semvec/pypi-packages huggingface dataset:
github-status-checker 1.0.2
Python module/tool for checking the status of GitHub.
License: MIT


# GitHub Status Checker

[![pre-commit](https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit&logoColor=white)](https://github.com/pre-commit/pre-commit)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
![Tests](https://github.com/rapid7/github-status-checker/workflows/Tests/badge.svg)
![Python Lint](https://github.com/rapid7/github-status-checker/workflows/Python%20Lint/badge.svg)
![Markdown Lint](https://github.com/rapid7/github-status-checker/workflows/Markdown%20Lint/badge.svg)

## What this is

A tool and Python module for checking the status of GitHub.

## Installation

### Install the module via `pip`

```
moose@rapid7:~$ pip install github-status-checker
...
```

## Usage

```
moose@rapid7:~$ github-status webhooks
Service 'Webhooks' is currently operational!
```

## Contributions

Contributions are welcome! This project utilizes [black](https://github.com/psf/black)
and [pre-commit](https://pre-commit.com/) for handling code
style. Simply follow the instructions for installing pre-commit and 
run `pre-commit install` in the repository after cloning and you will
be on your way to contributing!

## Changelog

* 1.0.2 - Update to handle crash on outage report | Code style: black
| Add contribution section
* 1.0.1 - Update homepage and author name
* 1.0.0 - Swap dataclasses for traditional classes for greater Python version 
compatibility (Python 3.x+)
* 0.1.0 - Initial development, support Summary
code-climate 0.0.1
CodeClimate API client for Python
License: MIT

.. image:: https://img.shields.io/badge/python-3-blue.svg
   :target: https://www.python.org/
   :alt: Python 3

.. image:: https://api.codeclimate.com/v1/badges/9290f8ea1c6f06da8dd9/maintainability
   :target: https://codeclimate.com/github/edukorg/code-climate-py/maintainability
   :alt: Maintainability


.. image:: https://api.codeclimate.com/v1/badges/9290f8ea1c6f06da8dd9/test_coverage
   :target: https://codeclimate.com/github/edukorg/code-climate-py/test_coverage
   :alt: Test Coverage


code-climate-py
jupyter-live-graph 0.1.0
Create and update graphs live in Jupyter
License: MIT
Requires Python: >=3.4

# jupyter-live-graph

Let's you create a graph in a Jupyter cell, and update that graph live in other cells. Uses matplotlib`.draw()`.

![](jupyterdraw.gif)

As simple as:

```
%matplotlib notebook
from draw import Draw

draw = Draw()
# ---
for i in range(1000):
  draw.graph([j for j in range(i)])
```

## License

MIT

## Contributing

This project is wide open to pull requests.
coloranalysis 1.3.2
coloranalysis is a package for calculating area of one or more colors in an image, provided the HEX codes.
License: MIT
Requires Python: >=3.5

# **coloranalysis**
coloranalysis is a python package for calculating the percentage of area covered by one or more colors in an image.

### **Prerequisites**
numpy, opencv and matplotlib are required to execute coloranalysis, you can download them using the following commands:
```
pip install numpy
pip install opencv-python
pip install matplotlib
```

### **Installing**
You can either clone or download this repository, or use this command:
```
pip install coloranalysis
```

### **Usage**

See [this notebook](https://github.com/sravyadhulipala/coloranalysis/blob/master/example/colorAreasExample.ipynb) for an example program on how to use this package. However, reading this document entirely is recommended.

Let us consider this image of a rainbow. 

<img src=https://github.com/sravyadhulipala/coloranalysis/blob/master/example/IPTestRainbow.jpg width="400" height="200">

To know the area covered by red color, or the area covered by multiple colors in the image, we should get the HEX codes using a [colorpicker.](https://imagecolorpicker.com/)

In the above image, HEX codes of all colors are: ["#FE0000", "#FD6400", "#FFFF02", "#008101", "#0000FE", "#4B0081", "#BC31FD"]

Import colorArea, the class that calculates the area of the colors we want, as follows. 
```
from coloranalysis.colors import colorAreas
```
*colorAreas takes no arguments.*

**getArea** 

*returns* a list of the percentages of area covered by the given colors.

