Collect data from one type of agents

[nada gh]

Hi,

I have two agent types, The datacollector will collect data from all agents.

Is there a way to specify an agent type for data collection?

Thanks

[Jeremy Foote]

Hi,

I've done this a couple of ways:

One is to create a list of the agents of each type on the model object, and to write datacollector functions that just access the agents in that list.

The other is to set an instance attribute like 'type' on each agent and then write the datacollector function so that it filters to just the agents with a certain type.

Best,

Jeremy

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[nada gh]

Thanks Jeremy,

 I tried the second option as follows:

self.datacollector = DataCollector(
    model_reporters={"model_var": lambda m: m.model_var},
    agent_reporters={"agent_v1": lambda a: a.agent_v1 if a.__class__.__name__ == 'Type1class' else None})

The type 2 is added to the dataframe with empty fields!

Regards

[Jeremy Foote]

Hi,

How I would do this is just have two agent classes, something like this:

class Type1(Agent):

    def __init__(self):

        self.type = "Type1"

        self.val = 1

class Type2(Agent):

    def __init__(self):

        self.type = "Type2"

        self.val = 2

Then, for the datacollector, you just do a lambda like:

{'type_1_sum': lambda a: a.val if a.type == 'Type1' else None}

Best,

Jeremy

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[nada gh]

Thanks a lot Jeremy!

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[Yaniv Proselkov]

Hi Jeremy,

I'm looking for a similar thing. Could I please ask to know how you do the first method you suggested? I've been trying to implement the second and it gives me a couple of issues.

Many Thanks,

Yaniv

[Jeremy Foote]

Hi Yaniv,

Sure - you'd just do something like this in the Model class for each agent type:

self.agent_type_1 = []

for i in range(num_agent_type_1):

    a = AgentType1()

    self.agent_type_1.append(a)

    self.schedule.add(a)

Then, when you want to collect data, you just make a model datacollector like:

{'type_1_sum': lambda m: sum([a.val for a in m.agent_type_1])}

Jeremy

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[Yaniv Proselkov]

Thank you very much Jeremy