Metadata-Version: 2.4
Name: auroraviz
Version: 0.1.3
Summary: A clean, modern Python visualization library with curated palettes and minimal ink.
Author: Gyanankur Baruah
License: MIT License
        
        Copyright (c) 2026 Gyanankur Baruah
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in
        all copies or substantial portions of the Software.
        
        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
        AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
        LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
        OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
        THE SOFTWARE.
        
Keywords: visualization,charts,matplotlib,data viz,design
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Intended Audience :: Developers
Classifier: Topic :: Scientific/Engineering :: Visualization
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: matplotlib>=3.7
Requires-Dist: numpy>=1.24
Requires-Dist: pandas>=2.0
Dynamic: license-file

# AuroraViz


[![Python Versions](https://img.shields.io/pypi/pyversions/auroraviz.svg)](https://pypi.org/project/auroraviz/)
[![License](https://img.shields.io/github/license/Gyanankur23/AuroraViz.svg)](https://github.com/Gyanankur23/AuroraViz/blob/main/LICENSE)
[![Build Status](https://github.com/Gyanankur23/AuroraViz/actions/workflows/publish.yml/badge.svg)](https://github.com/Gyanankur23/AuroraViz/actions)
[![Downloads](https://img.shields.io/pypi/dm/auroraviz.svg)](https://pypi.org/project/auroraviz/)
[![GitHub stars](https://img.shields.io/github/stars/Gyanankur23/AuroraViz.svg?style=social&label=Star)](https://github.com/Gyanankur23/AuroraViz)

AuroraViz is a modern Python visualization library with a unique dark/light theme toggle.  
Unlike other libraries, AuroraViz automatically inverts text colors when switching to dark mode, so your charts are always readable.

---

## PyPi Official Publication Badge

[![PyPI version](https://img.shields.io/pypi/v/auroraviz.svg)](https://pypi.org/project/auroraviz/)

## Quick Start in Google Colab

Run the following single block in Colab to clone the repo, import modules, and generate one chart in light mode and one chart in dark mode:

1. Clone the repository

```python

!git clone https://github.com/Gyanankur23/AuroraViz.git
import sys
sys.path.append("/content/AuroraViz/src")
```
2. Import modules
from auroraviz import theme, charts, palettes
import matplotlib.pyplot as plt

3. Run a chart in Light Mode
```python
theme.apply()
theme.set_palette("aurora")
fig, ax = charts.histogram(
    data=[1, 3, 2, 5, 4],
    title="AuroraViz Light Mode",
    xlabel="Index",
    ylabel="Value",
    color=palettes.CATEGORICAL[0]
)
plt.show()
```
4. Run a chart in Dark Mode
```python

theme.apply_dark()
theme.set_palette("vivid")
fig, ax = charts.histogram(
    data=[1, 3, 2, 5, 4],
    title="AuroraViz Dark Mode",
    xlabel="Index",
    ylabel="Value",
    color=palettes.CATEGORICAL[1]
)
plt.show()
`
```
---

## Notes

- The theme.apply() and theme.apply_dark() functions are the only commands you need to toggle between light and dark.  
- Palettes can be set globally with theme.setpalette("aurora") or theme.setpalette("vivid").  
- All chart types (charts.line, charts.bar, charts.scatter, etc.) respect the theme toggle automatically.

---

## Why AuroraViz is Different
- Auto text inversion — titles, labels, ticks, legends adapt instantly.  
- Palette flexibility — choose built‑in palettes or pass your own list of colors.  
- Consistent API — every chart type uses the same syntax:  
  `python
  charts.<chart_type>(data, title="...", xlabel="...", ylabel="...", color=...)
  `

---

## All Charts Display (Live Proof)

![AuroraViz Light Mode](charts/light.jpg)  
![AuroraViz Dark Mode](charts/dark.jpg)

## Showcase PDF

To generate a PDF showcase in Colab:
1. Run the light and dark examples above.
2. Use Colab’s File → Print → Save as PDF to export the notebook.
3. Share the PDF as a visual demo of AuroraViz.


## License

Protected by MIT License

## Created by
Gyanankur Baruah

Github:- [https://www.github.com/Gyanankur23]


---