*arguments* 

- **hexColours**: A list of strings representing the HEX codes.
- **path**: A string specifying the path of the image.
- **diff**: An integer to determine the lower and upper boundaries of the given colors, in the HSV color space.

*hexColours - colour with a 'u'*

For a digital image as above, the recommended *diff* value is 10. While the recommended *diff* value for images of real-life objects is 30-50. 
For more information on HSV color space, see [this link.](https://www.linuxtopia.org/online_books/graphics_tools/gimp_advanced_guide/gimp_guide_node51.html)

**detectColor**

*returns* a tuple with 'mask' and 'result' representing the pixels that match a single color

*arguments*
- **colour**: Values of the color in HSV color space, in the range H[0-360], S[0-100], V[0-100]
- **img**: A numpy array returned by cv2.imread()
- **hsv_img**: A numpy array returned by cv2.cvtColor()
- **diff**: An integer to determine the lower and upper boundaries of the given colors, in the HSV color space.

*colour - colour with a 'u'*

'mask' and 'result' can be used to visualize the presence of the color in the given image

**convertHEXColours**

*returns* a tuple with two lists representing values of the colors in HSV colorspace and RGB colorspace respectively.

*arguments* 
- **hexColours**: A list of strings representing the HEX codes.
pyautocast 0.1.1
Automatic function arguments casting
License: MIT
Requires Python: >=3.7

# pyautocast
Python library to automatically cast function arguments using decorator.

## Install

```bash
pip install pyautocast
```

## Usage

```py
>>> from pyautocast import autocast
>>> @autocast(x=str)
... def func(x):
...     assert(isinstance(x, str))
...     return "arg 'x' in func() is " + x
...
>>> func(2)
"arg 'x' in func() is 2"
>>> func([1, 2, 3])
"arg 'x' in func() is [1, 2, 3]"
```

```py
>>> from pyautocast import CustomCast
>>> mycast = CustomCast()
>>> mycast.add_cast_rule(int, tuple, lambda x: (x, x))
>>> @mycast.autocast(x=tuple)
... def func(x):
...     print(x)
>>> func(2)
(2, 2)
>>> func(-4.5)
Traceback (most recent call last):
...
TypeError: 'float' object is not iterable
```
regionconnect 0.12
IIT Human Brain Atlas regionconnect tool
License: MIT

# regionconnect
IIT Human Brain Atlas regionconnect app. 
Before using the regionconnect app, please register your data into the IIT space. See [IIT atlas manual](https://www.nitrc.org/frs/download.php/11488/IIT_Atlas_v.5.0_MANUAL.pdf) for more details.

## Getting the most probabel connections
1. First method: import regionconnect function.   
- To update your `pip` and create a virtual environment: 
```
python -m pip install --upgrade pip
python3 -m venv regionconnectTest
source regionconnectTest/bin/activate
python3 -m pip install regionconnect
```

- Launch python console and run `regionconnect`
```
python
>>from regionconnect import regionconnect
>>regionconnect.regionconnect('path_to_ROI_mask','path_to_output_text_file')
```

2. Second method: run the regionconnect.py script directly.
```
python3 -m pip install numpy nibabel
git clone https://github.com/SheridanQ/regionconnect.git
cd regionconnect
python ./regionconnect/regionconnect.py path_to_ROI_mask path_to_output_text_file

```

## Example
```
python ./regionconnect/regionconnect.py ./test/roi4.nii.gz ./test/results_roi4.txt
```

## Links
To know more details and other resources such as gray matter resources, diffusion tensor template and high angular resolution diffusion imaging template, please go to:
[MRIIT websit](https://www5.iit.edu/~mri/Home.html) and [Neuroimaging Tools & Resources Collaboratory(NITRC)](https://www.nitrc.org/projects/iit/).  

## References
Qi X., Zhang S., Arfanakis K. Enhancement and Evaluation of the White Matter Connectome of the IIT Human Brain Atlas. Proc. Int. Soc. for Magn. Reson. In Med. (ISMRM) 2019.  

## License
For non-commercial usage: IIT license (See LICENSE).  
For commercial usage: Please email to: mri@iit.edu