Improve Virus on Network documentation

examples/virus_on_network/README.md

@@ -2,7 +2,13 @@
 
 ## Summary
 
-This model is based on the NetLogo model "Virus on Network".
+This model is based on the NetLogo model "Virus on Network". It demonstrates the spread of a virus through a network and follows the SIR model, commonly seen in epidemiology.
+
+The SIR model is one of the simplest compartmental models, and many models are derivatives of this basic form. The model consists of three compartments:
+
+S: The number of susceptible individuals. When a susceptible and an infectious individual come into "infectious contact", the susceptible individual contracts the disease and transitions to the infectious compartment.
+I: The number of infectious individuals. These are individuals who have been infected and are capable of infecting susceptible individuals.
+R for the number of removed (and immune) or deceased individuals. These are individuals who have been infected and have either recovered from the disease and entered the removed compartment, or died. It is assumed that the number of deaths is negligible with respect to the total population. This compartment may also be called "recovered" or "resistant".
 
 For more information about this model, read the NetLogo's web page: http://ccl.northwestern.edu/netlogo/models/VirusonaNetwork.
 
@@ -26,6 +32,15 @@ To run the model interactively, run ``mesa runserver`` in this directory. e.g.
 
 Then open your browser to [http://127.0.0.1:8521/](http://127.0.0.1:8521/) and press Reset, then Run.
 
+or
+
+Directly run the file ``run.py`` in the terminal. e.g.
+
+```
+    $ python run.py
+```
+
+
 ## Files
 
 * ``run.py``: Launches a model visualization server.

examples/virus_on_network/app.py

@@ -48,7 +48,6 @@ def get_resistant_susceptible_ratio(model):
 
 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"]

examples/virus_on_network/virus_on_network/model.py

@@ -28,7 +28,9 @@ def number_resistant(model):
 
 
 class VirusOnNetwork(mesa.Model):
-    """A virus model with some number of agents"""
+    """
+    A virus model with some number of agents
+    """
 
     def __init__(
         self,
@@ -104,6 +106,10 @@ def run_model(self, n):
 
 
 class VirusAgent(mesa.Agent):
+    """
+    Individual Agent definition and its properties/interaction methods
+    """
+
     def __init__(
         self,
         unique_id,

examples/virus_on_network/virus_on_network/server.py

@@ -49,7 +49,11 @@ def get_agents(source, target):
     return portrayal
 
 
-network = mesa.visualization.NetworkModule(network_portrayal, 500, 500)
+network = mesa.visualization.NetworkModule(
+    portrayal_method=network_portrayal,
+    canvas_height=500,
+    canvas_width=500,
+)
 chart = mesa.visualization.ChartModule(
     [
         {"Label": "Infected", "Color": "#FF0000"},
@@ -71,63 +75,68 @@ def get_resistant_susceptible_ratio(model):
 
 model_params = {
     "num_nodes": mesa.visualization.Slider(
-        "Number of agents",
-        10,
-        10,
-        100,
-        1,
+        name="Number of agents",
+        value=10,
+        min_value=10,
+        max_value=100,
+        step=1,
         description="Choose how many agents to include in the model",
     ),
     "avg_node_degree": mesa.visualization.Slider(
-        "Avg Node Degree", 3, 3, 8, 1, description="Avg Node Degree"
+        name="Avg Node Degree",
+        value=3,
+        min_value=3,
+        max_value=8,
+        step=1,
+        description="Avg Node Degree",
     ),
     "initial_outbreak_size": mesa.visualization.Slider(
-        "Initial Outbreak Size",
-        1,
-        1,
-        10,
-        1,
+        name="Initial Outbreak Size",
+        value=1,
+        min_value=1,
+        max_value=10,
+        step=1,
         description="Initial Outbreak Size",
     ),
     "virus_spread_chance": mesa.visualization.Slider(
-        "Virus Spread Chance",
-        0.4,
-        0.0,
-        1.0,
-        0.1,
+        name="Virus Spread Chance",
+        value=0.4,
+        min_value=0.0,
+        max_value=1.0,
+        step=0.1,
         description="Probability that susceptible neighbor will be infected",
     ),
     "virus_check_frequency": mesa.visualization.Slider(
-        "Virus Check Frequency",
-        0.4,
-        0.0,
-        1.0,
-        0.1,
+        name="Virus Check Frequency",
+        value=0.4,
+        min_value=0.0,
+        max_value=1.0,
+        step=0.1,
         description="Frequency the nodes check whether they are infected by a virus",
     ),
     "recovery_chance": mesa.visualization.Slider(
-        "Recovery Chance",
-        0.3,
-        0.0,
-        1.0,
-        0.1,
+        name="Recovery Chance",
+        value=0.3,
+        min_value=0.0,
+        max_value=1.0,
+        step=0.1,
         description="Probability that the virus will be removed",
     ),
     "gain_resistance_chance": mesa.visualization.Slider(
-        "Gain Resistance Chance",
-        0.5,
-        0.0,
-        1.0,
-        0.1,
+        name="Gain Resistance Chance",
+        value=0.5,
+        min_value=0.0,
+        max_value=1.0,
+        step=0.1,
         description="Probability that a recovered agent will become "
         "resistant to this virus in the future",
     ),
 }
 
 server = mesa.visualization.ModularServer(
-    VirusOnNetwork,
-    [network, get_resistant_susceptible_ratio, chart],
-    "Virus Model",
-    model_params,
+    model_cls=VirusOnNetwork,
+    visualization_elements=[network, get_resistant_susceptible_ratio, chart],
+    name="Virus on Network Model",
+    model_params=model_params,
 )
 server.port = 8521