semvec/pypi-packages huggingface dataset:
codelingo 0.1
A library to convert code snippets into plain English.
Requires Python: >=3.6

# CodeLingo

CodeLingo is a Python library that converts code snippets into plain English. It also provides the ability to execute the code and capture its output. This library is designed to help beginners understand code by translating it into human-readable explanations.

---

## Features

- **Code to English**: Converts Python code snippets into plain English explanations.
- **Code Execution**: Executes the provided code and captures its output.
- **Easy to Use**: Simple and intuitive API for quick integration.

---

## Installation

You can install CodeLingo using `pip`:

```bash
pip install CodeLingo
Mathmodule590014164 0.1.0
Maths Functions
Requires Python: >=3.6

Math_module14164.py
Created by Manan Khandelwal, Batch 1, B.Tech CSE, 1st Year, UPES.

This Python module provides a collection of fundamental mathematical operations, including:

add: Perform addition of two numbers.
subtract: Compute the difference between two numbers.
multiply: Multiply two numbers.
divide: Divide one number by another.
factorial: Calculate the factorial of a given number.
is_prime: Check if a number is prime.
gcd: Find the greatest common divisor of two numbers.

Thank you for using and sharing this module! 😊 Let me know if you'd like any further enhancements!
quantummlsystem 0.1.3
A Flexible Quantum Machine Learning Framework
Requires Python: >=3.8
Keywords: quantum machine learning quantum-computing ml ai

# QuantumML: A Flexible Quantum Machine Learning Framework

## Overview

QuantumML is an advanced quantum machine learning framework built on top of PennyLane, designed to provide researchers and practitioners with a comprehensive toolkit for quantum machine learning experiments.

## Key Features

- Modular quantum machine learning architectures
- Multiple feature embedding techniques
- Variational quantum circuit implementations
- Advanced training and evaluation utilities
- Quantum-classical model comparisons
- Circuit complexity and barren plateau analysis

## Installation

```bash
pip install quantum-ml-system
```

## Project Structure

```
quantum-ml/
│
├── quantumml/
│   ├── __init__.py
│   ├── core/
│   │   ├── __init__.py
│   │   ├── base_model.py
│   │   └── quantum_circuit.py
│   │
│   ├── models/
│   │   ├── __init__.py
│   │   ├── classifier.py
│   │   ├── regressor.py
│   │   └── generative.py
│   │
│   ├── feature_maps/
│   │   ├── __init__.py
│   │   ├── zz_map.py
│   │   ├── amplitude_map.py
│   │   └── angle_map.py
│   │
│   ├── ansatzes/
│   │   ├── __init__.py
│   │   ├── strongly_entangling.py
│   │   ├── basic_entangler.py
│   │   └── custom_ansatz.py
│   │
│   ├── utils/
│   │   ├── __init__.py
│   │   ├── visualization.py
│   │   ├── complexity_analysis.py
│   │   └── gradient_analysis.py
│   │
│   └── metrics/
│       ├── __init__.py
│       ├── quantum_metrics.py
│       └── comparison_metrics.py
│
├── examples/
│   ├── classification_demo.py
│   ├── regression_demo.py
│   └── generative_model_demo.py
│
├── tests/
│   ├── test_core.py
│   ├── test_models.py
│   └── test_feature_maps.py
│
├── setup.py
├── requirements.txt
└── README.md
```

## Basic Usage Example

```python
from quantumml.models import QuantumClassifier
from quantumml.feature_maps import ZZFeatureMap
from quantumml.ansatzes import StronglyEntanglingAnsatz

# Create a quantum classifier
model = QuantumClassifier(
    n_qubits=4,
    feature_map=ZZFeatureMap(),
    ansatz=StronglyEntanglingAnsatz(n_layers=2)
)

# Train the model
model.fit(X_train, y_train)

# Make predictions
predictions = model.predict(X_test)
```

## Advanced Features

- Multiple Feature Embedding Techniques
- Flexible Variational Circuit Architectures
- Quantum-Classical Model Comparisons
- Circuit Complexity Analysis
- Barren Plateau Detection
- Model Serialization and Loading

## Contributing

Contributions are welcome! Please read our contributing guidelines and code of conduct.

## License

MIT License

## Citation

If you use QuantumML in your research, please cite our framework.
```
@software{quantum_ml_system,
  author = {Nikolaos Roufas},
  title = {Quantum Machine Learning System},
  year = {2025},
  url = {https://github.com/nikolasroufas/quantum-ml-system
/}
}
```
NodeVetting 0.0.1
A node vetting cli for Distributed Workloads
Requires Python: >=3.8

# Node Vetting for Distributed Workloads

Ensure allocated nodes are vetted before executing a distributed workload through a series of configurable sanity checks. These checks are designed to detect highly dynamic issues (e.g., GPU temperature) and should be performed immediately before executing the main distributed job.

## Features

- ⚡ **Fast and lightweight**
- 🛠️ **Modular and configurable** 
- 🚀 **Easy to extend**

## Usage

The sanity check should be embedded into your HPC `sbatch` script. It helps differentiate between healthy nodes and those that should be excluded.

