Add codespell support (config, workflow to detect/not fix) and make it fix few typos

.github/workflows/test-ci.yml

@@ -28,6 +28,18 @@ jobs:
         shell: bash
         run: ruff check src
 
+  codespell:
+    name: Check for spelling errors
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+      - name: Annotate locations with typos
+        uses: codespell-project/codespell-problem-matcher@v1
+      - name: Codespell
+        uses: codespell-project/actions-codespell@v2
+
   test-suite:
     runs-on: ${{ matrix.os }}
     needs: linter-check

README.rst

@@ -39,7 +39,7 @@ It includes several local-low-rank based denoising methods (see the `documentati
 4. Optimal Thresholding
 5. Raw Singular Value Thresholding
 
-A mathematical description of theses methods is available in the documentation.
+A mathematical description of these methods is available in the documentation.
 
 
 
@@ -58,7 +58,7 @@ After installing you can use the ``patch-denoise`` command-line.
 
    $ patch-denoise input_file.nii output_file.nii --mask="auto"
 
-See ``patch-denoise --help`` for detailled options.
+See ``patch-denoise --help`` for detailed options.
 
 Documentation and Examples
 ==========================

docs/denoisers.rst

@@ -1,7 +1,7 @@
 LLR Denoising Methods
 =====================
 
-Patch-denoise implemement several local-low-rank methods, based on singular values thresholding.
+Patch-denoise implement several local-low-rank methods, based on singular values thresholding.
 
 
 Singular Value Thresholding

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "patch-denoise"
-description = "Denoising method for sequence of images or volumes. Primarly targeting fMRI data."
+description = "Denoising method for sequence of images or volumes. Primarily targeting fMRI data."
 authors = [{name="Pierre-antoine Comby", email="[email protected]"}]
 
 readme = "README.rst"
@@ -29,12 +29,14 @@ classifiers = [
 [project.optional-dependencies]
 optional = ["modopt", "nipype", "numba"]
 test = ["pytest", "pytest-cov", "pytest-xdist", "pytest-sugar"]
+
 doc = [
   "pydata-sphinx-theme",
   "numpydoc",
   "sphinx_gallery",
   "sphinx",
 ]
+
 dev = ["black", "isort", "ruff"]
 
 [project.scripts]
@@ -80,3 +82,10 @@ addopts = [
     "--cov-report=term-missing",
     "--cov-report=xml"
 ]
+
+[tool.codespell]
+# Ref: https://github.com/codespell-project/codespell#using-a-config-file
+skip = '.git*'
+check-hidden = true
+ignore-regex = '\bND\b'
+ignore-words-list = 'fro'

src/patch_denoise/bindings/cli.py

@@ -182,6 +182,7 @@ def _get_parser():
         ),
         default=None,
     )
+
     denoising_group.add_argument(
         "--extra",
         metavar="key=value",

src/patch_denoise/bindings/utils.py

@@ -114,7 +114,7 @@ def __str__(self):
 
 
 def load_as_array(input):
-    """Load a file as a numpy array, and return affine matrix if avaiable."""
+    """Load a file as a numpy array, and return affine matrix if available."""
     import nibabel as nib
 
     if input is None:

src/patch_denoise/space_time/base.py

@@ -267,7 +267,7 @@ def denoise(self, input_data, mask=None, mask_threshold=50, progbar=None):
 
         #     # Replace all nan by mean value of patch.
         #     # FIXME this behaviour should be documented
-        #     # And ideally choosen by the user.
+        #     # And ideally chosen by the user.
 
         #     patch[np.isnan(patch)] = np.mean(patch)
         #     p_denoise, maxidx, noise_var = self._patch_processing(

src/patch_denoise/space_time/lowrank.py

@@ -40,7 +40,7 @@ def __init__(self, patch_shape, patch_overlap, threshold_scale, **kwargs):
         self.input_denoising_kwargs["threshold_scale"] = threshold_scale
 
     def _patch_processing(self, patch, patch_idx=None, threshold_scale=1.0):
-        """Process a pach with the MP-PCA method."""
+        """Process a patch with the MP-PCA method."""
         p_center, eig_vals, eig_vec, p_tmean = eig_analysis(patch)
         maxidx = 0
         meanvar = np.mean(eig_vals)
@@ -102,7 +102,7 @@ def denoise(
         return super().denoise(input_data, mask, mask_threshold, progbar=progbar)
 
     def _patch_processing(self, patch, patch_idx=None, var_apriori=None):
-        """Process a pach with the Hybrid-PCA method."""
+        """Process a patch with the Hybrid-PCA method."""
         varest = np.mean(var_apriori.get_patch(patch_idx))
         p_center, eig_vals, eig_vec, p_tmean = eig_analysis(patch)
         maxidx = 0
@@ -168,7 +168,7 @@ def denoise(
         return super().denoise(input_data, mask, mask_threshold, progbar=progbar)
 
     def _patch_processing(self, patch, patch_idx=None, **kwargs):
-        """Process a pach with the simple SVT method."""
+        """Process a patch with the simple SVT method."""
         # Centering for better precision in SVD
         u_vec, s_values, v_vec, p_tmean = svd_analysis(patch)