p_tqdm - parallel processing with progress bars

p_tqdm provides implementations of parallel and sequential map functions using tqdm progress bars.

Since p_tqdm uses pathos.multiprocessing instead of the regular python multiprocessing module, its parallel maps can apply almost any type of function, including lambda functions, nested functions, and class methods.

Installation

pip install p_tqdm

p_map - parallel ordered map

from p_tqdm import p_map

def func(a, b):
    return a + b

results = p_map(func, ['1', '2', '3'], ['a', 'b', 'c'])

  0%|                                    | 0/3 [00:00<?, ?it/s]
 33%|████████████                        | 1/3 [00:01<00:02, 1.00s/it]
 66%|████████████████████████            | 2/3 [00:02<00:01, 1.00s/it]
100%|████████████████████████████████████| 3/3 [00:03<00:00, 1.00s/it]

results == ['1a', '2b', '3c'] # True

p_imap - iterator for parallel ordered map

from p_tqdm import p_imap

def func(a, b):
    return a + b

iterator = p_imap(func, ['1', '2', '3'], ['a', 'b', 'c'])

for result in iterator:
    print(result) # prints '1a', '2b', '3c'

p_umap - parallel unordered map

from p_tqdm import p_umap

def func(a, b):
    return a + b

results = p_umap(func, ['1', '2', '3'], ['a', 'b', 'c'])

results == ['2b', '1a', '3c'] # an array with '1a', '2b', and '3c' in any order

p_uimap - iterator for parallel unordered map

from p_tqdm import p_uimap

def func(a, b):
    return a + b

iterator = p_uimap(func, ['1', '2', '3'], ['a', 'b', 'c'])

for result in iterator:
    print(result) # prints '1a', '2b', '3c' in any order

t_map - sequential map

from p_tqdm import t_map

def func(a, b):
    return a + b

results = t_map(func, ['1', '2', '3'], ['a', 'b', 'c'])

results == ['1a', '2b', '3c'] # True

t_imap - iterator for sequential map

from p_tqdm import p_imap

def func(a, b):
    return a + b

iterator = t_imap(func, ['1', '2', '3'], ['a', 'b', 'c'])

for result in iterator:
    print(result) # prints '1a', '2b', '3c'