TYP: add type hints to functional._dimension

.github/workflows/mypy.yml

@@ -0,0 +1,35 @@
+name: Type checking
+
+on:
+  push:
+    branches: [main]
+  pull_request:
+    branches:
+      - "*"
+  schedule:
+    - cron: "0 0 * * 1,4"
+
+jobs:
+  mypy:
+    runs-on: ubuntu-latest
+    defaults:
+      run:
+        shell: bash -l {0}
+
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: setup micromamba
+        uses: mamba-org/setup-micromamba@v1
+        with:
+          environment-file: ci/envs/312-latest.yaml
+          create-args: >-
+            mypy
+
+      - name: Install momepy
+        run: pip install .
+
+      - name: Check momepy
+        run: |
+            mypy momepy/ --install-types --ignore-missing-imports --non-interactive
+

momepy/functional/_dimension.py

@@ -1,6 +1,9 @@
 import numpy as np
-import pandas as pd
 import shapely
+from geopandas import GeoDataFrame, GeoSeries
+from libpysal.graph import Graph
+from numpy.typing import NDArray
+from pandas import Series
 
 __all__ = [
     "volume",
@@ -11,7 +14,10 @@
 ]
 
 
-def volume(area, height):
+def volume(
+    area: NDArray[np.float_] | Series,
+    height: NDArray[np.float_] | Series,
+) -> NDArray[np.float_] | Series:
     """
     Calculates volume of each object in given GeoDataFrame based on its height and area.
 
@@ -20,19 +26,24 @@ def volume(area, height):
 
     Parameters
     ----------
-    area : array_like
+    area : NDArray[np.float_] | Series
         array of areas
-    height : array_like
+    height : NDArray[np.float_] | Series
         array of heights
 
     Returns
     -------
-    array_like
+    NDArray[np.float_] | Series
+        array of a type depending on the input
     """
     return area * height
 
 
-def floor_area(area, height, floor_height=3):
+def floor_area(
+    area: NDArray[np.float_] | Series,
+    height: NDArray[np.float_] | Series,
+    floor_height: float | NDArray[np.float_] | Series = 3,
+) -> NDArray[np.float_] | Series:
     """Calculates floor area of each object based on height and area.
 
     The number of
@@ -45,42 +56,43 @@ def floor_area(area, height, floor_height=3):
 
     Parameters
     ----------
-    area : array_like
+    area : NDArray[np.float_] | Series
         array of areas
-    height : array_like
+    height : NDArray[np.float_] | Series
         array of heights
-    floor_height : float | array_like, optional
-        float denoting the uniform floor height or an array_like reflecting the building
+    floor_height : float | NDArray[np.float_] | Series, optional
+        float denoting the uniform floor height or an aarray reflecting the building
         height by geometry, by default 3
 
     Returns
     -------
-    array_like
+    NDArray[np.float_] | Series
+        array of a type depending on the input
     """
     return area * (height // floor_height)
 
 
-def courtyard_area(gdf):
+def courtyard_area(gdf: GeoDataFrame | GeoSeries) -> Series:
     """Calculates area of holes within geometry - area of courtyards.
 
     Parameters
     ----------
-    gdf : GeoDataFrame
-        A GeoDataFrame containing objects to analyse.
+    gdf : GeoDataFrame | GeoSeries
+        A GeoDataFrame or GeoSeries containing polygons to analyse.
 
     Returns
     -------
-    pandas.Series
+    Series
     """
-    return pd.Series(
+    return Series(
         shapely.area(shapely.polygons(shapely.get_exterior_ring(gdf.geometry.array)))
         - gdf.area,
         index=gdf.index,
         name="courtyard_area",
     )
 
 
-def longest_axis_length(gdf):
+def longest_axis_length(gdf: GeoDataFrame | GeoSeries) -> Series:
     """Calculates the length of the longest axis of object.
 
     Axis is defined as a
@@ -92,35 +104,34 @@ def longest_axis_length(gdf):
 
     Parameters
     ----------
-    gdf : GeoDataFrame
-        A GeoDataFrame containing objects to analyse.
+    gdf : GeoDataFrame | GeoSeries
+        A GeoDataFrame or GeoSeries containing polygons to analyse.
 
     Returns
     -------
-    pandas.Series
+    Series
     """
     return shapely.minimum_bounding_radius(gdf.geometry) * 2
 
 
-def perimeter_wall(gdf, graph=None):
+def perimeter_wall(gdf: GeoDataFrame | GeoSeries, graph: Graph | None = None) -> Series:
     """
     Calculate the perimeter wall length the joined structure.
 
     Parameters
     ----------
-    gdf : GeoDataFrame
-        GeoDataFrame containing objects to analyse
-    graph : libpysal.graph.Graph, optional
-        Graph encoding Queen contiguity of ``gdf``
+    gdf : GeoDataFrame | GeoSeries
+        A GeoDataFrame or GeoSeries containing polygons to analyse.
+    graph : Graph | None, optional
+        Graph encoding Queen contiguity of ``gdf``. If ``None`` Queen contiguity is
+        built on the fly.
 
     Returns
     -------
-    pandas.Series
+    Series
     """
 
     if graph is None:
-        from libpysal.graph import Graph
-
         graph = Graph.build_contiguity(gdf)
 
     isolates = graph.isolates
@@ -129,13 +140,13 @@ def perimeter_wall(gdf, graph=None):
     blocks = gdf.drop(isolates)
     component_perimeter = (
         blocks[[blocks.geometry.name]]
-        .set_geometry(blocks.buffer(0.01))
+        .set_geometry(blocks.buffer(0.01))  # type: ignore
         .dissolve(by=graph.component_labels.drop(isolates))
         .exterior.length
     )
 
     # combine components with isolates
-    results = pd.Series(np.nan, index=gdf.index, name="perimeter_wall")
+    results = Series(np.nan, index=gdf.index, name="perimeter_wall")
     results.loc[isolates] = gdf.geometry[isolates].exterior.length
     results.loc[results.index.drop(isolates)] = component_perimeter.loc[
         graph.component_labels.loc[results.index.drop(isolates)]