Metadata-Version: 2.1
Name: idsred
Version: 0.1.0
Summary: INT-IDS data-reduction pipeline
Home-page: https://github.com/temuller/idsred
Author: Tomás Enrique Müller-Bravo
Author-email: t.e.muller-bravo@ice.csic.es
License: MIT
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.8
Description-Content-Type: text/markdown
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: matplotlib
Requires-Dist: astropy
Requires-Dist: ccdproc
Requires-Dist: specutils
Requires-Dist: python-dotenv
Requires-Dist: scipy

# IDSRED: INT-IDS data-reduction pipeline

This is a spectroscopic data-reduction pipeline for the Isaac Newton Telescope (INT) Intermediate Dispersion Spectrograph (IDS) instrument. 
It is optimised for the blue detector (EEV10).

[![repo](https://img.shields.io/badge/GitHub-temuller%2Fidsred-blue.svg?style=flat)](https://github.com/temuller/idsred)
[![license](http://img.shields.io/badge/license-MIT-blue.svg?style=flat)](https://github.com/temuller/idsred/blob/master/LICENSE)
![Python Version](https://img.shields.io/badge/Python-3.8%2B-blue)
[![PyPI](https://img.shields.io/pypi/v/idsred?label=PyPI&logo=pypi&logoColor=white)](https://pypi.org/project/idsred/)

## Installation

It is recommended to install it on an anaconda environment:

```code
conda create -n idsred pip
conda activate idsred
```

and install it using pip:

```code
pip install "idsred"
```

or from source:

```code
git clone https://github.com/temuller/idsred.git
cd idsred
pip install .
```

Developer mode:

```code
pip install -e .
```

## Usage example

A notebook that explains how to use the pipeline is found in this repository [here](https://github.com/temuller/idsred/blob/main/reduction.ipynb).

## Contributing

To contribute, either open an issue or send a pull request (preferred option). You can also contact me directly.

## Acknowledgement

This pipeline is based on the [GROWTH school github repository](https://github.com/growth-astro/growth-school-2020), which nicely explains the entire reduction process for images and spectra, and on [INT-IDS-DataReduction](https://github.com/aayush3009/INT-IDS-DataReduction) for (long-slit) spectra reduction.
