Takes the hassle away from multiprocessing of functions and parsing of big dataset`s.
Running functions with multiprocessing hassle free
A nice package to help you run functions with multiprocessing and get their result.
pip install processio
Run function in own process
import time from processio import ProcessIO def get_company_name(): # Do work in own process time.sleep(5) return 'Adapted' def do_some_work(): get_name = ProcessIO(get_company_name) # do stuff or run a while loop to wait for result while get_name.doing_work(): print('Waiting for process to finnish') # You can also call .result() and the main thread will wait # for the thread to return your result. company_name = get_name.result() print(company_name) # Outputs -> Adapted if __name__ == '__main__': # <- its required to execute the function main do_some_work()
# Import the module from processio import ProcessIO # Start your function with or without arguments var = ProcessIO(function, args, kwargs) # Wait for the function to finnish or get the result if its finished var.result()
# Check if your thread is still working on the function. # This will return True if the function is not completed. var.doing_work()
Use parseIO to save a lot of time on list parsing
import time from processio import ParseIO def list_parser(list): result = 0 for line in list: result += get_total_amout(line) return result def do_some_work(): # this will split the list into 4 and run the function on # 4 different processes, that in most cases will almost speed # up the work time by 4. # you can define the number of processes you want to run, but as a # default the module runs on the systems cpu cores - 1 parser = ParseIO(list_parser, huge_list) # do stuff or run a while loop to wait for result while parser.doing_work(): print('Waiting for processes to finnish') # You can also call .result() and the main thread will wait # for the thread to return your result. result = parser.result() # result comes back as list pr process, so in this case we will get # a list with 4 numbers that we can loop thru. print(result) # Outputs -> [1000, 1000, 1000, 1000] <- example total = 0 for res in result: total += res print(total) # Outputs -> 4000 <- example if __name__ == '__main__': # <- its required to execute the function main do_some_work()
Use the following command to run tests.
python -m unittest threadit.tests.test_threadit
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