chore: resolve lint errors

---
type: pre_commit_static_analysis_report
description: Results of running static analysis checks when committing changes.
report:
  - task: lint_filenames
    status: passed
  - task: lint_editorconfig
    status: passed
  - task: lint_markdown
    status: na
  - task: lint_package_json
    status: na
  - task: lint_repl_help
    status: na
  - task: lint_javascript_src
    status: na
  - task: lint_javascript_cli
    status: na
  - task: lint_javascript_examples
    status: na
  - task: lint_javascript_tests
    status: na
  - task: lint_javascript_benchmarks
    status: na
  - task: lint_python
    status: passed
  - task: lint_r
    status: na
  - task: lint_c_src
    status: na
  - task: lint_c_examples
    status: na
  - task: lint_c_benchmarks
    status: na
  - task: lint_c_tests_fixtures
    status: na
  - task: lint_shell
    status: na
  - task: lint_typescript_declarations
    status: na
  - task: lint_typescript_tests
    status: na
  - task: lint_license_headers
    status: passed
---

Author: kgryte

lib/node_modules/@stdlib/stats/strided/dcovmatmtk/test/fixtures/script.py

@@ -15,37 +15,46 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+"""
+This script is meant to be copy-pasted into an IPython REPL session.
+
+Upon execution, enter `X` to print the contents of the generated array of random
+values.
+
+To generate an array containing the biased covariance, set the `bias` kwarg to
+`True`.
+"""
+
+import numpy as np
+
 # Define the list of known means:
-mean = [ 1.0, 1.0 ];
+mean = [1.0, 1.0]
 
 # Create a 2x3 matrix of mean values:
-broadcasted_mean = np.broadcast_to(mean, (3,2)).T;
+broadcasted_mean = np.broadcast_to(mean, (3, 2)).T
 
 # Define the matrix of standard deviations:
 sigma = [
-    [ 1.0, 0.7 ],
-    [ 0.7, 1.0 ]
-];
+    [1.0, 0.7],
+    [0.7, 1.0]
+]
 
 # Generate a random sample of normally distributed numbers:
-X = np.random.default_rng().multivariate_normal(mean, sigma, 3).T;
+X = np.random.default_rng().multivariate_normal(mean, sigma, 3).T
 
 # Center the generated values:
 for n in range(X.shape[0]):
     X[n] = X[n] - X[n].mean()
 
 # Transform the generated values via Cholesky decomposition (see https://stats.stackexchange.com/questions/120179/generating-data-with-a-given-sample-covariance-matrix and https://www.r-bloggers.com/2011/10/simulating-data-following-a-given-covariance-structure/)...
-L_inv = np.linalg.inv(np.linalg.cholesky(np.cov(X, bias = False)));
-X = np.dot(L_inv, X);
+L_inv = np.linalg.inv(np.linalg.cholesky(np.cov(X, bias=False)))
+X = np.dot(L_inv, X)
 
-L = np.linalg.cholesky(sigma);
-X = np.dot(L, X);
+L = np.linalg.cholesky(sigma)
+X = np.dot(L, X)
 
 # Un-center the transformed values:
-X += broadcasted_mean;
-
-# Display the generated values:
-X
+X += broadcasted_mean
 
 # Confirm that the generated values have the expected covariance matrix:
-np.cov(X, bias = False)
+np.cov(X, bias=False)