semvec/pypi-packages huggingface dataset:
meguro-resc 0.0.1
A package for visualization of Resource recovery amount and gain on sale data in Meguro
Requires Python: >=3.7

# meguro_resc
目黒区の年度別の資源ごみ回収量・売却益の可視化をしています。
範囲は2011~2019年の間です。
y軸は左が回収量(t)、右が売却益(1000円)です。

visualizes the amount of recyclable waste collected and proceeds from the sale of recyclable waste by year in Meguro City.
The range is between 2011~2019.
The y-axis is the amount collected (tons) on the left and the profit from sale (1000 yen) on the right.

# Dataset
・meguro_rescは以下のページからデータをダウンロードして利用しています。\
・meguro_resc uses the data downloaded from the following page.

https://catalog.data.metro.tokyo.lg.jp/dataset/t131105d0000000018/resource/31bb126f-6f10-433a-9f65-d00986c48385


・meguro_rescは、以下の著作物を改変・加工して利用しています。\
・meguro_resc uses the following copyrighted materials by modifying and processing them.

著作物のタイトル　資源回収量・収集拠点数・売却益、\
著作権者　東京都・目黒区、\
クリエイティブ・コモンズ・ライセンス 表示4.0国際（ https://creativecommons.org/licenses/by/4.0/deed.ja ）

# 実行例
```zsh
python meguro_resc.py
```
![びん](/img/びん.png)
deathcomp 0.0.3
A package compares the number of deaths due to COVID-19 in two countries
Requires Python: >=3.8

# How to install deathcomp
You may need matplotlib library.

$ pip install matplotlib

$ pip install deathcomp

# How to run milspend
Deathcomp program allows you to compare the number of deaths from COVID-19 for two specified countries and for a specified period (days).
For example, the following command can be executed to graph the number of deaths due to COVID-19 in Japan and China for 30 days.

$ deathcomp Japan China 30
EEGNetworks 0.1.0
基于PyTorch的各种用于EEG分类任务的简单网络的实现。

# EEG-PyTorch

基于PyTorch的各种用于EEG分类任务的简单网络的实现。
Implementation of various simple networks based on PyTorch to classify EEG signals.

## 已实现网络（Implemented networks）
- [x] eeg-inception
- [x] eeg-lstm
- [ ] eeg-gcn

## 使用方法（Usage）
### 准备工作

下载数据集 [GIB-UVa ERP-BCI dataset](https://www.kaggle.com/datasets/esantamaria/gibuva-erpbci-dataset?resource=download)



安装依赖包

```sh
pip install -r requirements.txt
```

### 训练

```sh
python train.py --model inception
```

```sh
python train.py --model lstm
```