semvec/pypi-packages huggingface dataset:
ndare 0.0.0
NDARE
Requires Python: >=3.8

NDARE
=====

NDARE is currently under development.

Check out the official documentation: `https://ndare.readthedocs.io/en/latest <https://ndare.readthedocs.io/en/latest/>`_

Explore the open source code repository: `https://github.com/vitorinojoao/ndare <https://github.com/vitorinojoao/ndare>`_

Install it from the Python Package Index: `https://pypi.org/project/ndare <https://pypi.org/project/ndare/>`_
rnaseqdata 0.0.8
New Data type known as SeqData for RNA-Seq data analysis
Keywords: pypi, cicd, python

\n# ernav2_seqdata
A new data type known as SeqData is designed for RNA-seq data analysis. The data type is designed for data integration from various sources and dimensions:

<img src="static/mrnaseq_data.png" width="900" height="250">

Expression of RNA is measured by read counts of transcripts. A typical bioinformatics pipeline of mRNA-seq determines reads counts (RC) of transcripts. The RCs are typically 2-D table, of which samples are in rows, and transcripts (or genes) are in columns, or in the reverse. After that, the RC table would be normalized as FPM or FPKM or somewhere else by a certain normalized method. The next, co-founding factors among samples would be removed using a certain method namely DESEQ2 or EdgeR etc. Moreover, those data would be transformed into various table, namely log, or partitioned into some subset. Bioinformatician should manage all those data sets during statistical anlaysis.

<img src="static/SeqData.png" width="600" height="400">

Biological scientists may be more care about significance of mRNA-seq data analysis, and what those significance reveals. In this case, sample informations, or patient information, or features of samples (namely single cells) shall be considered. Moreover, aside from transcript ID or Gene ID, other annotations would be integrated, for example, genomic annoations namely chromosome locus, protein annotations namely domain identification would be integrated, too. Those annoation data may not be used in statistical process, but really needed for further study.

SeqData is tree structure. The root contains data of phenotypes and annotations. Each node contains various attributes including X in m x n, and var (statistical aggregations). Nodes inherite the attributes of the root nodes. Data of children nodes is determined by those of parent nodes.

<img src="static/SeqData_data_structure.png" width="350" height="300">

## installation
It is convenient to install the repository using pip. The package could be found at [Pythone Package Index](https://pypi.org/manage/project/rnaseqdata/releases/).
```
pip install --upgrade rnaseqdata
```

## Development
```
git clone git@github.com:Tiezhengyuan/ernav2_seqdata.git
cd ernav2_seqdata
```

create virtual environment
```
virtualenv venv
source venv/bin/activate
pip install -r requirements.txt
```


Unit testing
```
pytest tests/unittests
```

## quick tourial
In Python3

```
from rnaseqdata import RootData, SeqData
import numpy as np
import pandas as pd
```

Create SeqData

```
root = RootData()
c = SeqData(root)
c.put_data('test', np.eye(3), root)
c.to_df('test)
```

          0    1    2
     0  1.0  0.0  0.0
     1  0.0  1.0  0.0
     2  0.0  0.0  1.0
hack4u-promixion 0.1.1
Biblioteca para consultar cursos de la academia hack4u.

# Hack4u Academy Python Library

Libreria para consultar cursos de la academia Hack4u.

## Cursos disponibles.

- Introducción a Linux [15 horas]
- Personalización de entorno en Linux [3 horas]
- Python Ofensivo [35 horas]
- Introducción al Hacking [53 horas]

## Instalación 

Instala el paquete con el gestor de paquetes `pip3`:

```python3
pip3 install hack4u_promixion
```

## Uso básico

### Listar todos los cursos

```python 
from hack4u_promixion import listarCursos

listarCursos()
```

### Listar curso por nombre

```python
from hack4u_promixion import listarCursoPorNombre

listarCursoPorNombre("Introducción a Linux")
```