```bash
# Run nodes vetting
srun python -m nodevetting diagnose ../templates/simple-config.yaml >> sanity-results.txt

# Extract node lists
grep '^Cordon:' sanity-results.txt | awk '{print $2}' > cordoned-nodes.txt
grep '^Vetted:' sanity-results.txt | awk '{print $2}' > vetted-nodes.txt

#Run on healthy nodes only
if [ $(wc -l < vetted-nodes.txt) -ge $REQUIRED_NODES ]; then
    srun -N $REQUIRED_NODES --exclude=./cordoned-nodes.txt $MAIN_JOB_COMMAND
else
    echo "Job canceled!"
    echo "Reason: too few vetted nodes."
fi
```

## Example: Running a Job

To run a demo job that outputs a list of vetted nodes, follow these steps:

```bash
# SSH into your HPC cluster

# Clone the repository
git clone https://github.com/theely/shrike.git

# Submit the job 
sbatch --account=csstaff shrike/examples/slurm-job-with-vetting/job.sh 
```

This will execute the sanity check and provide a vetted list of nodes before running the distributed workload.

Check the job status

```
squeue -j {jobid} --long
```


# Development


## Set-up Python Virtual environement

Create a virtual environment:
```console
python3.11 -m venv .venv
source .venv/bin/activate
python3 -m pip install --upgrade pip
pip install -r requirements.txt
```

## Run the CLI

```
cd src
python -m nodevetting diagnose ../templates/simple-config.yaml
```


## Running Tests
From the FirecREST root folder run pytest to execute all unit tests.
```console
source .venv/bin/activate
pip install -r ./requirements.txt -r ./requirements-testing.txt
pytest
```

## Distribute

```
python3 -m build --wheel
```
dig-ass-critic-protos 3.1.2
Proto files for dig_ass_critic_protos
Requires Python: >=3.13

# dig_ass_critic_protos

## Ответственный разработчик

@bakulin

## Зависимости

`python3.13 -m pip install -r requirements.txt`

## Тесты

- `sudo docker compose up --build`

### Линтеры

Работают частично, не удалось исключить [DigitalAssistantCritic_pb2_grpc.py](src/dig_ass_critic_protos/DigitalAssistantCritic_pb2_grpc.py) из проверки

```shell
pip install black flake8-pyproject mypy
black .
flake8
mypy .
```
dig-ass-text-protos 3.3.1
Proto files for dig_ass_text_protos
Requires Python: >=3.11

## Ответственный разработчик

@bakulin

## Зависимости

`python3.13 -m pip install -r requirements.txt`

## Тесты

- `sudo docker compose up --build`

### Линтеры

Работают частично, не удалось исключить [DigitalAssistantText_pb2_grpc.py](src/dig_ass_text_protos/DigitalAssistantText_pb2_grpc.py) из проверки

```shell
pip install black flake8-pyproject mypy
black .
flake8
mypy .
```
nonebot-plugin-animepush 0.0.3
将每日更新番剧渲染为图片

<!-- markdownlint-disable MD033 MD036 MD041 -->

<p align="center">
  <a href="https://huanxinbot.com/"><img src="https://raw.githubusercontent.com/huanxin996/nonebot_plugin_hx-yinying/main/.venv/hx_img.png" width="200" height="200" alt="这里放一张oc饭🤤"></a>
</p>

<div align="center">

# 日漫更新推送

_✨ 实时追踪最新动漫，自动推送更新信息 ✨_

</div>

## 📝 插件说明

这是一个基于 NoneBot2 的动漫更新推送插件，可以：

- 查看今日更新的番剧
- 获取本周番剧时间表
- 查询特定番剧的详细信息
- 设置自动推送更新提醒

## 💿 安装

### 使用 pip 安装（推荐）

```bash
pip install nonebot-plugin-animepush
```

## ⚙️ 配置说明

在 `.env` 文件中添加以下配置项：

| 配置项 | 类型 | 默认值 | 说明 | 必填 |
|:-----:|:----:|:----:|:----:|:----:|
| animepush_image_quality | int | 30 | 图片渲染质量(越高图片越大) | 否 |
| animepush_image_wait | int | 5 | 图片渲染超时时间(秒) | 否 |
| animepush_fonts_medium | str | None | 中等字重字体路径 | 否 |
| animepush_fonts_bold | str | None | 粗体字重字体路径 | 否 |

## 🎯 使用指南

### 基础命令

- `/今日番剧` - 查看今天更新的动漫
- `/本周番剧` - 查看本周动漫时间表
- `/番剧详情 [番剧id]` - 查看指定番剧的详细信息
- `/更新番剧数据` - 刷新一次番剧数据

### 使用示例

