function - The function to execute. It should accept two positional arguments; the first argument is the dictionary containing the input data for the respective process see process_data below, the second argument is the shared dictionary sent to all proceses see shared_data below.

process_data - A list of dictionaries where each dictionary describes the input data that will be sent to the respective process executing the function; the length of the list dictates the total number of processes that will be executed.

shared_data - A dictionary containing arbitrary data that will be sent to all processes.

processes_to_start - The number of processes to initially start; this represents the number of concurrent processes that will be running. If the total number of processes is greater than this number then the remaining processes will be queued and executed to ensure this concurrency is maintained. Defaults to the length of the process_data lsit.

regex - A dictionary whose key values are regular expressions for total, count and alias. The regular expressions will be checked against the log messages intercepted from the executing function, if matched the value will be used to assign the attribute for the respective progress bar. The total and count key values are required, the alias key value is optional. The default regex is:

{
    'total': r'^processing total of (?P<value>\d+) items$',
    'count': r'^processed .*$',
    'alias': r'^worker is (?P<value>.*)$'
}

**kwargs - Pass through any other supported ProgressBar keyword argument, refer to progress bar for supported arguments.

execute(raise_if_error=False)

Start execution the process’s activity. If raise_if_error is set to True, an exception will be raised if the function encounters an error during execution.

import time, random, logging
import names
from mppbar import MPpbar
logger = logging.getLogger(__name__)

def do_work(data, *args):
    # log our intentions - messages will be intercepted as designated by MPpbar regex
    logger.debug(f'worker is {names.get_last_name()}')
    total = data['total']
    logger.debug(f'processing total of {total} items')
    for index in range(total):
        # simulate work by sleeping
        time.sleep(random.choice([.1, .2, .4]))
        logger.debug(f'processed item {index}')
    return total

def main():
    # designate 6 processes total - each getting a different total
    process_data = [{'total': random.randint(8, 16)} for item in range(6)]
    print(f'>> Processing items using {len(process_data)} workers ...')
    pbars =  MPpbar(function=do_work, process_data=process_data, timeout=1)
    results = pbars.execute()
    # add up totals from all processes
    print(f">> {len(process_data)} workers processed a total of {sum(result for result in results)} items")

if __name__ == '__main__':
    main()

import time, random, logging
import names
from mppbar import MPpbar
logger = logging.getLogger(__name__)

def do_work(data, *args):
    # log our intentions - messages will be intercepted as designated by MPpbar regex
    logger.debug(f'processor is {names.get_last_name()}')
    total = data['total']
    logger.debug(f'processing total of {total}')
    for index in range(total):
        # simulate work by sleeping
        time.sleep(random.choice([.1, .2, .4]))
        logger.debug(f'processed item {index}')
    return total

def main():
    # designate 6 processes total - each getting a different total
    process_data = [{'total': random.randint(8, 16)} for item in range(6)]
    # supply custom regex to intercept and set values for total count and alias
    regex = {
        'total': r'^processing total of (?P<value>\d+)$',
        'count': r'^processed item \d+$',
        'alias': r'^processor is (?P<value>.*)$',
    }
    # designate fill factor for total - to make progress bar look nicer
    fill = {
        'max_total': 100
    }
    print(f'>> Processing items using {len(process_data)} workers ...')
    # set concurrency to 3 - max of 3 processes will be running at any given time
    pbars =  MPpbar(function=do_work, process_data=process_data, regex=regex, fill=fill, processes_to_start=3, timeout=1, show_fraction=False)
    results = pbars.execute()
    # add up totals from all processes
    print(f">> {len(process_data)} workers processed a total of {sum(result for result in results)} items")

if __name__ == '__main__':
    main()

import time, random, logging
from multiprocessing import Queue
from queue import Empty
import names
from mppbar import MPpbar
logger = logging.getLogger(__name__)

def do_work(total):
    # log our intentions - messages will be intercepted as designated by MPpbar regex
    logger.debug(f'worker is {names.get_last_name()}')
    logger.debug(f'processing total of {total} items')
    for index in range(total):
        # simulate work by sleeping
        time.sleep(random.choice([.001, .003, .005]))
        logger.debug(f'processed item {index}')
    return total

def prepare_queue():
    # create queue to add all the work that needs to be done
    queue = Queue()
    for _ in range(75):
        queue.put({'total': random.randint(100, 150)})
    return queue

def run_q(data, *args):
    queue = data['queue']
    result = 0
    while True:
        try:
            # get work from queue
            total = queue.get(timeout=1)['total']
            # process the work
            result += do_work(total)
            # this allows us to reset progress bar
            logger.debug('reset-mppbar')
        except Empty:
            logger.debug('reset-mppbar-complete')
            break
    return result

def main():
    queue = prepare_queue()
    # designate 3 processes total - each getting reference to the queue
    process_data = [{'queue': queue} for item in range(3)]
    print(f'>> Processing {queue.qsize()} totals using {len(process_data)} workers ...')
    pbars =  MPpbar(function=run_q, process_data=process_data, timeout=1, show_prefix=False, show_percentage=False)
    results = pbars.execute()
    # add up results from all workers
    print(f">> {len(process_data)} workers processed a total of {sum(result for result in results)} items")

if __name__ == '__main__':
    main()