Develop Reference

How to use other def values?

I want to use other def values.
For example, I added a 'pt' in the 'clean_beds_process' definition and add 'Patients' in the 'run' definition.
I want to patient information when the 'clean_beds_process' function is called.
However, this makes this error 'AttributeError: type object 'Patients' has no attribute 'id''
I don't know why this happen.
Maybe I have something wrong understanding of mechanism of simpy.
Please let me know how can I use a patient information when 'clean_beds_process' function is called.
Thank you.
import simpy
import random
class Patients:
def __init__(self, p_id):
self.id = p_id
self.bed_name = ""
self.admission_decision = ""
def admin_decision(self):
admin_decision_prob = random.uniform(0, 1)
if admin_decision_prob <= 0.7:
self.admission_decision = "DIS"
else:
self.dmission_decision = "IU"
return self.admission_decision
class Model:
def __init__(self, run_number):
self.env = simpy.Environment()
self.pt_ed_q = simpy.Store(self.env )
self.pt_counter = 0
self.tg = simpy.Resource(self.env, capacity = 4)
self.physician = simpy.Resource(self.env, capacity = 4)
self.bed_clean = simpy.Store(self.env)
self.bed_dirty = simpy.Store(self.env)
self.IU_bed = simpy.Resource(self.env, capacity = 50)
def generate_beds(self):
for i in range(77):
yield self.env.timeout(0)
yield self.bed_clean.put(f'bed{i}')
def generate_pt_arrivals(self):
while True:
self.pt_counter += 1
pt = Patients(self.pt_counter)
yield self.env.timeout(5)
self.env.process(self.process(pt))
def clean_beds_process(self, cleaner_id, pt):
while True:
print(pt.id)
bed = yield self.bed_dirty.get()
yield self.env.timeout(50)
yield self.bed_clean.put(bed)
def process(self, pt):
with self.tg.request() as req:
yield req
yield self.env.timeout(10)
bed = yield self.bed_clean.get()
pt.bed_name = bed
pt.admin_decision()
if pt.admission_decision == "DIS":
with self.IU_bed.request() as req:
dirty_bed_name = pt.bed_name
yield self.bed_dirty.put(dirty_bed_name)
yield self.env.timeout(600)
else:
dirty_bed_name = pt.bed_name
yield self.bed_dirty.put(dirty_bed_name)
def run(self):
self.env.process(self.generate_pt_arrivals())
self.env.process(self.generate_beds())
for i in range(2):
self.env.process(self.clean_beds_process(i+1, Patients))
self.env.run(until = 650)
run_model = Model(0)
run_model.run()

So if a patient can use either a clean bed or a dirty bed then the patient needs to make two request (one for each type of bed) and use env.any_of to wait for the first request to fire. You also need to deal with the case that both events fire at the same time. Don't forget to cancel the request you do not use. If the request that fires is for a clean bed, things stay mostly the same. But if the request is for a dirty bed, then you need to add a step to clean the bed. For this I would make the cleaners Resources instead of processes. So the patient would request a cleaner, and do a timeout for the cleaning time, release the cleaner. To collect patient data I would create a log with the patient id, key event, time, and crunch these post sim to get the stats I need. To process the log I often create a dataframe that filters the log for the first, a second dataframe that filters for the second envent, join the two dataframes on patient id. Now both events for a patient is on one row so I can get the delta. once I have have the delta I can do a sum and count. For example, if my two events are when a patient arrives, and when a patient gets a bed, get the sum of deltas and divide by the count and I have the average time to bed.
If you remember, one of the first answers I gave you awhile ago had a example to get the first available bed from two different queues
I do not have a lot of time right know, but I hope this dissertation helps a bit

