semvec/pypi-packages huggingface dataset:
stadincijfers 0.2.3
A python package to make data from stadincijfers easily available for data scientists. 
Requires Python: >=2.7

Stad in Cijfers, python API
================

Purpose
----

A python package to make data from stadincijfers easely available for data scientists. 

Intro
-----

Via the website 'Stad in cijfers' you can consult statistical data on various themes. In principle for the city of Antwerp, all data on 'Stad in cijfers' is open data and can therefore be used freely, unless stated otherwise.

Many local goverments in Flanders have a website like this:

- Gent: https://gent.buurtmonitor.be
- Antwerp: https://stadincijfers.antwerpen.be/databank
- The provinces: https://provincies.incijfers.be

Other websites that use the same platform:
- IMA-Atlas: https://atlas.ima-aim.be/
- Bevolkingsonderzoek: https://bevolkingsonderzoek.incijfers.be/

Installation:
--------------

from [pypi](https://pypi.org/project/stadincijfers/): 

    pip install stadincijfers

or from sources: 
    
    git clone https://github.com/warrieka/stadincijfers.py
    cd stadincijfers.py
    pip install -r requirements.txt
    pip install .

Usage:
-----

1) List periodlevels in the SIC site of Antwerp. 
```python
    from stadincijfers import stadincijfers
    sic = stadincijfers("antwerpen") #or gent or provincies or ima-atlas or bevolkingsonderzoek or url
    sic.periodlevels() #=> get all periodslevels
```

2) Export excel: 
```python
    from stadincijfers import stadincijfers
    sic = stadincijfers("antwerpen") #or gent or provincies or url
    bevdicht = sic.selectiontableasDataframe('bevdicht', geolevel='sector', periodlevel='year', period=2022)
    bevdicht.to_excel('data\\bevdicht2022.xlsx') # save to excel
```


For more see example jupyter notebook: [example_usage.ipynb](example_usage.ipynb)
toydl 0.3.0
ToyDL: Deep Learning from Scratch
Requires Python: <4.0,>=3.12
Keywords: engineering, machine learning, statistics

# ToyDL: Deep Learning from Scratch


[![Python](https://img.shields.io/pypi/pyversions/toydl.svg?color=%2334D058)](https://pypi.org/project/toydl/)
[![PyPI](https://img.shields.io/pypi/v/toydl?color=%2334D058&label=pypi%20package)](https://pypi.org/project/toydl/)

[![codecov](https://codecov.io/gh/ai-glimpse/toydl/graph/badge.svg?token=e2LQa7MF8s)](https://codecov.io/gh/ai-glimpse/toydl)
[![Build Docs](https://github.com/ai-glimpse/toydl/actions/workflows/build_docs.yml/badge.svg)](https://github.com/ai-glimpse/toydl/actions/workflows/build_docs.yml)
[![Test](https://github.com/ai-glimpse/toydl/actions/workflows/test.yml/badge.svg)](https://github.com/ai-glimpse/toydl/actions/workflows/test.yml)


[![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)
[![Checked with mypy](https://www.mypy-lang.org/static/mypy_badge.svg)](https://mypy-lang.org/)
[![pre-commit](https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit)](https://github.com/pre-commit/pre-commit)


ToyDL, as an education project, is aimed to make the
concepts behind the deep learning as clear as possible.
I do believe the key to reach the target is **SIMPLE**, as simple as possible.
I'm trying to use little code to build this library, although it's not simple enough yet.

### Installation

```bash
pip install toydl
```


## Related Projects
- [minitorch](https://github.com/minitorch/minitorch): The implementation of `toydl` is highly inspired by minitorch
- micrograd: [https://github.com/karpathy/micrograd](https://github.com/karpathy/micrograd)
- tinygrad: [https://github.com/tinygrad/tinygrad](https://github.com/tinygrad/tinygrad)
- nanograd: [https://github.com/e3ntity/nanograd](https://github.com/e3ntity/nanograd)
ragflow-mcp-server-continue 0.3.3
RAGFlow MCP Server Continue
Requires Python: >=3.10

# ragflow-mcp-server-continue MCP server

RAGFlow MCP Server Continue

## 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": {
    "ragflow-mcp-server-continue": {
      "command": "uv",
      "args": [
        "--directory",
        "D:\AIGC\Projects\ragflow-mcp-server-continue",
        "run",
        "ragflow-mcp-server-continue"
      ]
    }
  }
  ```
</details>

<details>
  <summary>Published Servers Configuration</summary>
  ```
  "mcpServers": {
    "ragflow-mcp-server-continue": {
      "command": "uvx",
      "args": [
        "ragflow-mcp-server-continue"
      ]
    }
  }
  ```
</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 D:\AIGC\Projects\ragflow-mcp-server-continue run ragflow-mcp-server-continue
```


Upon launching, the Inspector will display a URL that you can access in your browser to begin debugging.