refactor: more ruff fixes

Author: derkweijers

examples/boltzmann_wealth_model_network/boltzmann_wealth_model_network/model.py

@@ -5,8 +5,8 @@
 def compute_gini(model):
     agent_wealths = [agent.wealth for agent in model.agents]
     x = sorted(agent_wealths)
-    N = model.num_agents
-    B = sum(xi * (N - i) for i, xi in enumerate(x)) / (N * sum(x))
+    N = model.num_agents  # noqa: N806
+    B = sum(xi * (N - i) for i, xi in enumerate(x)) / (N * sum(x))  # noqa: N806
     return 1 + (1 / N) - 2 * B
 
 
@@ -45,7 +45,7 @@ def step(self):
         self.datacollector.collect(self)
 
     def run_model(self, n):
-        for i in range(n):
+        for _ in range(n):
             self.step()
 
 

examples/boltzmann_wealth_model_network/boltzmann_wealth_model_network/server.py

@@ -3,7 +3,7 @@
 from .model import BoltzmannWealthModelNetwork
 
 
-def network_portrayal(G):
+def network_portrayal(G):  # noqa: N803
     # The model ensures there is 0 or 1 agent per node
 
     portrayal = {}

examples/el_farol/el_farol.ipynb

@@ -24,7 +24,7 @@
     "    for m in memory_sizes\n",
     "]\n",
     "for model in models:\n",
-    "    for i in range(100):\n",
+    "    for _ in range(100):\n",
     "        model.step()"
    ]
   },
@@ -99,7 +99,7 @@
     "    for ns in num_strategies_list\n",
     "]\n",
     "for model in models:\n",
-    "    for i in range(100):\n",
+    "    for _ in range(100):\n",
     "        model.step()"
    ]
   },

examples/el_farol/el_farol/model.py

@@ -10,11 +10,11 @@ def __init__(
         crowd_threshold=60,
         num_strategies=10,
         memory_size=10,
-        N=100,
+        num_agents=100,
     ):
         super().__init__()
         self.running = True
-        self.num_agents = N
+        self.num_agents = num_agents
 
         # Initialize the previous attendance randomly so the agents have a history
         # to work with from the start.

examples/shape_example/shape_example/model.py

@@ -10,17 +10,17 @@ def __init__(self, model, heading=(1, 0)):
 
 
 class ShapeExample(mesa.Model):
-    def __init__(self, N=2, width=20, height=10):
+    def __init__(self, num_agents=2, width=20, height=10):
         super().__init__()
-        self.N = N  # num of agents
+        self.num_agents = num_agents  # num of agents
         self.headings = ((1, 0), (0, 1), (-1, 0), (0, -1))  # tuples are fast
         self.grid = OrthogonalMooreGrid((width, height), torus=True, random=self.random)
 
         self.make_walker_agents()
         self.running = True
 
     def make_walker_agents(self):
-        for _ in range(self.N):
+        for _ in range(self.num_agents):
             x = self.random.randrange(self.grid.dimensions[0])
             y = self.random.randrange(self.grid.dimensions[1])
             cell = self.grid[(x, y)]

gis/agents_and_networks/src/space/road_network.py

@@ -19,7 +19,7 @@ class RoadNetwork:
 
     def __init__(self, lines: gpd.GeoSeries):
         segmented_lines = gpd.GeoDataFrame(geometry=segmented(lines))
-        G = momepy.gdf_to_nx(segmented_lines, approach="primal", length="length")
+        G = momepy.gdf_to_nx(segmented_lines, approach="primal", length="length")  # noqa: N806
         self.nx_graph = G.subgraph(max(nx.connected_components(G), key=len))
         self.crs = lines.crs
 
@@ -70,7 +70,7 @@ def __init__(self, campus, lines, output_dir) -> None:
         self._path_cache_result = f"{output_dir}/{campus}_path_cache_result.pkl"
         try:
             with open(self._path_cache_result, "rb") as cached_result:
-                self._path_select_cache = pickle.load(cached_result)
+                self._path_select_cache = pickle.load(cached_result)  # noqa: S301
         except FileNotFoundError:
             self._path_select_cache = {}
 

gis/agents_and_networks/src/space/utils.py

@@ -22,7 +22,7 @@ def get_coord_matrix(
 def get_affine_transform(
     from_coord: np.ndarray, to_coord: np.ndarray
 ) -> tuple[float, float, float, float, float, float]:
-    A, res, rank, s = np.linalg.lstsq(from_coord, to_coord, rcond=None)
+    A, res, rank, s = np.linalg.lstsq(from_coord, to_coord, rcond=None)  # noqa: N806
 
     np.testing.assert_array_almost_equal(res, np.zeros_like(res), decimal=15)
     np.testing.assert_array_almost_equal(A[:, 2], np.array([0.0, 0.0, 1.0]), decimal=15)
@@ -57,7 +57,7 @@ def _segmented(linestring: LineString) -> list[LineString]:
 # reference: https://gis.stackexchange.com/questions/367228/using-shapely-interpolate-to-evenly-re-sample-points-on-a-linestring-geodatafram
 def redistribute_vertices(geom, distance):
     if isinstance(geom, LineString):
-        if (num_vert := int(round(geom.length / distance))) == 0:
+        if (num_vert := round(geom.length / distance)) == 0:
             num_vert = 1
         return LineString(
             [
@@ -78,9 +78,13 @@ class UnitTransformer:
     _degree2meter: pyproj.Transformer
     _meter2degree: pyproj.Transformer
 
-    def __init__(
-        self, degree_crs=pyproj.CRS("EPSG:4326"), meter_crs=pyproj.CRS("EPSG:3857")
-    ):
+    def __init__(self, degree_crs: pyproj.CRS | None, meter_crs: pyproj.CRS | None):
+        if degree_crs is None:
+            degree_crs = pyproj.CRS("EPSG:4326")
+
+        if meter_crs is None:
+            meter_crs = pyproj.CRS("EPSG:3857")
+
         self._degree2meter = pyproj.Transformer.from_crs(
             degree_crs, meter_crs, always_xy=True
         )

gis/geo_schelling/model.py

@@ -12,11 +12,11 @@
 def get_largest_connected_components(gdf):
     """Get the largest connected component of a GeoDataFrame."""
     # create spatial weights matrix
-    W = libpysal.weights.Queen.from_dataframe(
+    w = libpysal.weights.Queen.from_dataframe(
         gdf, use_index=True, silence_warnings=True
     )
     # get component labels
-    gdf["component"] = W.component_labels
+    gdf["component"] = w.component_labels
     # get the largest component
     largest_component = gdf["component"].value_counts().idxmax()
     # subset the GeoDataFrame

gis/geo_schelling_points/geo_schelling_points/model.py

@@ -15,11 +15,11 @@
 def get_largest_connected_components(gdf):
     """Get the largest connected component of a GeoDataFrame."""
     # create spatial weights matrix
-    W = libpysal.weights.Queen.from_dataframe(
+    w = libpysal.weights.Queen.from_dataframe(
         gdf, use_index=True, silence_warnings=True
     )
     # get component labels
-    gdf["component"] = W.component_labels
+    gdf["component"] = w.component_labels
     # get the largest component
     largest_component = gdf["component"].value_counts().idxmax()
     # subset the GeoDataFrame

pyproject.toml

@@ -2,6 +2,9 @@
 requires = ["hatchling"]
 build-backend = "hatchling.build"
 
+[tool.hatch.build.targets.wheel]
+    packages = ["examples", "gis", "rl"]
+
 [project]
 name = "mesa-models"
 description = "Importable Mesa models."