mirror of https://github.com/gsi-upm/soil
J. Fernando Sánchez
3 weeks ago
parent
f49be3af68
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from flask import Flask
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import solara.server.flask
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app = Flask(__name__)
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app.register_blueprint(solara.server.flask.blueprint, url_prefix="/solara/")
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@app.route("/")
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def hello_world():
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return "<p>Hello, World!</p>"
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'''
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This scenario has drivers driving around a city.
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In this model, drivers can only be at intersections, which are treated as nodes in the City Graph (grid).
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At the start of the simulation, drivers are randomly positioned in the city grid.
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The following models for agent behavior are included:
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* DummyDriver: In each simulation step, this type of driver can instantly move to any of the neighboring nodes in the grid, or stay in its place.
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'''
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import networkx as nx
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from soil import Environment, BaseAgent, state, time
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from mesa.space import NetworkGrid
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import mesa
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import statistics
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class CityGrid(NetworkGrid):
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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for (u, v, d) in self.G.edges(data=True):
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d["occupation"] = 0
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# self.dijkstras = dict(nx.all_pairs_dijkstra(self.G, weight="length"))
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# def eta(self, pos1, pos2):
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# return self.dijkstras[pos1][0][pos2]
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def travel_time(self, pos1, pos2):
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return float(min(d["travel_time"] for d in self.G.adj[pos1][pos2].values()))
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def node_occupation(self):
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return {k: len(v.get("agent", [])) for (k, v) in self.G.nodes(data=True)}
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def edge_occupation(self):
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return {(u,v): d.get('occupation', 1) for (u, v, d) in self.G.edges(data=True)}
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class Roamer(BaseAgent):
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waiting = False
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def step(self):
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'''
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A simple driver that just moves to a neighboring cell in the city
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'''
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yield from self.move_to(None)
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return self.delay(0)
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def choose_next(self):
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opts = self.model.grid.get_neighborhood(self.pos, include_center=False)
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pos = self.random.choice(opts)
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delay = self.model.grid.travel_time(self.pos, pos)
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return pos, delay
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def move_to(self, pos=None):
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self.waiting = True
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if pos is None:
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pos, delay = self.choose_next()
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if self.model.gradual_move:
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# Calculate how long it will take, and wait for that long
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if pos != self.pos:
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self.model.grid.G.edges[self.pos,pos,0]["occupation"] += 1
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yield delay
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if self.model.gradual_move and pos != self.pos:
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w1 = self.model.grid.G.edges[self.pos,pos,0]["occupation"]
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oldpos = self.pos
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self.model.grid.G.edges[self.pos,pos,0]["occupation"] = w1 - 1
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assert self.model.grid.G.edges[self.pos,pos,0]["occupation"] == w1-1
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self.model.grid.move_agent(self, pos)
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self.waiting = False
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class LazyRoamer(Roamer):
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waiting = False
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def choose_next(self):
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opts = self.model.grid.get_neighborhood(self.pos, include_center=False)
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times = [self.model.grid.travel_time(self.pos, other) for other in opts]
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idx = self.random.choices(range(len(times)), k=1, weights=[1/time for time in times])[0]
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return opts[idx], times[idx]
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def gini(values):
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s = sum(values)
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N = len(values)
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if s == 0:
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return 0
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x = sorted(values)
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B = sum(xi * (N - i) for i, xi in enumerate(x)) / (N * s)
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return 1 + (1 / N) - 2 * B
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class CityEnv(Environment):
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def __init__(self, *, G, side=20, n_assets=100, ratio_lazy=1, lockstep=True, gradual_move=True, max_weight=1, **kwargs):
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super().__init__(**kwargs)
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if lockstep:
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self.schedule = time.Lockstepper(self.schedule)
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self.n_assets = n_assets
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self.side = side
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self.max_weight = max_weight
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self.gradual_move = gradual_move
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self.grid = CityGrid(g=G)
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n_lazy = round(self.n_assets * ratio_lazy)
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n_other = self.n_assets - n_lazy
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self.add_agents(Roamer, k=n_other)
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self.add_agents(LazyRoamer, k=n_lazy)
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positions = list(self.grid.G.nodes)
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for agent in self.get_agents():
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pos = self.random.choice(positions)
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self.grid.place_agent(agent, pos)
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self.datacollector = mesa.DataCollector(
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model_reporters={
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"NodeGini": lambda model: gini(model.grid.node_occupation().values()),
