semvec/pypi-packages huggingface dataset:
azpipvar 0.1.6
A tool to extract and list variables from Azure Pipeline YAML files
Requires Python: >=3.8

# Azure Pipeline Variable List

[![Test](https://github.com/greatbody/macpy-scripts/actions/workflows/azpipvar-test.yml/badge.svg)](https://github.com/greatbody/macpy-scripts/actions/workflows/azpipvar-test.yml)

A tool to analyze and list variables used in Azure Pipeline YAML files.

[English](#features) | [中文](#功能说明)

## 功能说明

一个用于分析和列出 Azure Pipeline YAML 文件中使用的变量的工具。

### 功能特点

- 检测 `$(VARIABLE)` 格式的变量
- 识别变量组中定义的变量
- 支持嵌套目录结构
- 支持 `.yml` 和 `.yaml` 文件扩展名

例如，如果你有一个 `demo.yml` 流水线文件，内容如下：

```yaml
...
variables:
  group: infra
  ## - var1
  ## - var2
  ## - var3

stages:
  - stage: Build
    jobs:
      - job: Build
        steps:
          - script: echo $(var1)
...
```

azpipvar 将输出：

```
demo.yml:
  - var1 (by variable group)
```

使用说明：

```
## - var3
```

这是一个注释，告诉工具 `var3` 是在变量组中定义的，或者是在 Azure DevOps 流水线的变量中定义的（未存储在代码仓库中）。

该工具将列出流水线文件中使用的所有变量，并显示它们是否在变量组中定义。

这个工具可以帮助你找到流水线文件中所有未预定义的变量。

---

## Features

- Detects variables in `$(VARIABLE)` format
- Identifies variables defined in variable groups
- Supports nested directory structures
- Handles both `.yml` and `.yaml` file extensions

For example, if you have a pipeline file `demo.yml` like this:

```yaml
...
variables:
  group: infra
  ## - var1
  ## - var2
  ## - var3

stages:
  - stage: Build
    jobs:
      - job: Build
        steps:
          - script: echo $(var1)
...
```

azpipvar will output:

```
demo.yml:
  - var1 (by variable group)
```

Let me explain the usage:

```
## - var3
```

This is a comment, that tells the tool that `var3` is defined in a variable group or just defined in the variables of the azure devops pipeline(which is not stored in repository).

It will list all variables used in the pipeline files and show whether they are defined in variable groups or not.

This tool can help you find all variables used in the pipeline files that is not predefined.

## Requirements

- Python 3.8 or higher
- PyYAML 6.0.1 or higher

## Installation

You can install the package using pip:

```bash
pip install azpipvar
```

To help us improve the package by sharing anonymous installation statistics:

```bash
pip install azpipvar --install-option="--no-track-install"
```

Note: The installation tracking is completely optional and transparent. When enabled:
- A single GET request is made to count the installation
- No personal or system information is collected
- Failed tracking attempts will not affect installation
- The tracking server timeout is set to 2 seconds

For development installation from source:

```bash
git clone https://github.com/greatbody/macpy-scripts.git
cd macpy-scripts
pip install -e .
```

## Usage

Navigate to your Azure Pipelines directory and run:

```bash
azpipvar
```

This will scan all YAML files in the current directory and subdirectories, listing all variables used in the pipeline files.

## License

This project is licensed under the MIT License.

## Author

- greatbody (sunruicode@gmail.com)

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.
helpingai 0.0.1
Python client library for the HelpingAI API
Requires Python: >=3.7

# HelpingAI

The official Python library for the [HelpingAI](https://helpingai.co) API

[![PyPI version](https://badge.fury.io/py/helpingai.svg)](https://badge.fury.io/py/helpingai)
[![Python Versions](https://img.shields.io/pypi/pyversions/helpingai.svg)](https://pypi.org/project/helpingai/)


## Installation

```bash
pip install helpingai
```

## Quick Start

First, set your API key as an environment variable:

```bash
export HAI_API_KEY='your-api-key'
```

Basic usage example:

```python
from helpingai import HAI

hai = HAI()

response = hai.chat.completions.create(
    model="HelpingAI2.5-10B",
    messages=[
        {"role": "system", "content": "You are an expert in emotional intelligence."},
        {"role": "user", "content": "What makes a good leader?"}
    ]
)

print(response.choices[0].message.content)
```

## Advanced Usage

### Streaming Responses

```python
for chunk in hai.chat.completions.create(
    model="HelpingAI2.5-10B",
    messages=[{"role": "user", "content": "Tell me about empathy"}],
    stream=True
):
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="")
```

### Advanced Parameters

```python
response = hai.chat.completions.create(
    model="HelpingAI2.5-10B",
    messages=[{"role": "user", "content": "Write a story about empathy"}],
    temperature=0.7,        # Controls randomness (0-1)
    max_tokens=500,        # Maximum length of response
    top_p=0.9,            # Nucleus sampling parameter
    frequency_penalty=0.3, # Reduces repetition
    presence_penalty=0.3   # Encourages new topics
)
```

### Robust Error Handling

```python
from helpingai import HAI, HAIError, RateLimitError, InvalidRequestError
import time

def make_completion_with_retry(messages, max_retries=3):
    for attempt in range(max_retries):
        try:
            return hai.chat.completions.create(
                model="HelpingAI2.5-10B",
                messages=messages
            )
        except RateLimitError as e:
            if attempt == max_retries - 1:
                raise
            time.sleep(e.retry_after)
        except InvalidRequestError as e:
            print(f"Invalid request: {str(e)}")
            raise
        except HAIError as e:
            print(f"API error: {str(e)}")
            raise
```

## Requirements

- Python 3.7+
- `requests` library
- Valid HelpingAI API key

## Documentation

For detailed information, check out our comprehensive documentation:

- [📚 Getting Started Guide](docs/getting_started.md)
- [📖 API Reference](docs/api_reference.md)
- [💡 Example Code](docs/examples.md)
- [❓ FAQ](docs/faq.md)

## Support

- [Submit an issue](https://github.com/HelpingAI/HelpingAI-python/issues)
epitopegen 0.1.0
epitopegen: TCR-based epitope sequence prediction
Requires Python: >=3.8

# EpitopeGen

EpitopeGen is a deep learning model that predicts cognate epitope sequences from T-cell receptor (TCR) sequences. It helps functionally annotate TCRs in single-cell TCR sequencing data by generating potential binding epitopes and identifying phenotype associations.

