semvec/pypi-packages huggingface dataset:
netwix 0.1.8
Netwix is a python client for Netflix. With netwix by simply entering the Netflix ID the user can access data related to movies and tv shows available on Netflix
License: MIT
Requires Python: >=3.6
Keywords: python netflix movies series data retrieval module

# netwix ♨️

Netwix is a python client for Netflix. With netwix by simply entering the Netflix ID the user can access data related to movies and tv shows available on <a href="https://www.netflix.com">Netflix</a>.


## Installation 📥

Install netwix using pip

```bash
pip install netwix
```
## Authors ✍️

- [@new92](https://www.github.com/new92)


## Contributing 🤝

Contributions are always welcome!

See `contributing.md` for ways to get started.

Please adhere to this project's `code of conduct`. For more info please check the `CODE_OF_CONDUCT.md` file


## Feedback 💭

If you have any feedback, please reach out to us at <a href="mailto:new92github@gmail.com">this email address</a>.

**Feel free to contact us anytime ! You'll get a reply within a day. Please avoid using abusive or offensive language.
If you are reporting a bug or making a suggestion please make sure your report/suggestion is as much detailed as possible.**


## License 📜

[![License](https://img.shields.io/github/license/new92/netwix?style=for-the-badge)](https://github.com/new92/netwix/blob/main/LICENSE.md)


## Documentation 📄

### Example Movie ID

- **Movie:** Red Notice
- **URL:** `https://www.netflix.com/watch/81161626`
- **Movie ID:** `81161626`

#### Example Movies

```python
from netwix.types import Movies

movie = Movies("81161626")
print(movie.name) # Output: Red Notice
```

#### Example Movies

```python
from netwix.types import Movies

movie = Movies("81161626")
print(movie.allData) # Output: A dictionary with all the data of the specific movie
```

#### Movies Attributes

- `type`
- `name`
- `description`
- `url`
- `contentRating`
- `genre`
- `imgUrl`
- `awards`
- `startDate`
- `creationDate`
- `actors`
- `creators`
- `directors`
- `trailer`

### Example Series ID

- **Serie:** The Office
- **URL:** `https://www.netflix.com/watch/70126228`
- **Series ID:** `70126228`

#### Example Series

```python
from netwix.types import Series

serie = Series("70126228")
print(serie.name) # Output: The Office
```

#### Series Attributes

- `type`
- `name`
- `description`
- `url`
- `contentRating`
- `genre`
- `imgUrl`
- `startDate`
- `creationDate`
- `awards`
- `seasons`
- `actors`
- `creators`
- `directors`
sleepeeg 0.4.1
Sleep EEG preprocessing and analysis
License: MIT
Requires Python: >=3.10
Keywords: sleep,eeg

# sleepeeg
**sleepeeg** is a high-level package built on top of [mne-python](https://mne.tools/stable/index.html), [yasa](https://raphaelvallat.com/yasa/build/html/index.html) for preprocessing and analysis of sleep EEG data.
## Installation
0. Make sure you have [Python](https://www.python.org/downloads/) version installed. Requires Python 3.10 or higher.
1. Create a Python virtual environment, for more info you can refer to python [venv](https://docs.python.org/3/tutorial/venv.html), [virtualenv](https://virtualenv.pypa.io/en/latest/user_guide.html) or [conda](https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html).
2. Activate the environment
3. 
    ```
    pip install sleepeeg
    ```
4. [Download](https://minhaskamal.github.io/DownGit/#/home?url=https://github.com/NirLab-TAU/sleepeeg/tree/main/notebooks) notebooks.

## Quickstart
1. Open any of the downloaded notebooks using the created environment.
2. Follow the notebook's instructions.

