semvec/pypi-packages huggingface dataset:
nonebot-plugin-ocr 0.3.2
OCR plugin for nonebot2
License: MIT
Requires Python: >=3.10,<4.0
Keywords: nonebot,nonebot2

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# nonebot-plugin-ocr

基于 [NoneBot2](https://v2.nonebot.dev/) 的文字识别插件

![](https://img.shields.io/badge/license-MIT-yellow)
[![Python Compatability](https://img.shields.io/badge/Python-3.10-blue)](https://www.python.org/)
[![PyPI version](https://badge.fury.io/py/nonebot-plugin-ocr.svg)](https://pypi.org/project/nonebot-plugin-ocr/)
[![GitHub version](https://badge.fury.io/gh/NewYearPrism%2Fnonebot-plugin-ocr.svg)](https://github.com/NewYearPrism/nonebot-plugin-ocr)

</div>

## NoneBot 适配器支持
 + [OneBot V11](https://onebot.adapters.nonebot.dev/)

## OCR 应用支持
 + [百度智能云](https://cloud.baidu.com/product/ocr)（Web应用，内置）


## 部署指南
### 一. 安装插件
+ 方式一：使用 pip
  > 1.安装 nonebot-plugin-ocr
  > ```
  > pip install nonebot-plugin-ocr
  > ```
  > 2.使用 nonebot 加载插件 "nonebot_plugin_ocr" （[参考：加载插件](https://v2.nonebot.dev/docs/tutorial/plugin/load-plugin)）
+ 方式二：手动安装
  > 1.克隆本仓库
  > ```
  > git clone https://github.com/NewYearPrism/nonebot-plugin-ocr
  > ```
  > 2.安装依赖 requirements.txt
  > ```
  > pip install -r requirements.txt
  > ```
  > 3.复制插件本体到 Nonebot 环境中
  > ```
  > cp path/to/repo/nonebot-plugin-ocr/nonebot_plugin_ocr path/to/nonebot_plugins -r
  > ```
  > 4.使用 nonebot 加载插件（同上）
### 二. 创建配置文件
+ 需要手动创建配置文件，配置文件默认路径 ".ocr/config.toml" (相对当前工作目录)， 你可以在 NoneBot .env配置中使用变量OCR_CONFIG_PATH指定之：
  > <span># .env</span><br/>
  > HOST=0.0.0.0<br/>
  > PORT=8080<br/>
  > SUPERUSERS=["123456789", "987654321"]<br/>
  > <span># ...</span><br/>
  > OCR_CONFIG_PATH="path/to/config/ocr.toml"<br/>
    ```
    mkdir -p path/to/config
    vim path/to/config/ocr.toml 
    ```
+ 具体参考：[配置模板](config.template.toml)和[配置文档](docs/config.md)


# 使用方法
+ [使用演示](docs/usage.md)


# 特别感谢
+ [cq-picsearcher-bot](https://github.com/Tsuk1ko/cq-picsearcher-bot)
+ [baidu-aip](https://pypi.org/project/baidu-aip/)
Polspectra 1.1.0
Reading, writing, and manipulating radio astronomy polarization (Stokes) spectra.
License: MIT
Requires Python: >=3.5.0


# PolSpectra
A package for creating and manipulating tables of polarized radio spectra.

Detailed documentation is available on the wiki [here](https://github.com/CIRADA-Tools/PolSpectra/wiki).  
A full description of the PolSpectra standard is being written up and will be published soon.

This package was motivated for the need for a volume-efficient way of storing the Stokes IQUV spectra of compact sources: avoiding massive duplication of information (in FITS headers) and also avoiding the need for very large numbers of files (potentially half a billion files for the entirety of POSSUM!). The design set out to fulfill these goals: it would be space efficient (minimal duplication), easily readable (without requiring special software), and flexible in terms of combining data from separate instruments and observations into a single file if desired.

