semvec/pypi-packages huggingface dataset:
pyblocks-data 1.0.5
Interfaz visual para análisis de datos y Machine Learning en Streamlit
Requires Python: >=3.10

🧩 PyBlocks.Data (Pybs)
PyBlocks.Data (Pybs) es una interfaz visual interactiva para análisis de datos, Machine Learning y series temporales construida con Streamlit.
Permite cargar datos, explorarlos, procesarlos, modelarlos y exportar resultados sin escribir código, pero manteniendo la potencia de Python.

🚀 Características principales
✅ Gestión completa del flujo de datos

Carga de archivos CSV

Manejo de valores nulos

Análisis estadístico básico e inferencial

Visualizaciones interactivas

EDA automático con Sweetviz

✅ Machine Learning integrado

Transformaciones y preprocesamiento

Ingeniería de variables

Reducción de dimensionalidad (PCA)

Modelado con Scikit-learn

Clustering

Comparación y evaluación de modelos

Detección avanzada de outliers y anomalías

✅ Series temporales

Descomposición estacional

Pronósticos con Prophet y SARIMAX

Comparación de modelos de predicción

✅ Análisis avanzado

NLP y análisis de texto

Análisis de correlaciones

Conciliación y comparación de bases de datos

✅ Extras

Chatbot analítico con LLM

Exportación del proyecto

Resumen automático del flujo

📦 Instalación
Asegúrate de tener Python 3.10+ instalado.

# Crear entorno virtual (opcional pero recomendado)
python -m venv venv
source venv/bin/activate   # Linux/Mac
venv\Scripts\activate      # Windows

# Instalar el paquete desde PyPI
pip install pyblocks-data
▶️ Uso
Una vez instalado, puedes ejecutarlo con cualquiera de estos comandos:

pyblocks_data   # comando completo
pybs            # ✅ alias corto
Esto abrirá automáticamente la interfaz en tu navegador:

Local URL: http://localhost:8501
📂 Estructura interna (para desarrolladores)

pyblocks_data/
│
├── app.py
├── lego_blocks/
│   ├── load_data.py
│   ├── basic_stats.py
│   ├── ...
│   └── chatbot_llm.py
│
├── utils/
│   └── helpers.py
└── README.md
🛠️ Tecnologías usadas
Streamlit

Pandas

Scikit-learn

Prophet / SARIMAX

Sweetviz

Plotly

Wordcloud

NLTK

OpenAI API

❤️ Autor
Desarrollado con cariño por Ana Maraboli y Señor G (ChatGPT)

✨ ¿Quieres contribuir?

Haz un fork

Crea una rama nueva

git checkout -b feature-nueva
Envía un Pull Request
h5features 2.0.0
Easy and efficient storage of large features data in HDF5 file format
Requires Python: >=3.12
Keywords: HDF5, speech, machine learning

==========
h5features
==========

* The h5features library provides easy to use and efficient storage of **large
  features data** on the HDF5 binary file format.

* It has been primarly designed to store speech features extracted from huge
  corpora.

* It is implemented in **C++** and exposes **Python bindings**.

* The Python package is available on PyPI: ``pip install h5features``.

* See the complete documentation at https://docs.cognitive-ml.fr/h5features.
pini 1.5.2
An opinionated Python project initializer that sets up a new project with sensible defaults.
Requires Python: >=3.12

# 🚀 PIni — Project Initializer

**PIni** stands for **Project Initializer** — a blazing-fast CLI tool to scaffold new projects in your favorite frameworks **with zero setup fuss**.

Whether you're starting a new **FastAPI**, **Django**, **DRF**, **Next.js**, or plain **Python** project, PIni gets you a clean, standardized, production-ready starter in seconds.

<img src='pini-demo.gif' style="width: 100%; height: auto;" />

---

## ⚙️ Features

- ✅ Scaffolds common frameworks (FastAPI, Django, DRF, Next.js, Python)
- 🔧 Pre-configured **linters**, **formatters**, and **pre-commit hooks**
- 🧼 Opinionated defaults for **code style**, **project structure**, and **.gitignore**
- 📝 Auto-generates README and adds author info
- 🔒 Git initialized + first commit (so Commitizen doesn’t blow up)
- ✨ Commitizen + semantic commits ready out-of-the-box

---

## 💡 Why?

I got tired of rewriting the same setup boilerplate for every new idea?
PIni exists so you can **stop configuring** and **start building** without compromising on code quality and commits.
No need to create the same custom components for frontend, just import them into the templates directory and it will auto-replicate.

