💄 Linting library using ruff

The two commands run were:
- `ruff check .` then with `--fix`
- `ruff format .`

docs/conf.py

@@ -6,29 +6,28 @@
 # -- Project information -----------------------------------------------------
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information
 
-project = 'sagittal-average'
-copyright = '2019, Charlene Bultoc'
-author = 'Charlene Bultoc'
-release = '0.1'
+project = "sagittal-average"
+copyright = "2019, Charlene Bultoc"
+author = "Charlene Bultoc"
+release = "0.1"
 
 # -- General configuration ---------------------------------------------------
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration
 
 extensions = [
-    'sphinx.ext.autodoc',  # Support automatic documentation
-    'sphinx.ext.coverage', # Automatically check if functions are documented
-    'sphinx.ext.mathjax',  # Allow support for algebra
-    'sphinx.ext.viewcode', # Include the source code in documentation
-    'sphinx.ext.githubpages', # Publish HTML docs in GitHub Pages¶
+    "sphinx.ext.autodoc",  # Support automatic documentation
+    "sphinx.ext.coverage",  # Automatically check if functions are documented
+    "sphinx.ext.mathjax",  # Allow support for algebra
+    "sphinx.ext.viewcode",  # Include the source code in documentation
+    "sphinx.ext.githubpages",  # Publish HTML docs in GitHub Pages¶
 ]
 
-templates_path = ['_templates']
-exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']
-
+templates_path = ["_templates"]
+exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"]
 
 
 # -- Options for HTML output -------------------------------------------------
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output
 
-html_theme = 'alabaster'
-html_static_path = ['_static']
+html_theme = "alabaster"
+html_static_path = ["_static"]

src/sagittal_average/command.py

@@ -2,13 +2,24 @@
 
 from .sagittal_brain import run_averages
 
+
 def process():
-    parser = ArgumentParser(description="Calculates the average for each sagittal-horizontal plane.",
-                            formatter_class=ArgumentDefaultsHelpFormatter)
-    parser.add_argument('file_input', nargs='?', default="brain_sample.csv",
-                        help="Input CSV file with the results from scikit-brain binning algorithm.")
-    parser.add_argument('--file_output', '-o', default="brain_average.csv",
-                        help="Name of the output CSV file.")
+    parser = ArgumentParser(
+        description="Calculates the average for each sagittal-horizontal plane.",
+        formatter_class=ArgumentDefaultsHelpFormatter,
+    )
+    parser.add_argument(
+        "file_input",
+        nargs="?",
+        default="brain_sample.csv",
+        help="Input CSV file with the results from scikit-brain binning algorithm.",
+    )
+    parser.add_argument(
+        "--file_output",
+        "-o",
+        default="brain_average.csv",
+        help="Name of the output CSV file.",
+    )
     arguments = parser.parse_args()
 
     run_averages(arguments.file_input, arguments.file_output)

src/sagittal_average/sagittal_brain.py

@@ -1,9 +1,8 @@
-from argparse import ArgumentParser, ArgumentDefaultsHelpFormatter
 
 import numpy as np
 
 
-def run_averages(file_input='brain_sample.csv', file_output='brain_average.csv'):
+def run_averages(file_input="brain_sample.csv", file_output="brain_average.csv"):
     """
     Calculates the average through the coronal planes
     The input file should has as many columns as coronal planes
@@ -12,11 +11,11 @@ def run_averages(file_input='brain_sample.csv', file_output='brain_average.csv')
     The result is the average for each sagittal/horizontal plane (rows)
     """
     # Open the file to analyse
-    planes = np.loadtxt(file_input, dtype=int,  delimiter=',')
+    planes = np.loadtxt(file_input, dtype=int, delimiter=",")
 
     # Calculates the averages through the sagittal/horizontal planes
     # and makes it as a row vector
     averages = planes.mean(axis=1)[np.newaxis, :]
 
     # write it out on my file
-    np.savetxt(file_output, averages, fmt='%.1f', delimiter=',')
+    np.savetxt(file_output, averages, fmt="%.1f", delimiter=",")

tests/test_sagittal_brain.py

@@ -5,6 +5,7 @@
 
 TEST_DIR = Path(__file__).parent
 
+
 def test_average():
     # Creates input file
     data_input = np.zeros((20, 20))
@@ -13,12 +14,14 @@ def test_average():
     expected = np.zeros(20)
     expected[-1] = 1
 
-    np.savetxt(TEST_DIR / "brain_sample.csv", data_input, fmt='%d', delimiter=',')
+    np.savetxt(TEST_DIR / "brain_sample.csv", data_input, fmt="%d", delimiter=",")
 
     # run python program
-    run_averages(file_input=TEST_DIR / "brain_sample.csv",
-                 file_output=TEST_DIR / "brain_average.csv")
+    run_averages(
+        file_input=TEST_DIR / "brain_sample.csv",
+        file_output=TEST_DIR / "brain_average.csv",
+    )
 
     # Check result
-    result = np.loadtxt(TEST_DIR / "brain_average.csv",  delimiter=',')
+    result = np.loadtxt(TEST_DIR / "brain_average.csv", delimiter=",")
     np.testing.assert_array_equal(result, expected)