timeloop | An elegant periodic task executor | Job Scheduling library
kandi X-RAY | timeloop Summary
kandi X-RAY | timeloop Summary
Timeloop is a service that can be used to run periodic tasks after a certain interval. Each job runs on a separate thread and when the service is shut down, it waits till all tasks currently being executed are completed.
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timeloop Key Features
timeloop Examples and Code Snippets
Lines of Code : 2License : Strong Copyleft (CC BY-SA 4.0)
Copyglobal Min, Max
def quit_on_user_input():
input = raw_input("Press any key to quit.")
# thread will lock up and wait for user to input. That's why this is on a separate thread.
sys.exit(0)
quit_thread = threading.Thread(target=quit_on_user_inimport datetime
import time
from apscheduler.schedulers.background import BackgroundScheduler
scheduler = BackgroundScheduler()
def some_job():
time.sleep(5)
print(f"{datetime.datetime.utcnow()}: Every 10 seconds")
job = schedx'' - mu*(1-x^2)*x' + x = 0
mu*v = x' - mu*(1-x^2/3)*x
x' = mu*(v+(1-x^2/3)*x)
v' = -x/mu
#!/usr/bin/env python
import datetime, time, os, subprocess, random
from datetime import timedelta
from time import sleep
from omxplayer.player import OMXPlayer
def check_time () :
dt_now = datetime.datetime.now()
t_now = dt_now.timport tkinter as tk
from tkinter import Tk, ttk
def some_action():
# do what you want
# ...
print('Hi! Getting my urls here...')
# but don't block with sleeps or infinite loops,
# and then
next_check = get_your_cCommunity Discussions
Trending Discussions on timeloop
QUESTION
I am new to javascript and need to implement a script that calls my backend a total of 60 sec duration with 1 sec interval between the calls. The backend can respond with either true, false and 'Try again'. If the response is true I will redirect user to one location, if respond is false I will redirect user to another location. If response is 'Try again' I will stay in the loop until passing 60 sec. Below is the partial implemented code and I get an error in let paymentResponse = await verifyPayment();
Uncaught SyntaxError: await is only valid in async functions and the top level bodies of modules. Is it possible to accomplish what I want without using paymentResponse.then?
ANSWER
Answered 2021-Aug-06 at 06:32You need to define the setInterval callback function to be async
QUESTION
Min = 0
Sec = 0
if messages.content.startswith('y'):
await messages.channel.send("Start Counting. Wish you have a nice mood to learn!")
time.sleep(1)
await messages.channel.send("If you want to stop this timer you can type 'Stop',thank you.")
timeLoop = True
while timeLoop:
Sec += 1
await messages.channel.send(str(Min) + " Mins " + str(Sec) + " Sec ")
time.sleep(1)
if Sec == 60:
Sec = 0
Min += 1
await messages.channel.send(str(Min) + " Minute")
if messages.content.startswith('check'):
await messages.channel.send(str(Min) + str(Sec))
ANSWER
Answered 2021-Jul-27 at 04:08In your loops, when referring to Min and Max, it is creating a new variable. To make it use the already-made variable outside the loop do this:
Before referring to a variable inside a loop you have to make it global like this:
QUESTION
Ok, I really have no idea why this is happening. I'm currently implementing a thread container which runs an infinite loop in a detached manner, limited to a certain speed between each iteration.
Header:
...ANSWER
Answered 2021-Mar-28 at 13:20There are no guarantees on resolution of sleep_until, you are only guaranteed the thread will not be woken before the timepoint. If you are implementing the main game loop, read Fix your timestep.
Using sleep to guarantee timing is a terrible way to do it. You are at mercy of OS scheduler and e.g. Windows has a minimal sleep amount about 10 milliseconds I believe. (If the implementation actually asks the OS to put the thread to sleep and the OS decides to do a context switch.)
The lag might also be caused by VSync in the drawing thread if you are calling glfwSwapBuffers or similar. That would explain why your are limited to 60FPS, but not why commenting sleep solves the problem.
