semvec/pypi-packages huggingface dataset:
lark-connect 0.0.5
A Python package for integrating with Lark API to send messages, manage bots, and interact with Lark apps.
Requires Python: >=3.8
Keywords: lark, incode8

# Lark Connect
**Lark App**는 Lark API를 쉽게 연동하여 메시지 전송, 봇 관리, 앱 통합 등의 기능을 제공하는 Python 패키지입니다.  

## 🚀 설치  
```bash
pip install lark-connect


📌 주요 기능
✅ Lark 메시지 전송 (LarkClient)
✅ Lark Docs 문서 관리 (LarkDocManager)
✅ Lark Sheet 스프레드시트 관리 (LarkSheetManager)
✅ API 응답 타입 정의 및 예외 처리 (typings.py, exceptions.py)

# 사용 예시

python
from lark_connect.core import LarkCore

app_id = "YOUR_APP_ID"
app_access_secret = "YOUR_APP_SECRET"

client = LarkCore(app_id, app_access_secret)

client.send_message("안녕하세요! Lark App을 사용해보세요 🚀")
VarSim 1.0.1
Variant Simulator
Requires Python: >=3.8

# VarSim
VarSim generates simulations for all possible single nucleotide variants (SNVs) for the Matched Annotation from NCBI and EMBL-EBI (MANE) transcript, along with the corresponding protein using HGVS notation.

**INSTALLATION**
```powershell
pip install varsim
```
Variant Simulator

```python
cds("INS")
```
NM_000207.3:c.1A>G	NP_000198.1:p.M1V	NP_000198.1:p.Met1Val

NM_000207.3:c.2T>G	NP_000198.1:p.M1R	NP_000198.1:p.Met1Arg

M_000207.3:c.2T>A	NP_000198.1:p.M1K	NP_000198.1:p.Met1Lys

NM_000207.3:c.3G>A	NP_000198.1:p.M1I	NP_000198.1:p.Met1Ile

NM_000207.3:c.1A>T	NP_000198.1:p.M1L	NP_000198.1:p.Met1Leu

...	...	...	...

NM_000207.3:c.328A>T	NP_000198.1:p.N110Y	NP_000198.1:p.Asn110Tyr

NM_000207.3:c.329A>T	NP_000198.1:p.N110I	NP_000198.1:p.Asn110Ile

NM_000207.3:c.330C>T	NP_000198.1:p.N110=	NP_000198.1:p.Asn110=

NM_000207.3:c.328A>C	NP_000198.1:p.N110H	NP_000198.1:p.Asn110His

NM_000207.3:c.329A>C	NP_000198.1:p.N110T	NP_000198.1:p.Asn110Thr
```python
utr5("INS")
```
NM_000207.3:c.-59A>G

NM_000207.3:c.-59A>T

NM_000207.3:c.-59A>C

NM_000207.3:c.-58G>A

NM_000207.3:c.-58G>T

...	...	...	...

NM_000207.3:c.-2C>A

NM_000207.3:c.-2C>T

NM_000207.3:c.-1C>G

NM_000207.3:c.-1C>A

NM_000207.3:c.-1C>T
```python
utr3("INS")
```
NM_000207.3:c.*1A>G

NM_000207.3:c.*1A>T

NM_000207.3:c.*1A>C

NM_000207.3:c.*2C>G

NM_000207.3:c.*2C>A

...	...	...	...

NM_000207.3:c.*72G>T

NM_000207.3:c.*72G>C

NM_000207.3:c.*73C>G

NM_000207.3:c.*73C>A

NM_000207.3:c.*73C>T
```python
splicing("INS")
```
NM_000207.3:c.187+2T>G

NM_000207.3:c.188-2A>G

NM_000207.3:c.187+1G>A

NM_000207.3:c.187+2T>A

NM_000207.3:c.188-1G>A

NM_000207.3:c.187+1G>T

NM_000207.3:c.188-2A>T

NM_000207.3:c.188-1G>T

NM_000207.3:c.187+1G>C

NM_000207.3:c.187+2T>C

NM_000207.3:c.188-2A>C

NM_000207.3:c.188-1G>C
imaris-convert 1.0.1
A tool for converting images to Imaris format
Requires Python: >=3.6

# Imaris Convert

一个用于将图像转换为 Imaris 格式的 Python 工具包。

## 安装

```bash
pip install imaris-convert
```

## 使用方法

### 命令行使用

```bash
# 基本用法
imaris-convert input.tiff

# 指定输出目录
imaris-convert input.tiff -o /path/to/output/directory
```

### Python API 使用

```python
# 转换图像文件(.tiff)
from imaris_convert import tiff_to_imaris

tif_path = r'your_tiff_path'
tiff_to_imaris(tiff_path=tif_path)
# tiff_to_imaris(tiff_path=tif_path,out_path=out_path)

# 转换 np.ndarray 到 ims
from imaris_convert import numpy_to_imaris
import numpy as np

your_np_data = np.ones((100,100,100),dtype=np.uint16)
numpy_to_imaris(np_data=your_np_data,out_path='your_save_path')
```

## 依赖项

- Python >= 3.6
- numpy
- tifffile
- tqdm

## 许可证

MIT License
lark-app 0.0.2
A Python package for integrating with Lark API to send messages, manage bots, and interact with Lark apps.
Requires Python: >=3.8
Keywords: lark, incode8

# Lark Connect
**Lark Connect**는 Lark API를 쉽게 연동하여 메시지 전송, 봇 관리, 앱 통합 등의 기능을 제공하는 Python 패키지입니다.  

## 🚀 설치  
```bash
pip install lark-connect