## RAM requirements
For overnight, high density (256 channels) EEG recordings downsampled to 250 Hz expect at least 64 GB RAM expenditure for cleaning and spectral analyses, and at least 128 GB for event detection (or 64 GB if you downsample the data to 100 Hz before running the detection algorithm).
benchmarx 0.0.11
Tools for benchmarking optimization methods
License: MIT
Requires Python: >=3.7

![](./files/long_logo.svg)
# Benchmarx: Benchmarking Optimization Methods with Jax

Benchmarx is a Python library that enables you to benchmark various optimization methods in a reproducible manner using the Jax library. It provides a flexible framework for comparing the performance of different optimization algorithms on a wide range of optimization problems.

## Features

- Benchmarx allows you to easily benchmark optimization algorithms provided by the [Jaxopt](https://github.com/google/jaxopt) library as well as custom algorithms.
- Reproducibility is ensured by providing a comprehensive set of parameters to control the benchmarking process, including RNG states.
- Comes with a collection of standard optimization problems, making it simple to evaluate algorithms on diverse tasks.
- Supports tracking and visualization of key metrics such as function values, gradient norms(if possible) and solution trajectories.
- Generate customizable plots and reports to aid in analyzing and presenting benchmark results with [plotly](https://github.com/plotly/plotly.py) library.
- All the data from the experiments could be saved in the json or pandas format for the later analysis.

## Installation

You can install Benchmarx using pip:

```bash
pip install benchmarx
```

## Usage

To be done

## Examples

Refer to the examples provided in the repository for details on how to use this package.

- Gradient Descent on Quadratic problem [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/13bX2ZeOASEEKBWSbaTeY0Sm5g3ImaoLk?usp=sharing)

- Gradient Descent with custom Line Search [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1me6sE5W7SML6Ww-pBgpjmlJBKz8x3qUv?usp=sharing)

- Your own custom method [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1DCNgjb3_9ITQy93dZg0fUbV2BRB_MuLL?usp=sharing)

- Your own custom metric and interactive chart [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/12I_T09gELjZlslOP8nRV4v2ght2R6vDX?usp=sharing)

- Stochastic Gradient Descent on Quadratic Problem [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1bD7eM9NNZqEvMjlTl6OKvN_vcmtHSMAd?usp=sharing)

- Quadratic Problem based on real data [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1k3-ul8o9_wdsNko51t0-UNpHOwTemJ6o?usp=sharing)

- Neural Network training [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1W2vP5NRCnv6aFVgSm9pzmWZ7l0i3SCmm?usp=sharing)

## Documentation

Check out the full documentation at [benchmarx.fmin.xyz](https://benchmarx.fmin.xyz).


## Contribution

Contributions are welcome! If you have any suggestions or issues, feel free to open an issue or submit a pull request.
wol-redirect-proxy 1.0.1
A simple python Wake-on-LAN proxy
License: MIT
Requires Python: >=3.8,<4.0

## Wake-on-LAN redirect proxy

Brutally simple, single file, Fast API based proxy service. Create a simple `path` and/or  `hostname` based
redirects that send WoL packets if target URL is not reachable. Only works for `HTTP(S)` redirects.

Great complement for a simple [auto-suspend](https://autosuspend.readthedocs.io/) setup,
e.g. your home jellyfin server or any other `HTTP` service.

That's it!

### Install
```shell
# 1. install script - install wol-proxy into a new virtualenv inside your $HOME
 curl https://raw.githubusercontent.com/xyzjonas/wol-redirect-proxy/main/install.sh | bash
```
```shell
# 2. from PyPi
pip install wol-redirect-proxy
```
```shell
# 3. from source using Poetry
poetry install
```
### ...and run
```shell
wol-proxy --host "0.0.0.0" --port 12345
```
```shell
wol-proxy --help
```