In Simpy simulation, how can I escape from infinite loop

I have an endless loop problem in Simpy simulation.
My scenario is:
After finishing triage, a patient wait until getting empty bed.
However, if there is no empty bed, the patient should wait until an empty bed is available.
When a patient uses a bed and leaves the hospital, the bed turns into a dirty bed.
The dirty bed turns into an empty bed when the cleaner cleans it.
Bed cleaning work start after getting clean request.
I used "Store" for managing beds
I think I have an infinite loop when there is no empty bed and no dirty bed.
I think...
Add a queue (After triage)
From the queue, assign a bed based on the FIFO.
If we don't have empty or dirty bed, we should wait until a dirty bed is available.
But, I don't know how to implement this idea.
Please help me to solve this problem.
import simpy
import random
class Pre_Define:
warmup_period = 1440
sim_duration = 14400
number_of_runs = 3
class Patients:
def __init__(self, p_id):
self.id = p_id
self.bed_name = ""
self.admission_decision = ""
def admin_decision(self):
admin_decision_prob = random.uniform(0, 1)
if admin_decision_prob <= 0.7:
self.admission_decision = "DIS"
class Model:
def __init__(self, run_number):
self.env = simpy.Environment()
self.pt_counter = 0
self.tg = simpy.Resource(self.env, capacity = 4)
self.physician = simpy.Resource(self.env, capacity = 4)
self.bed_clean = simpy.Store(self.env, capacity = 77)
self.bed_dirty = simpy.Store(self.env, capacity = 77)
self.IU_bed = simpy.Resource(self.env, capacity = 50)
self.bed_cleaner = simpy.Resource(self.env, capacity = 2)
self.run_number = run_number
def generate_beds(self):
for i in range(77):
yield self.env.timeout(0)
yield self.bed_clean.put(f'bed{i}')
print(self.bed_clean.items)
def generate_pt_arrivals(self):
while True:
self.pt_counter += 1
pt = Patients(self.pt_counter)
yield self.env.timeout(1/7)
self.env.process(self.Process(pt))
def Process(self, Patients):
with self.tg.request() as req:
yield req
triage_service_time = random.expovariate(1.0/18)
yield self.env.timeout(triage_service_time)
if self.bed_clean.items != []:
get_empty_bed_name = yield self.bed_clean.get()
Patients.bed_name = get_empty_bed_name
elif self.bed_dirty.items != []:
get_dirty_bed_name = yield self.bed_dirty.get()
with self.bed_cleaner.request() as req:
yield req
yield self.env.timeout(50)
else:
print("NO BED, Should Wait!!")
no_bed = True
while no_bed:
#print("Waiting dirty bed")
if self.bed_dirty.items != []:
get_dirty_bed_name = yield self.bed_dirty.get()
print("Find dirty bed!")
with self.bed_cleaner.request() as req:
yield req
yield self.env.timeout(30)
Patients.bed_name = get_dirty_bed_name
no_bed = False
with self.physician.request() as req:
yield req
yield self.env.timeout(10)
Patients.admin_decision()
if Patients.admission_decision == "DIS":
with self.IU_bed.request() as req:
yield req
yield self.env.timeout(600)
get_dirty_bed_name = Patients.bed_name
yield self.bed_dirty.put(get_dirty_bed_name)
else:
get_dirty_bed_name = Patients.bed_name
yield self.bed_dirty.put(get_dirty_bed_name)
def run(self):
self.env.process(self.generate_pt_arrivals())
self.env.process(self.generate_beds())
self.env.run(until = Pre_Define.warmup_period + Pre_Define.sim_duration)
for run in range(Pre_Define.number_of_runs):
run_model = Model(run)
run_model.run()
print()

