semvec/pypi-packages huggingface dataset:
bgAna 0.0.4
Background Analyzer. Heuristics to get the background color of any image.
Requires Python: >=3.5
Keywords: computer vision

# bgAna - Background Analyzer
![travis-badge](https://travis-ci.org/Madoshakalaka/bgAna.svg?branch=master)

Heuristics to get the background color of any image

It's useful when you want to embed image in a bigger frame. This can be used to determine the color of the bigger frame.

## Showcase

## How to Use

It supports python 3.5 3.6 3.7 and most possibly future versions

`pip install bgAna`

```python
from bgAna import analyze_bg
img = cv2.imread('/home/matt/studyResource/.secretWeebFolder/waifu_no_435.jpg')
res = analyze_bg(img) # caution: color is in BGR


>>> print(type(res), res)
np.ndarray [125,125,125]
```

It also creates command line executable entry point by default

You should be able to run the following commands anywhere.

- `$ bgAna thonk.png`
    - output: `(125, 125, 125)`

- `$ bgAna -h` for more help
lajobsparser 0.3.0
Parser for Los Angeles city jobs bulletins
Requires Python: >=3.6, <4
Keywords: kaggle text processing

# lajobsparser

Parser for Los Angeles city jobs bulletins

* Source code - [Github][10]

[10]: https://github.com/gavinln/lajobsparser/

The city of Los Angeles (LA) is running a [Kaggle competion][20] that uses the
job bulletins for the city of LA jobs.

[20]: https://www.kaggle.com/c/data-science-for-good-city-of-los-angeles

The content, tone, and format of job bulletins can influence the quality of the
applicant pool. Overly-specific job requirements may discourage diversity. The
Los Angeles Mayor’s Office wants to reimagine the city’s job bulletins by using
text analysis to identify needed improvements.

The goal is to convert a folder full of plain-text job postings into a single
structured CSV file and then to use this data to:

1. identify language that can negatively bias the pool of applicants
2. improve the diversity and quality of the applicant pool
3. make it easier to determine which promotions are available to employees in
   each job class

This project helps parse the job bulletins and associated files.

## Miscellaneous

1. To install from github using pipenv

```
pipenv install -e git+https://github.com/gavinln/lajobsparser.git#egg=lajobsparser
```

## Push a new version to PyPI

1. Change version number in setup.py

2. Run tests

```
make test
```

3. Build a distribution

```
make sdist
```

4. Open a browser to PyPI: https://pypi.org/project/lajobsparser/

5. Run twine

```
make twine
```