semvec/pypi-packages huggingface dataset:
relnotegen 0.1.3
A tool to generate release notes from a set of markdown files
License: MIT
Requires Python: >=3.6
Keywords: release notes markdown

# relnotegen

## Overview

relnotegen is a Python-based tool designed to automate the process of generating and styling release notes for mobile applications. It extracts release information from markdown files stored in Azure DevOps wiki, formats this information into HTML using Jinja2 templates, and then applies Tailwind CSS for styling. The final HTML release notes can be uploaded to cloud storage or used in any other desired manner.

## Features

- **Markdown to HTML**: Converts markdown files from Azure DevOps wiki into HTML.
- **Dynamic Styling**: Uses Tailwind CSS to style the generated HTML release notes.
- **Customizable Templates**: Supports Jinja2 templates for HTML output, allowing for flexible and customizable formatting.
- **Configurable**: Easily configurable via a JSON file.


## Getting Started

### Prerequisites

- Python 3.6 or higher
- Jinja2, markdown (Installable via `pip`)
- Access to Azure DevOps wiki markdown files
- Node.js and npm (for Tailwind CSS)

### Installation

Install the package via pip:

```bash
$ pip install relnotegen
```

### Configuration

Create the `relnotegen.config.json` file to set your preferences. Here's an example (all fields are required):

```json
{
    "searchPath": "./mdown",
    "template": "template.html",
    "outputFile": "myapp-release-notes.html",
    "templateParameters": {
        "pageTitle": "MyApp Release Notes",
        "heading": "What's New",
        "package": "Your App Name"
    }
}
```

### Usage

Run relnotegen:

```bash
$ relnotegen
```

### Output

- The script will generate an HTML file as specified in the `outputFile` parameter.
- A corresponding CSS file (same name as `outputFile`, but with `.css` extension) will also be generated for styling.

## How It Works

1. **Collect Markdown Files**: Scans the specified directory for markdown files.
2. **Extract Release Info**: Parses each markdown file for release data.
3. **Render HTML**: Uses Jinja2 templates to format the extracted data into HTML.
4. **Generate CSS**: Runs Tailwind CSS to style the HTML file.

Only markdown files with the proper "frontmatter" are collected. The frontmatter starts and ends with three dashes. Here's an example (both fields are required):

```markdown
---
release-date: 2023-10-29
version: 2.0.8
---
We worked hard to bring you the following updates:

## Improvements <span class="ios"/><span class="android"/>
- You can now order peperoni pizza
- We removed pineapple as possible pizza toppings

## Bug Fixes <span class="ios"/>
- Fixed a crash issue when ordering more than 100 pizza's
```

## Customization

- **HTML Template**: Modify `template.html` to change the structure of the output HTML.
- **CSS Styling**: Edit `template.css` to customize styles. Run Tailwind CSS to compile these styles into the final CSS.

## License

This project is licensed under the MIT License.
perfcat 1.0.10
基于`QT` `pyside6` 开发的安卓游戏性能采集工具。
License: MIT
Requires Python: >=3.8,<4.0

# Performance Catcher 

![](asset/2023-11-12-22-42-19.png)

## 简介
基于`QT` `pyside6` 开发的安卓游戏性能采集工具。

## 安装

```shell
pip install perfcat
```

依赖安装失败就试一下更换清华源
```shell
pip install -i https://pypi.tuna.tsinghua.edu.cn/simple perfcat
```

## 运行

直接命令行里敲`perfcat`

```shell
>>perfcat
```
launart 0.8.2
Component lifetime manager for runtime.
License: MIT
Requires Python: >=3.8

# Launart - Launch for Graia Project

[![PyPI](https://img.shields.io/pypi/v/launart)](https://pypi.org/project/launart)
[![Docs](https://img.shields.io/badge/文档-here-blue)](https://graia.readthedocs.io/other/launart/)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
[![Imports: isort](https://img.shields.io/badge/%20imports-isort-%231674b1?style=flat&labelColor=ef8336)](https://pycqa.github.io/isort/)
[![codecov](https://codecov.io/gh/GraiaProject/launart/branch/master/graph/badge.svg?token=MW5U3JYXRA)](https://codecov.io/gh/GraiaProject/launart)
[![pdm-managed](https://img.shields.io/badge/pdm-managed-blueviolet)](https://pdm.fming.dev)

`Launart` 是实际实现了 `Launch API`, 即统一的应用实例启动时部件管理; 本部件为 Graia Project 内部使用.

在应用实例装载 `richuru` 后可获得更加缤纷多彩的控制台输出.
skxcs 1.0
SciKit learn wrapper for XCS algorithm implementation.
License: MIT
Requires Python: >=3.7
Keywords: XCS,xcs,SciKit,learn

# skxcs

skxcs is a SciKit learn wrapper for implementation of XCS algorithm [xcs](https://github.com/hosford42/xcs). 

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install skxcs. You need to have [Cython](https://pypi.org/project/Cython/) installed.

```bash
pip install skxcs
```

## Usage

Numeric Values

```python
from skxcs.classifiers import XcsClassifier
import pandas as pd
from sklearn.model_selection import train_test_split

# Numeric values
numerical_frame = pd.read_csv('https://raw.githubusercontent.com/kliegr/arcBench/master/data/datasets/iris.csv')
numerical_frame.dropna(inplace=True)
y = numerical_frame['class']
numerical_frame.drop('class', axis=1, inplace=True)
X_train, X_test, y_train, y_test = train_test_split(numerical_frame, y, test_size=0.33)

classifier = XcsClassifier()

# If data input is non binary, classifier automatically uses MLDP discretizer for numeric values
# and one hot encoding for categorical values to transform data in both fit and predict methods.

classifier.fit(X_train, y_train)

# Get prediction array
y_pred = classifier.predict(X_test)

# Get pretty rules
for rule in classifier.get_pretty_rules():
    print(rule)

# To use get_pretty_rules or pretty_print_prediction methods,
# classifier has to transform train and test data first.

```

Categorical values

```python
import pandas as pd
from skxcs.classifiers import XcsClassifier
from sklearn.model_selection import train_test_split

# Categorical values
categorical_frame = pd.read_csv('https://raw.githubusercontent.com/kliegr/arcBench/master/data/datasets/autos.csv')
categorical_frame.dropna(inplace=True)
y = categorical_frame['XClass']
categorical_frame = categorical_frame.select_dtypes(include=[object])
categorical_frame.drop('XClass', axis=1, inplace=True)
X_train, X_test, y_train, y_test = train_test_split(categorical_frame, y, test_size=0.25)
classifier = XcsClassifier()

# You can transform data yourself. You should either transform both training
# and testing data, or none of them. It is necessary to ensure correct values are passed to classifier.
X_train_bin = classifier.transform_df(X_train, y=y_train)
classifier.fit(X_train_bin, y_train)

# Note that we don't pass 'y' to transform method when we transform test data
X_test_bin = classifier.transform_df(X_test)

# pretty print prediction
result = classifier.pretty_print_prediction(X_test_bin)
print(result)
```

## Contributing
...

## License
[MIT](https://choosealicense.com/licenses/mit/)