I have a long code (430 lines) which is used to simulate an energy market following specific guidelines.
Four different processes: Home, Market, Weather, External.
Each process has a specific task listed below:
Home has an production and consumption float value, a trade policy as an integer and calculates energy exchanges between each home (multiple home processes are created for the simulation).
Market calculates the current energy price based on the production and consumption and external factors.
Weather determines random variables of temperature and season to be used in Market.
External is a child process of Market and provides random external factors I have created.
I have an issue in my code where I create a new thread to display the results of each day of the simulation (days pass every 2 seconds) but I feel my code doesn't launch the thread properly and I'm quite lost as to where the issue is occuring exactly and why. I have used various print(".") to show where the program goes and identify where it doesn't and I can't see why the thread doesn't launch properly.
I am on Windows and not Linux. If this could be the issues, please tell me. I will show a code snippet below of where the issue seems to be and the full code as well as a pdf explaining in more detail how the project should run in a github link (430 lines of code doesn't seem reasonable to post here).
def terminal(number_of_homes, market_queue, home_counter, clock_ready, energy_exchange_queue, console_connection, console_connection_2):
day = 1
while clock_ready.wait(1.5 * delay):
req1, req2, req3, req4 = ([] for i in range(4))
for i in range(number_of_homes):
a = market_queue.get()
req1.append(a)
req1 = sort(req1)
for i in range(number_of_homes):
a = market_queue.get()
req1.append(a)
req2 = sort(req2)
for i in range(number_of_homes):
a = market_queue.get()
req1.append(a)
req3 = sort(req3)
req1 = req1 + req2 + req3
for i in range(energy_exchange_queue.qsize()):
b = energy_exchange_queue.get()
req4.append(b)
req4 = sort(req4)
thread = threading.Thread(target = console_display, args = (number_of_homes, req1, day, req4, console_connection.recv(), console_connection_2.recv()))
thread.start()
thread.join()
day += 1
Github link: https://github.com/MaxMichel2/Energy-Market-Project
Related
How can I change the take profit or stop loss of an order already created via ccxt python in Binance futures?
I would like an already created order to be able to change the stop loss, as if I did it from the binance web cli, there is some way, I create my order like this
exchange.create_order(symbol=par, type='limit', side=side, price = precio, amount= monto, params={})
When detecting a certain pattern I would like to update the SL and TP, it's possible?
I have not found information in the ccxt wiki
There is an edit_order function that you may want to try.
import ccxt
exchange = ccxt.binanceusdm()
exchange.apiKey = 'YOUR_API_KEY'
exchange.secret = 'YOUR_API_SECRET'
symbol = 'BTC/USDT'
order_id = 'ORDER_ID'
order_type = 'limit'
side = 'buy'
amount = 0.001
price = 16000
stop_loss = 15000
take_profit = 17000
exchange.edit_order(order_id, symbol, order_type, side, amount, price, {'stopLossPrice': stop_loss, 'takeProfitPrice': take_profit})
I am using flask, and have a route that sends emails to people. I am using threading to send them faster. When i run it on my local machine it takes about 12 seconds to send 300 emails. But when I run it on lambda thorough API Gateway it times out.
here's my code:
import threading
def async_mail(app, msg):
with app.app_context():
mail.send(msg)
def mass_mail_sender(order, user, header):
html = render_template('emails/pickup_mail.html', bruger_order=order.ordre, produkt=order.produkt)
msg = Message(recipients=[user],
sender=('Sender', 'infor#example.com'),
html=html,
subject=header)
thread = threading.Thread(target=async_mail, args=[create_app(), msg])
thread.start()
return thread
#admin.route('/lager/<url_id>/opdater', methods=['POST'])
def update_stock(url_id):
start = time.time()
if current_user.navn != 'Admin':
abort(403)
if request.method == 'POST':
produkt = Produkt.query.filter_by(url_id=url_id)
nyt_antal = int(request.form['bestilt_hjem'])
produkt.balance = nyt_antal
produkt.bestilt_hjem = nyt_antal
db.session.commit()
orders = OrdreBog.query.filter(OrdreBog.produkt.has(func.lower(Produkt.url_id == url_id))) \
.filter(OrdreBog.produkt_status == 'Ikke klar').all()
threads = []
for order in orders:
if order.antal <= nyt_antal:
nyt_antal -= order.antal
new_thread = mass_mail_sender(order, order.ordre.bruger.email, f'Din bog {order.produkt.titel} er klar til afhentning')
threads.append(new_thread)
order.produkt_status = 'Klar til afhentning'
db.session.commit()
for thread in threads:
try:
thread.join()
except Exception:
pass
end = time.time()
print(end - start)
return 'Emails sendt'
return ''
AWS lambda functions designed to run functions within these constraints:
Memory– The amount of memory available to the function during execution. Choose an amount between 128 MB and 3,008 MB in 64-MB increments.
