semvec/pypi-packages huggingface dataset:
pysunday 0.4.0
一款目标明确的敏捷开发工具
Requires Python: >3

[![Downloads](https://pepy.tech/badge/pysunday/month)](https://pepy.tech/project/pysunday)
[![Supported Versions](https://img.shields.io/pypi/pyversions/pysunday.svg)](https://pypi.org/project/pysunday)
[![Contributors](https://img.shields.io/github/contributors/pysunday/pysunday.svg)](https://github.com/pysunday/pysunday/graphs/contributors)

# pysunday

官方文档[PySunday](https://pysunday.howduudu.tech)

## 安装

pysunday已在pypi上发布

安装：`python3 -m pip install pysunday`

卸载：`python3 -m pip uninstall pysunday`

更新：`python3 -m pip install --upgrade pysunday`

安装成功后需要执行`python3 -c '__import__("sunday.core.init")'`让sunday自动检查并初始化执行环境, 暂时只支持`zsh`、`bash`的环境初始化

或者手动修改shell的rc文件，添加：

```shell
export SUNDAY_ROOT=~/.sunday
export PATH=$SUNDAY_ROOT/bin:$PATH
```

## sunday支持及未来支持

[x] 敏捷工具核心库  
[x] 插件化  
[x] 日志系统集成  
[x] 持久化登录态  
[x] 开发环境代理  
[x] 系统PAC支持  
[x] 列表打印  
[x] 多线程运行  
[x] 密文加解密支持  
[X] 前端辅助开发工具  
[ ] LSP多语言支持  
[ ] 开发环境配置  
[ ] UI自动化测试工具  
[ ] 文档同步wiki  
[ ] 其它功能...

## 插件开发

插件主要指工具插件, 其它配套支持登录插件.

如A开发了一个网站的登录插件, 此后B和C可基于该登录插件开发工具插件

插件必须包含package.json的描述文件, 该文件配置如下:

1. name: 工具名称
2. type: login(登录)/tools(工具)
3. depend: 程序依赖，依赖包git地址的集合
4. keywords: 关键词
5. author: 作者

## 公共配置文件

新建文件`$SUNDAY_ROOT/config.ini`

配置格式如下:

```ini
# config.ini
[NAME1]
key1 = value
key2 = value

[NAME2]
key1 = value
key2 = value
```

### LOGGING

控制日志输出

#### key: level

控制程序日志输出级别, 默认级别为ERROR, 及只输出报错日志

### key: format

日志输出格式控制, 本人使用格式: `format = %%(blue)s[%%(asctime)s.%%(msecs)-3d] %%(log_color)s%%(levelname)-7s: %%(purple)s<%%(name)s> %%(log_color)s%%(message)s`

### PROXY

代理配置, 未设置时, 程序中的请求根据网络配置走系统的pac代理文件, 否则当设置了值后程序中的网络请求不走pac仅走配置的代理

#### key: proxy

配置程序中的请求是否走代理, 代理配置格式如`proxy = 127.0.0.1:8888`

### CRYPTO

加密程序相关配置

#### key=str

key为加密用的密钥, 默认是HOWDUUDU, 可自行修改增加密码安全性

### GIT

GIT相关配置, 如插件安装来源

#### base: git_url_base

配置git地址，当安装的插件或者依赖中的插件不存在git地址则使用该地址

## 内置命令

### `sunday_install`

用于安装sunday插件

```console
uusage: sunday_install [-v] [-h] [--giturl GIT_URL_BASE] [-l] [-N] [MODULE_URLS) [MODULE_URL(S ...]]

安装sunday模块

Positionals:
  MODULE_URL(S)          安装模块的本地模块路径或者仓库名称, 分支请用#字符拼接

Optional:
  -v, --version          当前程序版本
  -h, --help             打印帮助说明
  --giturl GIT_URL_BASE  git元地址, 取配置中的GIT.base字段，未配置则默认为ssh://git@github.com
  -l, --list             打印所有的已安装安装
  -N, --notdepend        是否跳过依赖安装，如果安装本地模块，且依赖的模块也是本地安装则可设置为不安装依赖

使用案例:
    sunday_install sunday/name1.git
    sunday_install https://website.com/sunday/name1.git https://website.com/sunday/name2.git
    sunday_install --giturl https://website.com sunday/name1.git https://website.com/sunday/name2.git sunday/name3.git https://website.com/sunday/name4.git
    sunday_install /path/to/package
```

## 插件收录

查看官方文档[插件列表](https://pysunday.howduudu.tech/plugins/)
urbancode 0.2.1
A package for universal urban analysis
Requires Python: >=3.8

# UrbanCode (v0.2.1)

A Python package for street view image perception analysis, providing tools for feature extraction and comfort prediction.

## Related Research

[Thermal Comfort in Sight: Thermal Affordance and Its Visual Assessment](https://github.com/Sijie-Yang/Thermal-Affordance)

## Features

### Street View Image (SVI) Analysis
- Semantic segmentation
- Object detection
- Color feature extraction
- Scene recognition
- Perception analysis (thermal_comfort, visual_comfort, safety, etc.)

## Examples

### 1. Street View Image Feature Extraction
`examples/test_svi_image_feature.ipynb`
- Demonstrates how to extract various features from street view images
- Includes semantic segmentation, object detection, color analysis, and scene recognition
- Shows how to process multiple images and save results

### 2. Street View Image Comfort Prediction
`examples/test_svi_comfort_prediction.ipynb`
- Shows how to predict comfort scores from street view images
- Demonstrates the use of the comfort function for both single images and folders
- Includes visualization of perception metrics
- Automatically normalizes perception scores to 0-5 range

## Installation

```bash
pip install urbancode
```

## Usage

### Feature Extraction
```python
import urbancode as uc
import pandas as pd

# Process a folder of images
df = uc.svi.filename("path/to/folder")
df = uc.svi.segmentation(df, folder_path="path/to/folder")
df = uc.svi.object_detection(df, folder_path="path/to/folder")
df = uc.svi.color(df, folder_path="path/to/folder")
df = uc.svi.scene_recognition(df, folder_path="path/to/folder")

# Save results
df.to_csv("svi_results.csv", index=False)
```

### Comfort Prediction
```python
import urbancode as uc

# Process a single image
df = uc.svi.comfort("path/to/image.jpg", mode='image')

# Process a folder of images
df = uc.svi.comfort("path/to/folder", mode='folder')

# Save results
df.to_csv("comfort_results.csv", index=False)
```

### Perception Metrics
The comfort function returns a DataFrame with the following perception metrics (normalized to 0-5 range):
- thermal_comfort
- visual_comfort
- temp_intensity
- sun_intensity
- humidity_inference
- wind_inference
- traffic_flow
- greenery_rate
- shading_area
- material_comfort
- imageability
- enclosure
- human_scale
- transparency
- complexity
- safe
- lively
- beautiful
- wealthy
- boring
- depressing