semvec/pypi-packages huggingface dataset:
gpiocontrol 1.1
Simple wrapper for bash commands to control GPIO.

# GPIO control

This is a simple python wrapper for pinctrl sysfs interface.
It is intended to be used on a Raspberry Pi.

Currently it only supports set and get pin state operations.

Because of how pinctrl works, there is no need to initialize pin direction.

## Purpose

This wrapper was created to control a relay board using a Raspberry Pi.
Using bash command keeps pin's state after the script ends.

## Usage

```python
from gpiocontrol import get, set, State

print(get(4))  # Get pin 4 state
set(4, State.HIGH)  # Set pin 4 to HIGH
```
smallgrad 1.0.2
Backpropagation from scratch

# smallgrad

smallgrad is a minimalistic engine that implements backpropagation from scratch for neural networks. It allows users to visualize the computational graph along with the gradients and includes a script for building a Multi-Layer Perceptron (MLP) and visualize its computational graph.

# Features
- **Backpropagation from Scratch**: Implements a simple and understandable version of backpropagation, making it a great learning resource.
- **Visualize Computational Graph**: Visualizes the computational graph and gradients during the forward and backward pass.
- **MLP Builder Script**: Easily build Multi-Layer Perceptrons (MLPs) and visualize their learning process.
# Installation
If you want the latest updates:
```
git clone https://github.com/lorenzo-delsignore/smallgrad.git
cd smallgrad
pip install -e .
```
If you want the stable version:
```
pip install smallgrad
```
To render the computational graph, you also need an external dependency [Graphviz](https://www.graphviz.org/).

If you want to install the dependency with conda you use the following command: ```conda install -c conda-forge pygraphviz```.

You can install smallgrad also with Docker, in which contains also the external Graphviz dependency:
```
cd smallgrad
docker -t smallgrad .
```
# Usage
To use smallgrad, you can run the provided script to create an MLP and visualize the computational graph:
```
usage: smallgrad [-h] [--save_dir SAVE_DIR] --input INPUT [INPUT ...] --output OUTPUT [OUTPUT ...] [--activation ACTIVATION]
```
## Options:
- **save_dir**: Directory to save the visualization of the computational graph.
- **input**: Specify the input features for the MLP.
- **output**: Specify the output dimensions of each hidden and output layer of the MLP.
- **activation**: Choose the activation function (only relu and tanh are supported at the moment) for the MLP layers.

## Example Usage:
```
smallgrad --input 2 4 --output 3 1 --activation relu --save_dir ./graphs
```
With Docker:
```
docker run --rm --name smallgrad -v host_path_to_save:/graphs smallgrad --input 1 1 --output 2 1 --activation relu --save_dir /graphs
```
Sqlite3Helper 2.3.0
一个不安全的 sqlite3 包装库
Requires Python: >=3.10

# Sqlite3Helper

一个不安全的 sqlite3 包装库

## 介绍

因本人总是记不住 SQLite 的那些 SQL 语法，在 Python 里使用 sqlite3 这个库的时候每次都得去查语法，甚是烦躁，于是就编写了这个包装库，把常用的数据库操作都封装成了函数。

说这个库不安全，是因为这个库没有处理 SQL 注入攻击的问题。本来也就是自己用一用，注入攻击这么复杂的问题，还是先放一放吧。

## 安装

```sh
pip install Sqlite3Helper
```

### 安装可选的加密功能

> 需 2.2.6 版本以上，2.2.6 版本之前是强制安装的

```sh
pip install Sqlite3Helper[crypto]
```


## 示例

点击 [这里](https://github.com/JulianFreeman/Sqlite3Helper/blob/main/docs/basic_usage.md) 查看基本使用。