📌 주요 기능
✅ Lark 메시지 전송 (LarkClient)
✅ Lark Docs 문서 관리 (LarkDocManager)
✅ Lark Sheet 스프레드시트 관리 (LarkSheetManager)
✅ API 응답 타입 정의 및 예외 처리 (typings.py, exceptions.py)

# 사용 예시

python
from lark_connect.core import LarkClient

webhook_url = "https://open.larksuite.com/open-apis/bot/v2/hook/YOUR_WEBHOOK"
client = LarkClient(webhook_url)

client.send_message("안녕하세요! Lark Connect를 사용해보세요 🚀")
nish 0.1.0
A CLI tool to combine PDFs in a folder
Requires Python: >=3.8
Keywords: pdf combine merge tool

# Nish

A simple command-line tool to combine PDF files in a folder.

## Installation

```bash
pip install nish
```

## Usage

Combine PDFs in the current directory:
```bash
nish .
```

Combine PDFs in a specific folder:
```bash
nish /path/to/pdfs
```

Combine PDFs with a custom output name:
```bash
nish /path/to/pdfs -o merged.pdf
```

## Features

- Combines all PDF files in a specified folder
- Sorts PDFs alphabetically before combining
- Simple command-line interface

## Requirements

- Python 3.8+

## License

MIT License
algozar 0.1.0
Collection of algorithm designs and some code to help ease the running of the program.
Requires Python: >=3.6

# Algozar

**Version:** 0.2703.25  
**Author:** [Izzar Suly Nashrudin](https://avezoor.vercel.app/)  
**Email:** [Izzarsuly@proton.me](mailto:Izzarsuly@proton.me)  
**Repository:** [GitHub - avezoor/algozar](https://github.com/avezoor/algozar)  

## Description
Algozar is a collection of algorithm designs and helper code to ease program execution. It includes implementations for various algorithms, making it a useful tool for developers, researchers, and students who need efficient algorithmic solutions.

## Installation
To install Algozar, use pip:

```sh
pip install algozar
```

## Usage
Import the package and use the provided algorithms:

```python
import algozar

# Example: Simple Algorithm
arr = [5, 3, 8, 1]
sorted_arr = algozar.bubble_sort(arr)
print(sorted_arr)  # Output: [1, 3, 5, 8]

# Example: Printing Code of bubble_sort
algozar.print.bubble_sort()
```

## Supported Platforms
Algozar is compatible with:
- Windows
- macOS
- Linux
- Unix

## Contribution
Contributions are welcome! Feel free to open issues and pull requests in the [GitHub repository](https://github.com/avezoor/algozar).
spacy-glossbert 0.1.0
A spaCy pipeline component for word sense disambiguation using the GlossBERT model
Requires Python: >=3.8

# Spacy-GlossBert

A spaCy pipeline component for word sense disambiguation using the GlossBERT model.

## Overview

This package provides a spaCy component that performs Word Sense Disambiguation (WSD) using the GlossBERT model. 
GlossBERT leverages BERT's contextual embeddings to disambiguate word senses by comparing the context with WordNet sense definitions (glosses).

## Installation

```bash
pip install spacy-glossbert
```

You'll also need to download the spaCy model:

```bash
python -m spacy download en_core_web_sm
```

## Usage

### Basic Usage

```python
import spacy
from spacy_glossbert import has_glossbert_wsd, get_synset_info

# Load spaCy with the GlossBERT component
nlp = spacy.load("en_core_web_sm")
nlp.add_pipe("glossbert_wsd", last=True)

# Process a text
doc = nlp("He went to the bank to deposit money.")

# Check if the document has been processed with GlossBERT
if has_glossbert_wsd(doc):
    # Print disambiguated senses
    for token in doc:
        synset = token._.glossbert_synset
        if synset:
            print(f"{token.text}: {token.pos_} -- {synset.name()} - {synset.definition()}")

    # Alternative: Get all sense information as a list of dictionaries
    senses = get_synset_info(doc)
    for sense in senses:
        print(f"{sense['text']}: {sense['synset']} - {sense['definition']}")
```

### Visualization

The package includes visualization utilities using spaCy's displaCy:

```python
from spacy_glossbert import visualize_wsd

# Visualize the disambiguated senses
visualize_wsd(doc)
```

### Configuration Options

```python
# Configure the component
nlp.add_pipe(
    "glossbert_wsd",
    config={
        "pos_filter": ["NOUN", "VERB", "ADJ"],  # Part-of-speech tags to process
        "supervision": True,  # Highlight the target word in context
        "model_name": "kanishka/GlossBERT"  # HuggingFace model name/path
    }
)
```

## How It Works

The GlossBERT component:

1. Identifies tokens with POS tags specified in `pos_filter`
2. Retrieves WordNet synsets (possible senses) for each token
3. For each candidate sense, creates an input of the form: `"context [SEP] definition"`
4. Scores each sense using the GlossBERT model
5. Assigns the highest-scoring sense to each token

## License

This project is licensed under the GNU General Public License v2.0 (GPLv2).

## Credits

- GlossBERT model: [Huang et al](https://aclanthology.org/D19-1355/)
- spaCy: [Explosion AI](https://spacy.io/)
- WordNet: [Princeton University](https://wordnet.princeton.edu/) 

## Author

Igor Morgado <morgado.igor@gmail.com>