semvec/pypi-packages huggingface dataset:
swiftascmaps 1.6.0
Taylor Swift inspired Matplotlib colormaps.

## Taylor Swift color map collection.

[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.5649259.svg)](https://doi.org/10.5281/zenodo.5649259)

Quick start: `pip install swiftascmaps`

Includes color maps based on the following albums:

+ Red (`red`, `red_r`)
+ 1989 (`nineteen_eighty_nine`, `nineteen_eighty_nine_r`)
+ Reputation (`reputation`, `reputation_r`)
+ Lover (`lover`, `lover_r`)
+ Folklore (`folklore`, `folklore_r`)
+ Evermore (`evermore`, `evermore_r`, `evermore_shifted`, `evermore_shifted_r`)
+ Fearless: Taylor's Version (`fearless_tv`, `fearless_tv_r`)
+ Red: Taylor's Version (`red_tv`, `red_tv_r`)
+ Midnights (`midnights`, `midnights_r`)
+ Speak Now: Taylor's Version (`speak_now_tv`, `speak_now_tv_r`)
+ 1989: Taylor's Version (nineteen_eighty_nine_tv, nineteen_eighty_nine_tv_r)

License: LGPLv3
Author: Josh Borrow (josh@joshborrow.com)

If you prefer to use `R`, there is an alternative package
maintained as [taloRswift](https://github.com/asteves/tayloRswift).

Usage
-----

To use these, you can import them and use them
with matplotlib as you would with any other color map.

```python
from swiftascmaps import red
from matplotlib.pyplot import imshow
from numpy import random

imshow(random.rand(128, 128), cmap=red)
```

The color maps can also be accessed in matplotlib using strings
by prefixing `swift`, e.g.

```python
import swiftascmaps

imshow(random.rand(128, 128), cmap="swift.red")
```

Examples
--------

![](images/maps.png)

Note
----

Of course, these aren't necessarily designed to be colorblind
friendly, or perceptually uniform, so use them with caution.
They are quite pretty though. To underline how much you should
_not_ use these in a real scientific publication (apart from
perhaps qualitative imaging), the lightness values are shown
below.

![](images/uniformity.png)

For quantitative comparisons, please ensure that you use a
perceptually uniform colour map (see e.g. those available
directly through [matplotlib](https://matplotlib.org/3.1.0/tutorials/colors/colormaps.html)).
py3mf-slicer 0.0.5
Functions which aids the process of using and slicing 3mf files
Requires Python: >=3.6

# py3mf_slicer

Provides functions useful to slice 3mf files


# To package
- Delete old build in \dist folder
- Update version in setup file
- run "python setup.py sdist bdist_wheel"
- upload to pip with "twine upload dist/*"
min-llm-server-client 0.3.10
A minimal API server for local HuggingFace LLMs
Requires Python: >=3.6




# Minimal LLM Server, for API calls  

The simplest possible Python code for running local LLM inference as a REST API server (with a simple client).

This package lets you start an inference server for Hugging Face–compatible models (like LLaMA, Qwen, GPT-OSS, etc.) on your own computer or server, and make it accessible to applications via HTTP.

See the [Tutorial](https://medium.com/@sadeghi.afshin/run-gpt-oss-20b-and-gpt-oss-120b-locally-with-a-minimal-api-server-in-the-style-of-openai-1872e68a93b7) page for extented info.
---

## Installation by pip ![Pepy Total Downloads](https://img.shields.io/pepy/dt/min_llm_server_client)

From PyPI  (recommended):

```bash
pip install min-llm-server-client
```

From source:

```bash
git clone https://github.com/afshinsadeghi/min_llm_server_client.git
cd min_llm_server_client
pip install .
```

---

## Usage

### Starting the Server

After installation, you can launch the server with the provided CLI entrypoint:

```bash
min-llm-server --model_name meta-llama/Llama-3.3-70B-Instruct --max_new_tokens 100 --device cuda:0
```

Options:
- `--model_name` : Hugging Face model name or local path
   suggested models:
    `openai/gpt-oss-20b`
    `openai/gpt-oss-120b`
    `meta-llama/Llama-3.3-70B-Instruct`
    `meta-llama/Llama-3.1-8B`
    `Qwen/Qwen3-0.6B`
    `Qwen/Qwen2-VL-72B-Instruct-AWQ`
    `deepseek-ai/DeepSeek-R1-Distill-Qwen-32B`
  
    or it can be local model with `/path/to/model`
- `--max_new_tokens` : maximum number of tokens to generate in response.
- `--device` : `cpu`, `cuda:0`, `cuda:1`, etc. if device is not given it finds available GPU core and uses them and if not available it uses CPU instead. 

Example (CPU run):

```bash
min-llm-server --model_name openai/gpt-oss-20b --max_new_tokens 50 --device cpu
```

---

### Sending Queries

Once the server is running (default: `http://127.0.0.1:5000/llm/q`), you can query it with `curl` or Python.

**Curl:**

```bash
curl -X POST http://127.0.0.1:5000/llm/q \
  -H "Content-Type: application/json" \
  -d '{"query": "What is Earth?", "key": "key1"}'
```

**Python client:**

```python
from min_llm_server_client.local_llm_inference_api_client import send_query

response = send_query("What is the capital of France?", user="user1", key="key1")
print(response)
```

---

### Performance notes

- Running **LLaMA 3.1 8B**:
  - Intel CPU → ~30 seconds per request, ~2.4 GB RAM
  - A100 GPU → <1 second per request, ~34 GB GPU memory, ~4.8 GB CPU RAM

---

## Project Structure

```
min_llm_server_client/
├── src/
│   ├── local_llm_inference_api_client.py
│   ├── local_llm_inference_server_api.py
│   └── ...
└── README.md
```

---

## License

This project is open source under the [Apache 2.0 License](./LICENSE).

---

## Author
Afshin Sadeghi  
📧 sadeghi.afshin@gmail.com  
🔗 [GitHub](https://github.com/afshinsadeghi)  
🔗 [Google Scholar](https://scholar.google.com/citations?user=uWTszVEAAAAJ&hl=en&oi=ao)  
🔗 [LinkedIn](https://www.linkedin.com/in/afshin-sadeghi)
pozansky-stock 0.1.0
Add your description here
Requires Python: >=3.10

# News MCP Server

基于MCP协议的新闻获取服务器，支持热点新闻获取和新闻搜索功能。

## 功能特性

- 🔥 获取热点新闻（多个分类）
- 🔍 搜索新闻
- 🌐 RESTful API 接口
- 🐳 Docker 容器化部署
- 📊 健康检查

## API接口

### 健康检查
GET /health

### 获取新闻分类
GET /news/categories


### 获取热点新闻
GET /news/hot?category=general&limit=10

### 搜索新闻
GET /news/search?keyword=科技&limit=10


### MCP接口
/mcp


## 本地测试


1. 安装依赖：
```bash
pip install -r requirements.txt
运行测试：

bash
python src/mcp_server.py
启动服务器：

bash
python app.py