semvec/pypi-packages huggingface dataset:
gpvisc 0.3.5
A Python module containing a Gaussian Process - Artificial Neural Network model of melt viscosity
Requires Python: >=3.8
Keywords: viscosity, liquid, glass, magma, phosphate, aluminosilicate

# GPVisc
=========

[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.13843250.svg)](https://zenodo.org/records/13843250) 
![GitHub](https://img.shields.io/github/license/charlesll/gpvisc)

GPVisc is a Python module containing a Gaussian Process - Artificial Neural Network of melt viscosity.

- **Documentation:** https://gpvisc.readthedocs.io
- **Examples:** https://github.com/charlesll/gpvisc/tree/master/examples
- **Web app** https://gpvisc.streamlit.app/
- **Source code:** https://github.com/charlesll/gpvisc/tree/master/src/gpvisc
- **License:** https://github.com/charlesll/gpvisc/tree/master/LICENSE.txt
- **Contributing:** https://github.com/charlesll/gpvisc/blob/master/CONTRIBUTING.md
- **Bug reports:** https://github.com/charlesll/gpvisc/issues
- **Contact:** lelosq@ipgp.fr

See the documentation for more information.
dnv-sesamx 1.0.3rc7
Python library for developing and running SesamX workflows.
Requires Python: >=3.10
Keywords: sesamx, sesam, workflow

# dnv-sesamx

dnv-sesamx is a Python library loading DNV SesamX .NET libraries that allows to work with DNV workspaces (*.sesx).

This is a release candidate version of the library.
It is intended for final testing before the official stable release.
While all major features are implemented, there may still be minor issues or bugs.
We encourage users to try this version and provide feedback, but it is not recommended for use in production environments.

Requirements: 
- DNV SesamX (distributed on request)
- Python 3.10 or later

## Usage/Examples

Installing the library:

`pip install dnv-sesamx --pre`

Usage example:

```python
import os

# loading SesamX assemblies
from dnv.sesamx import load_assemblies
load_assemblies()

# import from SesamX .NET libraries required to work with
from DNVS.Sesam.Commons.Workbench.UI.Shell import WorkspaceSession
from DNVS.Sesam.Commons.ConceptCore import PlotsFolder, Plot

session = any
try:
    session = WorkspaceSession(saveWorkspaceOnExit=True)
    workspace = session.CreateWorkspace(os.getcwd(), "demoWorkspace")

    # creates plot folder
    folder = session.CreateConcept[PlotsFolder](session.Workspace)

    # creates a plot
    session.CreateConcept[Plot](folder)
except Exception as e:
    print(f"An error occurred: {e}")
finally: #ensures workspace gets saved on exit
    session.Dispose()
```

## Build

Setup build tools:

```
python -m pip install --upgrade toml-cli
python -m pip install --upgrade build
```

Prepare build environment and version:

`toml set --toml-path src/dnv.sesamx/pyproject.toml project.version 1.0.0`

Build the package:

`python -m build src/dnv.sesamx`

Install package from a local source:

`pip install dnv-sesamx --no-index --find-links file:///C:\Git\SesamX.Python\src\dnv.sesamx\dist`


## License

[MIT](https://choosealicense.com/licenses/mit/)

## Support

If you encounter any issues, have questions, or want to provide feedback, please get in touch with our support team at software.support@dnv.com. We are committed to continuously improving SesamX and providing timely assistance to our users.
rhllasag 1.0.0
A Python package template.
Requires Python: >=3.8

# Python Package Template

This sample repo contains the recommended structure for a Python package.

## Setup Instructions 

This sample makes use of Dev Containers, in order to leverage this setup, make sure you have [Docker installed](https://www.docker.com/products/docker-desktop).

The code in this repo aims to follow Python style guidelines as outlined in [PEP 8](https://peps.python.org/pep-0008/).

To successfully run this example, we recommend the following VS Code extensions:

- [Dev Containers](https://marketplace.visualstudio.com/items?itemName=ms-vscode-remote.remote-containers)
- [Python](https://marketplace.visualstudio.com/items?itemName=ms-python.python)
- [Python Debugger](https://marketplace.visualstudio.com/items?itemName=ms-python.debugpy)
- [Pylance](https://marketplace.visualstudio.com/items?itemName=ms-python.vscode-pylance) 

In addition to these extension there a few settings that are also useful to enable. You can enable to following settings by opening the Settings editor (`Ctrl+,`) and searching for the following settings:

