ci: add docs build check

.github/workflows/integration-test.yml

@@ -28,6 +28,23 @@ env:
   TARGET_PYTHON_VERSION: "3.9"
 
 jobs:
+  docs-check:
+    # This job is used to check the documentation build with strict mode enabled
+    name: Docs check
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout repo
+        uses: actions/checkout@v4
+      - name: Setup python, and load cache
+        uses: ./.github/actions/setup-env
+        with:
+          python-version: ${{ env.TARGET_PYTHON_VERSION }}
+          cache-pre-commit: false
+          cache-venv: true
+          setup-poetry: true
+          install-deps: true
+      - name: Build documentation
+        run: mkdocs build --strict
   quality-test:
     # This job is used to run pre-commit checks to ensure that all files are
     # are formatted correctly.

pycytominer/cyto_utils/features.py

@@ -45,7 +45,7 @@ def label_compartment(cp_features, compartment, metadata_cols):
     cp_features : list of str
         All features being used.
     compartment : str
-       Measured compartment.
+        Measured compartment.
     metadata_cols : list
         Columns that should be considered metadata.
 
@@ -150,7 +150,7 @@ def drop_outlier_features(
         "Metadata_treatment == 'control'" (include all quotes).
         If "all", use all samples to calculate.
     outlier_cutoff : int or float, default 500
-    see https://github.com/cytomining/pycytominer/issues/237 for details.
+        see https://github.com/cytomining/pycytominer/issues/237 for details.
         Threshold to remove features if absolute values is greater
 
     Returns

pycytominer/normalize.py

@@ -56,15 +56,13 @@ def normalize(
         If provided, will write normalized profiles as a specified file type (either CSV or parquet).
         If not specified and output_file is provided, then the file will be outputed as CSV as default.
     compression_options : str or dict, optional
-        Contains compression options as input to
-        pd.DataFrame.to_csv(compression=compression_options). pandas version >= 1.2.
+        Contains compression options as input to `pd.DataFrame.to_csv(compression=compression_options)`.
     float_format : str, optional
         Decimal precision to use in writing output file as input to
         pd.DataFrame.to_csv(float_format=float_format). For example, use "%.3g" for 3
         decimal precision.
     mad_robustize_epsilon: float, optional
-        The mad_robustize fudge factor parameter. The function only uses
-        this variable if method = "mad_robustize". Set this to 0 if
+        The mad_robustize fudge factor parameter. The function only uses this variable if method = "mad_robustize". Set this to 0 if
         mad_robustize generates features with large values.
     spherize_center : bool
         If the function should center data before sphering (aka whitening). The
@@ -86,36 +84,36 @@ def normalize(
 
     Examples
     --------
+    ```python
     import pandas as pd
     from pycytominer import normalize
 
-    data_df = pd.DataFrame(
-        {
-            "Metadata_plate": ["a", "a", "a", "a", "b", "b", "b", "b"],
-            "Metadata_treatment": [
-                "drug",
-                "drug",
-                "control",
-                "control",
-                "drug",
-                "drug",
-                "control",
-                "control",
-            ],
-            "x": [1, 2, 8, 2, 5, 5, 5, 1],
-            "y": [3, 1, 7, 4, 5, 9, 6, 1],
-            "z": [1, 8, 2, 5, 6, 22, 2, 2],
-            "zz": [14, 46, 1, 6, 30, 100, 2, 2],
-        }
-    ).reset_index(drop=True)
+    data_df = pd.DataFrame({
+        "Metadata_plate": ["a", "a", "a", "a", "b", "b", "b", "b"],
+        "Metadata_treatment": [
+            "drug",
+            "drug",
+            "control",
+            "control",
+            "drug",
+            "drug",
+            "control",
+            "control",
+        ],
+        "x": [1, 2, 8, 2, 5, 5, 5, 1],
+        "y": [3, 1, 7, 4, 5, 9, 6, 1],
+        "z": [1, 8, 2, 5, 6, 22, 2, 2],
+        "zz": [14, 46, 1, 6, 30, 100, 2, 2],
+    }).reset_index(drop=True)
 
     normalized_df = normalize(
         profiles=data_df,
         features=["x", "y", "z", "zz"],
         meta_features="infer",
         samples="Metadata_treatment == 'control'",
-        method="standardize"
+        method="standardize",
     )
+    ```
     """
     # Load Data
     profiles = load_profiles(profiles)