semvec/pypi-packages huggingface dataset:
dynamics-odata-client 0.1.0
Cliente OData para Dynamics 365 con autenticación y exportación a DataFrame
Requires Python: >=3.7

# 🔗 dynamics_odata_client

**Cliente OData para Dynamics 365**, diseñado para conectarse fácilmente a la API OData con autenticación OAuth2, manejo automático de token, consultas paginadas y exportación directa a `pandas.DataFrame`.

Ideal para automatizar reportes, análisis o integraciones con Dynamics 365 desde Python.

---

## 🚀 Instalación

Instala el paquete directamente desde PyPI:

```bash
pip install dynamics-odata-client
```

O desde tu entorno local (para desarrollo):

```bash
pip install .
```

---

## ⚙️ Configuración

Este paquete requiere un archivo `.env` en la raíz del proyecto con las siguientes variables:

### 📁 `.env` (ejemplo)
```env
DYNAMICS_BASE_URL=https://empresa.crm.dynamics.com
DYNAMICS_AUTH_URL=https://login.microsoftonline.com/tu_tenant_id/oauth2/token
CLIENT_ID=tu_client_id
CLIENT_SECRET=tu_client_secret
RETRIES=5
DELAY=2
TIMEOUT=10
BATCH_SIZE=100
```

También puedes consultar el archivo `.env.example` incluido en el paquete.

---

## 🧪 Ejemplo de uso

```python
from dynamics_odata_client import DynamicsClient
import pandas as pd

# Crear cliente Dynamics (usa .env por defecto)
client = DynamicsClient()

# Definir entidad y filtros
entidad = "ReleasedProductCreationsV2"
query = "$select=ProductNumber,ProductDescription"

# Obtener los datos como DataFrame
df = client.get_data(entidad, query)

# Mostrar resultados
print(df.head())
```

---

## 📁 Estructura del paquete

| Archivo / Carpeta            | Descripción |
|-----------------------------|-------------|
| `client.py`                 | Cliente principal para consultas OData y manejo de token |
| `config.py`                 | Clase de configuración con parámetros dinámicos |
| `config_loader.py`          | Carga de configuración desde archivo `.env` |
| `__init__.py`               | Expone las clases principales del paquete |
| `.env.example`              | Plantilla de configuración para usuarios |
| `setup.py`                  | Archivo de instalación del paquete |
| `requirements.txt`          | Dependencias necesarias |

---

## 📥 Requisitos

- Python >= 3.7
- requests
- pandas
- python-dotenv

Instala las dependencias con:

```bash
pip install -r requirements.txt
```

---

## 📦 Publicación en PyPI (para desarrolladores)

1. Construir el paquete:
```bash
python -m build
```

2. Subir a PyPI:
```bash
python -m twine upload dist/*
```

3. Instalar desde PyPI:
```bash
pip install dynamics-odata-client
```

---

## 🧾 Licencia

MIT License - Libre uso para proyectos personales y comerciales.
3-04-2025-ttm 0.1.0
Add your description here
Requires Python: >=3.12

# Sourcery - Use Generative AI for Procurement
Soon to be on Y combinator, I kid, this is a full stack application showcasing how you might use Generative AI to help with procurement processes. 

## See it live and in action 📺
<img src="https://i.imgur.com/f97CRzO.gif"/>

# Startup 🚀
1. Install all the libraries `npm install && cd client && npm install`
2. Update your `WATSONX_AI_APIKEY` and `WATSONX_PROJECT_ID` in the `.example_env` file and rename the file to `.env`
3. Start a Postgres server, I find the easiest way to do this is with the <a href="https://postgresapp.com/">Postgres App</a>
4. Update the `DB_NAME` and `DB_PORT` in the `.env` file 
5. Create a test user by running `node tests/model/testuser.js`
5. Create a test vendor user by running `node tests/model/testvendor.js`
6. Start the app by running `npm run dev` this should start the backend express server and the front end Vite app which should run locally at <a href="http://localhost:5173/">http://localhost:5173/</a>
7. Login using the test user 
UN: user@gmail.com
PW: abc123

