In [ ]:

Actors' operation¶

In progress

This document is a work in progress if you see any errors, or exclusions or have any problems, please get in touch with us.

Manipulate an individual Actor¶

In [1]:

from abses import Actor, MainModel

# create a testing model
model = MainModel(name="actor_example", base="tests")

# actor creation should be bounded with a model.
actor = model.agents.new(Actor, singleton=True)

# actor has a class-property, -its breed, class's name.
actor.breed  # same as `actor.__class__.__name__`

from abses import Actor, MainModel # create a testing model model = MainModel(name="actor_example", base="tests") # actor creation should be bounded with a model. actor = model.agents.new(Actor, singleton=True) # actor has a class-property, -its breed, class's name. actor.breed # same as `actor.__class__.__name__`

[12:53:02][WARNING][nature] the nature's CRS has been changed to epsg:4326.

Out[1]:

'Actor'

In [2]:

# In an initial condition, this actor is not on the earth...
actor.on_earth

# In an initial condition, this actor is not on the earth... actor.on_earth

Out[2]:

False

Manipulate a group of Actors¶

ABSESpy provides two different container of actors:

• AgentsContainer: A unique dictionary-like class where all agents of the model are saving.
• ActorsList: A list-like class where referring some actors temporally.

AgentsContainer¶

When the model is created, there is a unique AgentsContainer obj bounding. It saves agents by their breeds. In the tutorial above, only one actor is added now.

In [3]:

repr(model.agents)

repr(model.agents)

Out[3]:

'<abses.container._ModelAgentsContainer object at 0x3213c2cd0>'

Container makes creating actors much easier:

In [4]:

# define a new breed of actor
class Seller(Actor):
    name = "seller"


class User(Actor):
    name = "user"


# You'd better to assign the `singleton=True` when only creating one actor.
# Otherwise you would get a list of actors (length = 1).
another_actor = model.agents.new(Actor, singleton=True)
seller = model.agents.new(Seller)  # default creating one actor.
users = model.agents.new(User, 5)  # creating 5 actors

model.agents

# define a new breed of actor class Seller(Actor): name = "seller" class User(Actor): name = "user" # You'd better to assign the `singleton=True` when only creating one actor. # Otherwise you would get a list of actors (length = 1). another_actor = model.agents.new(Actor, singleton=True) seller = model.agents.new(Seller) # default creating one actor. users = model.agents.new(User, 5) # creating 5 actors model.agents

Out[4]:

<abses.container._ModelAgentsContainer at 0x3213c2cd0>

While AgentsContainer mainly provides a way to store, most of manipulations for actors are implemented in another data type: ActorsList. AgentsContainer also has many ways to convert existing agents to this data type:

In [5]:

# select specific breeds.
model.agents.select(agent_type="Seller")

# select specific breeds. model.agents.select(agent_type="Seller")

Out[5]:

<ActorsList: (1)Seller>

In [6]:

model.agents.select(agent_type=["Seller", "User"])

model.agents.select(agent_type=["Seller", "User"])

Out[6]:

<ActorsList: (2)Actor; (1)Seller; (5)User>

ActorsList¶

ActorsList is a collection of Actors, facilitate to manipulate them at a batch.

In [7]:

lst = model.agents.select()
actor = lst[0]  # indexing -> an `Actor`
five_actors = lst[:5]  # slice -> another `ActorsList` object

five_actors  # (2)Actor; (1)Seller; (2)User
# five_actors contains this actor
actor in five_actors

lst = model.agents.select() actor = lst[0] # indexing -> an `Actor` five_actors = lst[:5] # slice -> another `ActorsList` object five_actors # (2)Actor; (1)Seller; (2)User # five_actors contains this actor actor in five_actors

Out[7]:

True

Convert the list to a dictionary: {breed: ActorsList}

In [8]:

# sort up mixed actors.
five_actors.to_dict()

# sort up mixed actors. five_actors.to_dict()

Out[8]:

{'Actor': <ActorsList: (2)Actor>,
 'Seller': <ActorsList: (1)Seller>,
 'User': <ActorsList: (2)User>}

Select by conditions.

In [ ]:

five_actors.to_dict()["User"]  # group then pick the 'User'

# select the first and the fourth actors via explicit collect
sel = [five_actors[0], five_actors[3]]
sel

five_actors.to_dict()["User"] # group then pick the 'User' # select the first and the fourth actors via explicit collect sel = [five_actors[0], five_actors[3]] sel

Out[ ]:

<ActorsList: (1)Actor; (1)User>

Actors' attributes are accessible as np.ndarray.

In [10]:

# get each Actor(object)'s unique id.
five_actors.array("unique_id")

# get each Actor(object)'s unique id. five_actors.array("unique_id")

Out[10]:

array([1, 2, 3, 4, 5])

You can access all actors in this list with id >= 7 by:

In [11]:

five_actors.update(attr="test", values=[1, 2, 3, 4, 5])
better_guys = five_actors.better(metric="test", than=3)
better_guys

five_actors.update(attr="test", values=[1, 2, 3, 4, 5]) better_guys = five_actors.better(metric="test", than=3) better_guys

Out[11]:

<ActorsList: (2)User>

Split the list in the second, third places: [1, 2, | 3, | 4, 5]

In [12]:

five_actors.split([2, 3])

five_actors.split([2, 3])

Out[12]:

[<ActorsList: (2)Actor>, <ActorsList: (1)Seller>, <ActorsList: (2)User>]