```
/今日番剧
> 返回今日更新的番剧列表图片

/番剧详情 114514
> 返回该番剧的详细信息
```

## ⚠️ 注意事项

1. 首次使用时请确保配置正确
2. 图片渲染可能需要一定时间，请耐心等待
3. 推荐使用自定义字体以获得最佳显示效果
4. 自定义字体请将字体文件放置于 插件目录的`fonts` 目录下，并修改 `.env` 文件中的配置项

## 🤝 贡献

欢迎提交 Issue 和 Pull Request

## 📄 许可证

MIT License
oapi3 1.5.3
Validator of openapi3 requests and responses
Requires Python: >=3.7.3

# oapi3
Validator of openapi3 Requests and Responses

1. Open and resolve openapi3 schema

```
>>>    import openapi3
>>>    schema = openapi3.open_schema('api.yaml')
```

2. Validate requests and responses


```
>>>    schema.validate_request(path, operation, query, media_type, body)
>>>    schema.validate_response(path, operation, status_code, media_type, body)
```

3. Create client to remote api

```
>>>    client = oapi3.Client('http://server/api', schema)
>>>    client.operations['some.operation_id'](
>>>        params={'item_id': 10},
>>>        body={'some_key': 'some_date'},
>>>    )
```
Mathmodule590011386 0.1.0
Maths Functions
Requires Python: >=3.6


Math_module.py
Created by Yashasvani Markanday, Batch 2, B.Tech CSE, 1st Year, UPES.

This Python module provides a collection of fundamental mathematical operations, including:

add: Perform addition of two numbers.
subtract: Compute the difference between two numbers.
multiply: Multiply two numbers.
divide: Divide one number by another.
factorial: Calculate the factorial of a given number.
is_prime: Check if a number is prime.
gcd: Find the greatest common divisor of two numbers.

Thank you for using and sharing this module! ðŸ˜Š Let me know if you'd like any further enhancements!
GraphVelo 0.1.11
Inferring manifold-constrained velocity and transforming vectors across representations
Requires Python: >=3.8

# GraphVelo allows for accurate inference of multimodal omics velocities and molecular mechanisms for single cells

[![Supported Python versions](https://img.shields.io/badge/python-3.8-blue)](https://python.org)

<img src="https://github.com/xing-lab-pitt/GraphVelo/blob/main/docs/source/_static/img/framework_fig.png" alt="GraphVelo" width="800" />

**GraphVelo** is a graph-based machine learning procedure that uses RNA velocities inferred from existing methods as input and infers velocity vectors that lie in the tangent space of the low-dimensional manifold formed by the single-cell data.

## Key Features

- Refine the velocity vectors estimated by any methods (e.g., splicing-based, metabolic labeling-based, pseudotime-based, lineage tracing-based, etc.) to the data manifold
- Infer modality dynamics that go beyond splicing events
    - Transcription rate of genes without introns or undergoing alternative splicing
    - Change rate of chromatin openness
    - More to be explored
- Serve as a plugin that can be seamlessly integrated into existing RNA velocity analysis pipelines
- Analyze dynamical systems in the context of multi-modal single-cell data

## Getting Started with GraphVelo

Check the pipeline of RNA velocity estimation and you will find the niche of `graphvelo`:

<img src="https://github.com/xing-lab-pitt/GraphVelo/blob/main/docs/source/_static/img/graphvelo_pipeline.png" alt="GraphVelo" width="800" />

Now let's get started with our [Tutorials](https://graphvelo.readthedocs.io/en/latest/index.html).

## Installation

You need to have Python 3.8 or newer installed on your system. 

To create and activate a new environment
```bash
conda create -n graphvelo python=3.8
conda activate graphvelo
```

Install via pip:
```bash
pip install graphvelo
```

## Citing GraphVelo

Please see our [manuscript](https://www.biorxiv.org/content/10.1101/2024.12.03.626638v1) for detailed explanation. 
If you find GraphVelo useful for your research, please consider citing our work as follows:

```
@article {Chen2024.12.03.626638,
	author = {Chen, Yuhao and Zhang, Yan and Gan, Jiaqi and Ni, Ke and Chen, Ming and Bahar, Ivet and Xing, Jianhua},
	title = {GraphVelo allows inference of multi-modal single cell velocities and molecular mechanisms},
	year = {2024},
	doi = {10.1101/2024.12.03.626638},
	publisher = {Cold Spring Harbor Laboratory},
	URL = {https://www.biorxiv.org/content/early/2024/12/07/2024.12.03.626638},
	journal = {bioRxiv}
}

```