- Python > Analysis > **Type Checking Mode** : `basic`
- Python > Analysis > Inlay Hints: **Function Return Types** : `enable`
- Python > Analysis > Inlay Hints: **Variable Types** : `enable`

## Running the Sample
- Open the template folder in VS Code (**File** > **Open Folder...**)
- Open the Command Palette in VS Code (**View > Command Palette...**) and run the **Dev Container: Reopen in Container** command
- Run the app using the Run and Debug view
- To test, navigate to the Test Panel to configure your Python test or by triggering the **Python: Configure Tests** command from the Command Palette
- Run tests in the Test Panel or by clicking the Play Button next to the individual tests in the `test_date_time.py` and `test_developer.py` file
mono-stubs 0.0.3
Unofficial stubs for the Metri3D repo.
Requires Python: >=3.8

# Metric3D stubs

[![PyPI](https://img.shields.io/pypi/v/mono-stubs?color=green&style=flat)](https://pypi.org/project/mon-stubs)
[![License](https://img.shields.io/pypi/l/mono-stubs?style=flat)](https://opensource.org/licenses/MIT)

Unofficial python stubs for the Metric3D repository

## Installation

The package is available on pypi [here](https://pypi.org/project/mono-stubs/), you can install it with:

```bash
pip install mono-stubs
```
qonto-mcp-server 0.1.4
MCP Server for the Qonto API.
Requires Python: >=3.10

# Qonto-MCP-Server

MCP Server for the Qonto API.

## Supported API Methods

This MCP server supports all endpoints of Qonto's Business API that are accessible via API key authentication. For a comprehensive list of these endpoints, please refer to the official Qonto documentation:

👉 [Endpoints accessible with an API key](https://docs.qonto.com/api-reference/business-api/authentication/introduction#endpoints-access)

## Installation

### Using uv (recommended)

When using [`uv`](https://docs.astral.sh/uv/) no specific installation is needed. We will
use [`uvx`](https://docs.astral.sh/uv/guides/tools/) to directly run _qonto-mcp-server_.

### Using PIP

Alternatively you can install `qonto-mcp-server` via pip:

```
pip install qonto-mcp-server
```

## Configuration

### Usage with Claude Desktop

Add this to your `claude_desktop_config.json`:

- Note: For details on how to obtain `API_LOGIN` and `API_SECRET_KEY` values, see the [Qonto API key docs](https://docs.qonto.com/api-reference/business-api/authentication/api-key).

#### Using uvx

```json
"mcpServers": {
  "qonto-mcp-server": {
    "command": "uvx",
    "args": ["qonto-mcp-server", "--api-key", "API_LOGIN:API_SECRET_KEY"]
  }
}
```

#### Using pip installation

```json
"mcpServers": {
  "qonto-mcp-server": {
    "command": "python",
    "args": ["-m", "qonto-mcp-server", "--api-key", "API_LOGIN:API_SECRET_KEY"]
  }
}
```
simple-math-calijaaa 0.3.3
Библиотека с математическими алгоритмами и численными методами
Requires Python: >=3.8

# Dict Library Package

Библиотека Python со встроенным словарем данных для быстрого доступа к информации.

## Возможности

- Получение значений из встроенного словаря библиотеки по двум ключам
- Встроенные категории: пользователи, настройки, города, предметы, животные
- Получение списка всех категорий
- Получение списка элементов в конкретной категории
- Безопасный доступ с проверкой типов и понятными ошибками

## Установка

```bash
pip install simple-math-calijaaa
```

## Использование

```python
from simple_math_calijaaa.dict_accessor import get_value, get_categories, get_items_in_category

# Получение математического кода из встроенного словаря
code_3_1 = get_value("3", "1")
print("Код для задачи 3.1:")
print(code_3_1[:200] + "...")  # Показываем первые 200 символов

code_4_1 = get_value("4", "1")
print("Код для задачи 4.1:")
print(code_4_1[:200] + "...")

# Получение списка всех категорий
all_categories = get_categories()
print(f"Доступные категории: {all_categories}")

# Получение элементов в категории
items_3 = get_items_in_category("3")
print(f"Элементы в категории 3: {items_3}")

items_4 = get_items_in_category("4")
print(f"Элементы в категории 4: {items_4}")
```

## Встроенные данные

Библиотека содержит математический контент, организованный в категории:

- **Категории**: 3, 4, 30, 14, 18, 29, 5, 7, 15
- **Элементы в каждой категории**: обычно 1, 2, 3
- **Содержимое**: Python код для решения математических задач

Каждый элемент содержит готовые алгоритмы и примеры реализации численных методов.

## Лицензия

MIT License