In other words, this project is aimed at doing things in the best and laziest way possible.

---

## 📦 Supported Frameworks

| Framework     | Stack   | Extras                                             |
| ------------- | ------- | -------------------------------------------------- |
| FastAPI       | Python  | uv, black, flake8, isort, pre-commit               |
| Django        | Python  | uv, black, flake8, isort, pre-commit               |
| DRF           | Python  | uv, black, flake8, isort, pre-commit               |
| Next.js       | Node/TS | Tailwind, Prettier, ESLint, Pre-commit, Commitizen |
| Python (base) | Python  | Linter config, Git, pyproject.toml, etc.           |
| Python (CV)   | Python  | TODO                                               |

---

## 🚀 Usage & Installation

```bash
uv tool install pini

pini configure
pini create

# Follow the prompts and enjoy!
```

---

## 🧰 Templates

All configs live under the `/templates` directory:

- `.gitignore`
- `.pre-commit-config.yaml`
- `README.md.tmpl`
- `prettierrc`, `prettierignore`
- `ISSUE_TEMPLATES`
- More to come (_maybe_)

---

## 🛣️ Roadmap

- [ ] Add testing frameworks (pytest, vitest)
- [ ] Add optional Dockerfile defaults
- [ ] Improve Commitizen integration in JS flows
- [ ] CLI flags for silent mode / auto init
- [ ] Add more frameworks
- [ ] Add Dockerized database initialization (MySQL, PostgreSQL, Supabase)

---

## 🤝 Contributing

PRs welcome — just follow the code style and keep templates clean.

---

## 📜 License

MIT — do whatever you want, just don't make it worse 😄
WeeLab 0.1.3
Unified control library for lab equipment (oscilloscopes, power supplies, lasers, etc.)
Requires Python: >=3.8

# Weeroc Lab Devices Package

This is an attempt to make a single python package containing all the tools for controlling the lab equipment as simply as possible.

## Install

### PyPI

To install the package use `pip install WeeLab`

## Currently available devices

- CAEN A7585 SiPM power supply **(device tested)**

    ``` python
    from WeeLab import SerialConnection, CAEN_A7585
    caen = CAEN_A7585(
        conn=SerialConnection(port="COM6", baudrate=115200))
    caen.set_voltage(30)  # Set voltage to 30V
    caen.wait_setpoint_reached()  # Wait until the setpoint is reached
    caen.get_IV()  # Get the current and voltage values
    ```

- Lecroy WaveRunner **(device tested)**

    ``` python
    from WeeLab import Vxi11Connection, LeCroyWaveRunner
    lecroy = LeCroyWaveRunner(conn=Vxi11Connection(ip="192.168.2.21"))
    p1_mean = lecroy.getMeasure(1, "MEAN") # Returns mean value of P1 measurement 
    lecroy.clear_sweeps() # Clear sweeps
    ```

- NKT Pilas Laser **(device tested)**

    ``` python
    from WeeLab import SerialConnection, NktPilasLaser
    laser = NktPilasLaser(
        conn=SerialConnection(port="COM6", baudrate=19200))
    laser.set_tune(500) # Sets tune 50%
    laser.set_frequency(20_000_00) # Sets frequency 20 MHz
    laser.enable() # Enables radiation
    ```

- Keithley 2100 digital multimeter **(device tested)**
    ``` python
    from WeeLab import KeithleyMultimetre, VisaConnection
    keithley = KeithleyMultimetre(
        conn=VisaConnection(resource_name='USB0::0x05E6::0x2100::8012509::INSTR'))
    keithley.configure_voltage(0.001, 0.001) # Configures voltage measurement with 1 mV range and 1 mV resolution
    keithley.configure_current(0.001, 0.001) # Configures current measurement with 1 mA range and 1 mA resolution
    keithley.set_sample_count(100)      # Collect 100 samples per measurement
    current = keithley.get_current()    # Get an average current from samples
    voltage = keithley.get_voltage()    # Get an average voltage from samples
    ```

- Tektronix AWG generator *(test TODO)*

    ``` python
    from WeeLab import VisaConnection, Tektronix_AFG3252
    afg = Tektronix_AFG3252(
        conn=VisaConnection("USB0::0x0699::0x0345::C010630::INSTR"))
    afg.do_set_voltage_ch1(5) # Sets voltage on ch1 to 5 V
    afg.set_frequency_ch1(10_000) # Sets frequency 10 kHz
    afg.do_set_status_ch1("on") # Enables generation in ch1
    ```

- WeeMove translation stage *(test TODO)*

    ``` python
    from WeeLab import SerialConnection, Stage
    stage = Stage(
        conn=SerialConnection(port="COM4", baudrate=9600))
    stage.move(10, 20) # Moves to x=10cm, y=20cm
    stage.wait_arrived() # Waits until the stage is no longer moving
    stage.ask_position() # Prints current position
    ```
DockForge 1.0
Preparation package for molecular docking
Requires Python: >=3.7
Keywords: molecule, docking, molecular docking, bioinformatics

# DockForge
A Python package for preparing proteins and ligands for molecular docking.  
Includes: hydrogen addition, charge assignment, format conversion, cleanup.

