Bump https://github.com/astral-sh/ruff-pre-commit from v0.16.4 to 0.16.6

.pre-commit-config.yaml

@@ -14,7 +14,7 @@ repos:
 #####
 # Python
 - repo: https://github.com/astral-sh/ruff-pre-commit
-  rev: v0.16.4
+  rev: v0.16.6
   hooks:
     # Sort imports
     - id: ruff-check

README.md

@@ -22,6 +22,7 @@ This project provides data tools and low friction access to versioned datasets w
 3. See which datasets are available:
    ```python
    from cfa.dataops import datacat
+
    print(datacat.__namespace_list__)
    ```
 4. Resolve a dataset reference from a full path or unique suffix:

docs/data_developer_guide.md

@@ -81,15 +81,13 @@ prefix = "path/to/transformed/data"
 ```python title="cfa/dataops/datasets/{team_dir}/schemas/{dataset_name}.py"
 import pandera.pandas as pa
 
-extract_schema = pa.DataFrameSchema({
-    "column1": pa.Column(str),
-    "column2": pa.Column(float)
-})
-
-load_schema = pa.DataFrameSchema({
-    "transformed_col1": pa.Column(str),
-    "transformed_col2": pa.Column(float)
-})
+extract_schema = pa.DataFrameSchema(
+    {"column1": pa.Column(str), "column2": pa.Column(float)}
+)
+
+load_schema = pa.DataFrameSchema(
+    {"transformed_col1": pa.Column(str), "transformed_col2": pa.Column(float)}
+)
 ```
 
 ### ETL script
@@ -156,33 +154,35 @@ import pandas as pd
 import pandera.pandas as pa
 
 # Define the schemas for validation
-extract_schema = pa.DataFrameSchema({
-    "date": pa.Column(pd.DatetimeTZDtype(tz='UTC')),
-    "value": pa.Column(float, checks=pa.Check.greater_than(0)),
-    "category": pa.Column(str, checks=pa.Check.isin(['A', 'B', 'C']))
-})
-
-load_schema = pa.DataFrameSchema({
-    "date": pa.Column(pd.DatetimeTZDtype(tz='UTC')),
-    "normalized_value": pa.Column(float),
-    "category": pa.Column(str)
-})
+extract_schema = pa.DataFrameSchema(
+    {
+        "date": pa.Column(pd.DatetimeTZDtype(tz="UTC")),
+        "value": pa.Column(float, checks=pa.Check.greater_than(0)),
+        "category": pa.Column(str, checks=pa.Check.isin(["A", "B", "C"])),
+    }
+)
+
+load_schema = pa.DataFrameSchema(
+    {
+        "date": pa.Column(pd.DatetimeTZDtype(tz="UTC")),
+        "normalized_value": pa.Column(float),
+        "category": pa.Column(str),
+    }
+)
+
 
 # Add mock data generation for testing
 # prefix with 'extract' or 'load'
-def extract_mock_data(output="pandas", size=10) -> pd.DataFrame|pl.DataFrame:
+def extract_mock_data(output="pandas", size=10) -> pd.DataFrame | pl.DataFrame:
     data = {
-            "date": pd.date_range(
-                start="2023-01-01",
-                periods=size,
-                tz='UTC'
-            ),
-            "value": np.random.uniform(1, 100, size),
-            "category": np.random.choice(['A', 'B', 'C'], size)
+        "date": pd.date_range(start="2023-01-01", periods=size, tz="UTC"),
+        "value": np.random.uniform(1, 100, size),
+        "category": np.random.choice(["A", "B", "C"], size),
     }
     df = pd.DataFrame(data)
     return df if output == "pandas" or output == "pd" else pl.from_pandas(df)
 
+
 # Validate synthetic data matches schema
 if __name__ == "__main__":
     test_df = extract_mock_data()

docs/data_user_guide.md

@@ -53,12 +53,14 @@ Use `datacat.get_ref(...)` when you want to resolve a dataset first and then reu
 The typical way to create a reference is as follows:
 ```python
 from cfa.dataops import datacat
+
 ref = datacat.public.team.data_trends
 ```
 
 This can be done much simpler using `get_ref`:
 ```python
 from cfa.dataops import datacat
+
 data_ref = datacat.get_ref("data_trends")
 ```
 
@@ -85,8 +87,8 @@ If you want to see which version will be returned before loading the dataframe,
 from cfa.dataops import datacat
 
 resolved = datacat.private.scenarios.covid19vax_trends.load.resolve_version(
-   version_spec=">=2025-05-01,<2025-06-01",
-   selection="newest",
+    version_spec=">=2025-05-01,<2025-06-01",
+    selection="newest",
 )
 
 print(resolved.version)
@@ -153,7 +155,7 @@ from cfa.dataops import datacat
 
 # newest match in the range (single version)
 df = datacat.private.scenarios.covid19vax_trends.load.get_dataframe(
-   version_spec=">=2025-05-01,<2025-06-01"
+    version_spec=">=2025-05-01,<2025-06-01"
 )
 ```
 
@@ -165,7 +167,7 @@ Used version: '2025-05-30T19-55-51'
 ```python
 # oldest match in the same range (selection=oldest)
 df_old = datacat.private.scenarios.covid19vax_trends.load.get_dataframe(
-   version_spec=">=2025-05-01,<2025-06-01", selection = "oldest"
+    version_spec=">=2025-05-01,<2025-06-01", selection="oldest"
 )
 ```
 
@@ -177,8 +179,7 @@ Used version: '2025-05-30T14-50-36'
 ```python
 # Preview the exact version that would be loaded for the same range
 resolved = datacat.private.scenarios.covid19vax_trends.load.resolve_version(
-   version_spec=">=2025-05-01,<2025-06-01",
-   selection="newest"
+    version_spec=">=2025-05-01,<2025-06-01", selection="newest"
 )
 
 resolved.version

docs/managing_catalogs.md

@@ -58,11 +58,10 @@ datacat.private.my_project.custom_dataset.load.get_dataframe()
 
 # Preview the version that will be loaded
 df_meta = datacat.private.scenarios.covid19vax_trends.load.resolve_version(
-   version_spec=">=2025-05-01,<2025-06-01",
-   selection="newest",
+    version_spec=">=2025-05-01,<2025-06-01",
+    selection="newest",
 )
 print(df_meta.version)
-
 ```
 
 ### Listing Available Resources

docs/release_notes/v2025.12.10.md

@@ -63,19 +63,22 @@ We're thrilled to announce the first official release of the **CFA DataOps** pro
 **List Available Datasets:**
 ```python
 from cfa.dataops import datacat
+
 print(datacat.__namespace_list__)
 ```
 
 **Load a Dataframe with Version Filtering:**
 ```python
 from cfa.dataops import datacat
+
 df = datacat.public.my_dataset.load.get_dataframe(version=">2024.12.01,<2025.08")
 ```
 
 **Generate a Report** *(removed in a later release)*:
 ```python
 from cfa.dataops import reportcat  # removed in a later release
-reportcat.examples.dataset_report_ipynb.nb_to_html_file('report.html')
+
+reportcat.examples.dataset_report_ipynb.nb_to_html_file("report.html")
 ```
 
 ## 📚 Documentation