semvec/pypi-packages huggingface dataset:
py-docstrings 0.1.0
Automatically generate NumPy-style docstrings
Requires Python: >=3.9
Keywords: docstring, documentation, cli, libcst

<h1 align="center">
<img src="https://raw.githubusercontent.com/Nimish-4/py-docstrings/main/logo.png" width="312" height="250"  border='0px'>
</h1>

A command-line tool for generating Python docstrings using Concrete Syntax Trees (CST).

`py-docstrings` analyzes Python source code with [**LibCST**](https://github.com/Instagram/LibCST) and inserts docstrings while preserving
original formatting, comments, and structure. `py-docstrings` uses Concrete Syntax Trees, which 
retain the exact structure of the original source code. The primary interface is the CLI, with a
few different options depending upon use.





## Installation

```bash
pip install py-docstrings
```

## Basic Usage

To generate docstrings directly in a file:

```bash
docgen main.py
```

To check for missing docstrings without modifying files:

```bash
docgen main.py --check
```

## Additional Options

* `--recursive`
  Recursively process Python files in a directory.

* `--verbose`
  Display information about processed files.

* `--full`
  Generate a more complete numpy-style docstring with more attributes.

---

## Example

### Before

```python
def add(a, b):
    return a + b
```

### After

```python
def add(a, b):
    """Summarize the function in one line.
    
    Several sentences providing an extended description. Refer to
    variables using back-ticks, e.g. `var`. For functions (also method and module),
    there should be no blank lines after closing the docstring.
    
    Parameters
    ----------
    var1 : array_like
        Array_like means all those objects -- lists, nested lists, etc. --
        that can be converted to an array.
    
    *args : iterable
        Other arguments.
    
    Returns
    -------
    describe : type
        Explanation of return value named `describe`.
    
    out : type
        Explanation of `out`.
    
    Examples
    --------
    These are written in doctest format, and should illustrate how to
    use the function.
    
    >>> a = [1, 2, 3]
    >>> print([x + 3 for x in a])
    [4, 5, 6]
    """
    return a + b
```

---

## Supported Targets

* Top-level functions
* Classes
* Class methods

Existing docstrings are **not overwritten**.


## Upcoming features

Planned improvements include:

* Docstrings include actual function arguments
* Generate docstring for specific functions/classes in a module

---

## License

MIT License
weblate-fonts 2026.1
Weblate fonts collection
Requires Python: >=3.11
Keywords: gettext, git, i18n, l10n, mercurial, translate

<a href="https://weblate.org/"><img alt="Weblate" src="https://s.weblate.org/cdn/Logo-Darktext-borders.png" height="80px" /></a>

**Weblate is libre software web-based continuous localization system,
used by over 2500 libre projects and companies in more than 165 countries.**

# Weblate Fonts Collection

Fonts used in Weblate.

[![Website](https://img.shields.io/badge/website-weblate.org-blue.svg)](https://weblate.org/)
[![CII Best Practices](https://bestpractices.coreinfrastructure.org/projects/552/badge)](https://bestpractices.coreinfrastructure.org/projects/552)
[![PyPI package](https://img.shields.io/pypi/v/weblate-fonts.svg)](https://pypi.org/project/weblate-fonts/)

## PIP Installation

Install using pip:

```console
pip3 install weblate-fonts
```

Sources are available at <https://github.com/WeblateOrg/fonts>.
gfa-ml 0.0.45
ML library for end-to-end ML pipeline
Requires Python: <3.14,>=3.10

# Green Factory AI ML Library - gfa-ml

This is the library to support data scientist in building end-to-end ML pipeline


<figure>
<p style="text-align:center">
<img src="docs/img/ML_pipeline.svg" alt="pipeline overview" width="1000"/>
</p>
<figcaption>
<p style="text-align:center">
Fig 1. Pipeline Overview
</p>
</figcaption>
</figure>


## 1. Data Analysis
More details can be found in the [data_analysis](docs/data_analysis.md) folder.
<figure>
<p style="text-align:center">
<img src="docs/img/data_analysis.svg" alt="data analysis" width="1000"/>
</p>
<figcaption>
<p style="text-align:center">
Fig 2. Overview of the data analysis process
</p>
</figcaption>
</figure>



## 2.Serving Pipeline
Progress: on going

<figure>
<p style="text-align:center">
<img src="docs/img/serving_pipeline.svg" alt="serving pipeline" width="1000"/>
</p>
<figcaption>
<p style="text-align:center">
Fig 3. End-to-end ML serving pipeline
</p>
</figcaption>
</figure>

## 3. DevOps
### 3.1. Lifecycle overview
More details can be found in the [devops_lifecycle](docs/dev.md) folder.

<figure>
<p style="text-align:center">
<img src="docs/img/lifecycle.svg" alt="DevOps Lifecycle" width="1000"/>
</p>
<figcaption>
<p style="text-align:center">
Fig 4. DevOps Lifecycle
</p>
</figcaption>
</figure>

### 3.2. [dev](dev)
Implements tools and utilities to support the DevOps lifecycle
- Generate UML diagrams for the codebase

### 3.3. [gfa_ml](gfa_ml)
Contains the main library code, including data model abstractions, data analysis, and serving pipeline. 

#### 3.3.1. [Lib](gfa_ml/lib)
- Common functions using the data model abstractions is implemented in `lib/common.py`
- Utilities (not using the data model abstractions) are implemented in `lib/utils.py`
- Constant values are defined in `lib/constant.py`

#### 3.3.2. [Data Model](gfa_ml/data_model)
This module provides abstract classes for the whole framework.
More details can be found in the [data_model](docs/data_model.md) folder

#### 3.3.2. [Custom](gfa_ml/custom)
This module provides the custom implementations for client-specific functions (e.g., cost evaluation)

## 5. Testing
Can be found in the [test](test) folder. It includes unit tests for data model abstractions, data analysis, and serving pipeline.