## Requirements
Requires **Open Babel** to be installed separately. Install via:  
```brew install open-babel``` or ```conda install -c conda-forge openbabel```  
See [Open Babel installation instructions](https://openbabel.org/docs/Installation/install.html).
  
Also requires PDBfixer. Install via:  
```conda install -c conda-forge pdbfixer```
TexIV 0.1.10
A machine learning–based method for transforming text into instrumental variables (IV).
Requires Python: >=3.11

<p align="center">
  <img src="source/img/TexIV-logo_16_9.png" width="256" alt="TexIV Logo">
</p>

---
A machine learning–based package for transforming text into instrumental variables (IV).

[![PyPI version](https://img.shields.io/pypi/v/texiv.svg)](https://pypi.org/project/texiv/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](LICENSE)
[![Issue](https://img.shields.io/badge/Issue-report-green.svg)](https://github.com/sepinetam/texiv/issues/new)
[![Wiki](https://img.shields.io/badge/Doc-Wiki-blue.svg)](https://github.com/sepinetam/texiv/wiki)
[![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/SepineTam/TexIV)

---

## 🌰 Example
Visit [Stata Example File](source/example/dofiles/main.do) and [Python Example File](source/example/pyscript/main.py).

There is a step-by-step example.

## ✨ Feature

- Support multiple Chinese word segmentation and embedding methods
- Customizable stopwords
- Support keyword relevance filtering and two-stage filtering
- Output includes frequency, total count, and ratio statistics

## 📦 Requirements

- Python 3.11+
- Recommended to use virtual environment (e.g., `venv` or `conda`)

## 🚀 Quickly Start

### Install
```bash
pip install texiv
```

### Usage
```python
from typing import List

from texiv import TexIV

texiv = TexIV()
content: str = "This is a test text..."
keywords: List[str] = ["keyword1", "keyword2", "keyword3"]
texiv.texiv_it(content, keywords)
```

Output example:

```
{'freq': 7, 'count': 34, 'rate': 0.20588235294117646}
```

## 🖥️ Command Line Tool

The project also provides a command-line interface that can be used directly after installation:

```bash
texiv --help
```

## 🛠️ Configuration

All models and parameters can be adjusted through configuration files in `～/.texiv/config.toml`.


## 📄 License

This project is licensed under the MIT License. See [LICENSE](./LICENSE) for details.
psifx 0.2.0
Psychological and Social Interactions Feature Extraction
Requires Python: >=3.10
Keywords: video, audio, language, machine-learning, pipeline, multimodal

# `psifx` - <u>P</u>sychological and <u>S</u>ocial <u>I</u>nteractions <u>F</u>eature e<u>X</u>traction

---

*`psifx` is a "plug-and-play" multi-modal feature extraction toolkit, aiming to facilitate and democratize the use of state-of-the-art machine learning techniques for human sciences research.
It is motivated by a need 
(a) to automate and standardize data annotation processes, otherwise involving expensive, lengthy, and inconsistent human labor, such as the transcription or coding of behavior changes from audio and video sources;
(b) to develop and distribute open-source community-driven psychology research software;
(c) to enable large-scale access and ease of use to non-expert users.
The framework contains an array of tools for tasks, such as speaker diarization, closed-caption transcription and translation from audio, as well as body, hand, and facial pose estimation and gaze tracking from video.
The package has been designed with a modular and task-oriented approach, enabling the community to add or update new tools easily.
We strongly hope that this package will provide psychologists a simple and practical solution for efficiently a range of audio, linguistic, and visual features from audio and video, thereby creating new opportunities for in-depth study of real-time behavioral phenomena.*

https://github.com/user-attachments/assets/f33cdac0-57f6-4936-acb2-8866ccfea7b2

This demo clip is not intended for commercial use, and is solely for demonstration in an academic or research context.

## Documentation, Reference & Quickstart

Visit https://psifx.github.io/psifx/

arXiv preprint:  https://www.arxiv.org/abs/2407.10266