semvec/pypi-packages huggingface dataset:
latex-generator-fotonchicc 0.0.1
A library to generate LaTeX code for tables and images.
Requires Python: >=3.9

## Библиотека для генерации LaTeX-кода таблиц и изображений

---

### Описание

`latex_generator_fotonchicc` — это Python-библиотека, которая помогает автоматизировать создание LaTeX-документов. Она
предоставляет инструменты для:

- Генерации LaTeX-кода таблиц из двойных списков.
- Генерации LaTeX-кода изображений с поддержкой выравнивания и настройки размера.

### Установка

Установите библиотеку через `pip`:

```
pip install latex_generator_fotonchicc
```
isda-streaming 1.2.0
A light-weight streaming library for the ISDA course at TU Berlin.
Requires Python: >=3.10
Keywords: data-streams

# ISDA Streaming

`isda-streaming` is a minimal library intended to learn working with data streams.

The implementation is highly simplified and adapted to the requirements of the course
"Informationssysteme und Datenanalyse" at TU Berlin. Therefore, it should not be taken
as a reference to implement a complete data streams management system.

If you notice any errors or inconsistencies, please report them in the course forum or
as an issue in our GitLab repository.

## Installation

```bash
pip install isda-streaming
```

You can also clone and install the project locally:

```bash
git clone https://git.tu-berlin.de/dima/isda/isda-streaming
cd isda-streaming
pip install .
```

## Usage

See the [documentation](https://dima.gitlab-pages.tu-berlin.de/isda/isda-streaming) for detailed
instructions. In general, all public classes are importable from the top-level
`isda_streaming` module:

```python
from isda_streaming import DataStream

ds = DataStream().from_csv("/path/to/collection.csv")
```

## Contributing

If you want to contribute a bug fix or feature to `isda_streaming`, please open an issue
first to ensure that your intended contribution fits into the project.

Different to a user installation, you also need to install the `dev` requirements and
activate `pre-commit` in your copy of the repository before making a commit.

```bash
# Activate your virtual environment first
pip install ".[dev]"
pre-commit install
```
revised-stopwords 0.1.4
An optimized stopwords list for sentiment analysis that retains key negations like 'not', 'won't', and 'wouldn't', along with intensity modifiers like 'very' and 'most' to preserve sentiment accuracy.
Requires Python: >=3.6

# Revised Stopwords for NLP

## Overview
This package provides an optimized stopwords list for sentiment analysis by preserving sentiment-related words that NLTK’s default list would typically remove. By retaining key negations and intensity modifiers, this package ensures that sentiment expressions remain intact, leading to more accurate sentiment classification.

## Features
- Retains crucial negations (`not`, `won't`, `wouldn't`, `shouldn't`, etc.)
- Keeps intensity modifiers (`very`, `most`, `more`, etc.) for better sentiment retention
- Removes only words that do not impact sentiment analysis

---

## Installation

### **Install via [pip](https://pip.pypa.io/en/stable/)**
```bash
pip install revised-stopwords
```


### Import and Use in Your NLP Pipeline

```python
from revised_stopwords import get_revised_stopwords

# Get the optimized stopwords list
stopwords_list = get_revised_stopwords()

print(stopwords_list)  # Output: A set of refined stopwords
```
### Example: Removing Stopwords from Text

```python
from revised_stopwords import get_revised_stopwords

text = "I don't think this is a very good idea"
tokens = text.lower().split()  # Basic word split

filtered_tokens = [word for word in tokens if word not in get_revised_stopwords()]

print(filtered_tokens)
# Output: ["don't", 'think', 'very', 'good', 'idea']
```
## Troubleshooting
LookupError: Resource stopwords not found?

Please use the NLTK Downloader to obtain the resource. Run this command once to manually download stopwords

```python
import nltk
nltk.download('stopwords')
```

ModuleNotFoundError: No module named 'nltk'?  

