np
 import pandas as pd
-from sklearn.linear_model import LogisticRegression
 from pandas import DataFrame
-import numpy as np
-import matplotlib.pyplot as plt
-from typing import Optional, Tuple
+from sklearn.linear_model import LogisticRegression
 
 
 def _prepare_data(data, x, y, confounders):

tests/test_plot.py

@@ -1,11 +1,10 @@
+import matplotlib.pyplot as plt
+import numpy as np
 import pandas as pd
+from lorepy.lorepy import _get_area_df, _get_dots_df, loreplot
 from pandas import DataFrame
-import numpy as np
 from sklearn.linear_model import LogisticRegression
 from sklearn.svm import SVC
-import matplotlib.pyplot as plt
-
-from lorepy.lorepy import loreplot, _get_dots_df, _get_area_df
 
 # Generate a sample dataset for testing
 X = np.concatenate([np.random.randint(0, 10, 50), np.random.randint(2, 12, 50)])

tests/test_uncertainty.py

@@ -1,11 +1,10 @@
+import numpy as np
 import pandas as pd
 import pytest
-import numpy as np
+from lorepy import uncertainty_plot
 from matplotlib.colors import ListedColormap
 from sklearn.svm import SVC
 
-from lorepy import uncertainty_plot
-
 # Generate a sample dataset for testing
 X = np.concatenate([np.random.randint(0, 10, 50), np.random.randint(2, 12, 50)])
 y = [0] * 50 + [1] * 50