semvec/pypi-packages huggingface dataset:
ingenialogger 0.3.0
Logger library for Ingenia projects
Requires Python: >=3.6

Ingenialogger
=============

Module to unify libraries, script and programs logs.

How to use it
-------------

### Module and libraries

```python
import ingenialogger

logger = ingenialogger.get_logger(__name__)

logger.info("Info log", axis=1, drive="EVE-XCR (192.168.2.22)")

logger.user_warning("User Warning log", code_error="0x2342")
```

### Final application

```python
import ingenialogger

log_queue = ingenialogger.configure_logger(level=ingenialogger.LoggingLevel.INFO, queue=True)
logger = ingenialogger.get_logger(__name__)
logger.user_info("User Info log", axis=2, drive="My drive")

log_msg = log_queue.get().msg
print(ingenialogger.LogParser.parse_log(log_msg))
```
farm-haystack-speech2text 1.0.1
Haystack node to convert audio files into Documents.
Requires Python: >=3.7

# speech2text

[![PyPI - Version](https://img.shields.io/pypi/v/farm-haystack-speech2text.svg)](https://pypi.org/project/farm-haystack-speech2text)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/farm-haystack-speech2text.svg)](https://pypi.org/project/farm-haystack-speech2text)

---

**Table of Contents**

- [Installation](#installation)
- [Usage](#usage)
- [License](#license)

## Installation

```console
pip install farm-haystack-speech2text
```

## Installation

Step 1. Install the audio system dependencies. On Debian-based systems:

```console
sudo apt-get install libsndfile1 ffmpeg
```

Step 2. Install the package. With pip:

```console
pip install farm-haystack-speech2text
```

## License

`haystack-speech2text` is distributed under the terms of the [Apache-2.0](https://spdx.org/licenses/Apache-2.0.html) license.
ckip-2-tei 1.0.0
A Python package that asynchronously segments JSON data into TEI XML format.
Requires Python: >=3.10,<4.0

# **ckip-2-tei**

This project segments the title, body, and comments from a JSON file and writes them to a TEI XML file, and
leverages asynchronous programming to achieve high performance and speed.

## 1. Installation


## 2. Documentation
simsnn 1.0.2
Radboud Spiking Neural Network Simulator
Requires Python: >=3.7

# SimSNN

[![License](https://img.shields.io/badge/license-MIT-blue.svg)](https://opensource.org/licenses/MIT) 
[![Python 3.6+](https://img.shields.io/badge/python-3.6-blue.svg)](https://www.python.org/downloads/release/python-360/)

A spiking neural network simulator developed and maintained by Radboud University staff and students.

## Requirements
To run the program, make sure you have installed the dependencies listed in environment.yml. 
We recommend creating a conda environment for every project. You can do this with the following command:
`conda env create --file environment.yml`

## Usage

### On Google Colab

Click on the badge to open the notebook in google colab. Then run every cell to see the example output. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/adiehl96/SNN-computing/blob/master/example.ipynb)


### Locally using jupyter notebooks
Clone this repository or download the zipped version. Unzip and open a terminal in the project directory. From there you can install the conda requirements, as mentioned before. You can also run the notebook by running 

`jupyter lab ./example.ipynb` 


### Locally from the command line
To run the example network, located in the examples directory, open up a terminal in the project directory and run the following command: 

`python -m simsnn`

You should be presented with a read out from the connected measuring tools like this:

![Default Readout](defaultreadout.png)
(see [homepage](https://gitlab.socsci.ru.nl/neuromorphiccomputing/simsnn) if the image doesn't render correctly.)
ee-export 1.0.3
export excel sheets to json
Requires Python: >=3.8

\n## TODO
- [x] 添加：根据cmd表单，形成任务列表的功能
- [x] 添加：获取Sheet信息
- [x] 添加：#注释掉列的功能
- [x] 添加：#注释掉行的功能

- [x] 增强：Header垂直到尾的判断能力

- [x] 添加：parse单元格内Json字符串的功能
- [x] 添加：根据表头的类型标注转换单元格value的功能

- [x] 添加：将多个相同ID表头的行合并伪一个list的功能
- [x] 添加：OneFilePerLine的功能
- [ ] 添加：命令行读取参数，处理Excel文件的功能
- [ ] 添加：bat脚本
- [ ] 打包发布到Pypi

