semvec/pypi-packages huggingface dataset:
dochere 0.0.2
DocHere is a Python package that automatically generates API documentation using LLM.
Requires Python: >=3.8

![image](assets/logo.png)

[![GitHub Code License](https://img.shields.io/github/license/Shulin-Zhang/dochere)](LICENSE)
[![GitHub last commit](https://img.shields.io/github/last-commit/Shulin-Zhang/dochere)](https://github.com/Shulin-Zhang/dochere/commits/main)
[![PyPI](https://img.shields.io/pypi/v/dochere)](https://pypi.org/project/dochere/)
[![GitHub pull request](https://img.shields.io/badge/PRs-welcome-blue)](https://github.com/Shulin-Zhang/dochere/pulls)


## dochere是一个用大模型自动生成API文档的python包，会自动扫描API的路由结构，生成或更新API文档。

## 安装

```bash
pip install dochere

或

pip install dochere -i https://pypi.org/simple
```

## 使用方法

### 方法一
```bash
export OPENAI_API_KEY=your_key

dochere generate server.py

dochere update server.py
```

### 方法二
```bash
dochere init
```

编辑`config.yaml`文件
```yaml
openai_api_key: xxx

openai_base_url: 'http://ip:port:v1'

openai_model: 'qwen-plus'
```
```bash
dochere generate server.py ./docs --lang zh --config config.yaml

dochere update server.py./docs --lang zh --config config.yaml
```

## 支持模型

- OpenAI

- AzureOpenAI

- 通义千问

## 支持API框架

- Flask

## TODO

- 支持文心一言、智普AI等大模型

- 支持fastapi、django等框架

- 支持文档在线web页面展示

- 支持自定义文档模版
ECOv002-L2T-STARS 1.0.1
ECOSTRESS Collection 2 JPL STARS Data Fusion Product Generating Executable (PGE)
Requires Python: >=3.11

# ECOSTRESS Collection 2 STARS Data Fusion Product Generating Executable (PGE)

This software package has been refactored from the [ECOSTRESS Collection 2 Gridded & Tiled Products Generator](https://github.com/ECOSTRESS-Collection-2/ECOSTRESS-Collection-2) as an independent product pipeline for the ECOSTRESS Collection 2 STARS data fusion product. This software package will serve as an ECOSTRESS Collection 2 reference for the development of the [ECOSTRESS Collection 3 STARS PGE](https://github.com/ECOSTRESS-Collection-3/ECOv003-L2T-STARS) and the [SBG Collection 1 JET PGE](https://github.com/sbg-tir/SBG-TIR-L2-STARS).

[Gregory H. Halverson](https://github.com/gregory-halverson-jpl) (they/them)<br>
[gregory.h.halverson@jpl.nasa.gov](mailto:gregory.h.halverson@jpl.nasa.gov)<br>
NASA Jet Propulsion Laboratory 329G

[Margaret C. Johnson](https://github.com/majohnso) (she/her)<br>
[maggie.johnson@jpl.nasa.gov](mailto:maggie.johnson@jpl.nasa.gov)<br>
NASA Jet Propulsion Laboratory 398L

[Evan Davis](https://github.com/evandjpl) (he/him)<br>
[evan.w.davis@jpl.nasa.gov](mailto:evan.w.davis@jpl.nasa.gov)<br>
NASA Jet Propulsion Laboratory 397K

[Kerry Cawse-Nicholson](https://github.com/kcawse) (she/her)<br>
[kerry-anne.cawse-nicholson@jpl.nasa.gov](mailto:kerry-anne.cawse-nicholson@jpl.nasa.gov)<br>
NASA Jet Propulsion Laboratory 329G

[Claire Villanueva-Weeks](https://github.com/clairesvw) (she/her)<br>
[claire.s.villanueva-weeks@jpl.nasa.gov](mailto:claire.s.villanueva-weeks@jpl.nasa.gov)<br>
NASA Jet Propulsion Laboratory 329G

## Prerequisites

This is a Python package that calls Julia code. Julia must be installed in order to run this package.

## Authentication

This package requires an [EarthData](https://urs.earthdata.nasa.gov/) account and reads EarthData credentials from `~/.netrc` in the following format:

```
machine urs.earthdata.nasa.gov
login <USERNAME>
password <PASSWORD>
```

## Environment

On macOS, there are issues with installing `pykdtree` using pip, so it's better to use a mamba environment and install the `pykdtree` mamba package.

```
mamba create -y -n ECOv002-L2T-STARS -c conda-forge python=3.11 jupyter pykdtree 
mamba activate ECOv002-L2T-STARS
```

## Installation

Install this package from PyPi using the name `ECOv002-L2T-STARS` with dashes:

```
pip install ECOv002-L2T-STARS
```

You can also install development versions of this package directly from a clone of this repository:

```
pip install .
```

## Usage

Import this package with the name `ECOv002_L2T_STARS` with underscores:

```
import ECOv002_L2T_STARS
```
celery-valkey-backend 0.1.0
A custom Celery result backend using Valkey as the storage engine
Requires Python: >=3.8

# Celery ValKey Backend

A custom result backend for Celery using ValKey as the storage engine. This backend provides a scalable, high-performance alternative to traditional result backends by leveraging ValKey's distributed key-value store capabilities.

## Requirements

- Python >= 3.8
- Celery >= 4.4.0
- ValKey >= 6.0.0

## Installation

Install the package using pip:

```bash
pip install celery-valkey-backend
```

## Quick Start

Use the ValKey backend in your Celery application:

```python
from celery import Celery

app = Celery('your_app', backend='valkey://localhost:6379/0')

@app.task
def add(x, y):
    return x + y

# Using the task
result = add.delay(4, 4)
print(result.get())  # Output: 8
```

## URL Format

The backend URL format:
```
valkey://host:port/db
```

Example:
```
valkey://localhost:6379/0
```

## Development

```bash
# Clone the repository
git clone https://github.com/vuonglv1612/celery-valkey-backend.git
cd celery-valkey-backend

# Install development dependencies
pip install -e ".[dev]"

# Run tests
pytest
```

## License

This project is licensed under the MIT License.

## Support

If you encounter any problems or have questions, please open an issue on [GitHub Issues](https://github.com/vuonglv1612/celery-valkey-backend/issues).
erp5_coverage_plugin 0.0.2
Coverage plugin for ERP5 python scripts and components
Requires Python: >=3.9

[`coverage.py`](https://coverage.readthedocs.io/) plugin to collect coverage from business templates.

# How it works ?

## Python Scripts

### Collecting

This depend on business template installation setting the `_erp5_coverage_filename`
property on the script instance in ZODB. This property is a string, the
full path of the python script.

### Reporting

During reporting, coverage needs to know the set of lines number containing code,
to compare it with the line number that were actually executed.
Because python scripts are compiled as a function, they can not be parsed
by the default Python reporter, we use a simple reporter which wraps the
code in a function definition to compile and collect the line numbers and then
subtract 1 to the line numbers.


## ZODB Components

This also depends on business template installation setting the
`_erp5_coverage_filename` property on the script instance in ZODB and the dynamic
module to have it set to its `__file__`, then coverage can load it like a
traditional python module.


## Page Templates, TALES Expressions

Not supported, no coverage is collected.