So you got the right idea where you use two Stores to track dirty and clean beds. The trick is to request both a clean and a dirty bed at the same time, and discard the request you do not use.
So the big changes I made was to request both a clean bed and a dirty bed and used env.any_of() to get me the first filled request. Note that both requests could get filled. Since I made two requests, that means I need to either cancel the request I do not use, or if filled, return the unused bed back to its queue. The other thing I did was to make a separate process for the cleaners. This means that the both the patients and the cleaners will be competing for dirty beds.
"""
Quick sim model of beds in a hosptial triage
beds have two states, clean and dirty
When a patient arrives they are assigned to a empty bed
If no beds are avail then the patient wait for first empty bed
If there is both a clean bed and a empty bed, the clean bed will be assigned
When the patient leaves triage the bed state is dirty.
empty dirty beads are queued to be clean, after cleaning bed state is clean
After being triaged, a patient is either admited or discharged.
If the patient is admitted, then they wait for a IU bed befor the triage bed is empty
Programmer: Michael R. Gibbs
"""
import simpy
import random
TRIAGE_TIME = 30
BED_CLEAN_TIME = 20
class Patient():
"""
has id to track patient progress
and bed when assigned to a bed
"""
next_id = 1
#classmethod
def get_next_id(cls):
id = cls.next_id
cls.next_id += 1
return id
def __init__(self):
self.id = self.get_next_id()
self.bed = None
class Bed():
"""
has id to track patient progress
and bed when assigned to a bed
"""
next_id = 1
#classmethod
def get_next_id(cls):
id = cls.next_id
cls.next_id += 1
return id
def __init__(self):
self.id = self.get_next_id()
self.bed_state = 'Clean'
def clean_beds(env, dirty_bed_q, clean_bed_q):
"""
Sim process for cleaning dirty beds from
the dirty queue, and putting the clean
beds into clean queue
A instance shoud be started for each cleaner
"""
while True:
bed = yield dirty_bed_q.get()
print(f'{env.now:.2f} bed {bed.id} is being cleaned')
# clean
yield env.timeout(BED_CLEAN_TIME)
bed.bed_state = "Clean"
clean_bed_q.put(bed)
print(f'{env.now:.2f} bed {bed.id} is clean')
def triage(env, pat, clean_bed_q, dirty_bed_q, ui_bed_q):
"""
models the patients life cycle in triage
stepss are:
get bed (clean perfered)
get triaged
leave
if addmited leaving is blocked until a ui bed is found
"""
# get bed
clean_req = clean_bed_q.get()
dirty_req = dirty_bed_q.get()
print(f'{env.now:.2f} patient {pat.id} has arrived')
fired = yield env.any_of([clean_req, dirty_req])
# see if we got a clean or dirty or both
if clean_req in fired:
# got a clean bead
pat.bed = fired[clean_req]
# need to deal with the dirty req
if dirty_req in fired:
# got two beds but dirty back
dirty_bed_q.put(fired[dirty_req])
else:
# stil need to cancel req
dirty_req.cancel()
else:
# we have a dirty bed
pat.bed = fired[dirty_req]
# need to deal with the dirty req
if clean_req in fired:
# got two beds but dirty back
clean_bed_q.put(fired[clean_req])
else:
# stil need to cancel req
clean_req.cancel()
print(f'{env.now:.2f} {pat.bed.bed_state} bed {pat.bed.id} is occupied and dirty with patient {pat.id}')
pat.bed.bed_state = 'Dirty'
# triage
yield env.timeout(TRIAGE_TIME)
admit = (random.uniform(0,1) < 0.7)
if admit:
# need to get a IU bed before
# patient gives up triage bed
print(f'{env.now:.2f} patient {pat.id} is being admitted')
ui_req = ui_bed_q.request()
yield ui_req
print(f'{env.now:.2f} patient {pat.id} got iu bed')
# patient leaves triage
# give up dirty bed
dirty_bed_q.put(pat.bed)
print(f'{env.now:.2f} bed {pat.bed.id} is empty and patient {pat.id} has left')
def gen_pats(env, clean_bed_q, dirty_bed_q, iu_bed_q):
"""
creates the arriving patients
"""
while True:
yield env.timeout(random.uniform(1,8))
pat = Patient()
# not each patient gets their own process
# so if one patient blocks while waiting for a iu bed, it does
# not block the other patients
env.process(triage(env, pat, clean_bed_q, dirty_bed_q, iu_bed_q))
def model():
env = simpy.Environment()
# make queues
clean_bed_q = simpy.Store(env)
dirty_bed_q = simpy.Store(env)
iu_bed_q = simpy.Resource(env,capacity=5)
clean_bed_q.items = [Bed() for _ in range(10)]
# start generating patients
env.process(gen_pats(env, clean_bed_q, dirty_bed_q, iu_bed_q))
# star up cleaners
for _ in range(2):
env.process(clean_beds(env, dirty_bed_q, clean_bed_q))
env.run(until=100)
print('simulation finish')
model()

Handling multiple objects in pytransitions

https://github.com/pytransitions/transitions
I was trying with the batman = NarcolepticSuperhero("Batman") example in the documentation.
But let's say if we have 100 batmans to handle, My approach is to create a list of every batman object and track their states individually using a loop.
IMO this is very naive.
Can some-one please suggest some effective solution.
Thanks