So my guess is the OS's sleep above. I would recommend to remove the sleep and rely on VSync, that's the right frequency you want to draw at anyway. Synchronization with logic threads will be a pain in... but that's always the case.
QUESTION
I'm intrigued on how less efficient are parallel loops in Python compared to parloop from Matlab.
Here I am presenting a simple root-finding problem brute-forcing initial 10^6 initial guesses between a and b.
ANSWER
Answered 2020-Oct-18 at 17:40From your single-process tests, your loop executes one million tasks in 90 seconds. Thus, each task takes your CPU about 90 microseconds in the average case.
In distributed computing frameworks like Dask or Spark that provide flexibility and resiliency, tasks have a small overhead associated with them. Dask's overhead is as low as 200 microseconds per task. The Spark 3.0 documentation suggests that Spark can support tasks as short as 200 milliseconds, which perhaps means Dask actually has 1000x less overhead than Spark. It sounds like Dask is actually doing really well here!
If your tasks are faster than the per-task overhead of your framework, you'll simply see worse performance using it relative to manually distributing your work across the same number of machines/cores. In this case, you're running into that scenario.
In your chunked data Dask example you have only a few tasks, so you see better performance from reduced overhead. But, you are either likely taking a small performance hit from the overhead of Dask relative to raw multiprocessing, or you're not using a Dask cluster and running the tasks a single process.
Multiprocessing (and Dask) Should HelpYour results with multiprocessing are generally unexpected for this kind of embarrassingly parallel problem. You may want to confirm the number of physical cores on your machine and in particular make sure nothing else is actively utilizing your CPU cores. Without knowing anything else, I would guess that's the culprit.
On my laptop with two physical cores, your example takes:
- 2min 1s for the single process loop
- 1min 2s for two processes
- 1min for four processes
- 1min 5s for a chunked Dask example with
nc=2to split into two chunks and a LocalCluster of two workers and one thread per worker. It may be worth double checking you're running on a cluster.
Getting a roughly 2x speedup with two processes is line with expectations on my laptop, as is seeing minimal or no benefit from more processes for this CPU bound task. Dask also adds a bit of overhead relative to raw multiprocessing.
QUESTION
This is a follow up to my previous question here however this question should be able to stand alone. I get the following error when I try to import tensorflow while there exists a file containing from tensorflow import keras.
ANSWER
Answered 2020-Jun-07 at 08:59Allright so this is a bug. I reproduced your issue using the python docker container, only installing the latest tensorflow. What fixed it, was renaming code.py to test.py (or anything else for that matter). This means this this is for sure a tensorflow issue. During import tensorflow, python will for some reason also import your code.py. Will you file an issue or should I?
QUESTION
I am currently trying to write some python code to solve an arbitrary system of first order ODEs, using a general explicit Runge-Kutta method defined by the values alpha, gamma (both vectors of dimension m) and beta (lower triangular matrix of dimension m x m) of the Butcher table which are passed in by the user. My code appears to work for single ODEs, having tested it on a few different examples, but I'm struggling to generalise my code to vector valued ODEs (i.e. systems).
In particular, I try to solve a Van der Pol oscillator ODE (reduced to a first order system) using Heun's method defined by the Butcher Tableau values given in my code, but I receive the errors
- "RuntimeWarning: overflow encountered in double_scalars
f = lambda t,u: np.array(... etc)" and- "RuntimeWarning: invalid value encountered in add
kvec[i] = f(t+alpha[i]*h,y+h*sum)"
followed by my solution vector that is clearly blowing up. Note that the commented out code below is one of the examples of single ODEs that I tried and is solved correctly. Could anyone please help? Here is my code:
...ANSWER
Answered 2020-Feb-23 at 13:16Your step size is not small enough. The Van der Pol oscillator with mu=100 is a fast-slow system with very sharp turns at the switching of the modes, so rather stiff. With explicit methods this requires small step sizes, the smallest sensible step size is 1e-5 to 1e-6. You get a solution on the limit cycle already for h=0.001, with resulting velocities up to 150.
You can reduce some of that stiffness by using a different velocity/impulse variable. In the equation
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Install timeloop
You can use timeloop like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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