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"EdgeGini": lambda model: gini(model.grid.edge_occupation().values()),
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"EdgeOccupation": lambda model: statistics.mean(model.grid.edge_occupation().values()),
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}#, agent_reporters={"Wealth": "wealth"}
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)
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class SquareCityEnv(CityEnv):
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def __init__(self, *, side=20, **kwargs):
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self.side = side
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G = nx.grid_graph(dim=[side, side])
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for (_, _, d) in G.edges(data=True):
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d["travel_time"] = self.random.randint(1, self.max_weight)
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for (k, d) in G.nodes(data=True):
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d["pos"] = k
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super().__init__(**kwargs, G=G)
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import osmnx as ox
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class NamedCityEnv(CityEnv):
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def __init__(self, *, location="Chamberi, Madrid", **kwargs):
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self.location = location
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super().__init__(**kwargs, G=load_city_graph(location))
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def load_city_graph(location='Chamberi, Madrid', **kwargs):
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G = ox.graph.graph_from_place(location, **kwargs)
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G = ox.add_edge_speeds(G)
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G = ox.add_edge_travel_times(G)
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largest = sorted(nx.strongly_connected_components(G), key=lambda x: len(x))[-1]
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G = G.subgraph(largest)
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return G
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if __name__ == "__main__":
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env = CityEnv()
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for i in range(100):
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env.step()
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import solara
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@solara.component
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def MainPage(clicks):
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color = "green"
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if clicks.value >= 5:
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color = "red"
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def increment():
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clicks.value += 1
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print("clicks", clicks) # noqa
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solara.Button(label=f"Clicked: {clicks}", on_click=increment, color=color)
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@solara.component
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def Page():
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v = Visualization()
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v.viz()
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class Visualization:
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def __init__(self):
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self.clicks = solara.reactive(0)
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def viz(self):
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from sol_lib import MainPage
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return MainPage(self.clicks)
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import solara
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@solara.component
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def MainPage(clicks):
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color = "green"
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if clicks.value >= 5:
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color = "red"
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def increment():
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clicks.value += 1
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print("clicks", clicks) # noqa
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solara.Button(label=f"Clicked: {clicks}", on_click=increment, color=color)
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Load Diff
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[tool.poetry]
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name = "soil"
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version = "1.0.0rc11"
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description = "An Agent-Based Social Simulator for Social Networks"
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authors = ["J. Fernando Sánchez"]
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license = "Apache 2.0"
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readme = "README.md"
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[tool.poetry.dependencies]
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python = "^3.10"
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networkx = ">=2.5"
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numpy = "^1.26.4"
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matplotlib = "^3.8.3"
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pyyaml = ">=5.1"
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pandas = ">=1"
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salib = ">=1.3"
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jinja2 = "^3.1.3"
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mesa = ">=1.2"
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pydantic = ">=1.9"
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sqlalchemy = ">=1.4"
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typing-extensions = ">=4.4"
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annotated-types = ">=0.4"
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tqdm = ">=4.64"
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[tool.poetry.group.dev.dependencies]
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pytest = "^8.1.1"
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pytest-profiling = "^1.7.0"
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scipy = ">=1.3"
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tornado = "^6.4"
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nbconvert = "7.3.1"
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nbformat = "5.8.0"
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jupyter = "1.0.0"
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osmnx = "^1.9.2"
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[build-system]
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requires = ["poetry-core"]
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build-backend = "poetry.core.masonry.api"
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@ -1 +1 @@
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1.0.0rc3
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1.0.0rc11
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from typing import Optional
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import sys
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import threading
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import matplotlib.pyplot as plt
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import reacton.ipywidgets as widgets
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import solara
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from solara.alias import rv
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import mesa.experimental.components.matplotlib as components_matplotlib
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from mesa.experimental.jupyter_viz import *
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from matplotlib.figure import Figure
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import networkx as nx
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class Controller:
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'''
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A visualization controller that holds a reference to a model so that it can be modified or queried while the simulation is still running.
