semvec/pypi-packages huggingface dataset:
Gamse 0.98
Generic Echelle Data Reduction Package
Requires Python: >=3.9


GAMSE
=====
<img src="https://github.com/wangleon/gamse/blob/master/docs/images/gamse.svg" width=250>

[![PyPI](https://img.shields.io/pypi/v/gamse.svg)](https://pypi.org/project/gamse/)
[![Read the Docs](https://img.shields.io/readthedocs/gamse.svg)](https://gamse.readthedocs.io/)
[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)

`GAMSE` is a data reduction package for high-resolution échelle spectrographs.
It contains necessary subroutines in spectral reduction process, including
overscan correction, bias subtraction, order detection, flat-fielding
correction, background correction, and optimal extraction.

Dependencies
------------
`GAMSE` is based on Python 3.4 or later, and does not work in Python 2.x.
To use `GAMSE` the following packages are required:

* [Numpy](http://www.numpy.org/) 1.16.1 or later: A Python library for
  multi-dimensional arrays and mathematics.
* [Scipy](https://www.scipy.org/) 0.17.0 or later: A Python library for
  scientific computing.
* [Matplotlib](https://matplotlib.org/) 2.2.0 or later: To display and generate
  output figures.
* [Astropy](http://www.astropy.org/) 3.1.1 or later: To read and write FITS
  files and ASCII tables.

Installation
------------
To install `GAMSE` package with `pip`, simply use the following command:

```bash
sudo pip install gamse
```

Or alternatively, clone the whole repository with GIT:

```bash
git clone https://github.com/wangleon/gamse.git
```

Then run the setup script in the cloned directory:

```bash
sudo python3 setup.py install
```
ribotools 2.0.0
Ribotools: a toolbox for translation efficiency (TE) and differential expression (DE) analyses
Requires Python: <3.14,>=3.11
Keywords: Bioinformatics, Ribo-seq/Ribosome profiling, RNA-seq/RNA sequencing, Translation efficiency (TE), Differential expression (DE)

# Ribotools

**Ribotools**, an _add-on_ to [Rp-Bp](http://rp-bp.readthedocs.io/en/latest/), is a toolbox for the analysis of matched ribosome profiling (Ribo-seq) and RNA sequencing (RNA-seq) data.

**Ribotools** can be used to generate count tables, starting from raw, trimmed reads, or existing alignments, taking into consideration Ribo-seq periodicity estimates and RNA-seq fragment length. **Ribotools** can perform translation efficiency (TE) or differential expression (DE) analysis, using standard features such as genes (exon or CDS), or Ribo-seq ORFs, accommodating for sample-to-sample variance, complex experimental designs, or confounding factors. For TE analysis, features are automatically classified into different regulatory layers.

<p align="center">
  <a href="https://ribotools.readthedocs.io/en/latest"><img alt="Ribotools" src="https://github.com/eboileau/ribotools/raw/master/docs/source/_static/ribotools-logo-light.png"></a>
</p>

<p align="center">
<a href="http://bioconda.github.io/recipes/ribotools/README.html"><img alt="Install with bioconda" src="https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg?style=flat"></a>
<a href="https://pypi.org/project/ribotools/"><img alt="PyPI" src="https://img.shields.io/pypi/v/ribotools"></a>
<a href="https://github.com/eboileau/ribotools/actions/workflows/ci.yml"><img alt="CI" src="https://github.com/eboileau/ribotools/actions/workflows/ci.yml/badge.svg"></a>
<a href='https://ribotools.readthedocs.io/en/latest/?badge=latest'>
    <img src='https://readthedocs.org/projects/ribotools/badge/?version=latest' alt='Documentation Status' />
</a>
</p>

---

## Documentation

Consult the [user guide](https://ribotools.readthedocs.io/en/latest) for instructions on how to install the package and detailed usage instructions.

## How to report issues

For bugs, issues, or feature requests, use the [bug tracker](https://github.com/eboileau/ribotools/issues). Follow the instructions and guidelines given in the templates.

## License

The MIT License (MIT). Copyright (c) 2019 Etienne Boileau.
tianmoucv 0.4.0.0
Algorithms library for Tianmouc sensor_preview version
Requires Python: >=3.8
Keywords: tianmoucv

TianmouCV is the official algorithm library of Tinamouc V1 (and soon will support Tianmouc V2..), including codec of the Tianmouc data (.tmdat files), deeplearning-friendly dataset reader, basic ISP tools, feature descriptions of TSD, classicle optical flow estimator and gray-sacle reconstructor, some basic but useful neural-network (reconstruction, opticla flow, deblur, instance segmentation and so on).