semvec/pypi-packages huggingface dataset:
testcord 0.0.1
A package that assists in writing tests for pycord
Keywords: discord pycord test

# Testcord

[![Build Status](https://travis-ci.com/KoalaBotUK/testcord.svg?branch=master)](https://travis-ci.com/KoalaBotUK/testcord)
[![Documentation Status](https://readthedocs.org/projects/testcord/badge/?version=latest)](https://testcord.readthedocs.io/en/latest/?badge=latest)
[![Discord Server](https://img.shields.io/discord/729325378681962576.svg?style=flat-square&logo=discord&logoColor=white&labelColor=697EC4&color=7289DA&label=%20)](https://discord.gg/5etEjVd)

This is a package to allow testing of pycord. 
If using pytest, it is suggested to use pytest-asyncio for test running, otherwise the user will need
to provide their own async test runner.

This repo is a maintained and updated version of dpytest for discord.py, created by CraftSpider.

# Documentation

Documentation can be found at [testcord.readthedocs.io](https://testcord.readthedocs.io/en/latest/), including examples and tutorials
velog-hits 1.0.0
Velog Hits
Requires Python: >= 3.8

# 🍕 velog-hits
**velog-hits**는 자신의 velog 게시글 조회수 통계를 종합해서 한 눈에 보기 쉽도록 HTML 파일로 만들어주는 프로젝트입니다.

## 📍 Demo Image
![Demo Image](./demo.png)

## 📍 시작하기
### 1) 파이썬 버전
- Python >= 3.8

### 2) 설치
- 조회수 종합 통계 데이터를 저장할 폴더 생성
  ```shell
  $ mkdir velog-hits-data
  ```

- 생성한 폴더로 이동
  ```shell
  $ cd velog-hits-data
  ```

- pip 설치
  ```shell
  $ pip install velog-hits
  ```

## 📍 사용방법
### 1) 도움말 명령어
```shell
$ velog-hits -h
```
```shell
usage: velog-hits [-h] -u USERNAME -at ACCESSTOKEN

Velog Hits

optional arguments:
  -h, --help            show this help message and exit
  -u USERNAME, --username USERNAME
                        Velog Username
  -at ACCESSTOKEN, --accesstoken ACCESSTOKEN
                        Your Velog Access Token
```

### 2) Velog Hits 실행

> ⚠️ Access Token을 모르신다면 해당 기능을 사용하실 수 없습니다.

Velog Hits를 사용하기 위해서는 자신의 **Velog Username(=ID)** 와 **Access Token**이 필요합니다.</br>
"조회수 통계"는 로그인 했을 때만 볼 수 있는 기능이기 때문에 Access Token이 필요합니다. Access Token을 통해 본인임을 인증할 수 있습니다.</br> 


```shell
$ velog-hits -u {username} -at {access_token}
```
```shell
'{username}'님의 조회수 데이터를 가져오고 있으니 잠시만 기다려주세요:)
'{username}'님의 조회수 데이터를 모두 가져왔습니다!!
HTML로 변환을 시작합니다...
Velog Hits Success!!
Velog Hits Result: {오늘날짜.html 파일 위치 경로}
```

### 3) 결과 확인
결과로 나온 `{오늘날짜.html 파일 위치 경로}` 부분을 복사한 후 **웹 브라우저를 열고 주소창에** 붙여넣기합니다.

조회수 종합 통계 데이터는 `.../velog-hits-data/htmlhits/`에 저장됩니다.</br>
- 명령어를 실행시킨 위치에 `htmlhits/` 폴더가 생성되고 `htmlhits/오늘날짜.html 파일`이 생성됩니다.
- 그렇기 때문에 `시작하기-설치` 부분에서 데이터 저장 폴더를 생성하고 해당 폴더로 이동한 것입니다.
shakelab 0.0.1
Python tools for engineering seismology
Requires Python: >=3.6

<img alt="ShakeLab - Tools for Engineering Seismology" class="right" style="width: 60%" src="https://raw.githubusercontent.com/klunk386/shakelab/master/logo/shakelab-header.png#1" />

[![AGPLv3](https://www.gnu.org/graphics/agplv3-88x31.png)](https://www.gnu.org/licenses/agpl.html)
[![Build Status](https://travis-ci.org/shakelab/shakelab.svg?branch=master)](https://travis-ci.org/shakelab/shakelab)
[![Supported Python versions](https://img.shields.io/pypi/pyversions/shakelab.svg)](https://pypi.python.org/pypi/shakelab)
[![PyPI Version](https://img.shields.io/pypi/v/shakelab.svg)](https://pypi.python.org/pypi/shakelab)

ShakeLab is an opensource project with the goal of providing a set of free and opensource tools for seismologists dealing with most common engineering problems.

Warning: This is still **WORK IN PROGRESS!**

## Installation

ShakeLab can be installed directly from the GitHub repository using pip:

```console
python3 -m pip install git+https://github.com/shakelab/shakelab.git
```
To upgrade an existing installation:

```console
python3 -m pip install --upgrade git+https://github.com/shakelab/shakelab.git
```

If you are a developer, you can simply install the cloned GIT repository with:

```console
python3 -m pip install --upgrade --editable shakelab
```

## Dependencies

ShakeLab requires the following dependencies:

  * [NumPy/Scipy](http://www.scipy.org/)
  * [Matplotlib](http://matplotlib.org/)
  * [Shapely](https://pypi.org/project/Shapely/)

## License

Copyright (c) 2019, ShakeLab Developers

ShakeLab is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

You should have received a copy of the GNU General Public License with this download. If not, see <http://www.gnu.org/licenses/>

## Disclaimer

ShakeLab is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Affero General Public License for more details.

The authors of the software assume no liability for use of the software.
auto-ts-ensemble 0.0.6
Auto Ensemble using Prophet and NeurlaProphet
Requires Python: >=3.6



# Dependencies 
### Make sure you have install the fbprophet and NeuralProphet
### fbprophet version == 0.7.1
### Steps to install fbprophet
```
conda env -n test_env python==3.8.5
```
```
conda install -c anaconda ephem
```
```
conda install -c conda-forge pystan
```
```
conda install -c conda-forge fbprophet
```

### if error degrade to pystan==2.19.1.1

###Steps to install NeuralProphet
```
pip install neuralprophet ==0.3.0
```

#import
```
from auto_ts_ensemble_rupakbob import auto_ts_ensemble
```

#call the package function 1
```
results = auto_ts_ensemble.neural_analysis(data,freq="H")
```
###access the model
```
neural_prophet = results[0]
```
###access the metrics
```
metrics = results[1]
```
###access the predictions
```
predictions = results[2]
```
####plot the components
```
neural_prophet.plot_components(predictions)
```

#call the package function 2
```
results2 = auto_ts_ensemble.ts_analysis(data,n_future=7)
```
#access the model
```
ts_model = results2[0]
```
#access the predictions
```
predictions2 = results2[1]
```
#plot the components
```
ts_model.plot_components(predictions2)
```

#call the package function 3 ensemble
```
ensemble_predictions = auto_ts_ensemble.ensemble_analysis(predictions,predictions2)
```