Metadata-Version: 2.4
Name: numba-progress
Version: 1.2.0
Summary: A tqdm-compatible progress bar implementation for numba functions
Author-email: Felix Igelbrink <felix.igelbrink@dfki.de>
License: MIT
Project-URL: Homepage, https://github.com/mortacious/numba-progress
Project-URL: Issues, https://github.com/mortacious/numba-progress/issues
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Programming Language :: Python :: 3.14
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: POSIX :: Linux
Classifier: Intended Audience :: Developers
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Software Development
Requires-Python: >=3.10
Description-Content-Type: text/markdown
Requires-Dist: numba>=0.52
Requires-Dist: numpy
Requires-Dist: tqdm
Provides-Extra: dev
Requires-Dist: pytest; extra == "dev"

# Numba-progress
 
A progress bar implementation for numba functions using tqdm.
The module provides the class `ProgressBar` that works as a wrapper around the 
`tqdm.tqdm` progress bar. 

It works by spawning a separate thread that updates the `tqdm` progress bar 
based on an atomic counter which can be accessed and updated in a numba nopython function.

The progress bar works with parallel as well as sequential numba functions.

## Installation

### Using pip
```
pip install numba-progress
```

### From source
```
git clone https://github.com/mortacious/numba-progress.git
cd numba-progress
python setup.py install
```

## Usage

```python
from numba import njit
from numba_progress import ProgressBar

num_iterations = 100

@njit(nogil=True)
def numba_function(num_iterations, progress_proxy):
    for i in range(num_iterations):
        #<DO CUSTOM WORK HERE>
        progress_proxy.update(1)

with ProgressBar(total=num_iterations) as progress:
    numba_function(num_iterations, progress)
```

The `ProgressBar` also works within parallel functions out of the box:

```python
from numba import njit, prange
from numba_progress import ProgressBar

num_iterations = 100

@njit(nogil=True, parallel=True)
def numba_function(num_iterations, progress_proxy):
    for i in prange(num_iterations):
        #<DO CUSTOM WORK HERE>
        progress_proxy.update(1)

with ProgressBar(total=num_iterations) as progress:
    numba_function(num_iterations, progress)
```

Refer to the `examples` folder for more usage examples.
