Helper Functions - Emergent

These top-level functions are included as reference implementations and starting points. They are not required — you can always write your own initial data and timestep functions.

from emergent.main import genInitialData, genTimestepData

`genInitialData(model)`

Generates initial data for a single node. Returns a dictionary with a random integer id between 1 and 100. Args:

model (AgentModel) — The model instance.

Returns: dict

{"id": <random int 1–100>}

Example:

from emergent import AgentModel
from emergent.main import genInitialData

model = AgentModel()
model.set_initial_data_function(genInitialData)

`genTimestepData(model, nodeData)`

Generates updated data for a single node by incrementing its id by 1. Args:

model (AgentModel) — The model instance.
nodeData (dict) — The node’s current data dictionary.

Returns: dict — The same nodeData dict with id incremented.

# Before: {"id": 42}
# After:  {"id": 43}

genTimestepData is a per-node function. To use it in a simulation, wrap it in a timestep function that iterates over the graph’s nodes.

Example:

from emergent.main import genTimestepData

def my_timestep(model):
    graph = model.get_graph()
    for node in graph.nodes():
        updated = genTimestepData(model, graph.nodes[node])
        graph.nodes[node].update(updated)

model.set_timestep_function(my_timestep)

​genInitialData(model)

​genTimestepData(model, nodeData)

`genInitialData(model)`

`genTimestepData(model, nodeData)`