gis: update four examples to use solara viz

gis/agents_and_networks/README.md

@@ -28,12 +28,12 @@ python3 -m pip install -r requirements.txt
 Then run the model:
 
 ```bash
-python3 scripts/run.py --campus ub
+solara run app.py -- --campus ub
 ```
 
 Change `ub` to `gmu` for a different campus map.
 
-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 `▶`.
 
 ## License
 

gis/agents_and_networks/app.py

@@ -0,0 +1,66 @@
+import sys
+
+from mesa.visualization import Slider, SolaraViz, make_plot_measure
+from mesa_geo.visualization import make_geospace_leaflet
+from src.model.model import AgentsAndNetworks
+from src.visualization.utils import agent_draw, make_plot_clock
+
+
+def parse_args():
+    campus = "ub"
+    if "--campus" in sys.argv:
+        campus = sys.argv[sys.argv.index("--campus") + 1]
+    return campus
+
+
+if __name__ == "__main__":
+    campus = parse_args()
+
+    if campus == "ub":
+        data_file_prefix = "UB"
+    elif campus == "gmu":
+        data_file_prefix = "Mason"
+    else:
+        raise ValueError("Invalid campus name. Choose from ub or gmu.")
+
+    campus_params = {
+        "ub": {"data_crs": "epsg:4326", "commuter_speed": 0.5, "zoom": 14},
+        "gmu": {"data_crs": "epsg:2283", "commuter_speed": 0.4, "zoom": 16},
+    }
+    model_params = {
+        "campus": campus,
+        "data_crs": campus_params[campus]["data_crs"],
+        "buildings_file": f"data/{campus}/{data_file_prefix}_bld.zip",
+        "walkway_file": f"data/{campus}/{data_file_prefix}_walkway_line.zip",
+        "lakes_file": f"data/{campus}/hydrop.zip",
+        "rivers_file": f"data/{campus}/hydrol.zip",
+        "driveway_file": f"data/{campus}/{data_file_prefix}_Rds.zip",
+        "output_dir": "outputs",
+        "show_walkway": True,
+        "show_lakes_and_rivers": True,
+        "show_driveway": True,
+        "num_commuters": Slider(
+            "Number of Commuters", value=50, min=10, max=150, step=10
+        ),
+        "commuter_speed": Slider(
+            "Commuter Walking Speed (m/s)",
+            value=campus_params[campus]["commuter_speed"],
+            min=0.1,
+            max=1.5,
+            step=0.1,
+        ),
+    }
+    model = AgentsAndNetworks()
+    page = SolaraViz(
+        model,
+        [
+            make_geospace_leaflet(agent_draw, zoom=campus_params[campus]["zoom"]),
+            make_plot_clock,
+            make_plot_measure(["status_home", "status_work", "status_traveling"]),
+            make_plot_measure(["friendship_home", "friendship_work"]),
+        ],
+        name="Agents and Networks",
+        model_params=model_params,
+    )
+
+    page  # noqa

gis/agents_and_networks/requirements.txt

@@ -2,7 +2,7 @@
 -e .
 
 # external requirements
-mesa-geo~=0.7
+mesa-geo~=0.9.0a0
 geopandas
 numpy
 pandas

gis/agents_and_networks/src/visualization/utils.py

@@ -3,6 +3,48 @@
 import matplotlib.pyplot as plt
 import pandas as pd
 import seaborn as sns
+import solara
+
+from ..agent.building import Building
+from ..agent.commuter import Commuter
+from ..agent.geo_agents import Driveway, LakeAndRiver, Walkway
+
+
+def make_plot_clock(model):
+    return solara.Markdown(f"**Day {model.day}, {model.hour:02d}:{model.minute:02d}**")
+
+
+def agent_draw(agent):
+    portrayal = {}
+    portrayal["color"] = "White"
+    if isinstance(agent, Driveway):
+        portrayal["color"] = "#D08004"
+    elif isinstance(agent, Walkway):
+        portrayal["color"] = "Brown"
+    elif isinstance(agent, LakeAndRiver):
+        portrayal["color"] = "#04D0CD"
+    elif isinstance(agent, Building):
+        portrayal["color"] = "Grey"
+        # if agent.function is None:
+        #     portrayal["color"] = "Grey"
+        # elif agent.function == 1.0:
+        #     portrayal["color"] = "Blue"
+        # elif agent.function == 2.0:
+        #     portrayal["color"] = "Green"
+        # else:
+        #     portrayal["color"] = "Grey"
+    elif isinstance(agent, Commuter):
+        if agent.status == "home":
+            portrayal["color"] = "Green"
+        elif agent.status == "work":
+            portrayal["color"] = "Blue"
+        elif agent.status == "transport":
+            portrayal["color"] = "Red"
+        else:
+            portrayal["color"] = "Grey"
+        portrayal["radius"] = "5"
+        portrayal["fillOpacity"] = 1
+    return portrayal
 
 
 def plot_commuter_status_count(model_vars_df: pd.DataFrame) -> None:

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 `▶`.