COMPAT: numpy 2.0 compatibility

.github/workflows/tests.yaml

@@ -15,6 +15,10 @@ on:
         default: test
         required: false
 
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: true
+
 jobs:
   Test:
     name: ${{ matrix.os }}, ${{ matrix.environment-file }}

ci/envs/312-dev.yaml

@@ -3,28 +3,24 @@ channels:
   - conda-forge
 dependencies:
   - python=3.12
-  - dask
-  - geopandas
-  - inequality
-  - libpysal>=4.11.0
-  - networkx
-  - osmnx
   - packaging
-  - pandas!=1.5.0
-  - shapely>=2
   - tqdm
-  - numba
+  # - numba
   # testing
   - codecov
   - pytest
   - pytest-cov
-  # for mapclassify
-  - scikit-learn
   - pip
   - pip:
+      - --pre  --index-url https://pypi.anaconda.org/scientific-python-nightly-wheels/simple --extra-index-url https://pypi.org/simple
+      - numpy
+      - pandas
+      - shapely
       - git+https://github.com/geopandas/geopandas.git
       - git+https://github.com/pysal/libpysal.git
       - git+https://github.com/networkx/networkx.git
-      - git+https://github.com/shapely/shapely.git
       - git+https://github.com/pysal/mapclassify.git
       - git+https://github.com/pysal/esda.git
+      - git+https://github.com/pysal/inequality.git
+      - osmnx
+      - scikit-learn

momepy/functional/_dimension.py

@@ -28,9 +28,9 @@
 
 
 def volume(
-    area: NDArray[np.float_] | Series,
-    height: NDArray[np.float_] | Series,
-) -> NDArray[np.float_] | Series:
+    area: NDArray[np.float64] | Series,
+    height: NDArray[np.float64] | Series,
+) -> NDArray[np.float64] | Series:
     """
     Calculates volume of each object in given GeoDataFrame based on its height and area.
 
@@ -39,24 +39,24 @@ def volume(
 
     Parameters
     ----------
-    area : NDArray[np.float_] | Series
+    area : NDArray[np.float64] | Series
         array of areas
-    height : NDArray[np.float_] | Series
+    height : NDArray[np.float64] | Series
         array of heights
 
     Returns
     -------
-    NDArray[np.float_] | Series
+    NDArray[np.float64] | Series
         array of a type depending on the input
     """
     return area * height
 
 
 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:
+    area: NDArray[np.float64] | Series,
+    height: NDArray[np.float64] | Series,
+    floor_height: float | NDArray[np.float64] | Series = 3,
+) -> NDArray[np.float64] | Series:
     """Calculates floor area of each object based on height and area.
 
     The number of
@@ -69,17 +69,17 @@ def floor_area(
 
     Parameters
     ----------
-    area : NDArray[np.float_] | Series
+    area : NDArray[np.float64] | Series
         array of areas
-    height : NDArray[np.float_] | Series
+    height : NDArray[np.float64] | Series
         array of heights
-    floor_height : float | NDArray[np.float_] | Series, optional
+    floor_height : float | NDArray[np.float64] | Series, optional
         float denoting the uniform floor height or an aarray reflecting the building
         height by geometry, by default 3
 
     Returns
     -------
-    NDArray[np.float_] | Series
+    NDArray[np.float64] | Series
         array of a type depending on the input
     """
     return area * (height // floor_height)
@@ -172,7 +172,7 @@ def perimeter_wall(
 
 
 def weighted_character(
-    y: NDArray[np.float_] | Series, area: NDArray[np.float_] | Series, graph: Graph
+    y: NDArray[np.float64] | Series, area: NDArray[np.float64] | Series, graph: Graph
 ) -> Series:
     """Calculates the weighted character.
 
@@ -191,9 +191,9 @@ def weighted_character(
 
     Parameters
     ----------
-    y : NDArray[np.float_] | Series
+    y : NDArray[np.float64] | Series
         The character values to be weighted.
-    area : NDArray[np.float_] | Series
+    area : NDArray[np.float64] | Series
         The area values to be used as weightss
     graph : libpysal.graph.Graph
         A spatial weights matrix for values and areas.
@@ -392,7 +392,7 @@ def _get_point_njit(x1, y1, bearing, dist):
 
 @njit
 def generate_ticks(list_points, end_markers, tick_length):
-    ticks = np.empty((len(list_points) * 2, 4), dtype=np.float64)
+    ticks = np.empty((len(list_points) * 2, 4), dtype=float)
 
     for i in range(len(list_points)):
         tick_pos = i * 2

momepy/functional/_diversity.py

@@ -79,8 +79,8 @@ def _percentile_limited_group_grouper(y, group_index, q=(25, 75)):
 
 
 def describe_agg(
-    y: NDArray[np.float_] | Series,
-    aggregation_key: NDArray[np.float_] | Series,
+    y: NDArray[np.float64] | Series,
+    aggregation_key: NDArray[np.float64] | Series,
     result_index: pd.Index = None,
     q: tuple[float, float] | list[float] | None = None,
     statistics: list[str] | None = None,
@@ -171,8 +171,8 @@ def describe_agg(
 
 
 def describe_reached_agg(
-    y: NDArray[np.float_] | Series,
-    graph_index: NDArray[np.float_] | Series,
+    y: NDArray[np.float64] | Series,
+    graph_index: NDArray[np.float64] | Series,
     graph: Graph,
     q: tuple[float, float] | list[float] | None = None,
     statistics: list[str] | None = None,

momepy/functional/_shape.py

@@ -32,7 +32,7 @@
 
 def form_factor(
     geometry: GeoDataFrame | GeoSeries,
-    height: NDArray[np.float_] | Series,
+    height: NDArray[np.float64] | Series,
 ) -> Series:
     """Calculates the form factor of each object given its geometry and height.
 