## 6. Data 
Contains some data from relevant projects (e.g., [enocell_pilot](data/enocell_pilot))
xhttpy 0.1.0
Unified sync/async HTTP client supporting httpx and aiohttp backends.
Requires Python: >=3.7

# xhttpy

统一的同步/异步 HTTP 客户端，一套 API 搞定所有场景。

## 安装

```bash
pip install xhttpy
```

## 快速开始

```python
from xhttpy import SmartHTTP

# 创建客户端
client = SmartHTTP()

# 同步请求
resp = client.get("https://httpbin.org/get")
print(resp.status_code, resp.json())

resp = client.post("https://httpbin.org/post", json={"key": "value"})
print(resp.json())
```

## 异步使用

```python
import asyncio
from xhttpy import SmartHTTP

async def main():
    async with SmartHTTP() as client:
        resp = await client.get("https://httpbin.org/get")
        print(resp.status_code, await resp.json_async())
        
        resp = await client.post("https://httpbin.org/post", json={"key": "value"})
        print(await resp.json_async())

asyncio.run(main())
```

## 流式请求

### 同步流式

```python
with client.stream("GET", "https://httpbin.org/stream/10") as resp:
    for line in resp.iter_lines():
        print(line)
```

### 异步流式

```python
async with client.stream("GET", "https://httpbin.org/stream/10") as resp:
    async for line in resp.iter_lines():
        print(line)
```

## 完整 API

### 请求方法

| 方法 | 说明 |
|------|------|
| `client.get(url, **kwargs)` | GET 请求 |
| `client.post(url, **kwargs)` | POST 请求 |
| `client.put(url, **kwargs)` | PUT 请求 |
| `client.patch(url, **kwargs)` | PATCH 请求 |
| `client.delete(url, **kwargs)` | DELETE 请求 |
| `client.head(url, **kwargs)` | HEAD 请求 |
| `client.options(url, **kwargs)` | OPTIONS 请求 |
| `client.stream(method, url, **kwargs)` | 流式请求 |

### 响应对象

**同步代码使用：**

| 属性/方法 | 说明 |
|-----------|------|
| `resp.status_code` | HTTP 状态码 |
| `resp.headers` | 响应头字典 |
| `resp.text` | 响应文本 |
| `resp.json()` | JSON 解析 |
| `resp.content` | 原始字节 |

**异步代码使用：**

| 属性/方法 | 说明 |
|-----------|------|
| `resp.status_code` | HTTP 状态码 |
| `resp.headers` | 响应头字典 |
| `await resp.text_async()` | 响应文本 |
| `await resp.json_async()` | JSON 解析 |
| `await resp.content_async()` | 原始字节 |

### 客户端配置

```python
client = SmartHTTP(
    base_url="https://api.example.com",  # 基础 URL（可省略路径前缀）
    timeout=30.0,                         # 超时时间（秒）
    headers={"Authorization": "Bearer xxx"},  # 默认请求头
    backend="httpx",                      # 后端引擎：httpx 或 aiohttp
)

# 使用 base_url 后，请求时只需写相对路径
resp = client.get("/users/1")  # 实际请求 https://api.example.com/users/1
```

## 设计理念

- **统一接口**：同步和异步使用相同的方法名，唯一区别是 `await`
- **简单规则**：同步用 `json()`，异步用 `await json_async()`
- **自动检测**：自动检测运行环境，无需手动指定同步/异步模式
- **流式支持**：`stream()` 方法同时支持 `with` 和 `async with`
- **响应封装**：统一的 `SmartResponse` 屏蔽底层差异
topsis-kashvi-102303564 1.0.0
TOPSIS implementation in Python (Command Line Tool)
Requires Python: >=3.7

# TOPSIS Implementation in Python

This project provides a **command-line implementation of the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution)** method using Python.  
It allows ranking of alternatives based on multiple criteria, weights, and impacts.

--------------------------------

# What is TOPSIS?

TOPSIS is a **multi-criteria decision-making (MCDM)** technique used to rank alternatives by comparing their distances from an **ideal best** and an **ideal worst** solution.

The best alternative:
- Has the **shortest distance from the ideal best**
- Has the **farthest distance from the ideal worst**

--------------------------------

# Project Structure
```
102303542_TOPSIS/
│
├── topsis/
│ ├── init.py
│ └── topsis.py
│ # Main TOPSIS implementation (CLI)
│
├── plot_graphs.py
│ # Script to generate graphs
│
├── README.md
│ # Project documentation
│
├── .gitignore
│
├── Topsis-Dataset.csv
│ # Sample input dataset
│
├── text_classification_result.csv
│ # Example TOPSIS output
│
├── modelResults.csv
│
├── topsis_scores.png
│ # TOPSIS score graph
│
└── topsis_ranking.png
# Ranking graph
```
-----------------------------------

# Requirements

- Python 3.x
- numpy
- pandas
- matplotlib(for graphs)

Install dependencies using:
pip install numpy pandas matplotlib

-------------------------------------

# Usage(Command Line)
python topsis.py <InputDataFile> <Weights> <Impacts> <ResultFileName>

Generate graphs using: 
python plot_graphs.py

Parameters:
InputDataFile → CSV file with alternatives and criteria
Weights → Comma-separated weights
Impacts → + for benefit, - for cost
ResultFileName → Output CSV file (auto-generated)

Input File Format:
First column: Alternative names
Remaining columns: Numeric criteria