semvec/pypi-packages huggingface dataset:
snngrow 0.1.1a1
Third-generation Artificial Intelligence SNN Universal Implementation
Requires Python: <=3.10,>=3.6

# SNNGrow

<p align="center">
  	<img alt="SNNGrow" src="./docs/en/source/_static/logo.png" width=50%>
</p>

English | [中文(Chinese)](./README_zh_CN.md) 

SNNGrow is a low-power, large-scale spiking neural network training and inference framework. It preserves minimal energy cosumption while providing the superior learning abilities of large spiking neural network.

The vision of SNNGrow is to decode human intelligence and the mechanisms of its evolution, and to provide support for the development of brain-inspired intelligent agents in a future society where humans coexist with artificial intelligence.

- **[Documentation](https://snngrow.readthedocs.io/)**
- **[Source](https://github.com/snngrow/snngrow/)**

## Install

SNNGrow offers two installation methods.
Running the following command in your terminal will install the project:
### Install from PyPI:

```
pip install snngrow
```
### Install from GitHub:

1.  Download or clone SNNGrow from github
```
git clone https://github.com/snngrow/snngrow.git
```
2.  Enter the folder of SNNGrow and install braincog locally with setuptools
```
cd snngrow
python setup.py install
```

## Quickstart

The code style of SNNGrow is consistent with Pytorch, allowing you to build spiking neural networks with simple code:
```
from snngrow.base.neuron import LIFNode
import torch

x = torch.randn(2, 3, 5, 5)

net = torch.nn.Sequential(
    nn.Conv2d(1, 32, kernel_size=3),
    LIFNode(),
    nn.Flatten(),
    nn.Linear(54, 1)
)

y = net(x)
```

## Development plans

SNNGrow is still under active development:
- [x] Large-scale deep spiking neural network training and inference
- [ ] Ultra-low energy consumption sparse spiking neural network computing
- [ ] Brain-inspired learning algorithm support
- [ ] Bionic neural network sparse structure support

## Cite

If you are using SNNGrow, please consider citing it as follows:
```
@misc{SNNGrow,
    title = {SNNGrow},
    author = {Lei, Yunlin and Gao, Lanyu and Yang, Xu and other contributors},
    year = {2024},
    publisher = {GitHub},
    journal = {GitHub repository},
    howpublished = {\url{https://github.com/snngrow/snngrow}},
}
```

## About

[Utarn Technology Co., Ltd.](https://www.utarn.com/w/home)and [Beijing Institute of Technology AETAS Laboratory](https://www.aetasbit.com/)are the main developers of SNNGrow.
pandas-tutorial 0.1.0
pandas tutorial

## 教程目录

<details>

        0. 配置环境
        1. Series和DataFrame对象的创建
        2. Series和DataFrame对象的查、改、增、删
        3. merge详解
        4. Index对象的创建，查、改、增、删和使用
        5. 普通列和行index的相互转化
        6. 数据结构总览
        7. 显示控制
        8. 快速查看整体信息
        9. 数值运算
        10. 数值统计运算
        11. mask与比较运算（待完成）
        12. Category型与离散化
        13. 时间型操作
        14. Object型操作
        15. groupby详解（待完成）
        16. resample详解（待完成）
        17. ……

</details>

## 教程说明

当今最热的职业是数据科学，数据科学领域应用最广泛的编程语言是python，python这么火的原因就是其有一个功能强大的数据科学库：pandas。

## 为什么写这套教程
然而，作为一名数据科学行业从业者，即使在pandas中浸淫日久，我常常还需要去查询官方文档，这严重影响了我的工作效率；甚至有时候迫不得已还得写循环操作，非常不pandas，这我忍不了，所以我觉得我得做点什么。

经过多次通读官方文档后，我认为问题根因在于：
- 官方文档组织杂而乱，知识框架不够精炼一致；
- 面面俱到，高价值信息被为了完整性而稀释；
- 文档更新不及时，API功能有时与文档描述不符。