Assuming all NarcolepticSuperheros should behave the same, I recommend to use one Machine for all 'batmen' instead of instantiating NarcolepticSuperhero a hundred times. This way, you can use Machine.dispatch to trigger an event on ALL models instead of looping through a list of superheroes (under the hood Machine also just loops through the model list though). I condensed the mentioned example a bit (all transitions in one list), removed the machine that was bound to NarcolepticSuperheros and introduced a SuperheroDen. Concerning state checking, you could use a dictionary to keep track of how many heroes are currently in what state. I added a hero_log to NarcolepticSuperhero and every time a hero changes its state, it will first log out of its current state and then log in with its new state.
from transitions import Machine
from collections import defaultdict
import random
class NarcolepticSuperhero(object):
hero_log = defaultdict(list)
def __init__(self, name):
self.name = name
self.kittens_rescued = 0
def update_journal(self):
self.kittens_rescued += 1
#property
def is_exhausted(self):
return random.random() < 0.5
#staticmethod
def change_into_super_secret_costume():
print("Beauty, eh?")
def log_out(self):
self.hero_log[self.state].remove(self)
def log_in(self):
self.hero_log[self.state].append(self)
class SuperheroDen(Machine):
states = ['asleep', 'hanging out', 'hungry', 'sweaty', 'saving the world']
transitions = [
['wake_up', 'asleep', 'hanging out'],
['work_out', 'hanging out', 'hungry'],
['eat', 'hungry', 'hanging out'],
['nap', '*', 'asleep'],
dict(trigger='distress_call', source='*', dest='saving the world', before='change_into_super_secret_costume'),
dict(trigger='complete_mission', source='saving the world', dest='sweaty', after='update_journal'),
dict(trigger='clean_up', source='sweaty', dest='asleep', conditions=['is_exhausted']),
['clean_up', 'sweaty', 'hanging out'],
]
def __init__(self):
super().__init__(model=[NarcolepticSuperhero('Clone warrior') for i in range(100)],
states=self.states,
transitions=self.transitions,
# tell each model to 'log_out' right before state change
before_state_change='log_out',
# and to 'log_in' right after
after_state_change='log_in',
initial='asleep')
# since our super heroes are asleep (and 'spawn' in their state), we have to log them in the first time
for model in self.models:
NarcolepticSuperhero.hero_log[self.initial].append(model)
machine = SuperheroDen()
# trigger event 'wake_up' on all models
machine.dispatch('wake_up')
assert len(NarcolepticSuperhero.hero_log['asleep']) == 0
assert len(NarcolepticSuperhero.hero_log['hanging out']) == 100
for i in range(10):
machine.models[i].work_out()
assert len(NarcolepticSuperhero.hero_log['hanging out']) == 90
assert len(NarcolepticSuperhero.hero_log['hungry']) == 10
assert machine.models[0] in NarcolepticSuperhero.hero_log['hungry']

What you suggest sounds reasonable to me.
heros = [NarcolepticSuperhero(f"Batman{i:02d}")
for i in range(100)]
Then either loop over all of them,
or use a while True: loop that sleeps a moment
then picks a hero at random.
Either way, you will have a hero
that you can trigger some new transition on.

Creating as many events as needed in Simpy simulation

I am working on a python simulation using Simpy but I am having somewhat of a brick wall. The simulation has an arrival process and a shipping process. The goal is to ship as many orders as they arrive and not leave orders waiting. This code gives me a process that ships more orders than they arrive.
class LC:
def __init__(self,env):
self.dispatch=simpy.Container(env,capacity=100000, init=0)
self.costs=simpy.Container(env, capacity=100000, init=0)
self.shipment_count=simpy.Container(env,capacity=10000, init=0)
self.orders=simpy.Container(env,capacity=10000,init=0)
def shipping(env, ship):
while True:
#yield env.timeout(i)
print('Shipment was sent at %d' % (env.now))
time,weight=get_weight_n_time() ####other function that assigns weight and time of shipment
if weight <=35:
cost=get_cost(weight,0)###other function that calculates costs
else:
cost=get_cost(weight,1)
yield env.timeout(time)
print ('Shipment of %d kgs where delivered after %d days with cost of $ %d' %(weight,time,cost))
yield ship.dispatch.put(weight)
yield ship.orders.get(1)
yield ship.costs.put(cost)
yield ship.shipment_count.put(1)
def arrival (env,ship):
while True:
print('Order arrived at %d'%(env.now))
yield ship.orders.put(1)
yield env.timeout(1)
def shipment_gen(env,ship):
env.process(arrival(env,ship))
num_trucks=5
for i in range(num_trucks):
env.process(shipping(env,ship))
yield env.timeout(0)
env = simpy.Environment()
ship=LC(env)
env.process(shipment_gen(env,ship))
env.run(until=100)
when I add a variable to determine weather or not to make a shipment based in the # of orders then the shipment process does not occur
class LC:
def __init__(self,env):
self.dispatch=simpy.Container(env,capacity=100000, init=0)
self.costs=simpy.Container(env, capacity=100000, init=0)
self.shipment_count=simpy.Container(env,capacity=10000, init=0)
self.orders=simpy.Container(env,capacity=10000,init=0)
self.order=0
def shipping(env, ship):
while True:
#yield env.timeout(i)
print('Shipment was sent at %d' % (env.now))
time,weight=get_weight_n_time()
if weight <=35:
cost=get_cost(weight,0)
else:
cost=get_cost(weight,1)
yield env.timeout(time)
print ('Shipment of %d kgs where delivered after %d days with cost of $ %d' %(weight,time,cost))
yield ship.dispatch.put(weight)
yield ship.orders.get(1)
yield ship.costs.put(cost)
yield ship.shipment_count.put(1)
def arrival (env,ship):
while True:
print('Order arrived at %d'%(env.now))
yield ship.orders.put(1)
yield env.timeout(1)
ship.order+=1
def shipment_gen(env,ship):
env.process(arrival(env,ship))
# if ship.orders.level >0:
# num_trucks=get_number_trucks(ship)
# else:
# num_trucks=get_number_trucks(ship)
num_trucks=5
for i in range(num_trucks):
if ship.order>0:
env.process(shipping(env,ship))
yield env.timeout(0)
ship.order-=1
else:
print('-')
env = simpy.Environment()
ship=LC(env)
env.process(shipment_gen(env,ship))
env.run(until=100)