</br>
# Other References 🔗 </br>
- Example: https://github.com/ibm-granite/granite-tsfm/blob/main/notebooks/hfdemo/ttm_getting_started.ipynb

# Who, When, Why?

👨🏾‍💻 Author: Nick Renotte <br />
📅 Version: 1.x<br />
📜 License: This project is licensed under the MIT License </br>
reading 2025.4.3
一个文本离线朗读软件。用户既可以在界面上输入文本进行朗读，也能选择本地的文本文件进行全文朗读
Requires Python: >=3.12

# 描述

一个文本离线朗读软件。用户既可以在界面上输入文本进行朗读，也能选择本地的文本文件进行全文朗读。

# 作者

作者: 许灿标

链接: [主页](https://lcctoor.com) \| [Github](https://github.com/lcctoor) \| [PyPi](https://pypi.org/user/lcctoor) \| [微信](https://lcctoor.com/cdn/WeChatQRC.jpg) \| [邮箱](mailto:lcctoor@outlook.com) \| [捐赠](https://lcctoor.com/cdn/DonationQRC-0rmb.jpg)

# 教程

## 安装

```
pip install reading
```

## 运行

```python
from reading import reading_server

reading_server(port=9355)  # 可通过 port 参数指定服务端口
```

此时, 将自动打开浏览器, 并访问 `http://localhost:9355/` .
django-otp-agents 1.1.0
Integration of django-otp and django-agent-trust.
Requires Python: >=3.7

.. image:: https://img.shields.io/pypi/v/django-otp-agents?color=blue
   :target: https://pypi.org/project/django-otp-agents/
   :alt: PyPI
.. image:: https://img.shields.io/readthedocs/django-otp-agents
   :target: https://django-otp-agents.readthedocs.io/
   :alt: Documentation
.. image:: https://img.shields.io/badge/github-django--otp--agents-green
   :target: https://github.com/django-otp/django-otp-agents
   :alt: Source

This uses `django-agent-trust`_ to add OTP-based machine authorization to
django-otp.

See `django-otp`_ for more information on the OTP framework.

.. _django-agent-trust: http://pypi.org/project/django-agent-trust
.. _django-otp: http://pypi.org/project/django-otp

.. end-of-doc-intro


Development
-----------

This project is built and managed with `hatch`_. If you don't have hatch, I
recommend installing it with `pipx`_: ``pipx install hatch``.

``pyproject.toml`` defines several useful scripts for development and testing.
The default environment includes all dev and test dependencies for quickly
running tests. The ``test`` environment defines the test matrix for running the
full validation suite. Everything is executed in the context of the Django
project in test/test\_project.

As a quick primer, hatch scripts can be run with ``hatch run [<env>:]<script>``.
To run linters and tests in the default environment, just run
``hatch run check``. This should run tests with your default Python version and
the latest Django. Other scripts include:

* **manage**: Run a management command via the test project. This can be used to
  generate migrations.
* **lint**: Run all linters.
* **fix**: Run all fixers to address linting issues. This may not fix every
  issue reported by lint.
* **test**: Run all tests.
* **check**: Run linters and tests.
* **warn**: Run tests with all warnings enabled. This is especially useful for
  seeing deprecation warnings in new versions of Django.
* **cov**: Run tests and print a code coverage report.

To run the full test matrix, run ``hatch run test:run``. You will need multiple
specific Python versions installed for this.

You can clean up the hatch environments with ``hatch env prune``, for example to
force dependency updates.


.. _hatch: https://hatch.pypa.io/
.. _pipx: https://pypa.github.io/pipx/
svsbench 0.1.0
Benchmarking for Scalable Vector Search
Requires Python: >=3.12

# Scalable Vector Search Benchmarking
[![OpenSSF Scorecard](https://api.scorecard.dev/projects/github.com/IntelLabs/ScalableVectorSearchBenchmarking/badge)](https://scorecard.dev/viewer/?uri=github.com/IntelLabs/ScalableVectorSearchBenchmarking)
![GitHub License](https://img.shields.io/github/license/IntelLabs/ScalableVectorSearchBenchmarking)
![python-support](https://img.shields.io/badge/Python-3.12-3?logo=python)

Scalable Vector Search Benchmarking enables the benchmarking or evaluation of the [Scalable Vector Search](https://github.com/intel/ScalableVectorSearch) library.