@@ -50,7 +50,7 @@ def form_factor(
     ----------
     geometry : GeoDataFrame | GeoSeries
         A GeoDataFrame or GeoSeries containing polygons to analyse.
-    height : NDArray[np.float_] | Series
+    height : NDArray[np.float64] | Series
         array of heights
 
     Returns
@@ -178,7 +178,7 @@ def convexity(geometry: GeoDataFrame | GeoSeries) -> Series:
 
 def courtyard_index(
     geometry: GeoDataFrame | GeoSeries,
-    courtyard_area: NDArray[np.float_] | Series | None = None,
+    courtyard_area: NDArray[np.float64] | Series | None = None,
 ) -> Series:
     """Calculates the courtyard index of each object given its geometry.
 
@@ -191,7 +191,7 @@ def courtyard_index(
     ----------
     geometry : GeoDataFrame | GeoSeries
         A GeoDataFrame or GeoSeries containing polygons to analyse.
-    courtyard_area : NDArray[np.float_] | Series | None, optional
+    courtyard_area : NDArray[np.float64] | Series | None, optional
         array of courtyard areas. If None, it will be calculated, by default None
 
     Returns
@@ -227,7 +227,7 @@ def rectangularity(geometry: GeoDataFrame | GeoSeries) -> Series:
 
 def shape_index(
     geometry: GeoDataFrame | GeoSeries,
-    longest_axis_length: NDArray[np.float_] | Series | None = None,
+    longest_axis_length: NDArray[np.float64] | Series | None = None,
 ) -> Series:
     """Calculates the shape index of each object given its geometry.
 
@@ -238,7 +238,7 @@ def shape_index(
     ----------
     geometry : GeoDataFrame | GeoSeries
          A GeoDataFrame or GeoSeries containing polygons to analyse.
-    longest_axis_length : NDArray[np.float_] | Series | None, optional
+    longest_axis_length : NDArray[np.float64] | Series | None, optional
         array of longest axis lengths. If None, it will be calculated, by default None
 
     Returns
@@ -475,7 +475,7 @@ def linearity(geometry: GeoDataFrame | GeoSeries) -> Series:
 
 def compactness_weighted_axis(
     geometry: GeoDataFrame | GeoSeries,
-    longest_axis_length: NDArray[np.float_] | Series | None = None,
+    longest_axis_length: NDArray[np.float64] | Series | None = None,
 ) -> Series:
     """Calculates the compactness-weighted axis of each object in a given GeoDataFrame.
 
@@ -487,7 +487,7 @@ def compactness_weighted_axis(
     ----------
     geometry : GeoDataFrame | GeoSeries
          A GeoDataFrame or GeoSeries containing polygons to analyse.
-    longest_axis_length : NDArray[np.float_] | Series | None, optional
+    longest_axis_length : NDArray[np.float64] | Series | None, optional
         array of longest axis lengths. If None, it will be calculated, by default None
 
     Returns
@@ -506,13 +506,13 @@ def compactness_weighted_axis(
 
 
 def _true_angles_mask(
-    points: NDArray[np.float_], eps: float, return_degrees: bool = False
-) -> NDArray[np.bool_] | tuple[NDArray[np.bool_], NDArray[np.float_]]:
+    points: NDArray[np.float64], eps: float, return_degrees: bool = False
+) -> NDArray[np.bool_] | tuple[NDArray[np.bool_], NDArray[np.float64]]:
     """Calculates the mask of true angles.
 
     Parameters
     ----------
-    points : NDArray[np.float_]
+    points : NDArray[np.float64]
         array of points
     eps : float
         Deviation from 180 degrees to consider a corner
@@ -521,7 +521,7 @@ def _true_angles_mask(
 
     Returns
     -------
-    NDArray[np.bool_] | tuple[NDArray[np.bool_], NDArray[np.float_]]
+    NDArray[np.bool_] | tuple[NDArray[np.bool_], NDArray[np.float64]]
         boolean array or a tuple of boolean array and float array of degrees
     """
     a = np.roll(points, 1, axis=0)

momepy/functional/tests/test_distribution.py

@@ -56,7 +56,7 @@ def test_alignment(self):
             "max": 21.32311946014944,
         }
         r = mm.alignment(orientation, self.graph)
-        assert_result(r, expected, self.df_buildings)
+        assert_result(r, expected, self.df_buildings, check_names=False)
 
     def test_neighbor_distance(self):
         expected = {
@@ -66,7 +66,7 @@ def test_neighbor_distance(self):
             "max": 42.164831456311475,
         }
         r = mm.neighbor_distance(self.df_buildings, self.graph)
-        assert_result(r, expected, self.df_buildings)
+        assert_result(r, expected, self.df_buildings, check_names=False)
 
     def test_mean_interbuilding_distance(self):
         expected = {

momepy/preprocessing.py

@@ -1518,7 +1518,7 @@ class FaceArtifacts:
         ['circlular_compactness', 'isoperimetric_quotient', 'diameter_ratio'], by
         default "circular_compactness"
     height_mins : float, optional
-        Required depth of valleys, by default np.NINF
+        Required depth of valleys, by default -np.inf
     height_maxs : float, optional
         Required height of peaks, by default 0.008
     prominence : float, optional
@@ -1560,7 +1560,7 @@ def __init__(
         self,
         gdf,
         index="circular_compactness",
-        height_mins=np.NINF,
+        height_mins=-np.inf,
         height_maxs=0.008,
         prominence=0.00075,
     ):