add python 3.12 and fix warnings

.github/workflows/build-test.yml

@@ -18,7 +18,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.10", "3.11"]
+        python-version: ["3.10", "3.11", "3.12"]
         os: [ubuntu-latest, windows-latest, macos-latest]
     steps:
     - uses: actions/checkout@v3

.github/workflows/coverage.yml

@@ -14,7 +14,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: [ "3.10" ]
+        python-version: [ "3.11" ]
     steps:
     - uses: actions/checkout@v3
     - name: "pip: Python ${{ matrix.python-version }} coverage"

.github/workflows/mpi-test.yml

@@ -14,7 +14,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.10"]
+        python-version: ["3.11"]
     steps:
       - uses: actions/checkout@v3
       - name: "pip: Python ${{ matrix.python-version }}"

examples/boids/boids2d.py

@@ -74,7 +74,7 @@ def step(self) -> None:
     self.__normalise()
 
     # set colours
-    self.boids.c = 0
+    self.boids.c = 0.0
     self.boids.loc[in_range[0:self.N_predators].sum(axis=0) != 0, "c"] = 1/3
     self.boids.loc[too_close[0:self.N_predators].sum(axis=0) != 0, "c"] = 2/3
     self.boids.loc[0:self.N_predators - 1, "c"] = 1

examples/conway/conway.py

@@ -10,7 +10,7 @@ class Conway(no.Model):
 
   __glider = np.array([[0, 0, 1], [1, 0, 1], [0, 1, 1]], dtype=int)
 
-  def __init__(self, nx: int, ny: int, n: int, edge: no.Edge=no.Edge.WRAP) -> None:
+  def __init__(self, nx: int, ny: int, edge: no.Edge=no.Edge.WRAP) -> None:
     super().__init__(no.LinearTimeline(0, 1), no.MonteCarlo.nondeterministic_stream)
 
     # create n automata at regular positions

examples/conway/run_model.py

@@ -8,9 +8,7 @@
 # saturation (proportion initially alive)
 sat = 0.36
 
-n = int(nx * ny * sat)
-
 # edges wrap - try with no.Edge.CONSTRAIN
-m = Conway(nx, ny, n, no.Edge.WRAP)
+m = Conway(nx, ny, no.Edge.WRAP)
 
 no.run(m)

examples/infection/infection.py

@@ -3,13 +3,13 @@
 from time import sleep
 import matplotlib.pyplot as plt
 import numpy as np
-
-from neworder.geospatial import GeospatialGraph
+import pandas as pd
 import osmnx as ox
 from shapely import line_interpolate_point
 import geopandas as gpd
 
 import neworder as no
+from neworder.geospatial import GeospatialGraph
 
 class Status(Enum):
   SUSCEPTIBLE = 0
@@ -54,17 +54,16 @@ def __init__(self,
     # create the agent data, which is stored in a geopandas geodataframe
     start_positions = self.domain.all_nodes.sample(n=n_agents, random_state=self.nprand, replace=True).index.values
     speeds = self.nprand.lognormal(np.log(speed), 0.2, n_agents)
-    agents = gpd.GeoDataFrame(data={"node": start_positions, "speed": speeds, "status": Status.SUSCEPTIBLE, "t_infect": no.time.NEVER})
+    agents = pd.DataFrame(data={"node": start_positions, "speed": speeds, "status": Status.SUSCEPTIBLE, "t_infect": no.time.NEVER})
     agents["dest"] = agents["node"].apply(self.__random_next_dest)
     agents["path"] = agents[["node", "dest"]].apply(lambda r: self.domain.shortest_path(r["node"], r["dest"], weight="length"), axis=1)
-    agents["dist"] = agents.path.apply(lambda p: p.length)
+    agents["dist"] = agents.path.apply(lambda p: p.length).astype(float)
     agents["offset"] = 0.0
-    agents["geometry"] = agents["path"].apply(lambda linestr: line_interpolate_point(linestr, 0))
     infected = self.nprand.choice(agents.index, n_infected, replace=False)
     agents.loc[infected, "status"] = Status.INFECTED
     agents.loc[infected, "t_infect"] = self.timeline.index
 
-    self.agents = agents
+    self.agents = gpd.GeoDataFrame(agents, geometry=agents["path"].apply(lambda linestr: line_interpolate_point(linestr, 0)))
     self.fig, self.g = self.__init_visualisation()
 
   def step(self) -> None:
@@ -111,7 +110,7 @@ def __update_position(self) -> None:
       # path <- (node, dest), dist <- new_dist
       self.agents.loc[overshoots, "path"] = self.agents.loc[overshoots, ["node", "dest"]] \
         .apply(lambda r: self.domain.shortest_path(r["node"], r["dest"], weight="length"), axis=1)
-      self.agents.loc[overshoots, "dist"] = self.agents.loc[overshoots, "path"].apply(lambda p: p.length)
+      self.agents.loc[overshoots, "dist"] = self.agents.loc[overshoots, "path"].apply(lambda p: p.length).astype(float)
       # finally update position
       self.agents.loc[overshoots, "geometry"] = self.agents.loc[overshoots, "path"].apply(lambda linestr: line_interpolate_point(linestr, 0))
 

examples/people/population.py

@@ -152,7 +152,7 @@ def check(self) -> bool:
 
   def plot_pyramid(self) -> None:
     a = np.arange(86)
-    s = self.population.groupby(by=["DC1117EW_C_SEX", "DC1117EW_C_AGE"])["DC1117EW_C_SEX"].count()
+    s = self.population.groupby(by=["DC1117EW_C_SEX", "DC1117EW_C_AGE"], observed=False)["DC1117EW_C_SEX"].count()
     m = s[s.index.isin([1], level="DC1117EW_C_SEX")].values
     f = s[s.index.isin([2], level="DC1117EW_C_SEX")].values
 

pyproject.toml

@@ -22,6 +22,7 @@ requires-python = ">=3.10"
 classifiers = [
     "Programming Language :: Python :: 3.10",
     "Programming Language :: Python :: 3.11",
+    "Programming Language :: Python :: 3.12",
     "License :: OSI Approved :: MIT License",
     "Operating System :: OS Independent",
 ]
@@ -60,8 +61,8 @@ dev = [
 
 
 [project.urls]
-"Homepage" = "https://github.com/pypa/sampleproject"
-"Bug Tracker" = "https://github.com/pypa/sampleproject/issues"
+"Homepage" = "https://neworder.readthedocs.io/"
+"Bug Tracker" = "https://github.com/virgesmith/neworder/issues"
 
 [tool.pytest.ini_options]
 minversion = "6.0"

test/test_mc.py

@@ -1,3 +1,4 @@
+from math import factorial
 import numpy as np
 import numpy.typing as npt
 import neworder as no
@@ -132,7 +133,7 @@ def test_mc_counts(base_model: no.Model) -> None:
 
   def poisson_pdf(x: range, l: float) -> np.ndarray:
     y = np.exp(-l)
-    return np.array([l**k * y / np.math.factorial(k) for k in x]) # type: ignore # Module has no attribute "math"; maybe "emath" or "mat"?
+    return np.array([l**k * y / factorial(k) for k in x]) # type: ignore # Module has no attribute "math"; maybe "emath" or "mat"?
 
   tests = [(1.0, 1.0, 10000), (3.0, 0.5, 10000), (0.2, 2.0, 10000), (10.0, 1.0, 1000), (3.0, 1.0, 100000)]