### Configuration
Specify a list of proxy mappings using a yaml config, e.g.:

```yaml
#  This will WoL redirect any HTTP request and carry over the path.
#  e.g.: POST: http://my-proxy.home/login -> POST "http://my-jellyfin.home:8096/login"

targets:
- handler: "wol"
  source_url: "http://my-proxy.home/*"
  target_url: "http://my-jellyfin.home:8096"
  methods: [GET, POST, DELETE, PATCH]
  options:
    mac: "75:55:39:a4:33:27"
    timeout_s: 2
```

```yaml
#  Multiple redirects to different services by hostname
#  (requires working DNS setup)

targets:
- handler: "plain"
  source_url: "http://jellyfin.home/*"
  target_url: "http://192.168.0.124:8096"
  methods: [GET, POST, DELETE, PATCH]
  
- handler: "plain"
  source_url: "http://nextcloud.home/*"
  target_url: "http://192.168.0.129:8080"
  methods: [GET, POST]
  
- handler: "plain"
  source_url: "http://torrent-box.home/*"
  target_url: "http://192.168.0.135:8080"
  methods: [GET, POST]
```

### Use as a systemd service

```shell
# use install script and follow instructions
curl https://raw.githubusercontent.com/xyzjonas/wol-redirect-proxy/main/install.sh | bash
```

```shell
# use the sample systemd unit file
git clone https://github.com/jonasbrauer/wol-redirect-proxy.git && cd wol-redirect-proxy 
sudo cp sample-systemd.unit /etc/systemd/system/wol-redirect-proxy.service  # ...and edit user/group
sudo systemctl daemon-reload
sudo systemctl enable --now wol-redirect-proxy.service
sudo systemctl status wol-redirect-proxy.service
```
pyday 0.0.33
PyDay aims to provide a unified interface and feature set to make code concise and easy to maintain. It is a comprehensive toolkit primarily used for data analysis and data visualization, encapsulating popular packages such as pandas, numpy, and matplotlib. In addition to data analysis and data visualization, PyDay also provides useful functionalities like Simplified Chinese to English translation and PDF generation.
License: MIT
Keywords: pyday,Data,Tool

<p align="center">
    <img width="192px" src="./doc/Logo/AnsonLogo01.png" >
</p>
<h1 align="center"><b>PyDay</b></h1>

<p align="center">Python 工具一條龍</p>
<p align="center">v0.0.33</p>

---

## 宗旨
PyDay 的宗旨，是提供統一接口和功能集，使代碼更簡潔和易於維護。  
<!-- PyDay 是一個綜合工具包，主要用於數據分析和數據可視化，封裝了pandas, numpy, matplotlib等常用包。   -->
<!-- 除了數據分析和數據可視化之外， -->
Pyday 提供了一些有用的功能，例如䌓簡英轉換、解壓縮（想當年泰迪杯...）等。  

---

## 簡介
### 框架由以下基本模組構成：
1. DataReader (開發中)
2. DataVis (未開發)
3. Data2PDF 用於生成PDF (開發中)
4. Tool 
   1. ChageLang 用於翻譯 (完工)
   2. dirTree 生成文件樹 (完工)
   3. unzip 解壓縮 (開發中)
<!-- Machine Learning 機器學習 -->

### 優勢
有時庫可能會不支持顯示中文字體，需要自已再設定字體。  
而本項目使用了google開源字體 Noto Sans，全面支持中文字體。

---

## 項目結構
```
*
├── LICENSE 開源證明
├── requirements.txt
├── test 
├── script 有用腳本
├── README.md
├── setup.py 打包庫
├── .gitignore
├── .gitattributes
├── doc 
├── setup.cfg
└── src
    └── pyday
```

---

## 使用方法
### 安裝
```python 
pip install pyday
```

### 測試
```python
import pyday
print(pyday.__version__)
```

---

## 線上資源
**查看 [pypi/pyday主頁](https://pypi.org/project/pyday/)**
**查看 [pyday線上文檔](https://ansoncar.github.io/AC-Note/Document/pyday/guide_tc.html)**

---

## 查閱更多
**查看 [Change Log(更新日志)](https://github.com/AnsonCar/pyday/blob/main/doc/CHANGLOG.md)**