# p_tqdm

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`p_tqdm` makes parallel processing with progress bars easy.

`p_tqdm` is a wrapper around [pathos.multiprocessing](https://github.com/uqfoundation/pathos/blob/master/pathos/multiprocessing.py) and [tqdm](https://github.com/tqdm/tqdm). Unlike Python's default multiprocessing library, pathos provides a more flexible parallel map which can apply almost any type of function --- including lambda functions, nested functions, and class methods --- and can easily handle functions with multiple arguments. tqdm is applied on top of pathos's parallel map and displays a progress bar including an estimated time to completion.

## Installation

```pip install p_tqdm```

`p_tqdm` works with Python versions 2.7, 3.4, 3.5, 3.6.

## Example

Let's say you want to add two lists element by element. Without any parallelism, this can be done easily with a Python `map`.

```python
l1 = ['1', '2', '3']
l2 = ['a', 'b', 'c']

def add(a, b):
    return a + b
    
added = map(add, l1, l2)
# added == ['1a', '2b', '3c']
```

But if the lists are much larger or the computation is more intense, parallelism becomes a necessity. However, the syntax is often cumbersome. `p_tqdm` makes it easy and adds a progress bar too.

```python
from p_tqdm import p_map

added = p_map(add, l1, l2)
# added == ['1a', '2b', '3c']
```

```
  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]
```

## p_tqdm functions

### Parallel maps

* [p_map](#p_map) - parallel ordered map
* [p_imap](#p_imap) - iterator for parallel ordered map
* [p_umap](#p_umap) - parallel unordered map
* [p_uimap](#p_uimap) - iterator for parallel unordered map

### Sequential maps
* [t_map](#t_map) - sequential ordered map
* [t_imap](#t_imap) - iterator for sequential ordered map

### p_map

Performs an ordered map in parallel.

```python
from p_tqdm import p_map

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

added = p_map(add, ['1', '2', '3'], ['a', 'b', 'c'])
# added = ['1a', '2b', '3c']
```

### p_imap

Returns an iterator for an ordered map in parallel.

```python
from p_tqdm import p_imap

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

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

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

### p_umap

Performs an unordered map in parallel.

```python
from p_tqdm import p_umap

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

added = p_umap(add, ['1', '2', '3'], ['a', 'b', 'c'])
# added is an array with '1a', '2b', '3c' in any order
```

### p_uimap

Returns an iterator for an unordered map in parallel.

```python
from p_tqdm import p_uimap

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

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

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

### t_map

Performs an ordered map sequentially.

```python
from p_tqdm import t_map

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

added = t_map(add, ['1', '2', '3'], ['a', 'b', 'c'])
# added == ['1a', '2b', '3c']
```

### t_imap

Returns an iterator for an ordered map to be performed sequentially.

```python
from p_tqdm import p_imap

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

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

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

## Shared properties

### Arguments

All `p_tqdm` functions accept any number of lists (of the same length) as input, as long as the number of lists matches the number of arguments of the function. Additionally, if any non-list variable is passed as an input to a `p_tqdm` function, the variable will be passed to all calls of the function. See the example below.

```python
l1 = ['1', '2', '3']
l2 = ['a', 'b', 'c']

def add(a, b, c):
    return a + b + c

added = p_map(add, l1, l2, '!')
# added == ['1a!', '2b!', '3c!']
```

### CPUs

All the parallel `p_tqdm` functions can be passed the keyword `num_cpus` to indicate how many CPUs to use. The default is all CPUs. `num_cpus` can either be an integer to indicate the exact number of CPUs to use or a float to indicate the proportion of CPUs to use.