<img src="./overview.PNG" alt="EpitopeGen" style="zoom:150%;" />

## Installation

```bash
pip install epitopegen
```

## Quick Start

```python
from epitopegen import EpitopeGenPredictor

# Initialize predictor
predictor = EpitopeGenPredictor()

# Predict epitopes for TCR sequences
tcrs = ["CASIPEGGRETQYF", "CAVRATGTASKLTF"]
results = predictor.predict(tcrs)
```

## Basic Usage

### 1. Prepare Input Data

Create a CSV file with TCR sequences:
```csv
text,label
CASIPEGGRETQYF,ZZZZZ
CAVRATGTASKLTF,ZZZZZ
```

### 2. Run Predictions

```python
import pandas as pd
from epitopegen import EpitopeGenPredictor

# Initialize predictor
predictor = EpitopeGenPredictor()

# Read TCR sequences
tcrs = pd.read_csv("input.csv")["text"].tolist()

# Generate predictions
results = predictor.predict(
    tcr_sequences=tcrs,
    output_path="predictions.csv",
    top_k=50  # num of epitopes to generate
)
```

### 3. Annotate Phenotypes

```python
from epitopegen import EpitopeAnnotator

# Initialize annotator with reference database
annotator = EpitopeAnnotator("epitopes_db.csv")

# Annotate predictions
results = annotator.annotate(
    predictions_df=results,
    method='substring',
    output_path="annotations.csv"
)
```

## Key Features

- Generate potential epitope sequences for TCRs
- Support for multiple model checkpoints
- Phenotype annotation using reference databases
- Ensemble predictions for robust results
- Built-in analysis tools

## Resources

- [Detailed Tutorial](https://github.com/Regaler/EpitopeGen/blob/main/tutorials/epitopegen_basic_usage.ipynb)
- [API Reference](https://regaler.github.io/EpitopeGen/index.html)
- [Model Checkpoints](https://zenodo.org/records/14853949)
- [Reference Databases](https://zenodo.org/records/14861398)
- [Development Resources](https://zenodo.org/records/14286754)

## Requirements

- Python ≥ 3.8
- PyTorch ≥ 1.12
- transformers ≥ 4.39.0
- pandas ≥ 1.5.0

## Citation

If you use EpitopeGen in your research, please cite:
```bibtex
@article{epitopegen2024,
  title={Generating cognate epitope sequences of T-cell receptors with a generative transformer},
  year={2024}
}
```

## License

MIT License

## Support

For questions and issues:
- Open an issue on [GitHub](https://github.com/regaler/epitopegen/issues)
- Contact: minukma@cs.ubc.ca
pku-lib 0.1.2
PKMN Utility Library
Requires Python: >=3.12
Keywords: pk6, pk7, pk8, pk9, pkmn, pkx, pokemon, pokémon, pyukumuku, rng, wry

# PKU - PKMN Utility Library

[![Codeberg repository](https://img.shields.io/badge/Repository-Codeberg-blue?logo=codeberg&labelColor=white&style=plastic)][repo]
[![CPython 3.12 to 3.14](https://img.shields.io/badge/CPython-3.12%20|%203.13%20|%203.14-blue?style=plastic)][python]
[![PyPI version](https://img.shields.io/pypi/v/pku-lib?label=PyPI&color=blue&style=plastic)][pypi]
[![AGPLv3+ license](https://img.shields.io/pypi/l/pku-lib?label=License&color=blue&style=plastic)][license]

<p align=center>
    <img src="https://codeberg.org/wry/pku-lib/raw/branch/main/assets/icon.png" title="Pyukumuku" alt="Pyukumuku sprite">
</p>

## Installation

This library supports [CPython][python] 3.12 to 3.14.

1. Install the package from [PyPI] **(recommended)**

- The latest stable release.
- Potentially outdated.

```shell
python3 -m pip install pku-lib
```

2. Install the package from [Codeberg][repo]

- The most recent changes.
- Potentially unstable.

```shell
python3 -m pip install git+https://codeberg.org/wry/pku-lib.git
```

## Usage

To use the library, import `pku`.

```python
import pku

pku.hello()
```

## Development

1. **Install uv**

[uv](https://docs.astral.sh/uv/) is a tool for managing dependencies and
running tests. See the uv documentation for installation instructions.

2. **Set Up the Development Environment**

Once uv is installed, run the following command to ensure that all project
dependencies are installed and up-to-date.

```shell
uv sync
```

3. **Lint and Run Tests**

To ensure your code is consistent with the project and passes current tests,
run nox:

```shell
uv run nox
```

## License

This library is licensed under the [GNU Affero General Public License][license]
version 3.0 or later.

<!-- Hyperlinks! -->

[python]: https://python.org/downloads/ "Download Python"
[pypi]: https://pypi.org/project/pku-lib/ "Python Package Index"
[repo]: https://codeberg.org/wry/pku-lib/ "Codeberg repository"
[license]: https://www.gnu.org/licenses/agpl-3.0.en.html "GNU Affero General Public License"