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'''
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def __init__(self):
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self.model = None
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def JupyterViz(*args, **kwargs):
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c = Controller()
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page = JupyterPage(*args, controller=c, **kwargs)
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page.controller = c
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return page
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@solara.component
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def JupyterPage(
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model_class,
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model_params,
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controller=None,
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measures=None,
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name="Mesa Model",
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agent_portrayal=None,
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space_drawer="default",
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play_interval=150,
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columns=2,
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):
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"""Initialize a component to visualize a model.
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Args:
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model_class: class of the model to instantiate
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model_params: parameters for initializing the model
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measures: list of callables or data attributes to plot
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name: name for display
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agent_portrayal: options for rendering agents (dictionary)
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space_drawer: method to render the agent space for
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the model; default implementation is the `SpaceMatplotlib` component;
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simulations with no space to visualize should
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specify `space_drawer=False`
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play_interval: play interval (default: 150)
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"""
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if controller is None:
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controller = Controller()
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current_step = solara.use_reactive(0)
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# 1. Set up model parameters
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user_params, fixed_params = split_model_params(model_params)
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model_parameters, set_model_parameters = solara.use_state(
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{**fixed_params, **{k: v["value"] for k, v in user_params.items()}}
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)
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# 2. Set up Model
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def make_model():
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model = model_class(**model_parameters)
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current_step.value = 0
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controller.model = model
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return model
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reset_counter = solara.use_reactive(0)
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model = solara.use_memo(
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make_model, dependencies=[*list(model_parameters.values()), reset_counter.value]
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)
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def handle_change_model_params(name: str, value: any):
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set_model_parameters({**model_parameters, name: value})
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# 3. Set up UI
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with solara.AppBar():
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solara.AppBarTitle(name)
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with solara.GridFixed(columns=2):
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UserInputs(user_params, on_change=handle_change_model_params)
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ModelController(model, play_interval, current_step, reset_counter)
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solara.Markdown(md_text=f"###Step: {current_step} - Time: {model.schedule.time } ")
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with solara.GridFixed(columns=columns):
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# 4. Space
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if space_drawer == "default":
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# draw with the default implementation
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components_matplotlib.SpaceMatplotlib(
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model, agent_portrayal, dependencies=[current_step.value]
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)
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elif space_drawer:
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# if specified, draw agent space with an alternate renderer
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space_drawer(model, agent_portrayal, dependencies=[current_step.value])
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# otherwise, do nothing (do not draw space)
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# 5. Plots
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for measure in measures:
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if callable(measure):
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# Is a custom object
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measure(model)
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else:
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components_matplotlib.make_plot(model, measure)
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@solara.component
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def NetworkDrawer(model, network_portrayal, dependencies: Optional[list[any]] = None):
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space_fig = Figure()
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space_ax = space_fig.subplots()
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graph = model.grid.G
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nx.draw(
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graph,
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ax=space_ax,
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**network_portrayal(graph),
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)
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solara.FigureMatplotlib(space_fig, format="png", dependencies=dependencies)
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try:
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import osmnx as ox
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@solara.component
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def GeoNetworkDrawer(model, network_portrayal, dependencies: Optional[list[any]] = None):
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space_fig = Figure()
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space_ax = space_fig.subplots()
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graph = model.grid.G
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ox.plot_graph(
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graph,
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ax=space_ax,
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**network_portrayal(graph),
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)
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solara.FigureMatplotlib(space_fig, format="png", dependencies=dependencies)
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except ImportError:
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pass
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Reference in New Issue