semvec/pypi-packages huggingface dataset:
qredtea 0.3.13
Quantum TEA's python tensor library beyond numpy/cupy
Requires Python: >=3.11

[![License](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)

qredtea
=======

The quantum red tea library of Quantum TEA provides tensor beyond the dense
tensor with numpy or cupy, which could be either tensor for systems with
symmetries or tensor using another backend, e.g., pytorch.

Documentation
=============

[Here](https://quantum_red_tea.baltig-pages.infn.it/py_api_quantum_red_tea/)
is the documentation. The documentation can also be built locally via sphinx.

Backends
========

We list here the supported backends with the tested version:

* `pytorch==2.5.1`
* `jax==0.4.38`
* `tensorflow==2.18.0`

The version allow to install all four tensor backends (numpy, torch, jax, tensorflow)
within python3.11 environment (for CPUs).

License
=======

The project `qredtea` is hosted at the repository
``https://baltig.infn.it/quantum_red_tea/py_api_quantum_red_tea.git``,
and is licensed under the following license:

[Apache License 2.0](LICENSE)

The license applies to the files of this project as indicated
in the header of each file, but not its dependencies.


Installation
============

The `qredtea` library is never used as stand-alone package; it replaces
the tensor backend for one of the quantum TEA applications in agreement
with a `qtealeaves` version. Therefore, the minimal use-case to explore
the library is together with `qtealeaves`. Moreover, it can be used as
well with `qmatchatea`.

Local installation via pip
--------------------------

The package is available via a local pip installation as `pip install .`,
i.e., after cloning the repository.

Dependencies
------------

The python dependencies can be found in the [``requirements.txt``](requirements.txt)
and are required independently of the following use-cases.

Depending on your use-case, more requirements might be necessary at runtime. As
we implement the API to other packages via `qredtea`, we do not require the user
to install all of them.

qtealeaves simulations
----------------------

If you want to use the `qtealeaves` toolchain, a compatible version of 
`qtealeaves` has to be installed. The correct version is fetched
automatically if this package is installed via pip. Otherwise, see
[``requirements.txt``](requirements.txt).

qmatchatea simulations
----------------------

Quantum circuit simulations via `qmatchatea` require both `qredtea` and `qtealeaves`
as a dependency. Follow the instructions contained in the `qmatchatea` repository.
pyrangeyes 0.1.9
'Genomic intervals data visualization package for dataframe objects generated with PyRanges.'
Requires Python: >=3.12

# Pyrangeyes
Gene visualization package for dataframe objects generated with [PyRanges](https://pyranges1.readthedocs.io/).

Pyrangeyes significantly facilitates genomic data
interpretation by providing powerful, customizable, and user-friendly visualizations. It
effectively covers the gap between data manipulation and visualization, thereby accelerating
the analysis workflow in genomic research.

## Overview
The goal is getting a plot displaying a series of genes, transcripts, or any kind
of ranges contained in a PyRanges object. It displays the genes' intron-exon structure 
in its corresponding chromosome, enabling easy visualization of your PyRanges data. The 
Pyranges version compatible with Pyrangeyes is >= 1.0.0 (find it at https://github.com/pyranges/pyranges_1.x.git).

To obtain a plot, the variable `engine` must be specified by the user first. This variable 
defines the graphic library on which the plots will be based: the valid `engine` options 
are "matplotlib" or "plt" for Matplotlib and "plotly" or "ply" for Plotly. 

Every other functionality can be defined during the `plot` function call. These 
functionalities include the ID column to group the intervals belonging to the same item 
(transcript, gene, protein...), items disposition, coloring criteria and palette, labels 
and output form among others. The input for the `plot` fucntion is 1 or more PyRanges 
objects, and the output is by default an interactive plot with zooming options and tooltip 
information, but if desired the plot can be directly exported to a png or pdf file.



<p align="center">
    <img src="https://github.com/pyranges/pyrangeyes/blob/main/images/general_ex.png">
</p>




## Installation
Pyrangeyes can be installed using pip. To install all dependencies in order to be able to 
use all the functionalities of the package and both engines, the `[all]` option must be 
specified:

```
pip install pyrangeyes[all]
```

If the user wishes to use only one of the engines, the installation of all dependencies 
can be avoided by using the engine-specific installation options:
```
# For matplotlib
pip install pyrangeyes[matplotlib]

# For plotly
pip install pyrangeyes[plotly]
```

Note that the minimal installation by `pip install pyrangeyes` is not able to produce plots 
since the graphical dependencies are not installed.


## Documentation
Pyrangeyes documentation and tutorial can be found at https://pyrangeyes.readthedocs.io/.