## Installation

Requires Python >= 3.12.

```sh
python -m pip install \
    git+https://github.com/IntelLabs/ScalableVectorSearchBenchmarking
```

## Usage

### Building an index

```sh
python -m svsbench.build \
    --vecs_file /path/to/vectors.fvecs \
    --svs_type leanvec4x8 --leanvec_dims -4 \
    --proportion_vectors_init 0.5 --batch_size 10000
```

### Computing the ground truth

For the query vectors used in performance measurements:
```sh
python -m svsbench.generate_ground_truth \
    --vecs_file vectors.fvecs \
    --query_file query_vectors.fvecs
```

For the query vectors used in the calibration of search parameters:
```sh
python -m svsbench.generate_ground_truth \
    --vecs_file vectors.fvecs \
    --query_file calibration_query_vectors.fvecs
```

### Searching

Calibrating the search parameters for a given recall and then searching:
```sh
python -m svsbench.search \
    --idx_dir /path/to/index_dir_from_build \
    --svs_type leanvec4x8 \
    --query_file /path/to/query_vectors.fvecs \
    --ground_truth_file /path/to/ground_truth.ivecs \
    -k 5 \
    --recall 0.95 \
    --calibration_query_file /path/to/calibration_query_vectors.fvecs \
    --calibration_ground_truth_file /path/to/calibration_ground_truth.ivecs
```

Searching using specified search parameters:
```sh
python -m svsbench.search \
    --idx_dir /path/to/index_dir_from_build \
    --svs_type leanvec4x8 \
    --query_file /path/to/query_vectors.fvecs \
    --ground_truth_file /path/to/ground_truth.ivecs \
    -k 5 \
    --batch_size 1 \
    --search_window_size 14 \
    --search_buffer_capacity 34 \
    --prefetch_lookahead 1 \
    --prefetch_step 0 \
    --batch_size 10000 \
    --search_window_size 15 \
    --search_buffer_capacity 36 \
    --prefetch_lookahead 10 \
    --prefetch_step 4
```
ppp-run 0.2.1
A CLI tool to run scripts from pyproject.toml

# ppp-run

`ppp` is a simple script/alias runner for Python - akin to `npm run` in javascript world.

## Installation

```sh
uv add --dev ppp-run
```

## Usage

### CLI

`ppp` has only 2 commands

- `ppp run {script}` - run a script (where `{script}` is a script defined in pyproject.toml)
- `ppp list` - list available scripts along with their descriptions

### Defining scripts

Define your scripts/aliases in `pyproject.toml` in `[tool.ppp.scripts]`.

You can use comments to specify command or argument descriptions.

#### Example

```toml
[tool.ppp.scripts]
# Run a docker image with http server
"docker-up" = "docker run -it -v bob:the_builder nvm" # [start|stop]
"type" = "echo"                                       # any text
```

Will result in

![List result](documentation/list.png)
pumpkin-supmcu 1.2.3
A library to interface with Pumpkin SupMCU modules via I2C
Requires Python: >=3.7

# pumpkin_supmcu Package

The `pumpkin_supmcu` package has the following functionality:

* Discover all telemetry items on a module, and put into telemetry definitions.
* Parse any telemetry item on a module given a module telemetry definition.
* Provide a universal I2C interface to integrate multiple I2C Masters.
* Request telemetry from any SupMCU module via I2C interface.
* Write commands to SupMCU modules via I2C interface.

The documentation for the `pumpkin_supmcu` package can be [found here](https://pumpkin-supmcu.readthedocs.io/en/latest/index.html).

### Telemetry Benchmark Utility
`utils/benchmark.py` can be used to measure the response time of each telemetry item on the bus.