Lambda allocates CPU power linearly in proportion to the amount of memory configured. At 1,792 MB, a function has the equivalent of one full vCPU (one vCPU-second of credits per second).
Timeout – The amount of time that Lambda allows a function to run before stopping it. The default is 3 seconds. The maximum allowed value is 900 seconds.
To put this in your mail sending multi threaded python code. The function will terminate automatically either when your function execution completes successfully or it reaches configured timeout.
I understand you want single python function to send n number of emails "concurrently". To achieve this with lambda try the "Concurrency" setting and trigger your lambda function through a local script, S3 hosted html/js triggered by cloud watch or EC2 instance.
Concurrency – Reserve concurrency for a function to set the maximum number of simultaneous executions for a function. Provision concurrency to ensure that a function can scale without fluctuations in latency.
https://docs.aws.amazon.com/lambda/latest/dg/configuration-console.html
Important: All above settings will affect your lambda function execution cost significantly. So plan and compare before applying.
If you need any more help, let me know.
Thank you.
I have the following function from a colleague who was previously working for the company and the comments are self explanatory, the problem is I'm right now using windows, and there issues with the synchornization with the device.
Would someone address or know a solution in windows for sync with a device ?
def sync_time(self):
"""Sync time on SmartScan."""
# On a SmartScan time can be set only by the precision of seconds
# So we need to wait for the next full second until we can send
# the packet on it's way to the scanner.
# It's not perfect, but the error should be more or less constant.
message = Maint()
message.state = message.OP_NO_CHANGE
now = datetime.datetime.utcnow()
epoch = datetime.datetime(1970, 1, 1)
# int and datetime objects
seconds = int((now - epoch).total_seconds()) + 1 # + sync second
utctime = datetime.datetime.utcfromtimestamp(seconds)
# wait until next full second
# works only on Linux with good accuracy
# Windows needs another approach
time.sleep((utctime - datetime.datetime.utcnow()).total_seconds())
command = MaintRfc()
command.command = command.SET_CLOCK
command.data = (seconds, )
message.add_message(command)
self._handler.sendto(message)
LOG.debug("Time set to: %d = %s", seconds, utctime)
I am running grinder to test a POST URI with 10 different json bodies. The response times given by grinder are not proper.Individual json body tests are giving a reasonable response time though 10 the script with with 10 different json bodies is giving a very high response time and a very low tps. I am using 1 agent with 5 worker processes and 15 threads.Can someone help me figure out where the problem might be?
