How to use aiomas - 10 common examples

To help you get started, we’ve selected a few aiomas examples, based on popular ways it is used in public projects.

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github frnsys / system_designer / tests / test_node.py View on Github external
def test_gets_local_agent(self):
        addr = self.agents[0].addr
        agent_proxy = aiomas.run(self.node.connect(addr))
        self.assertTrue(isinstance(agent_proxy, LocalProxy))
github frnsys / system_designer / tests / test_node.py View on Github external
def test_agent_update(self, mock_method):
        aiomas.run(self.node.update_agents())
        self.assertEquals(mock_method.call_count, 2)
github frnsys / system_designer / tests / test_agent.py View on Github external
def test_multiple_behaviors_called(self):
        class NewAgent(syd.Agent):
            state_vars = ['cash']
            behaviors = [behavior,
                         partial(behavior_with_params, param=2)]

        agent = NewAgent(self.node, {'cash': 100})
        self.assertEquals(agent.state.cash, 100)

        aiomas.run(agent.decide())
        agent.apply_updates()
        self.assertEquals(agent.state.cash, 20)
github frnsys / system_designer / syd / node / node.py View on Github external
import aiomas
import asyncio
import logging
import operator
import itertools
from .proxy import LocalProxy

logger = logging.getLogger(__name__)


class Node(aiomas.Container):
    """a node manages a population of agents"""
    def __init__(self, addr, *args, **kwargs):
        super().__init__(addr, *args, **kwargs)
        self._local_agent_proxies = {}

        # prepare logging for each node
        _, port = addr[:-1].rsplit(':', 1)
        handler = logging.FileHandler('/tmp/sydspawn_{}.log'.format(port))
        handler.setLevel(logging.DEBUG)
        self.logger = logging.getLogger(__name__)
        self.logger.addHandler(handler)
        self.logger.debug('node up at {}'.format(addr))


    @classmethod
    async def start(cls, addr, **container_kwargs):
github frnsys / system_designer / syd / agent / base.py View on Github external
# extract vars required by behaviors
        # can handle partials as well
        all_required_vars = set(sum((
            b.required_vars if not isinstance(b, partial)
            else b.func.required_vars
            for b in behaviors
        ), []))

        missing = [var for var in all_required_vars
                   if var not in state_vars]
        if missing:
            raise MissingVarError('required vars {} are missing in agent `state_vars`'.format(missing))
        return type.__new__(cls, name, parents, dct)


class Agent(aiomas.Agent, metaclass=AgentMeta):
    state_vars = []
    behaviors = []

    def __init__(self, container, state, *args, world_addr=None, **kwargs):
        super().__init__(container)
        self.state = self.State(**state)
        self.world_addr = world_addr
        self._updates = []
        self.init(state, *args, **kwargs)

    def init(self, state, *args, **kwargs):
        """initialization for an agent. you should
        override this method instead of `__init__`."""
        pass

    async def emit(self, name, *args, **kwargs):
github frnsys / system_designer / syd / simulation.py View on Github external
def irun(self, steps, reports=None):
        """run the simulation lazily (as an iterator)"""
        # reports = {name: (fn, mod step)}
        aggs = defaultdict(dict)
        reports = reports or {}
        for name, (fn, mod_step) in reports.items():
            aggs[mod_step][name] = fn

        aiomas.run(self.container.setup_agents())

        for i in range(steps):
            aiomas.run(self._step())

            # TODO any way to make this more efficient?
            report = {}
            states = None
            for mod_step, agg_fns in aggs.items():
                if i % mod_step == 0:
                    for name, fn in agg_fns.items():
                        if states is None:
                            states = self.container.states()
                        report[name] = fn(states)
            yield report
github frnsys / system_designer / syd / node / cluster.py View on Github external
def states(self):
        """iterate over all agent states across the cluster"""
        tasks = [asyncio.ensure_future(manager.states())
                 for manager in self._managers]

        for states in aiomas.run(asyncio.gather(*tasks)):
            yield from states
github frnsys / system_designer / syd / simulation.py View on Github external
def irun(self, steps, reports=None):
        """run the simulation lazily (as an iterator)"""
        # reports = {name: (fn, mod step)}
        aggs = defaultdict(dict)
        reports = reports or {}
        for name, (fn, mod_step) in reports.items():
            aggs[mod_step][name] = fn

        aiomas.run(self.container.setup_agents())

        for i in range(steps):
            aiomas.run(self._step())

            # TODO any way to make this more efficient?
            report = {}
            states = None
            for mod_step, agg_fns in aggs.items():
                if i % mod_step == 0:
                    for name, fn in agg_fns.items():
                        if states is None:
                            states = self.container.states()
                        report[name] = fn(states)
            yield report
github frnsys / system_designer / syd / cli.py View on Github external
def start_node(port):
    """start a node"""
    task = Node.start(('0.0.0.0', port),
                      codec=aiomas.codecs.MsgPackBlosc,
                      extra_serializers=[serializers.get_np_serializer])

    # terminates when the node's manager is given the 'stop' command
    aiomas.run(until=task)
github frnsys / system_designer / syd / node / cluster.py View on Github external
def shutdown(self):
        """shutdown the cluster"""
        tasks = [asyncio.ensure_future(manager.stop())
                 for manager in self._managers]
        aiomas.run(asyncio.gather(*tasks))
        self._container.shutdown()

aiomas

Asyncio-based, layered networking library providing request-reply channels, RPC, and multi-agent systems.

MIT
Latest version published 7 years ago

Package Health Score

42 / 100
Full package analysis