# parabar
Progress bars from `tqdm` for multiprocessing with `pathos` in Python.
This is similar in spirit to `tqdm_pathos` but more simplified.
## Installation
`pip install parabar`
## Usage
There is a single function `parabar.map`.
The two required arguments are the function you want to map and the iterable(s) you want to map over: `parabar.map(func, iterable)`.
If you want to iterate over multiple arguments, you should zip them: `parabar.map(func, zip(iterable1, iterable2))`.
It has two optional arguments:
- `ncpus = 1`: the number of processes for the pool
- `tqdm_kwargs = {}`: dictionary of extra arguments to pass to `tqdm`.
If the length of your iterable is `total`, you can tell `tqdm` by using `tqdm_kargs = {'total': total}`. Otherwise, `parabar.map` will convert your iterables to a list and use `len`.
You can provide other fixed positional and keywords arguments to your function that you do not want to iterate over as the reminaing position and keyword arguments: `parabar.map(func, iterable, arg1, arg2, kwarg1=None, ncpus=1)`.
## Examples
### Function of a single iterable:
```
f = lambda x: x**2
iterable = [1, 2, 3]
# Serial
y = [f(x) for x in iterable]
print(y)
# Parallel
y = parabar.map(f, iterable)
print(y)
```
### Function of a single iterable, with non-iterable args and kwargs:
```
f = lambda x, a, b = 0: x**2 * a + b
iterable = [1, 2, 3]
a = 1
b = 0
# Serial
y = [f(x, a, b = b) for x in iterable]
print(y)
# Parallel
y = parabar.map(f, iterable, a, b = b)
print(y)
```
### Function of multiple iterables:
```
f = lambda x, y: x * y
iterable1 = [1, 2, 3]
iterable2 = [4, 5, 6]
# Serial
z = [f(x, y) for x, y in zip(iterable1, iterable2)]
print(z)
# Parallel
z = parabar.map(f, zip(iterable1, iterable2))
print(z)
```
### Function of multiple iterables, with non-iterable args and kwargs
```
f = lambda x, y, a, b = 0: x * y * a + b
iterable1 = [1, 2, 3]
iterable2 = [4, 5, 6]
a = 1
b = 0
# Serial
z = [f(x, y, a, b = b) for x, y in zip(iterable1, iterable2)]
print(z)
# Parallel
z = parabar.map(f, zip(iterable1, iterable2), a, b = b)
print(z)
```
### Specify number of processes and keyword arguments for progress bar
```
from tqdm.auto import tqdm
f = lambda x: x
iterable = [1, 2, 3]
tqdm_kwargs = dict(total = 3, desc = 'iterating')
# Serial
y = [f(x) for x in tqdm(iterable, **tqdm_kwargs)]
print(y)
# Parallel
y = parabar.map(f, iterable, ncpus=2, tqdm_kwargs=tqdm_kwargs)
print(y)
```
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"description": "# parabar\n\nProgress bars from `tqdm` for multiprocessing with `pathos` in Python.\n\nThis is similar in spirit to `tqdm_pathos` but more simplified.\n\n## Installation\n\n`pip install parabar`\n\n## Usage\n\nThere is a single function `parabar.map`.\n\nThe two required arguments are the function you want to map and the iterable(s) you want to map over: `parabar.map(func, iterable)`.\n\nIf you want to iterate over multiple arguments, you should zip them: `parabar.map(func, zip(iterable1, iterable2))`.\n\nIt has two optional arguments:\n- `ncpus = 1`: the number of processes for the pool\n- `tqdm_kwargs = {}`: dictionary of extra arguments to pass to `tqdm`.\n\nIf the length of your iterable is `total`, you can tell `tqdm` by using `tqdm_kargs = {'total': total}`. Otherwise, `parabar.map` will convert your iterables to a list and use `len`.\n\nYou can provide other fixed positional and keywords arguments to your function that you do not want to iterate over as the reminaing position and keyword arguments: `parabar.map(func, iterable, arg1, arg2, kwarg1=None, ncpus=1)`.\n\n## Examples\n\n### Function of a single iterable:\n\n```\nf = lambda x: x**2\niterable = [1, 2, 3]\n\n# Serial\ny = [f(x) for x in iterable]\nprint(y)\n\n# Parallel\ny = parabar.map(f, iterable)\nprint(y)\n```\n\n### Function of a single iterable, with non-iterable args and kwargs:\n\n```\nf = lambda x, a, b = 0: x**2 * a + b\niterable = [1, 2, 3]\na = 1\nb = 0\n\n# Serial\ny = [f(x, a, b = b) for x in iterable]\nprint(y)\n\n# Parallel\ny = parabar.map(f, iterable, a, b = b)\nprint(y)\n```\n\n### Function of multiple iterables:\n\n```\nf = lambda x, y: x * y\niterable1 = [1, 2, 3]\niterable2 = [4, 5, 6]\n\n# Serial\nz = [f(x, y) for x, y in zip(iterable1, iterable2)]\nprint(z)\n\n# Parallel\nz = parabar.map(f, zip(iterable1, iterable2))\nprint(z)\n```\n\n### Function of multiple iterables, with non-iterable args and kwargs\n\n```\nf = lambda x, y, a, b = 0: x * y * a + b\niterable1 = [1, 2, 3]\niterable2 = [4, 5, 6]\na = 1\nb = 0\n\n# Serial\nz = [f(x, y, a, b = b) for x, y in zip(iterable1, iterable2)]\nprint(z)\n\n# Parallel\nz = parabar.map(f, zip(iterable1, iterable2), a, b = b)\nprint(z)\n```\n\n### Specify number of processes and keyword arguments for progress bar\n\n```\nfrom tqdm.auto import tqdm\n\nf = lambda x: x\niterable = [1, 2, 3]\ntqdm_kwargs = dict(total = 3, desc = 'iterating')\n\n# Serial\ny = [f(x) for x in tqdm(iterable, **tqdm_kwargs)]\nprint(y)\n\n# Parallel\ny = parabar.map(f, iterable, ncpus=2, tqdm_kwargs=tqdm_kwargs)\nprint(y)\n```\n",
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