### Calcular duración total de los cursos

```python3
from hack4u_promixion import duracionTotal 

print(f"La duración total de los cursos es de {duracionTotal()} horas.")
```
biofile 0.1.0
Process various file format for RNA-Seq data analysis
Keywords: pypi, cicd, python

\n# Bioinformatics Tool: bioFile

## Introduction
Retrieve data from various file formats used in RNA-Seq data analysis. The tool currently support:
- GTF file: genomic annotations
- GFF file: genomic annoations

quick installation
```
pip install biofile
```


## Development

```
git clone git@github.com:Tiezhengyuan/bio_file.git
cd bio_file
source venv/bin/activate
```

Run unit testing:
```
pytest tests/unittests
```

## Quick tour


### Process GFF:
Retrieve annotations by features from <gff_file>. Multiple json files would be stored in <out_dir>
```
from biofile import GFF
g = GFF(gff_file, out_dir)
g.split_by_features()
```

Given an attribute, retrieve annotations from <gff_file>. and save dataframe in <out_dir>. Here, search all mRNA according to transcript_id. All related annotations are included. The output is transcript_id_mRNA.txt.
```
from biofile import GFF
g = GFF(gff_file, out_dir)
g.parse_attributes('transcript_id', 'mRNA')
```

### Process GTF:
Retrieve annotations by features from <gtf_file>. Multiple json files would be stored in <out_dir>
```
from biofile import GTF
g = GTF(gtf_file, out_dir)
g.split_by_features()
```

Given an attribute, retrieve annotations from <gtf_file>. and save dataframe in <out_dir>. Here, search all mRNA according to transcript_id. All related annotations are included. The output is transcript_id_mRNA.txt.
```
from biofile import GTF
g = GTF(gtf_file, out_dir)
g.parse_attributes('transcript_id', 'mRNA')
```
jaraco.postgres 5.2.0
PostgreSQL services by jaraco
Requires Python: >=3.8

.. image:: https://img.shields.io/pypi/v/jaraco.postgres.svg
   :target: https://pypi.org/project/jaraco.postgres

.. image:: https://img.shields.io/pypi/pyversions/jaraco.postgres.svg

.. image:: https://github.com/jaraco/jaraco.postgres/actions/workflows/main.yml/badge.svg
   :target: https://github.com/jaraco/jaraco.postgres/actions?query=workflow%3A%22tests%22
   :alt: tests

.. image:: https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/charliermarsh/ruff/main/assets/badge/v2.json
    :target: https://github.com/astral-sh/ruff
    :alt: Ruff

.. image:: https://readthedocs.org/projects/jaracopostgres/badge/?version=latest
   :target: https://jaracopostgres.readthedocs.io/en/latest/?badge=latest

.. image:: https://img.shields.io/badge/skeleton-2024-informational
   :target: https://blog.jaraco.com/skeleton

Routines and fixtures for launching and managing
`PostgreSQL <https://postgresql.org>`_ instances.

Pytest Plugin
=============

This library includes a pytest plugin. To enable it, simply
include this library in your test requirements.

Then, in your tests, simply add a ``postgresql_instance``
parameter to your functions.

Instance
--------

The ``postgresql_instance`` is a ``jaraco.postgres.PostgresServer``
instance with ``host`` and ``port`` properties.
gronckle 0.0.3
Quality of content LLM application infrastructure
Requires Python: >=3.8

# What Is Gronckle?
<img src="https://static.wikia.nocookie.net/howtotrainyourdragon/images/b/b8/HTTYD_Gronckle.png/revision/latest?cb=20171031142609" width="200" height="200" alt="dragon">
Gronckle is a layer of LLM application infrastructure designed to help developers get higher quality content from their underlying LLMs without having to change which LLMs they're using or how their application works

# Requirements
We're building this package with the assumption that developers will be using a specific stack:
**Vector Database** --> Cassandra
**Vector Database Provider** --> Astra by Datastax