You do not need to add extra ship.order in the class. The container has the information about the number of orders(ship.orders.level).So delete the ship.order and delete the loop in shipment_gen ,your code will be executed normally. By using of multi-thread,the multi-process is realized by Simpy. If you add extra variable in the ship class,it may lead to errors.

do you want to move your
yield ship.orders.get(1)
line to the top of the function so your truck waits for a order to arrive before it does anything else?
also
shipment_gen is doing its if statement at time 0, but your order counter is not =+1 till time 1
so at time 0 your order counter is still 0 so your if will always do the else

Issue with sharing data between Python processes with multiprocessing

I've seen several posts about this, so I know it is fairly straightforward to do, but I seem to be coming up short. I'm not sure if I need to create a worker pool, or use the Queue class. Basically, I want to be able to create several processes that each act autonomously (which is why they inherit from the Agent superclass).
At random ticks of my main loop I want to update each Agent. I'm using time.sleep with different values in the main loop and the Agent's run loop to simulate different processor speeds.
Here is my Agent superclass:
# Generic class to handle mpc of each agent
class Agent(mpc.Process):
# initialize agent parameters
def __init__(self,):
# init mpc
mpc.Process.__init__(self)
self.exit = mpc.Event()
# an agent's main loop...generally should be overridden
def run(self):
while not self.exit.is_set():
pass
print "You exited!"
# safely shutdown an agent
def shutdown(self):
print "Shutdown initiated"
self.exit.set()
# safely communicate values to this agent
def communicate(self,value):
print value
A specific agent's subclass (simulating an HVAC system):
class HVAC(Agent):
def __init__(self, dt=70, dh=50.0):
super(Agent, self).__init__()
self.exit = mpc.Event()
self.__pref_heating = True
self.__pref_cooling = True
self.__desired_temperature = dt
self.__desired_humidity = dh
self.__meas_temperature = 0
self.__meas_humidity = 0.0
self.__hvac_status = "" # heating, cooling, off
self.start()
def run(self): # handle AC or heater on
while not self.exit.is_set():
ctemp = self.measureTemp()
chum = self.measureHumidity()
if (ctemp < self.__desired_temperature):
self.__hvac_status = 'heating'
self.__meas_temperature += 1
elif (ctemp > self.__desired_temperature):
self.__hvac_status = 'cooling'
self.__meas_temperature += 1
else:
self.__hvac_status = 'off'
print self.__hvac_status, self.__meas_temperature
time.sleep(0.5)
print "HVAC EXITED"
def measureTemp(self):
return self.__meas_temperature
def measureHumidity(self):
return self.__meas_humidity
def communicate(self,updates):
self.__meas_temperature = updates['temp']
self.__meas_humidity = updates['humidity']
print "Measured [%d] [%f]" % (self.__meas_temperature,self.__meas_humidity)
And my main loop:
if __name__ == "__main__":
print "Initializing subsystems"
agents = {}
agents['HVAC'] = HVAC()
# Run simulation
timestep = 0
while timestep < args.timesteps:
print "Timestep %d" % timestep
if timestep % 10 == 0:
curr_temp = random.randrange(68,72)
curr_humidity = random.uniform(40.0,60.0)
agents['HVAC'].communicate({'temp':curr_temp, 'humidity':curr_humidity})
time.sleep(1)
timestep += 1
agents['HVAC'].shutdown()
print "HVAC process state: %d" % agents['HVAC'].is_alive()
So the issue is that, whenever I run agents['HVAC'].communicate(x) within the main loop, I can see the value being passed into the HVAC subclass in its run loop (so it prints the received value correctly). However, the value never is successfully stored.
So typical output looks like this:
Initializing subsystems
Timestep 0
Measured [68] [56.948675]
heating 1
heating 2
Timestep 1
heating 3
heating 4
Timestep 2
heating 5
heating 6
When in reality, as soon as Measured [68] appears, the internal stored value should be updated to output 68 (not heating 1, heating 2, etc.). So effectively, the HVAC's self.__meas_temperature is not being properly updated.
Edit: After a bit of research, I realized that I didn't necessarily understand what is happening behind the scenes. Each subprocess operates with its own virtual chunk of memory and is completely abstracted away from any data being shared this way, so passing the value in isn't going to work. My new issue is that I'm not necessarily sure how to share a global value with multiple processes.
I was looking at the Queue or JoinableQueue packages, but I'm not necessarily sure how to pass a Queue into the type of superclass setup that I have (especially with the mpc.Process.__init__(self) call).
A side concern would be if I can have multiple agents reading values out of the queue without pulling it out of the queue? For instance, if I wanted to share a temperature value with multiple agents, would a Queue work for this?
Pipe v Queue

