gis: update three examples to use solara viz

gis/geo_schelling/README.md

@@ -16,10 +16,10 @@ NUTS-2 regions are the GeoAgents. The neighbors of a polygon are considered thos
 
 ## How to Run
 
-To run the model interactively, run `mesa runserver` in this directory. e.g.
+To run the model interactively, run `solara run app.py` in this directory. e.g.
 
 ```bash
-mesa runserver
+solara run app.py
 ```
 
-Then open your browser to [http://127.0.0.1:8521/](http://127.0.0.1:8521/) and press `Start`.
+Then open your browser to [http://127.0.0.1:8765/](http://127.0.0.1:8765/) and press the play button `▶`.

gis/geo_schelling/app.py

@@ -0,0 +1,44 @@
+import solara
+from mesa.visualization import Slider, SolaraViz, make_plot_measure
+from mesa_geo.visualization import make_geospace_leaflet
+from model import GeoSchelling
+
+
+def make_plot_happiness(model):
+    return solara.Markdown(f"**Happy agents: {model.happy}**")
+
+
+model_params = {
+    "density": Slider("Agent density", 0.6, 0.1, 1.0, 0.1),
+    "minority_pc": Slider("Fraction minority", 0.2, 0.00, 1.0, 0.05),
+    "export_data": False,
+}
+
+
+def schelling_draw(agent):
+    """
+    Portrayal Method for canvas
+    """
+    portrayal = {}
+    if agent.atype is None:
+        portrayal["color"] = "Grey"
+    elif agent.atype == 0:
+        portrayal["color"] = "Red"
+    else:
+        portrayal["color"] = "Blue"
+    return portrayal
+
+
+model = GeoSchelling()
+page = SolaraViz(
+    model,
+    [
+        make_geospace_leaflet(schelling_draw, zoom=4),
+        make_plot_happiness,
+        make_plot_measure(["happy"]),
+    ],
+    model_params=model_params,
+    name="GeoSchelling",
+)
+
+page  # noqa

gis/geo_schelling/requirements.txt

@@ -1 +1 @@
-mesa-geo~=0.7
+mesa-geo~=0.9.0a0

gis/geo_schelling_points/README.md

@@ -14,12 +14,12 @@ The NUTS-2 regions are considered as a shared definition of neighborhood among a
 
 There are two types of GeoAgents: people and regions. Each person resides in a randomly assigned region, and checks the color ratio of its region against a pre-defined "happiness" threshold at every time step. If the ratio falls below a certain threshold (e.g., 40%), the agent is found to be "unhappy", and randomly moves to another region. People are represented as points, with locations randomly chosen within their regions. The color of a region depends on the color of the majority population it contains (i.e., point in polygon calculations).
 
-## How to run
+## How to Run
 
-To run the model interactively, run `mesa runserver` in this directory. e.g.
+To run the model interactively, run `solara run app.py` in this directory. e.g.
 
 ```bash
-mesa runserver
+solara run app.py
 ```
 
-Then open your browser to [http://127.0.0.1:8521/](http://127.0.0.1:8521/) and press `Start`.
+Then open your browser to [http://127.0.0.1:8765/](http://127.0.0.1:8765/) and press the play button `▶`.

gis/geo_schelling_points/app.py

@@ -0,0 +1,46 @@
+import solara
+from geo_schelling_points.agents import PersonAgent, RegionAgent
+from geo_schelling_points.model import GeoSchellingPoints
+from mesa.visualization import Slider, SolaraViz, make_plot_measure
+from mesa_geo.visualization import make_geospace_leaflet
+
+
+def make_plot_happiness(model):
+    return solara.Markdown(f"**Happy agents: {model.happy}**")
+
+
+model_params = {
+    "red_percentage": Slider("% red", 0.5, 0.00, 1.0, 0.05),
+    "similarity_threshold": Slider("% similar wanted", 0.5, 0.00, 1.0, 0.05),
+}
+
+
+def schelling_draw(agent):
+    portrayal = {}
+    if isinstance(agent, RegionAgent):
+        if agent.red_cnt > agent.blue_cnt:
+            portrayal["color"] = "Red"
+        elif agent.red_cnt < agent.blue_cnt:
+            portrayal["color"] = "Blue"
+        else:
+            portrayal["color"] = "Grey"
+    elif isinstance(agent, PersonAgent):
+        portrayal["radius"] = 1
+        portrayal["shape"] = "circle"
+        portrayal["color"] = "Red" if agent.is_red else "Blue"
+    return portrayal
+
+
+model = GeoSchellingPoints()
+page = SolaraViz(
+    model,
+    [
+        make_geospace_leaflet(schelling_draw, zoom=4),
+        make_plot_happiness,
+        make_plot_measure(["happy", "unhappy"]),
+    ],
+    model_params=model_params,
+    name="GeoSchellingPoints",
+)
+
+page  # noqa

