semvec/pypi-packages huggingface dataset:
mathforuni 0.1.0
Описание вашей библиотеки
Requires Python: >=3.6

# Mathforuni

## Применение
Данная библиотека создана в основном для студентов, изучающих математику в высших учебных заведениях, в частности такие ее разделы как Теория конечных графов и Элементы теории вероятности

## Использование
Библиотека содержит **два** основных модуля - графы и теория вероятностей.
- Раздел графы включает в себя такие функции как нахождение минимального остовного дерева(алгоритм Краскала и Прима), алгоритмы поиска кратчайших путей -  Уоршала-Флойда и Дэйкстры, транзитивное замыкание, поиск Гамильтонова и Эйлерова циклов.
-  Раздел теории вероятности включает в себя комбинаторные функции, формулу Бернулли и формулу Пуассона.

## Установка

Для установки пакета достаточно воспользоваться pip install и импортировать все необходимое.
weather-query-xiaomei 0.1.0
query weather of different city for mt users
Requires Python: >=3.10

# weather_query_xiaomei MCP server

query weather of different city for mt users

## Components

### Resources

The server implements a simple note storage system with:
- Custom note:// URI scheme for accessing individual notes
- Each note resource has a name, description and text/plain mimetype

### Prompts

The server provides a single prompt:
- summarize-notes: Creates summaries of all stored notes
  - Optional "style" argument to control detail level (brief/detailed)
  - Generates prompt combining all current notes with style preference

### Tools

The server implements one tool:
- add-note: Adds a new note to the server
  - Takes "name" and "content" as required string arguments
  - Updates server state and notifies clients of resource changes

## Configuration

[TODO: Add configuration details specific to your implementation]

## Quickstart

### Install

#### Claude Desktop

On MacOS: `~/Library/Application\ Support/Claude/claude_desktop_config.json`
On Windows: `%APPDATA%/Claude/claude_desktop_config.json`

<details>
  <summary>Development/Unpublished Servers Configuration</summary>
  ```
  "mcpServers": {
    "weather_query_xiaomei": {
      "command": "uv",
      "args": [
        "--directory",
        "/Users/shelmin/PycharmProjects/xiaomei-mcp-server/weather_query_xiaomei",
        "run",
        "weather_query_xiaomei"
      ]
    }
  }
  ```
</details>

<details>
  <summary>Published Servers Configuration</summary>
  ```
  "mcpServers": {
    "weather_query_xiaomei": {
      "command": "uvx",
      "args": [
        "weather_query_xiaomei"
      ]
    }
  }
  ```
</details>

## Development

### Building and Publishing

To prepare the package for distribution:

1. Sync dependencies and update lockfile:
```bash
uv sync
```

2. Build package distributions:
```bash
uv build
```

This will create source and wheel distributions in the `dist/` directory.

3. Publish to PyPI:
```bash
uv publish
```

Note: You'll need to set PyPI credentials via environment variables or command flags:
- Token: `--token` or `UV_PUBLISH_TOKEN`
- Or username/password: `--username`/`UV_PUBLISH_USERNAME` and `--password`/`UV_PUBLISH_PASSWORD`

### Debugging

Since MCP servers run over stdio, debugging can be challenging. For the best debugging
experience, we strongly recommend using the [MCP Inspector](https://github.com/modelcontextprotocol/inspector).


You can launch the MCP Inspector via [`npm`](https://docs.npmjs.com/downloading-and-installing-node-js-and-npm) with this command:

```bash
npx @modelcontextprotocol/inspector uv --directory /Users/shelmin/PycharmProjects/xiaomei-mcp-server/weather_query_xiaomei run weather-query-xiaomei
```


Upon launching, the Inspector will display a URL that you can access in your browser to begin debugging.
license-bb 0.1.0
A description of your package
Requires Python: >=3.8

# Python SDK licensing

This SDK helps to manage client licenses, generate keys, and interact with a license server.

## Installation

You can install My Package using pip:

### For all systems:
```sh
pip install ./dist/license_bb-0.1.0-py3-none-any.whl
```

### or:
```sh
pip install git+https://github.com/Adannak24/python_SDK_licensing
```

## Functions

### `init(base_url, license_key, client_details)`

Initializes the client's configuration by creating an init file and generating necessary keys.

- **Args:**
  - `base_url` (str): License server API base URL without the API endpoints.
  - `license_key` (str): License key received by the user on requesting or subscribing.
  - `client_details` (dict): Dictionary containing 'email', 'org_id', 'phone', 'user_name', 'org_name', and 'server_name' (optional).

- **Returns:**
  - `dict`: Combined information including client and system details.

### `do_exchange(org_id, client_details)`

Exchanges the client's public key with the license server and retrieves the server's public key.

- **Args:**
  - `org_id` (str): Unique ID for each organization.
  - `client_details` (dict): Full configuration from the init() function.

- **Returns:**
  - `dict`: Response from the licensing server.

### `get_license(org_id, client_details)`

Retrieves the available/assigned license for the client from the license server.

- **Args:**
  - `org_id` (str): Unique ID for each organization.
  - `client_details` (dict): Full configuration from the init() function.

- **Returns:**
  - `dict`: Response from the licensing server.

### `update_license(license_key, org_id, assign_type)`

Updates the client's license key and assigns type in the init() configuration.