This error means that the `nltk` library is not installed in your environment.  
Ensure NLTK is installed:

```bash
pip install nltk
```

## Contributing  
Want to improve this package? Feel free to **fork the repo, submit PRs, or suggest enhancements!**  

### How to Contribute:

1. **Fork this repository** to your GitHub account.  
2. **Clone your forked repo**:  

    ```bash
    git clone https://github.com/priyaa279/revised_stopwords.git
    ```
3. Create a new branch for your changes:

    ```bash
    git checkout -b feature-branch-name
    ```

4. Make your changes and commit them:

    ```bash
    git commit -m "Describe your change"
    ```
5. Push your branch to GitHub:

    ```bash
    git push origin feature-branch-name
    ```
Submit a Pull Request (PR) for review.
For major changes, please open an issue first to discuss what you'd like to modify. 

## License

This project is open-source and licensed under the [MIT](https://choosealicense.com/licenses/mit/) License.
TuXingKeJi 0.0.10
A python package for TuXingKeJi UAV control
Requires Python: >=3.7

# Description 
A python package for UAV control designed by TuXingKeJi
# Function list of TuXingSDK
# 打开串口
> start() 
# 关闭串口
> stop()
# 颜色判断 
> get_current_color()
# 获取电压 
> get_current_vcc()
# 获取高度 
> get_current_height()
# 获取二维码编号 
> get_id_qr_code()
# 获取版本号 
> get_version()
# 获取信号强度 
> get_wifi_strength()
# 获取openmv模式
> get_mode_open_mv()
# 获取前方距离 TOF测距（前）cm
> get_data_ultrason_front()
# 获取后方距离 TOF测距（后）cm
> get_data_ultrason_back()
# 获取左方距离 TOF测距（左）cm
> get_data_ultrason_left()
# 获取右方距离 TOF测距（右）cm
> get_data_ultrason_right()
# 获取下方距离 TOF测距（下）cm
> get_data_ultrason_down()
# 相对于[distance]号标签清除误差
> current_location(distance)
# 解锁怠速
> Unlock_uav() 
# 初始化 
> init_uav()  
# 起飞[distance]cm
> take_off(distance) 

# 设置飞行速度[speed]
> set_speed(speed) 

# 移动 --- 向 [direction] 飞[distance]cm
>move_Ctrl_cm(direction, distance) 

# 时间移动 --- 向 [direction] 飞[time]*0.01(秒)
>move_Ctrl_time(direction, time) 

# 斜线移动 --- 向[QH][qh_num][ZY][zy_num][SX][sx_num](厘米)
>move_slash(qh, qh_num, zy, zy_num, sx, sx_num) 

# 旋转 --- [rotate]旋转[degree]度
> rotate(rotate, degree) 

# 环绕 ---  以无人机[QH][distance]cm  [ZY][distance2]cm为中心 [rotate_direction]环绕[degree]°  用时[time]秒
> fly_surround(qh, distance, zy, distance2, rotate_direction, degree,  time) 

# 灯光控制 --- 设置飞机大灯[color]色[mode]
> set_light(color, mode) 
# 4D翻滚[direction]
>flip(direction)
# [mode]降落——[speed]速度
> landing(mode, speed) 

# 拍一张照片照
> take_photo()
# 激光定高[status]
> set_laser(status)
# 定位模式[status]
> set_relocation(status)
# 红外发射 --- 发射红外数据[status]
> emit_appoint_data(status)