This has been implemented using the FITS table and VOTable formats. The use of variable-length array columns allow the spectra to be stored efficiently, even if different rows have different numbers of channels (although this has only limited official support under the FITS standard). The basic unit for a row is a single source-observation (the same source observed multiple times gets a row for each individual observation), with a 'source_number' column that can group together multiple observations under the same source.

The resulting tables should be readable by anything that supports FITS tables (with the variable-length array column standard) or VOTables. This Python package offers a streamlined way to create these tables as objects in Python and read/write them to FITS and VOTable format.

Designed for use by the [CIRADA](cirada.org) Polarization pipeline, for processing of data for the POSSUM and VLASS radio surveys, and for use in the [RM-Tools](https://github.com/CIRADA-Tools/RM-Tools) polarization analysis package (not yet supported).

Cameron Van Eck (cameron.van.eck (at) utoronto.ca)
code-mixed-text-toolkit 0.5.5
A library for processing Code Mixed Text. Still in development!
License: MIT

### This repository has been archived! 
You can find the latest version of the source code [inside the CMTT repository](https://github.com/Reuben27/CMTT), where it will continue to be developed.


[![forthebadge made-with-python](http://ForTheBadge.com/images/badges/made-with-python.svg)](https://www.python.org/)
<!-- [![forthebadge](https://forthebadge.com/images/badges/made-with-java.svg)](https://forthebadge.com) -->

<div align = center>
<img width="600px" height="180px" src= "https://user-images.githubusercontent.com/76529011/185376373-787f65d5-b78b-4f11-a7fb-e9aa19dc3a04.png">
</div>

-----------------------------------------
[![code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)![Compatibility](https://img.shields.io/badge/compatible%20with-python3.9.x-blue.svg)

CMTT is a wrapper library that makes code-mixed text processing more efficient than ever. More documentation incoming!

### Installation
```
pip install code-mixed-text-toolkit
```

### Get started
How to use this library:

```Python
import code_mixed_text_toolkit.data as cmtt_data
import code_mixed_text_toolkit.preprocessing as cmtt_pp

# Loading json files
result_json = cmtt_data.load('https://world.openfoodfacts.org/api/v0/product/5060292302201.json')

# Loading csv files
result_csv = cmtt_data.load('https://gist.githubusercontent.com/rnirmal/e01acfdaf54a6f9b24e91ba4cae63518/raw/b589a5c5a851711e20c5eb28f9d54742d1fe2dc/datasets.csv')

# List the key properties available for the datasets provided by the cmtt library
keys = cmtt_data.list_dataset_keys()

# List all datasets provided by cmtt
# Specifying the 'key' property allows to return the dataset names with the respective 'key' value
# Specifying the 'key' as 'all' returns all the information pertaining to all the datasets
data = cmtt_data.list_cmtt_datasets()
print(data)

# Download multiple datasets provided by cmtt, returning a list of paths where the datasets get downloaded
# The Datasets are downloaded into a new 'cmtt' directory inside the root directory of the operating system
lst = cmtt_data.download_cmtt_datasets(["linc_ner_hineng", "L3Cube_HingLID_all", "linc_lid_spaeng"])

# Download a dataset from a url, returning the path where the dataset gets downloaded
# The Dataset is downloaded into a new directory 'datasets' inside the current working directory
path = cmtt_data.download_dataset_url('https://world.openfoodfacts.org/api/v0/product/5060292302201.json')

# Load and preprocess txt dataset
result_txt = cmtt_data.load('https://www.w3.org/TR/PNG/iso_8859-1.txt')
result_txt_tokenized = cmtt_pp.tokenizer.word_tokenize(result_txt)

# Search target word in txt corpus
cmtt_pp.search.search_word(result_txt, 'with', tokenize = True, width = 3)
```

### Contributors
 - [Paras Gupta](https://github.com/paras-gupt)
 - [Tarun Sharma](https://github.com/tarun2001sharma)
 - [Reuben Devanesan](https://github.com/Reuben27)