## 说明
### cmd表单说明
* cmd是管理导出任务的默认表单
* 保证第一行和第一列没有被空单元格间断（合并单元格里的所有单元格都是非空）
    * 第一行：h标明表头的范围
* 表头说明：
    * Task：任务标记
    * SheetName：任务所对应的表单名称
    * Export：是否执行导出
    * OneFilePerLine：是否将一行导出为一个文件
    * IgnoreEmptyCell：是否忽略空的单元格
    * OutputFolder：导出json所放置的文件夹

### 对象表单说明
* Id区：Id（不区分大小写）表头是数据标志列，该列的数据会作为数据的唯一标识符
* Header区：从第1行到h标志结束所在行，从Id列后一列到数据中断的列，是Header区域
* Content区：Header末行以下、Id列以右的区域
* 保证第一行和第一列没有被空单元格间断（合并单元格里的所有单元格都是非空）
    * 第一列的最后一个h，标记表头结束的行
* 使用#注释行列：
    * 如果用#开头，则该行或该列会被忽略；合并单元的话，该合并单元格所覆盖的列都会被忽略。
* 表头数据填写：
    * 默认分隔符是"-"：没有分隔符，则该表头的名称是整个单元格；有，则是分隔后的第一个
    * 标注数值类型：支持标注int/str/float三种数值类型
    * 标注数据结构类型：
        * 支持标注dict/list两种数据结构类型
        * dict类型可以不标注，因为默认就是dict类型
* 支持单元格类填写json数据
    * 默认会检查单元格内的数据，如果是被[]/{}包围，将以json5解析该单元格的数据
    * 因为是用json5作为解析工具，所以单元格里字典的key可以不用加""，默认都会加，即使是用None、null做为key


### 其他
* 合并单元格直接对应多列数据：如果一个合并单元格已经是表头的垂直末尾，则该表头对应的多列数据会默认被放到一个list里
* 重复Id处理：
    * OneFilePerLine：后边的数据会覆盖前边的数据
    * 非OneFilePerLine：重复的数据会被按顺序放到一个list里
* 空表头处理：
    * 如果数据向上追溯碰到的第一个非空表头是list，则该数据会被放到一个list里
    * 如果数据向上追溯碰到的第一个非空表头是dict，则该数据会被放到一个以None为Key的键值对里。注意：多个空表头会导致最后一个空表头覆盖前边的空表头
    * 如果在表头的拆分字符串里出现None/Null（不区分大小写），该表头会被当做空表头处理。（这么做是方便做备注，同时保留所需要的空表头）
* 按Id列输出列表（只适用于不是OneFilePerLine的情形）：
    * 在(1, 1)输入{abcd:[]}（abcd可以用其他字母替换），脚本会以该格式作为包裹，将id列的值放到列表里；id列的健会被略去，因为外层是一个list。
    * 这个功能主要用于：从根节点开始就非常需要把内容进行列表排序，而不需要键值对查询的。外包了一层字典，保留拓展性
* 暂不实现忽略空单元格的功能，涉及比较复杂的情况

### 测试
* clone下来后，在src文件夹下，执行python setup.py install
* 执行Sheet.py
criptoGR 0.1.0
This project refers to a password encryptor, which also serves to encrypt and decrypt files through a password entered by users.
Requires Python: >=3.8

# CRIPTOGR
=============



This project refers to a password encryptor, which also serves to encrypt and decrypt files through a password entered by users. <br>
[Project Cripto](https://github.com/NaraAndrad3/Cripto/tree/main/Package)
to write your content.


<h3><li>1 - Para instalar o pacote use:</li></h3>

``````
pip install criptoGR==0.0.1
``````


<h3><li>2 - import o modulo usando:</li></h3>

``````
from criptoGR import *
``````



<h3><li>3 - Formas de usar o pacote:</li></h3>

``````
#Encriptando e decriptando o arquivo

f = FilesDec()



print('Criptografar: ')
arq = 'arquivo.pdf'
f.encryptFile(arq,'1234')


print('Decriptografar: ')
f.decryptFiles(arq,'1234')


#Gerando senhas
senha1 = pwd.password_letters(8)

``````