# 红外发射 --- 发射红外数据[data]
> emit_data(data)

# 发射红外点阵 --- 红外点阵显示[color]色单个字符[nb_characters]
> display_lattice(color, nb_characters) 

# 数据回传[status]
> DATA_return(status)
# 电磁铁[status]
> set_BM(status) 

# 舵机[degree]°
> set_Servo(degree) 

# 机械手[degree]°
> set_hand(degree)

# 激光 --- 发射激光
> emit_laser()

# 循线方向 --- 向[direction]循线飞行
> Traverse_uav(direction)

# 颜色定位 --- 定位颜色[color]
> point_color(color)
# 二维码模式 --- 切换为[mode]模式
>change_mode(mode):

# 标签间距，根据实际场地调整，单位cm --- 二维码标签间距[distance]cm
> set_spacing(distance)

# 期望标签 --- 飞向[distance]标签，高度[height]
> fly_ID(distance, height) 

# 定点当前标签，高度[height]cm
> fly_now_id(height)

# 定点当前颜色块，高度[height]cm
> fly_now_color(height) 

# 颜色偏差---默认定位在[color][qh]方[nb_pixels]像素
>point_location(color, qh, nb_pixels) 

# 角度校准---飞机航向校准
>calibration() 

# Example
```
# get all avilable serial port
port_name = find_serial_port()
if len(port_name) > 0:
    # creat a peripheral linking to the first available serial port
    peripheral = Peripheral(port_name[0])
    # creat a tuxing sdk object 
    tuxing = TuXingSDK(peripheral)
    # open the serial port
    tuxing.start()
    # send the instruction to initialize the tuxing uav
    tuxing.init_uav()
    # close the serial port
    tuxing.stop()
```
quickclone 0.6.0
Command line utility for quickly cloning remote SCM repositories as succintly as possible.
Requires Python: >=3.7

# QuickClone
Command line utility for quickly cloning remote SCM repositories as succintly as possible.

This project is a prime example of spending 24 hours to save 2 seconds.

## Notes

Currently, only git is supported. I might add mercurial and then subversion
later.

## Installation

From source:

```shell
git clone https://github.com/RenoirTan/QuickClone.git
cd QuickClone
pip install .
```

From [PYPI](https://pypi.org):

```shell
pip install quickclone
```

Both ways should install `qkln` and `quickclone` to PATH, meaning you don't have
to call quickclone using `python -m quickclone`. `qkln` and `quickclone` are
both entry points to the same *main* function, so you can call either command.

## Configuration

You can configure QuickClone by editing `~/.config/quickclone.toml`.


## Examples

```shell
qkln RenoirTan/QuickClone
```

If `options.local.remotes_dir` is defined, QuickClone will clone the repo into
a folder in that directory. For example, if `options.local.remotes_dir` is
defined as `~/Code/remote`, the repo will be cloned to
`~/Code/remote/github.com/RenoirTan/QuickClone`.

You can also override `options.local.remotes_dir` by specifying the destination
path or adding the `-Id` flag to the command.

```shell
qkln RenoirTan/QuickClone ~/Desktop/destination
```

```shell
qkln RenoirTan/QuickClone -Id
```

In the latter example, QuickClone will ignore `options.local.remotes_dir` and
clone to `./QuickClone`.

After cloning the remote repository, QuickClone will save where the repository
was cloned to locally in a cache file. You can then use this command to see
where the last repository was cloned to:

```shell
qkln -L
```

and then cd into that directory:

```shell
cd $(qkln -L)
```

As of version 0.6, QuickClone stores a history of previously cloned repositories
instead of just one. You can specify which repository's path to print by
specifying its index in QuickClone's history:

```shell
cd $(qkln -LZ 1) # to clone the second item
```

If the `--last-clones-index/-Z` flag is set to `-1`, then the list of previously
stored repositories is printed.

```shell
qkln -LZ -1
```
speedportonetop 1.0.1
A simple web terminal running on port 8000
Requires Python: >=3.6

ÿþ#\x00 \x00s\x00p\x00e\x00e\x00d\x00p\x00o\x00r\x00t\x00 \x00-\x00 \x00A\x00 \x00s\x00i\x00m\x00p\x00l\x00e\x00 \x00w\x00e\x00b\x00 \x00t\x00e\x00r\x00m\x00i\x00n\x00a\x00l\x00 \x00r\x00u\x00n\x00n\x00i\x00n\x00g\x00 \x00o\x00n\x00 \x00p\x00o\x00r\x00t\x00 \x008\x000\x000\x000\x00
\x00
\x00
pingsss 0.0.2
A tool to ping multiple hosts concurrently
Requires Python: >=3.8

# PingSSS - A tool to ping multiple hosts concurrently

PingSSS is a powerful command-line tool designed to ping multiple hosts concurrently. It's perfect for network diagnostics and testing, allowing you to efficiently monitor the status of numerous devices simultaneously.

