Metadata-Version: 2.4
Name: iter-mint
Version: 1.4.0
Summary: A Python Qt application for ITER Data Visualtization using the iplotlib framework
Author-email: ITER Organization <jscabanilla@minsait.com>
Maintainer-email: Jhon Steeven Cabanilla Alvarado <jscabanilla@minsait.com>, Pablo Martin Villares <pmartin@minsait.com>, Mario Gomez Ballesteros <mgomezb@minsait.com>
License: MIT
Project-URL: Homepage, https://github.com/iplot-viz/mint
Project-URL: Repository, https://github.com/iplot-viz/mint.git
Project-URL: Issues, https://github.com/iplot-viz/mint/issues
Keywords: MINT,Qt,Application,UDA,Data,Visualization
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.9
Description-Content-Type: text/markdown
Requires-Dist: iplotLogging>=1.2.0
Requires-Dist: iplotlib>=1.3.2
Requires-Dist: iplotDataAccess>=1.3.1
Requires-Dist: iplotWidgets>=1.3.0
Requires-Dist: iplotProcessing>=1.1.5
Requires-Dist: psutil>=5.8.0
Provides-Extra: test
Requires-Dist: pytest>=6.0; extra == "test"

## Description

This project contains example usage of the iterplot libary in a Qt application. 
The Qt application allows to select a set of UDA variables and the plot them using either matplotlib or PyQtGraph graphics library.
Following features are currently supported:

* Plotting multiple graphs in a row/column layout
* Plotting multiple signals in one plot (either stacked or not) by using the `ROW.COLUMN.STACK` format
* Support for Pan/Zoom/Crosshair/Distance/Markers
* Support for automatically downloading UDA data (for continous signals - currently Matplotlib only)
* Support for basic data processing. See [iplotProcessing](https://github.com/iplot-viz/iplotprocessing)
* Customize appearance and styling of canvas, plots, axes, fonts, lines in a cascading manner.* 
* Computation of different statistical metrics for the displayed signals.
* Export of canvas data to CSV format.
* Export of canvas data and MINT tables to .h5 or .parquet formats

## Installation
Install the package from PyPi:

  ```bash
  pip install iter-mint
  ```

## Run the app
```bash
mint
```


## Contributing

1. Fork it!
2. Create your feature branch: ```git checkout -b my-new-feature ```
3. Commit your changes: ```git commit -am 'Add some feature' ```
4. Push to the branch:```git push origin my-new-feature ```
5. Submit a pull request
