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

.codespellrc

@@ -0,0 +1,6 @@
+[codespell]
+# Ref: https://github.com/codespell-project/codespell#using-a-config-file
+skip = .git*,.codespellrc
+check-hidden = true
+ignore-regex = ^\s*"image/\S+": ".*
+# ignore-words-list =

.github/workflows/codespell.yml

@@ -0,0 +1,25 @@
+# Codespell configuration is within .codespellrc
+---
+name: Codespell
+
+on:
+  push:
+    branches: [main]
+  pull_request:
+    branches: [main]
+
+permissions:
+  contents: read
+
+jobs:
+  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

README.md

@@ -81,7 +81,7 @@ training/derivatives/sub-verse000/
 5. (VerSe'20) https://s3.bonescreen.de/public/VerSe-complete/dataset-verse20validation.zip
 6. (VerSe'20) https://s3.bonescreen.de/public/VerSe-complete/dataset-verse20test.zip
 
-### OSF Reporsitories:
+### OSF Repositories:
 
 1. VerSe'19: https://osf.io/nqjyw/
 2. VerSe'20: https://osf.io/t98fz/
@@ -99,7 +99,7 @@ We provide helper code and guiding notebooks.
 
 * Data reading, standardising, and writing: [Data utilities](https://github.com/anjany/verse/blob/main/utils/data_utilities.py)
 * Evaluation (as employed in the 2020 challenge): [Evaluation utilities](https://github.com/anjany/verse/blob/main/utils/eval_utilities.py)  
-* Notebooks: [Data preperation](https://github.com/anjany/verse/blob/main/utils/prepare_data.ipynb), [Evaluation](https://github.com/anjany/verse/blob/main/utils/evaluate.ipynb)
+* Notebooks: [Data preparation](https://github.com/anjany/verse/blob/main/utils/prepare_data.ipynb), [Evaluation](https://github.com/anjany/verse/blob/main/utils/evaluate.ipynb)
 
 ## Contact
 For queries and issues not fit for a github issue, please email [Anjany Sekuboyina](mailto:[email protected]) or [Jan Kirschke](mailto:[email protected]) .

utils/eval_utilities.py

@@ -83,8 +83,8 @@ def get_hits(cent_list_gt, cent_list_pred, max_vert_idx):
         Successful identification defined as the correct label
         being closest and < 20mm away.
     hit_list: array-like
-        A 1-d array of lenght max_vert_idx. Contains nan (if vertebra is absent in GT),
-        1 (if vertebra is succesfully identified), and 0 (if failed to identify)
+        A 1-d array of length max_vert_idx. Contains nan (if vertebra is absent in GT),
+        1 (if vertebra is successfully identified), and 0 (if failed to identify)
 
     Notes
     -----

utils/evaluate.ipynb

@@ -13,7 +13,7 @@
     "Please look at the data_utilities notebook for data-preperation!  \n",
     "\n",
     "**Note**: We do not provide scripts for Hausdorff distance and localisation distance.\n",
-    "    Eventhough they were used for the benchamrking process, these were not used for VerSe'20. \n",
+    "    Even though they were used for the benchmarking process, these were not used for VerSe'20. \n",
     "    This is because of their invalidity when evaluating 'missed vertebrae'. "
    ]
   },
@@ -155,7 +155,7 @@
     "verts_in_gt        = np.argwhere(~np.isnan(true_ctd_arr[:, 0])).reshape(-1) + 1  # list of vertebrae present in annotation\n",
     "\n",
     "print('id.rate:{:.2f}\\n'.format(num_hits/len(verts_in_gt)))\n",
-    "print('Hits:\\n', hit_list) # nan : vertebrae is absent. 1 : successful identifcation. 0 : failed identification\n"
+    "print('Hits:\\n', hit_list) # nan : vertebrae is absent. 1 : successful identification. 0 : failed identification\n"
    ]
   },
   {