First you must generate a bus definition using [pumqry](https://github.com/PumpkinSpace/PuTDIG-CLI): `pumqry -t kubos -p 1 -e -d -f bus.json` (example bus.json provided for reference).

Then you can run a benchmark: `python utils/benchmark.py -p /dev/ttyUSBX -f /path/to/save/to.json`
cfdverify 0.0.2
A package to conduct solution verification for CFD
Requires Python: >=3.8

CFDverify
=========

Efficient solution verification tools for computational fluid dynamics (CFD).

CFDverify is a python package designed to make verifying computational fluid dynamics (CFD) simulations easier! Solution verification is essential for trustworthy CFD results, but you need to spend your time running simulations, not fiddling with error analysis. CFDverify provides tested methods, batteries included! Check out the tutorial notebooks, use the provided utilities for easy plotting and output, or peruse the documentation to catch up on the theory. CFDverify is in early stages of development, so make sure to open an `issue <https://github.com/ORNL/cfd-verify/issues>`_ if it doesn't have a feature you need; but, be warned, the interface may change unexpectedly until the first major release!

Installation
------------

To get the latest release of CFDverify, use pip :code:`pip install cfdverify`. The latest release is hosted on the Python Package Index (PyPI) `here <https://pypi.org/project/cfdverify/>`_.

If you prefer to install from the `source <https://github.com/ORNL/cfd-verify>`_, download this repository to a suitable location on your computer, (optionally) activate your environment, and install with pip.

.. code-block:: bash

    git clone git@github.com:ORNL/cfd-verify.git
    cd cfd-verify
    source /path/to/your/venv/bin/activate
    pip install .

To install dependencies for testing the code, install with the command :code:`pip install .[tests]`. Likewise, to install documentation dependencies use the command :code:`pip install .[docs]`. Alternatively, install all optional dependencies using the command :code:`pip install .[full]`.

Documentation
-------------

Documentation is available on `Read the Docs <https://cfd-verify.readthedocs.io>`_.

To build CFDverify's documentation locally, execute the command :code:`make html` in the docs directory of CFDverify.

.. code-block:: bash

    cd cfd-verify/docs
    make html
    # or make latexpdf to generate a PDF

The documentation can then be read using any web browser by opening the file cfd-verify/docs/build/html/index.html. Install CFDverify using :code:`pip install .[docs]` to ensure you have all the required dependencies to build the documentation. If you choose to build a PDF version, you will need to make sure your system has the necessary LaTeX executables.

Testing
-------

To run CFDverify's tests, execute the command :code:`pytest` in the top level of the CFDverify directory or in the tests sub-directory. Install CFDverify using :code:`pip install .[tests]` to ensure you have all the required dependencies to run tests.

Contributing
------------

More information coming soon! For now, feel free to open an `issue <https://github.com/ORNL/cfd-verify/issues>`_ or contact the author.

Author
------

Justin Weinmeister: <weinmeistejr@ornl.gov>
code-reviewer-tool 0.1.0
A tool for code review and documentation generation
Requires Python: >=3.6

# Code Reviewer: Your AI-Powered Code Polishing Assistant

This project is designed to be your personal AI-powered code reviewer, helping you clean, refine, and improve your code quality as you develop. It leverages AI to provide suggestions, identify potential issues, and ensure code adheres to best practices.

## Features

-   **Automated Code Review:** Automatically analyzes your code for common errors, style inconsistencies, and potential bugs.
-   **AI-Powered Suggestions:** Receives intelligent suggestions for code improvements, refactoring, and optimization.
-   **Customizable Rules:** Tailor the review process to match your specific coding style and project requirements.
-   **Learning and Adaptation:** Continuously learns from your feedback to provide increasingly relevant and accurate reviews.
-   **Integration:** Designed for easy integration into your existing development workflow.

## Installation

```bash
pip install code-reviewer-tool
```

## Usage

```bash
code-reviewer --help
```

## Contributing

Contributions are welcome! If you have ideas for new features, improvements, or bug fixes, please feel free to submit a pull request.

## License

This project is licensed under the [MIT License](LICENSE).