gis/geo_schelling_points/geo_schelling_points/model.py

@@ -1,6 +1,8 @@
 import random
 from pathlib import Path
 
+import geopandas as gpd
+import libpysal
 import mesa
 import mesa_geo as mg
 
@@ -10,6 +12,21 @@
 script_directory = Path(__file__).resolve().parent
 
 
+def get_largest_connected_components(gdf):
+    """Get the largest connected component of a GeoDataFrame."""
+    # create spatial weights matrix
+    W = libpysal.weights.Queen.from_dataframe(
+        gdf, use_index=True, silence_warnings=True
+    )
+    # get component labels
+    gdf["component"] = W.component_labels
+    # get the largest component
+    largest_component = gdf["component"].value_counts().idxmax()
+    # subset the GeoDataFrame
+    gdf = gdf[gdf["component"] == largest_component]
+    return gdf
+
+
 class GeoSchellingPoints(mesa.Model):
     def __init__(self, red_percentage=0.5, similarity_threshold=0.5):
         super().__init__()
@@ -26,7 +43,9 @@ def __init__(self, red_percentage=0.5, similarity_threshold=0.5):
         # Set up the grid with patches for every NUTS region
         ac = mg.AgentCreator(RegionAgent, model=self)
         data_path = script_directory / "../data/nuts_rg_60M_2013_lvl_2.geojson"
-        regions = ac.from_file(data_path)
+        regions_gdf = gpd.read_file(data_path)
+        regions_gdf = get_largest_connected_components(regions_gdf)
+        regions = ac.from_GeoDataFrame(regions_gdf)
         self.space.add_regions(regions)
 
         for region in regions:

gis/geo_schelling_points/requirements.txt

@@ -1 +1 @@
-mesa-geo~=0.7
+mesa-geo~=0.9.0a0

gis/geo_sir/README.md

@@ -15,12 +15,12 @@ Susceptible agents (those who have never been infected) who come in proximity wi
 Neighbourhood agents represent neighbourhoods in the Toronto, and become hot-spots (colored red) if there are infected agents inside them.
 Data obtained from [this link](http://adamw523.com/toronto-geojson/).
 
-## How to run
+## How to Run
 
-To run the model interactively, run `mesa runserver` in this directory. e.g.
+To run the model interactively, run `solara run app.py` in this directory. e.g.
 
 ```bash
-mesa runserver
+solara run app.py
 ```
 
-Then open your browser to [http://127.0.0.1:8521/](http://127.0.0.1:8521/) and press `Start`.
+Then open your browser to [http://127.0.0.1:8765/](http://127.0.0.1:8765/) and press the play button `▶`.

gis/geo_sir/app.py

@@ -0,0 +1,42 @@
+from geo_sir.agents import PersonAgent
+from geo_sir.model import GeoSir
+from mesa.visualization import Slider, SolaraViz, make_plot_measure
+from mesa_geo.visualization import make_geospace_leaflet
+
+model_params = {
+    "pop_size": Slider("Population size", 30, 10, 100, 10),
+    "init_infected": Slider("Fraction initial infection", 0.2, 0.00, 1.0, 0.05),
+    "exposure_distance": Slider("Exposure distance", 500, 100, 1000, 100),
+}
+
+
+def infected_draw(agent):
+    """
+    Portrayal Method for canvas
+    """
+    portrayal = {}
+    if isinstance(agent, PersonAgent):
+        portrayal["radius"] = "2"
+    if agent.atype in ["hotspot", "infected"]:
+        portrayal["color"] = "Red"
+    elif agent.atype in ["safe", "susceptible"]:
+        portrayal["color"] = "Green"
+    elif agent.atype in ["recovered"]:
+        portrayal["color"] = "Blue"
+    elif agent.atype in ["dead"]:
+        portrayal["color"] = "Black"
+    return portrayal
+
+
+model = GeoSir()
+page = SolaraViz(
+    model,
+    [
+        make_geospace_leaflet(infected_draw, zoom=12),
+        make_plot_measure(["infected", "susceptible", "recovered", "dead"]),
+    ],
+    name="Basic agent-based SIR model",
+    model_params=model_params,
+)
+
+page  # noqa

gis/geo_sir/model.py → gis/geo_sir/geo_sir/model.py

@@ -13,7 +13,7 @@ class GeoSir(mesa.Model):
     """Model class for a simplistic infection model."""
 
     # Geographical parameters for desired map
-    geojson_regions = script_directory / "data/TorontoNeighbourhoods.geojson"
+    geojson_regions = script_directory / "../data/TorontoNeighbourhoods.geojson"
     unique_id = "HOODNUM"
 
     def __init__(

gis/geo_sir/requirements.txt

@@ -1 +1 @@
-mesa-geo~=0.7
+mesa-geo~=0.9.0a0