solara: Add virus_on_network network example

Author: rht

examples/virus_on_network/app.py

@@ -0,0 +1,135 @@
+import math
+
+import solara
+from matplotlib.figure import Figure
+from matplotlib.ticker import MaxNLocator
+from mesa.experimental import JupyterViz, make_text
+from virus_on_network.model import State, VirusOnNetwork, number_infected
+
+
+def agent_portrayal(graph):
+    def get_agent(node):
+        return graph.nodes[node]["agent"][0]
+
+    edge_width = []
+    edge_color = []
+    for u, v in graph.edges():
+        agent1 = get_agent(u)
+        agent2 = get_agent(v)
+        w = 2
+        ec = "#e8e8e8"
+        if State.RESISTANT in (agent1.state, agent2.state):
+            w = 3
+            ec = "black"
+        edge_width.append(w)
+        edge_color.append(ec)
+    node_color_dict = {
+        State.INFECTED: "tab:red",
+        State.SUSCEPTIBLE: "tab:green",
+        State.RESISTANT: "tab:gray",
+    }
+    node_color = [node_color_dict[get_agent(node).state] for node in graph.nodes()]
+    return {
+        "width": edge_width,
+        "edge_color": edge_color,
+        "node_color": node_color,
+    }
+
+
+def get_resistant_susceptible_ratio(model):
+    ratio = model.resistant_susceptible_ratio()
+    ratio_text = r"$\infty$" if ratio is math.inf else f"{ratio:.2f}"
+    infected_text = str(number_infected(model))
+
+    return "Resistant/Susceptible Ratio: {}<br>Infected Remaining: {}".format(
+        ratio_text, infected_text
+    )
+
+
+def make_plot(model):
+    # This is for the case when we want to plot multiple measures in 1 figure.
+    # We could incorporate this into core Mesa.
+    fig = Figure()
+    ax = fig.subplots()
+    measures = ["Infected", "Susceptible", "Resistant"]
+    colors = ["tab:red", "tab:green", "tab:gray"]
+    for i, m in enumerate(measures):
+        color = colors[i]
+        df = model.datacollector.get_model_vars_dataframe()
+        ax.plot(df.loc[:, m], label=m, color=color)
+    fig.legend()
+    # Set integer x axis
+    ax.xaxis.set_major_locator(MaxNLocator(integer=True))
+    solara.FigureMatplotlib(fig)
+
+
+model_params = {
+    "num_nodes": {
+        "type": "SliderInt",
+        "value": 10,
+        "label": "Number of agents",
+        "min": 10,
+        "max": 100,
+        "step": 1,
+    },
+    "avg_node_degree": {
+        "type": "SliderInt",
+        "value": 3,
+        "label": "Avg Node Degree",
+        "min": 3,
+        "max": 8,
+        "step": 1,
+    },
+    "initial_outbreak_size": {
+        "type": "SliderInt",
+        "value": 1,
+        "label": "Initial Outbreak Size",
+        "min": 1,
+        "max": 10,
+        "step": 1,
+    },
+    "virus_spread_chance": {
+        "type": "SliderFloat",
+        "value": 0.4,
+        "label": "Virus Spread Chance",
+        "min": 0.0,
+        "max": 1.0,
+        "step": 0.1,
+    },
+    "virus_check_frequency": {
+        "type": "SliderFloat",
+        "value": 0.4,
+        "label": "Virus Check Frequency",
+        "min": 0.0,
+        "max": 1.0,
+        "step": 0.1,
+    },
+    "recovery_chance": {
+        "type": "SliderFloat",
+        "value": 0.3,
+        "label": "Recovery Chance",
+        "min": 0.0,
+        "max": 1.0,
+        "step": 0.1,
+    },
+    "gain_resistance_chance": {
+        "type": "SliderFloat",
+        "value": 0.5,
+        "label": "Gain Resistance Chance",
+        "min": 0.0,
+        "max": 1.0,
+        "step": 0.1,
+    },
+}
+
+page = JupyterViz(
+    VirusOnNetwork,
+    model_params,
+    measures=[
+        make_plot,
+        make_text(get_resistant_susceptible_ratio),
+    ],
+    name="Virus Model",
+    agent_portrayal=agent_portrayal,
+)
+page  # noqa