semvec/pypi-packages huggingface dataset:
test-sf-etl-py3 0.0.11
surfin test
Requires Python: >=3.7

## **SURFIN 封装一些通用的方法, 要求python3**
> 测试版本  
  
#### pypi地址
https://pypi.org/project/test-sf-etl-py3/  

#### 依赖 
发送图片(必须)：需安装wkhtmltopdf,下载地址如下(windows需加入环境变量)  
> https://wkhtmltopdf.org/downloads.html  

#### 使用案例
示例：钉钉机器人发送消息
```python
# pip install test_sf_etl_py3
from sf_etl_py3.bot import DingBot 

# ------ 发送文本 ------
ding_obj = DingBot()
ding_obj.set_kwargs(url='*机器人的url',sec='机器人的加签')
ding_obj.send_text('需要发送的消息')

# ------ 发送图片(pg数据库查询到的数据, 以图片形式发送) ------
ding_obj = DingBot()
ding_obj.set_kwargs(url='*机器人的url',
                    sec='机器人的加签',
                    access_key_id='*阿里云的oss账号',
                    access_key_secret='*阿里云的oss账号',
                    endpoint='阿里云的oss地区, 默认 http://oss-ap-southeast-1.aliyuncs.com',
                    bucket_name='阿里云的ossbucket_name, 默认 surfin-bigdata'
                    )
# 参数1: pg数据库配置
# 需要2: 需要执行的sql   
ding_obj.send_img_simple({'host': 'xxx', 'port': 8756, 'user': 'xxx', 'password': 'xxx', 'database': 'xxx'},
                         "select 1 as test;")
```
<br><br> 
---
#### **版本**
##### 0.0.9
> Test version: kettle xml
contactapi 0.0.24
Just a demo project
Requires Python: >=3.10
Keywords: fastapi,project

# fastapi

# Runing API via podman

```bash

### create vars

export PORT="8000"

export DB_IMAGE="docker.io/library/postgres"
export DB_CONTAINER="postgresdb"

export DB_PASS="password"
export DATABASE_URL="postgresql://postgres:$DB_PASS@$DB_CONTAINER:5432"

export APP_IMAGE="ghcr.io/yulai202020/contactapi"

### create network

podman network create contactapi

### pull images

podman pull $DB_IMAGE
podman pull $APP_IMAGE
podman images

### run containers

podman run --network contactapi -e POSTGRES_PASSWORD=$DB_PASS -d --name $DB_CONTAINER $DB_IMAGE
podman run --network contactapi -e DATABASE_URL=$DATABASE_URL -e PORT=$PORT -p $PORT:$PORT -d $APP_IMAGE

### check

podman ps
curl http://localhost:8000/

```
cluster-challenge 0.0.1.dev0
Comparison of cluster algorithm performances on cosmoDC2 and DC2 datasets.
Requires Python: >=3.8

# cluster_challenge
Comparison of cluster algorithm performances on cosmoDC2 and DC2 datasets   
Developed for the DC2 project: https://portal.lsstdesc.org/DESCPub/app/PB/show_project?pid=248   
Common framework for the analysis of cosmoDC2, redMaPPer, WaZP and AMICO catalogs   
Using CLEVAR package   
For CLEVAR installation: pip install -e clevar  (after python setup.py install --user)

## DESC environment
### Create a DESC conda environment (to be done only once)   
source /pbs/throng/lsst/software/desc/common/miniconda/setup_current_python.sh    
conda create --clone desc -p desc_july_2022_v0   
conda activate desc_july_2022_v0   
conda install mysql-connector-python -c conda-forge   
### Setup (to be done every time you log in)  
source /pbs/throng/lsst/software/desc/common/miniconda/setup_current_python.sh   
conda activate desc_july_2022_v0
pyenopt 0.0.1
Pyenopt package
Requires Python: >=3.8

# Pyenopt code
This package contains code related to the pyenopt package for ensemble based
optimization. 

Currently it contains a plugin for the main pyenopt package to provide backwards
compatibility with a legacy sqlite database interface ot pyenopt.

The main pyenopt optimizer code itself is currently an internal project, but
will be released as open-source in the near future. Until then, adding parts of
the code that can be released already, like this plugin, are added
incrementally.

All files in this repository are released under the The GNU General Public
License v3.0 (a copy is provided in the LICENSE file).
datareuse 0.0.3
Reuse computed datasets

# DataReuse
Tool to reuse saved computations with various file formats.

It can be used as follows:

```
    from datareuse import Reuse
    
    with Reuse("data.csv", index=False, index_col=1) as reuse:
        df = reuse(computation, *args, **kwargs)
```

At the moment, the following file formats are available:
- csv (pandas)
playwright-spy 1.0.2
A plugin for playwright to prevent detection.
Requires Python: >=3.5

# playwright-spy [![GitHub Workflow Status](https://img.shields.io/badge/playwright-python-8A2BE2)

> A plugin for [playwright-python](https://img.shields.io/badge/just%20the%20message-8A2BE2) to prevent detection.

<p align="center"><img src="https://i.imgur.com/q2xBjqH.png" /></p>

## Install

```bash
pip install playwright-spy
```

## Usage

### Page
```python
from playwright.sync_api import sync_playwright
import playwright_spy

with sync_playwright() as p:
    browser = p.chromium.launch()
    page = browser.new_page()
    playwright_spy.load_sync(page)
    page.goto("https://bot.sannysoft.com/")
    page.screenshot(path="example.png", full_page=True)
    browser.close()
```

### Context
```python
from playwright.sync_api import sync_playwright
import playwright_spy

with sync_playwright() as p:
    browser = p.chromium.launch()
    context = browser.new_context()
    playwright_spy.load_sync(context)

    page = context.new_page()
    page.goto("https://bot.sannysoft.com/")
    page.screenshot(path="example.png", full_page=True)
    browser.close()
```
rebyu 0.1.6
Review Analysis Made Easy
Requires Python: >=3.8

# Rebyu (レビュー)
🌟 Review Analysis Made Easy

```python
from rebyu import Rebyu
from rebyu.pipeline import NLTK_PIPELINE

rb = Rebyu(data='twitter.csv', pipeline=NLTK_PIPELINE) # Initialize the data and pipeline
rb.run() # Run the pipeline

print(rb.data['vader_polarity'])
```

Rebyu, a Review Analysis Library equipped with text processing, transformers, and diverse analyses like sentiment, topics, emotion, and named-entities. It plans to be language-flexible, highly scalable, and offers both preset pipelines and customizable options.

**Version 0.1.6 (Released)** - [Change Log](CHANGELOG.md)

```bash
pip install -U rebyu
```

---

### Concept

![Rebyu Concept](https://raw.githubusercontent.com/abhishtagatya/rebyu/master/docs/images/Rebyu_Concept_sm.png)


The essence of Rebyu for Review Analysis lies in its structured 
pipeline approach to enhancing existing data. By leveraging a range of 
tools within this pipeline, we streamline the process, avoiding repetitive 
analyses and accelerating the generation of insights.


---

### Documentation

| Documentation      | Description |      |
| :---:        |    :----:   |          :---: |
| Read the Docs      | Official Documentation       | [Link](https://rebyu.readthedocs.io/)   |
| Replit Example   | Multiple examples to run on Replit        | [Link]()      |


---

### Author

- Abhishta Gatya ([Email](mailto:abhishtagatya@yahoo.com)) - Software and Machine Learning Engineer