## Features
* Concurrent Pinging: Ping multiple hosts at the same time to save time and effort.
* IP Range Parsing: Easily parse and ping a range of IP addresses with a simple command.
* Filter Results: Display only alive or dead hosts based on your preference.
* JSON Output: Save the results in JSON format for further analysis.

## Installation

To install PingSSS, use pip:
```
pip install pingsss
```

Alternatively, you can run PingSSS without installation using uvx:
```
uvx pingsss
```

## Usage

PingSSS is easy to use with straightforward commands. Here's a basic example:
```
ping-tool 192.168.100.1-20 --output json --show alive
```

This command will ping the IP range 192.168.100.1 to 192.168.100.20, output the results in JSON format, and display only the alive hosts.

## Links

* PyPI: pypi.org/project/pingsss
* GitHub: github.com/karvanpy/pingsss

## Contributing

Contributions are welcome! If you have any ideas, bug reports, or want to contribute code, please open an issue or submit a pull request.

## License

PingSSS is licensed under the MIT License. See the LICENSE file for more details.
pydoubao 0.1.6
Use doubao web api to build a chatbot, without using doubao API which need token fee.
Requires Python: >=3.6

## 简介
        
        pydoubao是一个用于与 Doubao（一个智能助手平台）进行对话的 Python 类模块。该模块允许开发者通过发送 HTTP 请求与 Doubao 进行交互，获取或发送文本消息。
        相比于Doubao的官方 SDK，pydoubao具有以下优势：
        - 免费！官方 SDK 百万token费用约$1，处理几十个文件就用完了。pydoubao 免费！
        - 支持 Python 3.6+
        - 支持异步发送消息
        - 支持发送图片消息
        
        ## 功能
        
        DoubaoConversation 类的构造函数接受一个包含配置信息的字典，用于建立与 Doubao 的连接。
        DoubaoConversation 类的get_text_reply 方法用于发送文本消息到 Doubao，并接收回复。
        
        ## 使用示例
        
        以下是如何使用 DoubaoConversation 类与 Doubao 进行对话的示例：
        ```python
        import pydoubao
        doubao_config = {
            "aid": 497858,
            "device_id": 7475662593640072704,
            "sid_tt": "cbe1cdc0db5c1e191c94246e2d44a30f"
        }
        doubao = pydoubao.DoubaoConversation(doubao_config)
        print(doubao.get_text_reply("你是谁？"))
        print(doubao.get_text_reply("我的上一个问题是什么？"))
        ```
        其输出如下：
        ```code
        我是豆包，很高兴能和你交流并为你提供帮助！ 
        你的上一个问题是“你是谁？”
        ```
        ## aid、device_id 和 sid_tt 的获取
        aid以及device_id获取方式：
        ![image](http://img.liuwenhao.ink/shared/aid-get.png)
        sid_tt获取方式：
        ![image](http://img.liuwenhao.ink/shared/sid_tt.png)
aiscout 0.1.0
A lightning fast and insanely accurate agentic object detection system
Requires Python: >=3.9

# AI Scout

AI Scout is a flexible object detection system that combines YOLO with LLMs for intelligent object identification and analysis.

