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Emergent ships two module-level helper functions as reference implementations. They are intentionally simple and are intended to illustrate the expected function signatures for initial_data_function and timestep_function. They are not designed for production use.

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genInitialData

genInitialData(model: AgentModel) -> dict
A sample initial data function. Returns a dictionary assigning each node a random integer id between 1 and 100 (inclusive). This function matches the signature required by set_initial_data_function: it accepts the model instance and returns a dictionary that is merged into the node’s attribute store.
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model
AgentModel
required
The model instance. Passed automatically by initialize_graph when this function is registered as the initial data function. The function does not read any data from the model, but the signature is required by the framework.
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return
dict
A dictionary of the form {"id": int} where id is a random integer between 1 and 100.
This is a reference implementation. For real simulations, write your own initial data function that returns the attributes relevant to your model.
from emergent.main import AgentModel, genInitialData

model = AgentModel()
model.set_initial_data_function(genInitialData)
model.initialize_graph()

# Each node now has an "id" attribute set to a random integer 1–100
graph = model.get_graph()
for node, data in graph.nodes(data=True):
    print(node, data["id"])

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genTimestepData

genTimestepData(model: AgentModel, nodeData: dict) -> dict
A sample per-node update function. Increments nodeData["id"] by 1 and returns the updated dictionary.
This function is not automatically called by the framework. It does not match the signature expected by set_timestep_function, which requires a function of the form (model: AgentModel) -> None. To use it, you must wrap it inside a timestep function that iterates over the graph’s nodes manually.
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model
AgentModel
required
The model instance. Not used by this function, but included to reflect a common pattern where per-node helpers receive the model for access to parameters.
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nodeData
dict
required
The current attribute dictionary for a single node. Must contain an "id" key with a numeric value.
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return
dict
The same nodeData dictionary with nodeData["id"] incremented by 1.
from emergent.main import AgentModel, genInitialData, genTimestepData

model = AgentModel()
model.set_initial_data_function(genInitialData)

# Wire genTimestepData into a compliant timestep function
def my_timestep(model):
    graph = model.get_graph()
    for node, data in graph.nodes(data=True):
        updated = genTimestepData(model, data)
        graph.nodes[node].update(updated)

model.set_timestep_function(my_timestep)
model.initialize_graph()
model.timestep()

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