Merge branch 'v2' into typing

.github/workflows/ci.yml

@@ -2,9 +2,9 @@ name: CI
 
 on:
   push:
-    branches: [main]
+    branches: [main, typing]
   pull_request:
-    branches: [main]
+    branches: [main, v2]
   schedule:
     - cron: "0 6 * * 1" # Every Monday at 06:00 UTC
 
@@ -15,7 +15,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
+        python-version: ["3.9", "3.10", "3.11", "3.12"]
         os: [ubuntu-latest]
 
     defaults:

CHANGELOG.md

@@ -7,7 +7,9 @@
 ## 1.x.x (Unreleased)
 
 - fix a bug in the features module's polygon handling (#1104)
+- update obsolete numpy random number generation (#1108)
 - deprecate return_coords argument in graph.graph_from_address function (#1105)
+- deprecate return_hex argument in plot.get_colors function (#1109)
 
 ## 1.8.1 (2023-12-31)
 

osmnx/_version.py

@@ -1,3 +1,3 @@
 """OSMnx package version information."""
 
-__version__ = "1.8.1"
+__version__ = "2.0.0-dev"

osmnx/utils_geo.py

@@ -53,9 +53,9 @@ def sample_points(G: nx.MultiGraph, n: int) -> gpd.GeoSeries:
         warn("graph should be undirected to avoid oversampling bidirectional edges", stacklevel=2)
     gdf_edges = utils_graph.graph_to_gdfs(G, nodes=False)[["geometry", "length"]]
     weights = gdf_edges["length"] / gdf_edges["length"].sum()
-    idx = np.random.choice(gdf_edges.index, size=n, p=weights)
+    idx = np.random.default_rng().choice(gdf_edges.index, size=n, p=weights)
     lines = gdf_edges.loc[idx, "geometry"]
-    return lines.interpolate(np.random.rand(n), normalized=True)
+    return lines.interpolate(np.random.default_rng().random(n), normalized=True)
 
 
 def interpolate_points(geom: LineString, dist: float) -> Generator[tuple[float, float], None, None]:

pyproject.toml

@@ -87,11 +87,13 @@ extend-select = [
     "EM",   # check raw literals inside exception raising
     "ERA",  # check commented-out code from python files a la eradicate
     "F",    # check python source code for errors a la pyflakes
+    "FA",   # check from __future__ import annotations
     "FIX",  # check temporary developer notes a la flake8-fixme
    #"FURB", # check code improvements a la refurb (preview)
     "G",    # check logging string formatting a la flake8-logging-format
     "I",    # check isort imports
    #"LOG",  # check logging module usage a la flake8-logging (preview)
+    "NPY",  # check numpy usage
     "PD",   # check pandas linting a la pandas-vet
     "PERF", # check performance anti-patterns a la perflint
     "PGH",  # check pygrep hooks

tests/environments/env-test-minimal.yml

@@ -12,7 +12,7 @@ dependencies:
   - networkx=2.5
   - numpy=1.20
   - pandas=1.1
-  - python=3.8 # pin to minimum version from /pyproject.toml
+  - python=3.9 # pin to minimum version from /pyproject.toml
   - requests=2.27
   - shapely=2.0
 

tests/test_osmnx.py

@@ -330,8 +330,8 @@ def test_routing() -> None:
     # test multiple origins-destinations
     n = 5
     nodes = np.array(G.nodes)
-    origs = [int(x) for x in np.random.choice(nodes, size=n, replace=True)]
-    dests = [int(x) for x in np.random.choice(nodes, size=n, replace=True)]
+    origs = [int(x) for x in np.random.default_rng().choice(nodes, size=n, replace=True)]
+    dests = [int(x) for x in np.random.default_rng().choice(nodes, size=n, replace=True)]
     paths1 = ox.shortest_path(G, origs, dests, weight="length", cpus=1)
     paths2 = ox.shortest_path(G, origs, dests, weight="length", cpus=2)
     paths3 = ox.shortest_path(G, origs, dests, weight="length", cpus=None)
@@ -506,16 +506,16 @@ def test_graph_save_load() -> None:
     # create random boolean graph/node/edge attributes
     attr_name = "test_bool"
     G.graph[attr_name] = False
-    bools = np.random.randint(0, 2, len(G.nodes))
+    bools = np.random.default_rng().integers(low=0, high=2, size=len(G.nodes))
     node_attrs = {n: bool(b) for n, b in zip(G.nodes, bools)}
     nx.set_node_attributes(G, node_attrs, attr_name)
-    bools = np.random.randint(0, 2, len(G.edges))
+    bools = np.random.default_rng().integers(low=0, high=2, size=len(G.edges))
     edge_attrs = {n: bool(b) for n, b in zip(G.edges, bools)}
     nx.set_edge_attributes(G, edge_attrs, attr_name)
 
     # create list, set, and dict attributes for nodes and edges
-    rand_ints_nodes = np.random.randint(0, 10, len(G.nodes))
-    rand_ints_edges = np.random.randint(0, 10, len(G.edges))
+    rand_ints_nodes = np.random.default_rng().integers(low=0, high=10, size=len(G.nodes))
+    rand_ints_edges = np.random.default_rng().integers(low=0, high=10, size=len(G.edges))
     list_node_attrs = {n: [n, r] for n, r in zip(G.nodes, rand_ints_nodes)}
     nx.set_node_attributes(G, list_node_attrs, "test_list")
     list_edge_attrs = {e: [e, r] for e, r in zip(G.edges, rand_ints_edges)}