The script I am using are :-
`from net.grinder.script.Grinder import grinder
from net.grinder.script import Test
from net.grinder.plugin.http import HTTPRequest
from HTTPClient import NVPair
from java.io import FileInputStream
test1 = Test(1, "Request resource")
request1 = HTTPRequest()
#response1 = HTTPResponse()
test1.record(request1)
log = grinder.logger.info
class TestRunner:
def __call__(self):
#request1.setDataFromFile("ReqBody.txt")
payload1 = FileInputStream("/home/ubuntu/grinder-3.11/scripts/Req.txt")
payload2 = FileInputStream("/home/ubuntu/grinder-3.11/scripts/Req2.txt")
payload3 = FileInputStream("/home/ubuntu/grinder-3.11/scripts/Req3.txt")
payload4 = FileInputStream("/home/ubuntu/grinder-3.11/scripts/Req4.txt")
payload5 = FileInputStream("/home/ubuntu/grinder-3.11/scripts/Req5.txt")
payload6 = FileInputStream("/home/ubuntu/grinder-3.11/scripts/Req6.txt")
payload7 = FileInputStream("/home/ubuntu/grinder-3.11/scripts/Req7.txt")
payload8 = FileInputStream("/home/ubuntu/grinder-3.11/scripts/Req8.txt")
payload9 = FileInputStream("/home/ubuntu/grinder-3.11/scripts/Req9.txt")
payload10 = FileInputStream("/home/ubuntu/grinder-3.11/scripts/Req10.txt")
headersPost = [NVPair('Content-Type', ' application/json')]
#request1.setHeaders(headersPost)
#request1.setHeaders
myload = [payload1, payload2, payload3, payload4, payload5, payload6, payload7, payload8, payload9, payload10]
for f in myload:
result1 = request1.POST("http://XX.XX.XX.XX:8080/api/USblocks/ORG101/1/N/",f,headersPost)
log(result1.toString())`
First step for you is to run it with 1 thread 1 process and 1 agent . I hope it will run properly.
It looks since for loop is used all the scripts are going to run for each and every thread.
I think what you want /what should be done is that each thread should be sending one request .
You can move the request out to a global method and take some random number like Grinder.threadNo and use it to return the script to be executed . This then also demands that you remove the for loop since you can control the number of times the script is executed or leave it running for a duration .
Running 10 threads in parallel is a good number to start with then slowly you can see how many requests are actually getting accepted.
I would like to use the library threads (or perhaps parallel) for loading/preprocessing data into a queue but I am not entirely sure how it works. In summary;
Load data (tensors), pre-process tensors (this takes time, hence why I am here) and put them in a queue. I would like to have as many threads as possible doing this so that the model is not waiting or not waiting for long.
For the tensor at the top of the queue, extract it and forward it through the model and remove it from the queue.
I don't really understand the example in https://github.com/torch/threads enough. A hint or example as to where I would load data into the queue and train would be great.
EDIT 14/03/2016
In this example "https://github.com/torch/threads/blob/master/test/test-low-level.lua" using a low level thread, does anyone know how I can extract data from these threads into the main thread?
Look at this multi-threaded data provider:
https://github.com/soumith/dcgan.torch/blob/master/data/data.lua
It runs this file in the thread:
https://github.com/soumith/dcgan.torch/blob/master/data/data.lua#L18
by calling it here:
https://github.com/soumith/dcgan.torch/blob/master/data/data.lua#L30-L43
And afterwards, if you want to queue a job into the thread, you provide two functions:
https://github.com/soumith/dcgan.torch/blob/master/data/data.lua#L84
The first one runs inside the thread, and the second one runs in the main thread after the first one completes.
Hopefully that makes it a bit more clear.
If Soumith's examples in the previous answer are not very easy to use, I suggest you build your own pipeline from scratch. I provide here an example of two synchronized threads : one for writing data and one for reading data:
local t = require 'threads'
t.Threads.serialization('threads.sharedserialize')
local tds = require 'tds'
local dict = tds.Hash() -- only local variables work here, and only tables or tds.Hash()
dict[1] = torch.zeros(4)
local m1 = t.Mutex()
local m2 = t.Mutex()
local m1id = m1:id()
local m2id = m2:id()
m1:lock()
local pool = t.Threads(
1,
function(threadIdx)
end
)
pool:addjob(
function()
local t = require 'threads'
local m1 = t.Mutex(m1id)
local m2 = t.Mutex(m2id)
while true do
m2:lock()
dict[1] = torch.randn(4)
m1:unlock()
print ('W ===> ')
print(dict[1])
collectgarbage()
collectgarbage()
end
return __threadid
end,
function(id)
end
)
-- Code executing on master:
local a = 1
while true do
m1:lock()
a = dict[1]
m2:unlock()
print('R --> ')
print(a)
end