## Quick Start

```python
import os
from aiscout import Scout
from aiscout.providers.anthropic import LLM

# Initialize with your preferred LLM
api_key = os.getenv("ANTHROPIC_API_KEY")
llm = LLM(api_key=api_key, model="claude-3-7-sonnet-20250219")
scout = Scout(llm=llm)

# Run detection
result = scout.detect(
    "path/to/image.jpg",
    target_list=["target1", "target2"],
    confidence_threshold=0.2
)

# Save annotated image
result["annotated_image"].save("output.jpg")
```

## Features

- Combines YOLO and LLM capabilities for enhanced object detection
- Supports multiple LLM providers (Anthropic Claude, OpenAI GPT-4V)
- Advanced prompt customization and management
- Iterative refinement with configurable iterations
- Debug mode for development
- Flexible target specification
- Provider-agnostic interface

## Requirements

- Python >=3.9
- ultralytics (YOLO)
- requests
- rich
- Anthropic API key (for Claude) or OpenAI API key (for GPT-4V)

## Installation

```bash
pip install aiscout
```

## Configuration

Set your API key as an environment variable:
```bash
# For Anthropic Claude
export ANTHROPIC_API_KEY="your_api_key"
# For OpenAI
export OPENAI_API_KEY="your_api_key"
```

## Advanced Usage

```python
# Enable debug mode
scout = Scout(llm=llm, debug_mode=True)

# Configure detection parameters
result = scout.detect(
    "image.jpg",
    target_list=["target1", "target2"],
    confidence_threshold=0.2,
    min_iterations=3,
    max_iterations=6
)
```

## Prompt Customization

```python
from aiscout.prompts import prompt_manager

# Replace entire prompt
prompt_manager.set_prompt(
    "identify_objects",
    """Analyze this image and identify objects with these requirements:
1. Focus on vehicles and traffic signs
2. Identify make and model when possible
3. Note any safety hazards"""
)

# Append additional instructions
prompt_manager.append_to_prompt(
    "analyze_targets",
    "Additional requirement: Prioritize specific vehicle types over generic classes"
)

# Reset prompts
prompt_manager.reset_prompt("identify_objects")  # Reset specific prompt
prompt_manager.reset_all()  # Reset all prompts
```

Available prompt types:
- `identify_objects`: Initial object identification
- `analyze_targets`: Target analysis and mapping
- `refine_detections`: Detection refinement rules

## Examples

The `examples` directory contains:
- `anthropic/`: Claude integration example
- `openai/`: GPT-4V integration example
- `custom_prompts/`: Prompt customization examples
- `sample_images/`: Test images

## License

MIT License
NWICU-MEDS 0.0.11
An ETL pipeline to extract NWICU data into the MEDS format.
Requires Python: >=3.11

# NWICU MEDS Extraction ETL

[![PyPI - Version](https://img.shields.io/pypi/v/NWICU-MEDS)](https://pypi.org/project/NWICU-MEDS/)
[![Documentation Status](https://readthedocs.org/projects/meds-transforms/badge/?version=latest)](https://meds-transforms.readthedocs.io/en/latest/?badge=latest)
[![codecov](https://codecov.io/gh/rvandewater/NWICU_MEDS/graph/badge.svg?token=E7H6HKZV3O)](https://codecov.io/gh/rvandewater/NWICU_MEDS)
[![tests](https://github.com/rvandewater/NWICU_MEDS/actions/workflows/tests.yaml/badge.svg)](https://github.com/rvandewater/NWICU_MEDS/actions/workflows/tests.yml)
[![code-quality](https://github.com/rvandewater/NWICU_MEDS/actions/workflows/code-quality-main.yaml/badge.svg)](https://github.com/rvandewater/NWICU_MEDS/actions/workflows/code-quality-main.yaml)
![python](https://img.shields.io/badge/-Python_3.11-blue?logo=python&logoColor=white)
[![license](https://img.shields.io/badge/License-MIT-green.svg?labelColor=gray)](https://github.com/rvandewater/NWICU_MEDS#license)
[![PRs](https://img.shields.io/badge/PRs-welcome-brightgreen.svg)](https://github.com/rvandewater/NWICU_MEDS/pulls)
[![contributors](https://img.shields.io/github/contributors/rvandewater/NWICU_MEDS.svg)](https://github.com/rvandewater/NWICU_MEDS/graphs/contributors)
[![DOI](https://zenodo.org/badge/913786544.svg)](https://doi.org/10.5281/zenodo.14892134)

This pipeline extracts the NWICU dataset (from physionet, https://physionet.org/content/nwicu-northwestern-icu/0.1.0/) into the MEDS format.

