[pre-commit.ci] pre-commit autoupdate (#7)

* [pre-commit.ci] pre-commit autoupdate

updates:
- [github.com/astral-sh/ruff-pre-commit: v0.1.3 → v0.1.4](astral-sh/ruff-pre-commit@v0.1.3...v0.1.4)

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

.pre-commit-config.yaml

@@ -15,7 +15,7 @@ repos:
   # Lint / autoformat: Python code
   - repo: https://github.com/astral-sh/ruff-pre-commit
     # Ruff version.
-    rev: "v0.1.3"
+    rev: "v0.1.4"
     hooks:
       - id: ruff
         args: [--exit-non-zero-on-fix]

docs/api.md

@@ -30,12 +30,14 @@ show_source: false
 heading_level: 3
 
 ## importer.py
+
 ::: osm_rawdata.importer.MapImporter
 options:
 show_source: false
 heading_level: 3
 
 ## overture.py
+
 ::: osm_rawdata.overture.Overture
 options:
 show_source: false

docs/overture.md

@@ -54,11 +54,11 @@ fresh data.
 
 The data appears to not be processed for duplicates or bad geometries,
 but that is what the [Conflator](https://github.com/hotosm/conflator)
-and [Underpass ](https://github.com/hotosm/underpass/wiki) projects
+and [Underpass](https://github.com/hotosm/underpass/wiki) projects
 are for, to clean the data for possible imports. The license is Odbl,
 so suitable for OSM.
 
-### The current list of buildings datasets in V1 (July 2023) is:
+### The current list of buildings datasets in V1 (July 2023) is
 
 - Austin Building Footprints Year 2013 2D Buildings
 - Boston BPDA 3D Buildings
@@ -75,7 +75,7 @@ so suitable for OSM.
 - USGS Lidar
 - Washington DC Open Data 3D Buildings
 
-### The current list of buildings datasets in V2 (Oct 2023) is:
+### The current list of buildings datasets in V2 (Oct 2023) is
 
 - Portland Building Footprint 2D Buildings
 - Esri Community Maps

osm_rawdata/importer.py

@@ -178,7 +178,7 @@ def parquetThread(
         hex = wkb.loads(geom, hex=True)
         gdata = geoalchemy2.shape.from_shape(hex, srid=4326, extended=True)
         # geom_type = wkb.loads(geom).geom_type
-        scalar = select(cast(tags['properties'], JSONB)).scalar_subquery()
+        scalar = select(cast(tags["properties"], JSONB)).scalar_subquery()
         sql = None
         if hex.geom_type == "Polygon":
             sql = insert(ways).values(