simpy

dated_simulation.py

import simpy
import time
import pandas as pd
import datetime as dt
import numpy as np
import metrics

class DatedEnvironment(simpy.Environment):

    def __init__(self, start_time, end_time, offset='h', initial_time=0):

        self.start_time = start_time
        self.end_time = end_time

        self.offset = offset

        super(DatedEnvironment, self).__init__(initial_time)

    @property
    def offset_td(self):
        return np.timedelta64(1, self.offset)

    @property
    def steps(self):
        """ How man steps between start_time and end_time, measured in the offset span. """
        return int((self.end_time - self.start_time) / self.offset_td)

    def run(self):
        super(DatedEnvironment, self).run(until=self.steps)

    @property
    def now_date(self):
        return self.start_time + (self.now * self.offset_td)

    @property
    def now_ts(self):
        return int(self.now_date.tolist().strftime("%s")) * 1000

def producer(env, store):
    for i in range(env.steps):
        yield env.timeout(1)
        yield store.put('job %s' % i)

def consumer(name, env, store):
    while True:
        yield env.timeout(1)
        item = yield store.get()

def log_store_size(simulation_name, env, store):

    columns = ["simulation_name", "size", "time"]

    while True:
        points = [[simulation_name, len(store.items), env.now_ts]]
        metrics.write_metrics("store", columns, points)
        yield env.timeout(1)

start = np.datetime64('2015-06-01 00:00:00')
end = np.datetime64('2015-07-01 00:00:00')
env = DatedEnvironment(start, end)

store = simpy.Store(env)

prod = env.process(producer(env, store))
consumers = [env.process(consumer(i, env, store)) for i in range(2)]

env.process(log_store_size("sim c", env, store))

env.run()