updated documentation

README.md

@@ -88,8 +88,7 @@ class MyAgent(AgentDF):
     def step(cls):
         wealthy_agents = cls.model.agents.loc[cls.mask, "wealth"] > 0
         if wealthy_agents.any():
-            #The next line finds the mask of a random sample of agents which ù
-            is of the same length as wealthy agents
+            #The next line finds the mask of a random sample of agents which is of the same length as wealthy agents
             other_agents = cls.model.agents.index.isin(
                 cls.model.agents.sample(n=wealthy_agents.sum()).index
             )

mesa_frames/agent.py

@@ -12,7 +12,7 @@ class AgentDF:
     """The AgentDF class is the base class for other agents.
     It should be used as inherited class for new agents classes.
 
-    Attributes:
+    Attributes
     ----------
     dtypes : dict[str, str]
         The attributes of the Agent as a dictionary of columns and data types. It contains:
@@ -62,7 +62,7 @@ class GeoAgentDF(AgentDF):
     """The GeoAgentDF extends the AgentDF class to include a geometry attribute.
     The agents will be stored in a GeoDataFrame.
 
-    Attributes:
+    Attributes
     ----------
     dtypes : dict[str, str]
         The attributes of the Agent as a dictionary of columns and data types. It contains:

mesa_frames/model.py

@@ -10,6 +10,25 @@
 
 
 class ModelDF:
+    """The base class for all models
+
+    Attributes
+    ----------
+    unique_id : int
+        The unique_id of the model.
+    running : bool
+        Indicates if the model is running or not.
+    agents : pd.DataFrame | gpd.GeoDataFrame | None
+        The dataframe containing the agents of the model.
+    agent_types : list[tuple[type[AgentDF], float]] | None
+        The list of agent types and their proportions.
+    p_agents : dict[type[AgentDF], float] | None
+        The dictionary of agents to create. The keys are the types of agents,
+        the values are the percentages of each agent type. The sum of the values should be 1.
+    space
+        The space where the agents will be placed. Can be None if model does not have a space.
+    """
+
     def __new__(cls, *args, **kwargs):
         """Create a new model object and instantiate its RNG automatically
         (adds supports to numpy with respect to base model)."""
@@ -35,22 +54,6 @@ def __init__(self, unique_id: int | None = None, space=None):
             If None, a random unique_id is assigned using a 64-bit random integer.
         space
             The space where the agents will be placed. Can be None if model does not have a space.
-
-        Attributes
-        ----------
-        unique_id : int
-            The unique_id of the model.
-        running : bool
-            Indicates if the model is running or not.
-        agents : pd.DataFrame | gpd.GeoDataFrame | None
-            The dataframe containing the agents of the model.
-        agent_types : list[tuple[type[AgentDF], float]] | None
-            The list of agent types and their proportions.
-        p_agents : dict[type[AgentDF], float] | None
-            The dictionary of agents to create. The keys are the types of agents,
-            the values are the percentages of each agent type. The sum of the values should be 1.
-        space
-            The space where the agents will be placed. Can be None if model does not have a space.
         """
         # Initialize default attributes
         self.running: bool = True
@@ -116,7 +119,7 @@ def step(self, merged_mro: bool = False) -> None:
 
         Parameters
         ----------
-        merged_mro: bool
+        merged_mro : bool
             If False, the model will execute one step for each class in p_agent. This is the default behaviour.
             If True, the model will execute one step for each inherited agent type in the order of a "merged" MRO.
             This may increase performance if there are multiple and complex inheritance as each agent_type (even if parents of different classes),
@@ -136,9 +139,10 @@ def step(self, merged_mro: bool = False) -> None:
     def reset_randomizer(self, seed: int | None = None) -> None:
         """Reset the model random number generator.
 
-        Parameters:
+        Parameters
         ----------
-            seed: A new seed for the RNG; if None, reset using the current seed
+        seed : int | None
+            A new seed for the RNG; if None, reset using the current seed
         """
         if seed is None:
             seed = self._seed
@@ -264,29 +268,6 @@ def create_agents(
 
         print("Created agents: " + "--- %s seconds ---" % (time() - start_time))
 
-    # TODO: implement different data collection frequencies (xw, xd, xh, weekly, daily, hourly, per every step):
-    """def initialize_data_collector(
-        self,
-        model_reporters=None,
-        agent_reporters=None,
-        tables=None,
-    ) -> None:
-        if not hasattr(self, "schedule") or self.schedule is None:
-            raise RuntimeError(
-                "You must initialize the scheduler (self.schedule) before initializing the data collector."
-            )
-        if self.schedule.get_agent_count() == 0:
-            raise RuntimeError(
-                "You must add agents to the scheduler before initializing the data collector."
-            )
-        self.datacollector = DataCollector(
-            model_reporters=model_reporters,
-            agent_reporters=agent_reporters,
-            tables=tables,
-        )
-        # Collect data for the first time during initialization.
-        self.datacollector.collect(self)"""
-
     def _initialize_data_collection(self, how="2d") -> None:
         """Initializes the data collection of the model.
 
@@ -309,3 +290,26 @@ def update_agents_masks(self) -> None:
             )
         for agent_type in self.agent_types:
             agent_type[0].mask = self.get_agents_of_type(agent_type[0])
+
+    # TODO: implement different data collection frequencies (xw, xd, xh, weekly, daily, hourly, per every step):
+    """def initialize_data_collector(
+        self,
+        model_reporters=None,
+        agent_reporters=None,
+        tables=None,
+    ) -> None:
+        if not hasattr(self, "schedule") or self.schedule is None:
+            raise RuntimeError(
+                "You must initialize the scheduler (self.schedule) before initializing the data collector."
+            )
+        if self.schedule.get_agent_count() == 0:
+            raise RuntimeError(
+                "You must add agents to the scheduler before initializing the data collector."
+            )
+        self.datacollector = DataCollector(
+            model_reporters=model_reporters,
+            agent_reporters=agent_reporters,
+            tables=tables,
+        )
+        # Collect data for the first time during initialization.
+        self.datacollector.collect(self)"""