Here's a suggested solution assuming that you want the following:
a centralized manager / main process which controls lifetimes of the workers
worker processes to do something self-contained and then report results to the manager and other processes
Before I show it though, for the record I want to say that in general unless you are CPU bound multiprocessing is not really the right fit, mainly because of the added complexity, and you'd probably be better of using a different high-level asynchronous framework. Also, you should use python 3, it's so much better!
That said, multiprocessing.Manager, makes this pretty easy to do using multiprocessing. I've done this in python 3 but I don't think anything shouldn't "just work" in python 2, but I haven't checked.
from ctypes import c_bool
from multiprocessing import Manager, Process, Array, Value
from pprint import pprint
from time import sleep, time
class Agent(Process):
def __init__(self, name, shared_dictionary, delay=0.5):
"""My take on your Agent.
Key difference is that I've commonized the run-loop and used
a shared value to signal when to stop, to demonstrate it.
"""
super(Agent, self).__init__()
self.name = name
# This is going to be how we communicate between processes.
self.shared_dictionary = shared_dictionary
# Create a silo for us to use.
shared_dictionary[name] = []
self.should_stop = Value(c_bool, False)
# Primarily for testing purposes, and for simulating
# slower agents.
self.delay = delay
def get_next_results(self):
# In the real world I'd use abc.ABCMeta as the metaclass to do
# this properly.
raise RuntimeError('Subclasses must implement this')
def run(self):
ii = 0
while not self.should_stop.value:
ii += 1
# debugging / monitoring
print('%s %s run loop execution %d' % (
type(self).__name__, self.name, ii))
next_results = self.get_next_results()
# Add the results, along with a timestamp.
self.shared_dictionary[self.name] += [(time(), next_results)]
sleep(self.delay)
def stop(self):
self.should_stop.value = True
print('%s %s stopped' % (type(self).__name__, self.name))
class HVACAgent(Agent):
def get_next_results(self):
# This is where you do your work, but for the sake of
# the example just return a constant dictionary.
return {'temperature': 5, 'pressure': 7, 'humidity': 9}
class DumbReadingAgent(Agent):
"""A dumb agent to demonstrate workers reading other worker values."""
def get_next_results(self):
# get hvac 1 results:
hvac1_results = self.shared_dictionary.get('hvac 1')
if hvac1_results is None:
return None
return hvac1_results[-1][1]['temperature']
# Script starts.
results = {}
# The "with" ensures we terminate the manager at the end.
with Manager() as manager:
# the manager is a subprocess in its own right. We can ask
# it to manage a dictionary (or other python types) for us
# to be shared among the other children.
shared_info = manager.dict()
hvac_agent1 = HVACAgent('hvac 1', shared_info)
hvac_agent2 = HVACAgent('hvac 2', shared_info, delay=0.1)
dumb_agent = DumbReadingAgent('dumb hvac1 reader', shared_info)
agents = (hvac_agent1, hvac_agent2, dumb_agent)
list(map(lambda a: a.start(), agents))
sleep(1)
list(map(lambda a: a.stop(), agents))
list(map(lambda a: a.join(), agents))
# Not quite sure what happens to the shared dictionary after
# the manager dies, so for safety make a local copy.
results = dict(shared_info)
pprint(results)