- **Args:**
  - `license_key` (str): New license key.
  - `org_id` (str): Unique ID for each organization.
  - `assign_type` (str): Assign type, either 'update' or 'default'.

- **Returns:**
  - `dict`: Response from the licensing server.

### `get_feature(org_id, feature_name)`

Retrieves the specified feature details from the license file.

- **Args:**
  - `org_id` (str): Unique ID for each organization.
  - `feature_name` (str): Specific feature name or 'all' for all features.

- **Returns:**
  - `dict`: Feature details.

## Example Usage

```python


command to make whl: python setup.py sdist bdist_wheel
automate-scalable-unsupervised-dataset-generation 0.10.0
A brief description
Requires Python: >=3.6

# automate-scalable-unsupervised-dataset-generation
-----

## Table of Contents

- [Installation](#installation)
- [License](#license)

## Installation

```console
pip install automate-scalable-unsupervised-dataset-generation
```

## Start the app
```
import automate_scalable_unsupervised_dataset_generation.automate
query = ["Artificial Intelligence","recipe of lemon tea!"]
num_page = 5
sleep_time = 10
results = automate_scalable_unsupervised_dataset_generation.automate.parallel_scraping(query,num_page,sleep_time)
```

## License

`automate-scalable-unsupervised-dataset-generation` is distributed under the terms of the [MIT](https://spdx.org/licenses/MIT.html) license.
PyHEADTAIL 1.16.5
CERN PyHEADTAIL numerical n-body simulation code for simulating macro-particle beam dynamics with collective effects.

   
PyHEADTAIL
==========

CERN PyHEADTAIL macro-particle code
for simulating beam dynamics in particle accelerators with collective effects.

PyHEADTAIL is written in Python with some C extensions, compiled with Cython.
Currently, PyHEADTAIL is compatible with Python v3.6 or later.

Installation for Users
----------------------

For using PyHEADTAIL without modifying the source code,
we recommend to install the latest version via PyPI:

.. code-block:: bash

    $ pip install PyHEADTAIL

Installation for Developers
---------------------------

For developers of PyHEADTAIL, we recommend to install a stand-alone
package from the source code using git. For GPU usage, the developer
version is required (the Makefile is included in the source code
version only).

We recommend to use the Anaconda package manager to simplify installing.
You can obtain it from anaconda.org .

Installation of PyHEADTAIL on linux (having git installed)
is straight forward.

- Clone the repository in a local folder:

.. code-block:: bash

    $ git clone https://github.com/PyCOMPLETE/PyHEADTAIL

- Go to the folder and run the installation script:

.. code-block:: bash

    $ cd PyHEADTAIL

    $ make

And there you go, start using PyHEADTAIL!

.. code-block:: bash

    $ cd ..

    $ ipython

    ...

    In [1]: import PyHEADTAIL

For a single installation of PyHEADTAIL we recommended to add
the PyHEADTAIL path to your PYTHONPATH.
automate-supervised-dataset-generation 0.9.0
A brief description
Requires Python: >=3.6

# automate-scalable-unsupervised-dataset-generation
-----

## Table of Contents

- [Installation](#installation)
- [License](#license)

## Installation

```console
pip install automate-scalable-unsupervised-dataset-generation
```

## Start the app
```
import automate_scalable_unsupervised_dataset_generation.automate
query = ["Artificial Intelligence","recipe of lemon tea!"]
num_page = 5
sleep_time = 10
results = automate_scalable_unsupervised_dataset_generation.automate.parallel_scraping(query,num_page,sleep_time)
```

## License

`automate-scalable-unsupervised-dataset-generation` is distributed under the terms of the [MIT](https://spdx.org/licenses/MIT.html) license.
automate-unsupervised-dataset-generation 0.5.0
A brief description
Requires Python: >=3.6

# automate-unsupervised-dataset-generation
-----

## Table of Contents

- [Installation](#installation)
- [License](#license)

## Installation

```console
pip install automate-unsupervised-dataset-generation
```

## Start the app
```
import automate_unsupervised_dataset_generation.automate
query = "Artificial Intelligence"
num_page = 5
sleep_time = 10
results = automate_unsupervised_dataset_generation.automate.parallel_scraping(query,num_page,sleep_time)
```

## License

`automate-unsupervised-dataset-generation` is distributed under the terms of the [MIT](https://spdx.org/licenses/MIT.html) license.
DashboardServerReportWebsiteSide 0.1.0
A website monitoring tool for performance, security, SEO, and UI
Requires Python: >=3.6

# DashboardServerReportWebsiteSide

**Package Name**: `DashboardServerReportWebsiteSide`

DashboardServerReportWebsiteSide is a web-based tool that allows users to monitor and generate reports for website performance, security, SEO, and UI details. It provides a comprehensive dashboard for monitoring various aspects of a website.

## Features

- **Performance Monitoring**: Analyze website performance metrics.
- **Security Reports**: View security details and headers of a website.
- **SEO Insights**: Gather SEO-related data such as on-page analysis, meta tags, etc.
- **UI Monitoring**: Check for UI accessibility, image alt text, etc.
- **Internal & External Link Analysis**: Get internal and external link data for better SEO and navigation.
- **Report Generation**: Generate downloadable reports in PDF and Excel formats.

## Requirements

- Python 3.6+
- Flask
- Requests
- BeautifulSoup4
- Other dependencies mentioned in `requirements.txt`