black formatted

setup.py

@@ -42,10 +42,10 @@ def read_version(filepath: str) -> str:
 setup(
     name="sparsesurv",
     version=read_version("sparsesurv/__init__.py"),  # single place for version
-    license='BSD-3-Clause',
+    license="BSD-3-Clause",
     description="Installable sparsesurv package via pip or source. Modify as needed.",
     long_description=open("README.md").read(),
-    long_description_content_type='text/markdown',
+    long_description_content_type="text/markdown",
     url="https://github.com/BoevaLab/sparsesurv",
     authors="David Wissel, Nikita Janakarajan",
     author_email="[email protected], [email protected]",
@@ -74,21 +74,26 @@ def read_version(filepath: str) -> str:
         ],
     },
     classifiers=[
-        'Topic :: Software Development :: Libraries :: Python Modules',
-        'License :: OSI Approved :: BSD License',
-	'Development Status :: 5 - Production/Stable',
-        'Intended Audience :: Science/Research',
-	'Operating System :: OS Independent',
-	'Programming Language :: Python :: 3.8',
-        'Programming Language :: Python :: 3.9',
-        'Programming Language :: Python :: 3.10',
+        "Topic :: Software Development :: Libraries :: Python Modules",
+        "License :: OSI Approved :: BSD License",
+        "Development Status :: 5 - Production/Stable",
+        "Intended Audience :: Science/Research",
+        "Operating System :: OS Independent",
+        "Programming Language :: Python :: 3.8",
+        "Programming Language :: Python :: 3.9",
+        "Programming Language :: Python :: 3.10",
     ],
     install_requires=[
         "numpy>=1.23.4",
-	"pandas>=1.5.1",
-	"celer>=0.7.2",
-	"numba>=0.56.4",
-	"scikit-learn>=1.2.1",
+        "pandas>=1.5.1",
+        "celer>=0.7.2",
+        "numba>=0.56.4",
+        "scikit-learn>=1.2.1",
+    ],
+    keywords=[
+        "Survival",
+        "Knowledge Distillation",
+        "Sparse Survival",
+        "High Dimensional Data",
     ],
-    keywords=['Survival', 'Knowledge Distillation',  'Sparse Survival', 'High Dimensional Data']
 )