与此同时，我也通读了国内外各种pandas教程，不过总体而言这些教程多数浅尝辄止，不够实用。所以，我决定编写一套pandas教程，提高自己能力的同时，也能帮助大家少走弯路。

## 教程编写核心原则
这套教程编写的核心原则是：
- 首重知识体系逻辑，没有组织、不成体系的信息是无效信息，很难记住和使用；
- 知识粒度大小适中，即不流于表面也不深入过多细节;
- 示例精炼短小（能看出操作效果），方便手打练习；
- 在示例位置都会注上解释，辅助理解。

## 这套教程适合谁
这套教程包含从初级到进阶的内容，适合初学者和希望进阶建立知识体系的数据科学从业者阅读。为确保教程的高可用性和准确性，我花了大量时间精心准备，但仍难免有错漏，非常欢迎各位读者能够跟我反馈。

## 知乎主页
花半楼：https://www.zhihu.com/people/HANGZS

## 交流可以加我微信
#### 微信号：204078950
#### 公众号：花半楼

![image](./resource/204078950.png)
raft-python 0.1.3
raft python实现, 用于测试学习

# 说明

非拜占庭节点的分布式共识算法Raft的python实现。

Raft官网：https://raft.github.io/


## raft
### 整体架构
![](./resource/raft.svg)


### 文件说明

- node.py, log.py为raft核心代码
- node.py 实现raft的节点消息收发、角色转换等功能
- log.py 实现具体的日志存储
- rpc.py 实现具体消息编码和转发
- master.py 控制面中心节点, 进行group创建和管理
- slave.py 每个server上部署的控制面节点

## test
环境：windows 10系统 单机

### 测试步骤
1. 运行 python -m raft.master, 启动master节点
2. 运行 python -m raft.slave, 启动slave节点
3. 运行 python -m test.test_master **param**
    - param: create_group, create一个raft group
    - param: stop_group, 安全终止 一个raft group
    - param: stop_master, 安全终止master
4. 运行 python -m test.test_slave **param**
    - param: stop_slave, 安全终止slave
5. 运行 python -m test.client, 用户写入log

## 结果说明
- data/master, master存储的数据
- data/slvae, slave存储的数据
- data/node 
    - .log 各node交互过程监控数据, 可以在git bash中用tail -f filename.log查看
    - persistent.json, node持久化数据
    - log.json, 存储的日志
influx-line 1.0.0
Influx line protocol builder
Keywords: python, influx, influxdb, line protocol, flux, influx line protocol


# influx-line

Lightweight [influxdb line protocol](https://docs.influxdata.com/influxdb/v2.3/write_protocols/line_protocol_tutorial) builder

## Installation

```bash
pip install influx-line
```

## Usage

```python
from influx_line import InfluxLine 

line = InfluxLine("weather")

line.add_tag("location", "CA")
line.add_tag("season", "summer")
line.add_field("temperature", 82, is_integer=True)
line.add_field("error", 0.1)
line.add_field("time_zone", "PDT")

line.set_timestamp(1556813561098000000)

str(line)
"""
weather,location=CA,season=summer temperature=82i,error=0.1,time_zone="PDT" 1556813561098000000
"""

```

## Contributing

The default repository is on [gitlab](https://gitlab.com/functionoffunction/influx-line) but mirrored on [github](https://github.com/functionoffunction/influx-line)

[Google style guidelines](https://google.github.io/styleguide/) has been chosen as the style and contribution guidelines for submitting additions. In general, we follow the "fork-and-pull" Git workflow.

 1. **Fork** the repo on GitLab
 2. **Clone** the project to your own machine
 3. **Commit** changes to your own branch
 4. **Push** your work back up to your fork
 5. Submit a **Merge request** so that we can review your changes

NOTE: Be sure to merge the latest from "upstream" before making a pull request!

## License

See [LICENSE](https://github.com/functionoffunction/influx-line/blob/main/LICENSE) file.