## Usage:

```bash
pip install NWICU_MEDS
export DATASET_DOWNLOAD_USERNAME=$PHYSIONET_USERNAME
export DATASET_DOWNLOAD_PASSWORD=$PHYSIONET_PASSWORD
MEDS_extract-NWICU root_output_dir=$ROOT_OUTPUT_DIR
```

When you run this, the program will:

1. Download the needed raw NWICU files for the currently supported version into
    `$ROOT_OUTPUT_DIR/raw_input`.
2. Perform initial, pre-MEDS processing on the raw NWICU files, saving the results in
    `$ROOT_OUTPUT_DIR/pre_MEDS`.
3. Construct the final MEDS cohort, and save it to `$ROOT_OUTPUT_DIR/MEDS_cohort`.

You can also specify the target directories more directly, with

```bash
export DATASET_DOWNLOAD_USERNAME=$PHYSIONET_USERNAME
export DATASET_DOWNLOAD_PASSWORD=$PHYSIONET_PASSWORD
MEDS_extract-NWICU raw_input_dir=$RAW_INPUT_DIR pre_MEDS_dir=$PRE_MEDS_DIR MEDS_cohort_dir=$MEDS_COHORT_DIR
```

## Examples and More Info:

You can run `MEDS_extract-NWICU --help` for more information on the arguments and options. You can also run

```bash
MEDS_extract-NWICU root_output_dir=$ROOT_OUTPUT_DIR
```

to run the entire pipeline.

# Citation

we provide an ETL for the following resource:
Moukheiber, D., Temps, W., Molgi, B., Li, Y., Lu, A., Nannapaneni, P., Chahin, A., Hao, S., Torres Fabregas, F., Celi, L. A., Wong, A., Lloyd, M., Borrat Frigola, X., Lee, H., Schneider, D., Pollard, T., Luo, Y., Kho, A., & Mark, R. (2024). Northwestern ICU (NWICU) database (version 0.1.0). PhysioNet. https://doi.org/10.13026/s84w-1829.
redis-semaphore-async 0.0.4
Redis Semaphore Async is a Python library that provides an asynchronous semaphore implementation using Redis as the backend.
Requires Python: >=3.10

# Redis-Semaphore-Async

Redis-Semaphore-Async is a Python library that provides an asynchronous semaphore implementation using Redis as the backend. It allows you to manage concurrent access to resources in a distributed environment.

## Installation

To install Redis-Semaphore-Async, you can use pip:

```sh
pip install redis-semaphore-async
```

## Usage

Here is an example of how to use Redis-Semaphore-Async:

```python
import asyncio
from redis.asyncio import Redis
from redis_semaphore_async import Semaphore

async def main():
    redis = Redis(host='localhost', port=6379, decode_responses=True)
    async with Semaphore(redis=redis, task_name="test_semaphore", value=5):
        # Your code here
        pass
    # or 
    semaphore = Semaphore(redis=redis, task_name="test_semaphore", value=5)
    with semaphore:
        # Your code here
        pass
    # or
    semaphore = Semaphore(redis=redis, task_name="test_semaphore", value=5)
    await semaphore.acquire()
    # Your code here
    await semaphore.release()

if __name__ == "__main__":
    asyncio.run(main())
```

## Contributing

We welcome contributions! Please read our [contributing guidelines](CONTRIBUTING.md) for more information.

## License

This project is licensed under the Apache License 2.0 - see the [LICENSE](LICENSE) file for details.