[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

Author: pre-commit-ci[bot]

gis/geo_pathogen/README.md

@@ -1,37 +1,37 @@
 # Geographic Disease Spread Model
 
 A fast-spreading pathogen outbreak geo-model with no exposed/latency period.
-The model simulates how quarantine compliance rates at the population level affect outbreak dynamics in a real geo mapped environment, 
+The model simulates how quarantine compliance rates at the population level affect outbreak dynamics in a real geo mapped environment,
 analyzing changes in infection, death and immunity rates here the the dots represent not population directly but sets of highly populated areas.
 
 **It showcases:**
 - **Disease spread on a real geographic map**Disease spread using real geographic proximity(not grid cells) and agent movement is bounded within the Eurasian continent.
-- **Two-threshold quarantine system** - quarantine triggers when infected 
-  count exceeds an upper threshold and lifts only when it drops below a 
-  lower one, preventing quarantine from lifting the moment a single agent 
-  recovers 
-- **Compliance rate** - a configurable fraction of citizens actually follow 
+- **Two-threshold quarantine system** - quarantine triggers when infected
+  count exceeds an upper threshold and lifts only when it drops below a
+  lower one, preventing quarantine from lifting the moment a single agent
+  recovers
+- **Compliance rate** - a configurable fraction of citizens actually follow
   quarantine orders, attempting to simulate real-world partial adherence. The rest
   keep moving freely by not following quarantine instructions.
 - **Geographic flee behavior** — compliant healthy agents moves away from quarantined zones during lockdown.
-- **Infected agents freeze** — simulating a government isolating/quarantining an infected 
+- **Infected agents freeze** — simulating a government isolating/quarantining an infected
   zone. Non-compliant agents ignore this entirely.
 - **Emergence of different outcomes** — combination of different configurations produce dramatically different outbreak curves.
 
 ## How It Works
 
 1. **Initialization** — citizens are placed randomly(more or less realistically) on a geographic map using mesa-geo. A configurable number are set initially as infected.
-2. **Disease Spread** — each step, healthy agents check their neighbours within a certain proximity for infected if its within the set infection radius, if there's an infection there is a configurable chance of transmission. 
+2. **Disease Spread** — each step, healthy agents check their neighbours within a certain proximity for infected if its within the set infection radius, if there's an infection there is a configurable chance of transmission.
    No latency period — infection is immediate on contact,chance of getting an infection is 60%.
-3. **Quarantine System** — the model monitors total infected count each step. 
-   When it exceeds the upper threshold, quarantine activates. It only lifts 
+3. **Quarantine System** — the model monitors total infected count each step.
+   When it exceeds the upper threshold, quarantine activates. It only lifts
    when infected drops below the lower threshold.
 4. **Agent Behaviour during Quarantine:**
    - Compliant agents - flee away from all infected agents.
    - Non-compliant agents - move randomly, ignoring quarantine
    - Infected agents - compliant ones, freeze in place, simulating isolation or a lockdowned zone
-5. **Recovery** — after 10 steps of infection, agents recover to full 
-   immunity or die with the probability of 10%. Dead agents remain 
+5. **Recovery** — after 10 steps of infection, agents recover to full
+   immunity or die with the probability of 10%. Dead agents remain
    on the grid as red circles(this is an intentional mechanic) as a visual indicator to assess how compliance affects the model.
 
 ## Interesting Cases to Observe

gis/geo_pathogen/agents.py

@@ -1,10 +1,10 @@
+import random
+
 import mesa_geo as mg
 from shapely.geometry import Point
-import random
 
 
 class Citizen(mg.GeoAgent):
-
     def __init__(self, model, geometry, crs):
         super().__init__(model, geometry, crs)
 
@@ -13,7 +13,6 @@ def __init__(self, model, geometry, crs):
         self.compliant = random.random() < self.model.compliance_rate
 
     def step(self):
-
         if self.model.quarantine_status and self.compliant:
             self.quarantine()
         else:
@@ -33,7 +32,9 @@ def step(self):
 
         cell_state = "healthy"
 
-        cell_neigh = self.model.space.get_neighbors_within_distance(self, self.model.exposure)
+        cell_neigh = self.model.space.get_neighbors_within_distance(
+            self, self.model.exposure
+        )
 
         for i in cell_neigh:
             if isinstance(i, Citizen) and i.state == "infected":
@@ -45,11 +46,11 @@ def step(self):
                 self.state = "infected"
 
     def quarantine(self):
-       
         if self.state == "dead" or self.state == "infected":
             return
         infected = [
-            a for a in self.model.agents
+            a
+            for a in self.model.agents
             if isinstance(a, Citizen) and a.state == "infected"
         ]
         if not infected:
@@ -58,25 +59,26 @@ def quarantine(self):
         nearest = min(infected, key=lambda a: self.geometry.distance(a.geometry))
         dx = self.geometry.x - nearest.geometry.x
         dy = self.geometry.y - nearest.geometry.y
-        dist = max((dx**2 + dy**2)**0.5, 1)
+        dist = max((dx**2 + dy**2) ** 0.5, 1)
         scale = self.model.mobility_range / dist
-        np = Point(
-            self.geometry.x + dx * scale,
-            self.geometry.y + dy * scale
-        )
+        np = Point(self.geometry.x + dx * scale, self.geometry.y + dy * scale)
         if self.model.asia_boundary.contains(np):
-                self.geometry = np
+            self.geometry = np
 
     def move(self):
         if self.state != "dead":
-
-            change_x = self.random.randint(-self.model.mobility_range, self.model.mobility_range)
-            change_y = self.random.randint(-self.model.mobility_range, self.model.mobility_range)
+            change_x = self.random.randint(
+                -self.model.mobility_range, self.model.mobility_range
+            )
+            change_y = self.random.randint(
+                -self.model.mobility_range, self.model.mobility_range
+            )
             np = Point(self.geometry.x + change_x, self.geometry.y + change_y)
             if self.model.asia_boundary.contains(np):
                 self.geometry = np
 
+
 class CountryAgent(mg.GeoAgent):
     def __init__(self, model, geometry, crs):
         super().__init__(model, geometry, crs)
-        self.atype = "safe"
+        self.atype = "safe"

gis/geo_pathogen/app.py

@@ -1,12 +1,13 @@
+from agents import Citizen
 from mesa.visualization import SolaraViz, make_plot_component
 from mesa_geo.visualization import make_geospace_component
 from model import Geo_Model
-from agents import Citizen
+
 
 def citizen_draw(agent):
     if not isinstance(agent, Citizen):
         return {"color": "Gray", "fillOpacity": 0.1, "weight": 1}
-    
+
     if agent.state == "healthy":
         return {"color": "Green", "radius": 3}
     elif agent.state == "infected":
@@ -16,6 +17,7 @@ def citizen_draw(agent):
     elif agent.state == "dead":
         return {"color": "Black", "radius": 3}
 
+
 model_params = {
     "compliance": {
         "type": "SliderFloat",
@@ -40,7 +42,7 @@ def citizen_draw(agent):
         "min": 1,
         "max": 20,
         "step": 1,
-    }
+    },
 }
 
 model = Geo_Model(n=100, infn=5)