atpbar 0.9.5

Progress bars for threading and multiprocessing tasks

atpbar

Progress bars for threading and multiprocessing tasks on terminal and Jupyter Notebook.

 100.00% :::::::::::::::::::::::::::::::::::::::: |     7811 /     7811 |:  task 1
 100.00% :::::::::::::::::::::::::::::::::::::::: |    23258 /    23258 |:  task 0
  65.62% ::::::::::::::::::::::::::               |     8018 /    12219 |:  task 4
  60.89% ::::::::::::::::::::::::                 |    31083 /    51048 |:  task 2
  25.03% ::::::::::                               |    23884 /    95421 |:  task 3

atpbar can display multiple progress bars simultaneously growing to show the progresses of iterations of loops in threading or multiprocessing tasks. atpbar can display progress bars on terminal and Jupyter Notebook. The code in atpbar started its development in 2015 as part of alphatwirl. It had been a sub-package, progressbar, of alphatwirl until it became an independent package in February 2019.

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• Requirement
• Install
• How to use
  • Import libraries
  • One loop
  • Nested loops
  • Threading
  • Multiprocessing
• License
• Contact

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Requirement

• Python 2.7, 3.6, or 3.7

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Install

You can install with pip.

$ pip install -U atpbar

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How to use

I will show here how to use atpbar by simple examples.

These examples can be also run on Jupyter Notebook.

Import libraries

To create simple loops in the examples, we use two python standard libraries, time and random. Import the two packages as well as atpbar.

import time, random
from atpbar import atpbar

Note: import the object atpbar from the package atpbar rather than importing the package atpbar itself.

One loop

The object atpbar is an iterable that can wrap another iterable and shows the progress bars for the iterations. (The idea of making the interface iterable was inspired by tqdm.)

n = random.randint(5, 10000)
for i in atpbar(range(n)):
    time.sleep(0.0001)

The task in the above code is to sleep for 0.0001 seconds in each iteration of the loop. The number of the iterations of the loop is randomly selected from between 5 and 10000.

A progress bar will be shown by atpbar.

  51.25% ::::::::::::::::::::                     |     4132 /     8062 |:  range(0, 8062) 

In order for atpbar to show a progress bar, the wrapped iterable needs to have a length. If the length cannot be obtained by len(), atpbar won't show a progress bar.

Nested loops

atpbar can show progress bars for nested loops as in the following example.

for i in atpbar(range(4), name='outer'):
    n = random.randint(5, 10000)
    for j in atpbar(range(n), name='inner {}'.format(i)):
        time.sleep(0.0001)

The outer loop iterates 4 times. The inner loop is similar to the loop in the previous example---sleeps for 0.0001 seconds. You can optionally give the keyword argument name to specify the label on the progress bar.

 100.00% :::::::::::::::::::::::::::::::::::::::: |     3287 /     3287 |:  inner 0
 100.00% :::::::::::::::::::::::::::::::::::::::: |     5850 /     5850 |:  inner 1
  50.00% ::::::::::::::::::::                     |        2 /        4 |:  outer  
  34.42% :::::::::::::                            |     1559 /     4529 |:  inner 2

In the snapshot of the progress bars above, the outer loop is in its 3rd iteration. The inner loop has completed twice and is running the third. The progress bars for the completed tasks move up. The progress bars for the active tasks are growing at the bottom.

Threading

atpbar can show multiple progress bars for loops concurrently iterating in different threads.

The function run_with_threading() in the following code shows an example.

from atpbar import flush
import threading

def run_with_threading():
    def task(n, name):
        for i in atpbar(range(n), name=name):
            time.sleep(0.0001)
    nthreads = 5
    threads = [ ]
    for i in range(nthreads):
        name = 'thread {}'.format(i)
        n = random.randint(5, 100000)
        t = threading.Thread(target=task, args=(n, name))
        t.start()
        threads.append(t)
    for t in threads:
        t.join()
    flush()

run_with_threading()

The task to sleep for 0.0001 seconds is defined as the function task. The task is concurrently run 5 times with threading. atpbar can be used in any threads. Five progress bars growing simultaneously will be shown. The function flush() returns when the progress bars have finished updating.

 100.00% :::::::::::::::::::::::::::::::::::::::: |     8042 /     8042 |:  thread 3 
  33.30% :::::::::::::                            |    31967 /    95983 |:  thread 0 
  77.41% ::::::::::::::::::::::::::::::           |    32057 /    41411 |:  thread 1 
  45.78% ::::::::::::::::::                       |    31816 /    69499 |:  thread 2 
  39.93% :::::::::::::::                          |    32373 /    81077 |:  thread 4 

As a task completes, the progress bar for the task moves up. The progress bars for active tasks are at the bottom.

Multiprocessing

atpbar can be used with multiprocessing.

The function run_with_multiprocessing() in the following code shows an example.

import multiprocessing
from atpbar import register_reporter, find_reporter, flush

def run_with_multiprocessing():
    def task(n, name):
        for i in atpbar(range(n), name=name):
            time.sleep(0.0001)
    def worker(reporter, task, queue):
        register_reporter(reporter)
        while True:
            args = queue.get()
            if args is None:
                queue.task_done()
                break
            task(*args)
            queue.task_done()
    nprocesses = 4
    processes = [ ]
    reporter = find_reporter()
    queue = multiprocessing.JoinableQueue()
    for i in range(nprocesses):
        p = multiprocessing.Process(target=worker, args=(reporter, task, queue))
        p.start()
        processes.append(p)
    ntasks = 10
    for i in range(ntasks):
        name = 'task {}'.format(i)
        n = random.randint(5, 100000)
        queue.put((n, name))
    for i in range(nprocesses):
        queue.put(None)
        queue.join()
    flush()

run_with_multiprocessing()

It starts four workers in subprocesses with multiprocessing and have them run ten tasks.

In order to use atpbar in a subprocess, the reporter, which can be found in the main process by the function find_reporter(), needs to be brought to the subprocess and registered there by the function register_reporter().

Simultaneously growing progress bars will be shown.

 100.00% :::::::::::::::::::::::::::::::::::::::: |    44714 /    44714 |:  task 3
 100.00% :::::::::::::::::::::::::::::::::::::::: |    47951 /    47951 |:  task 2
 100.00% :::::::::::::::::::::::::::::::::::::::: |    21461 /    21461 |:  task 5
 100.00% :::::::::::::::::::::::::::::::::::::::: |    73721 /    73721 |:  task 1
 100.00% :::::::::::::::::::::::::::::::::::::::: |    31976 /    31976 |:  task 4
 100.00% :::::::::::::::::::::::::::::::::::::::: |    80765 /    80765 |:  task 0
  58.12% :::::::::::::::::::::::                  |    20133 /    34641 |:  task 6
  20.47% ::::::::                                 |    16194 /    79126 |:  task 7
  47.71% :::::::::::::::::::                      |    13072 /    27397 |:  task 8
  76.09% ::::::::::::::::::::::::::::::           |     9266 /    12177 |:  task 9

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License

• atpbar is licensed under the BSD license.

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Contact

• Tai Sakuma - [e-mail]