semvec/pypi-packages huggingface dataset:
hypervehicle 0.7.1
Parametric geometry generation tool for hypersonic vehicles
Requires Python: >=3.11

[![x43](docs/source/images/logo-dark.png)](docs/hangar.md)

<p align="center">
  
  <a href="https://pypi.org/project/hypervehicle/">
    <img src="https://img.shields.io/pypi/v/hypervehicle?color=blue&style=plastic" alt="Latest version" width=95 height=20>
  </a>
  
  <a href="https://github.com/psf/black">
    <img alt="Code style: black" src="https://img.shields.io/badge/code%20style-black-000000.svg">
  </a>

  <a href="https://kieran-mackle.github.io/hypervehicle/pytest_report">
    <img src="https://github.com/kieran-mackle/hypervehicle/actions/workflows/tests.yml/badge.svg" alt="Test Status" >
  </a>

  <a href="https://kieran-mackle.github.io/hypervehicle/coverage">
    <img src="https://github.com/kieran-mackle/hypervehicle/raw/gh-pages/coverage.svg?raw=true" alt="Test Coverage" >
  </a>
  
</p>

A Python package to rapidly generate parametric geometries
defined by hyperpatches. Check out the 
[hangar](docs/source/hangar.md) for some examples.


## Installation

To install `hypervehicle` from [PyPI](https://pypi.org/project/hypervehicle/), use the command below.

```
pip install hypervehicle
```

### Developing HyperVehicle
Contributions to HyperVehicle are welcome. Please refer to the [contribution guidelines](https://hypervehicle.readthedocs.io/en/latest/other/contributing.html) for guidance.


## Citing HyperVehicle
If you use HyperVehicle in any published work, please cite it using the BibTex reference below.

```text
@inproceedings{Mackle2024,
  author    = {Mackle, Kieran and Jahn, Ingo},
  booktitle = {AIAA Science and Technology Forum and Exposition},
  title     = {Efficient and Flexible Methodology for the Aerodynamic Shape Optimisation of Hypersonic Vehicle Concepts in a High-Dimensional Design Space},
  year      = {2024},
}
```

## License
HyperVehicle is licensed under [GPLv3](COPYING).
ChineseErrorCorrector 1.6.0
Syntax Error Data Enhancement
Requires Python: >=3.8

#  一键语法错误增强工具
欢迎使用一键语法错误增强工具，该工具可以进行14种语法错误的增强，不同行业可以根据自己的数据进行错误替换，来训练自己的语法和拼写模型。

使用：`pip install ChineseErrorCorrector`

开源不易，欢迎 star🌟

pypi:https://pypi.org/project/ChineseErrorCorrector/

---

## 介绍

一键语法错误增强工具，支持：
- [1.缺字漏字](#1缺字漏字)
- [2.错别字错误](#2错别字错误)
- [3.缺少标点](#3缺少标点)
- [4.错用标点](#4错用标点)
- [5.主语不明](#5主语不明)
- [6.谓语残缺](#6谓语残缺)
- [7.宾语残缺](#7宾语残缺)
- [8.其他成分残缺](#8其他成分残缺)
- [9.虚词多余](#9虚词多余)
- [10.其他成分多余](#10其他成分多余)
- [11.主语多余](#11主语多余)
- [12.语序不当](#12语序不当)
- [13.动宾搭配不当](#13动宾搭配不当)
- [14.其他搭配不当](#14其他搭配不当)




## 注意

如果没有进行数据增强，则返回None

---
## API



### 1.缺字漏字


```python
from ChineseErrorCorrector.dat import GrammarErrorDat

cged_tool = GrammarErrorDat()
print(cged_tool.lack_word("小明住在北京"))

# 输出：小明在北京

```
### 2.错别字错误


```python
from ChineseErrorCorrector.dat import GrammarErrorDat

cged_tool = GrammarErrorDat()
print(cged_tool.wrong_word("小明住在北京"))
# 输出：小明住在北鲸

```
### 3.缺少标点


```python
from ChineseErrorCorrector.dat import GrammarErrorDat

cged_tool = GrammarErrorDat()
print(cged_tool.lack_char("小明住在北京，热爱NLP。"))
# 输出：小明住在北京热爱NLP。

```
### 4.错用标点


```python
from ChineseErrorCorrector.dat import GrammarErrorDat

cged_tool = GrammarErrorDat()
print(cged_tool.wrong_char("小明住在北京，热爱NLP。"))
# 输出：小明住在北京。热爱NLP。

```
### 5.主语不明


```python
from ChineseErrorCorrector.dat import GrammarErrorDat

cged_tool = GrammarErrorDat()
print(cged_tool.unknow_sub("小明住在北京"))
# 输出：住在北京

```
### 6.谓语残缺


```python
from ChineseErrorCorrector.dat import GrammarErrorDat

cged_tool = GrammarErrorDat()
print(cged_tool.unknow_pred("小明住在北京"))
# 输出：小明在北京
```
### 7.宾语残缺


```python
from ChineseErrorCorrector.dat import GrammarErrorDat

cged_tool = GrammarErrorDat()
print(cged_tool.lack_obj("小明住在北京，热爱NLP。"))
# 输出：小明住在北京，热爱。
```
### 8.其他成分残缺


```python
from ChineseErrorCorrector.dat import GrammarErrorDat

cged_tool = GrammarErrorDat()
print(cged_tool.lack_others("小明住在北京，热爱NLP。"))
# 输出：小明住北京，热爱NLP。
```
### 9.虚词多余

```python
from ChineseErrorCorrector.dat import GrammarErrorDat

cged_tool = GrammarErrorDat()
print(cged_tool.red_fun("小明住在北京，热爱NLP。"))
# 输出：小明所住的在北京，热爱NLP。
```
### 10.其他成分多余


```python
from ChineseErrorCorrector.dat import GrammarErrorDat

cged_tool = GrammarErrorDat()
print(cged_tool.red_component("小明住在北京，热爱NLP。"))
# 输出：小明住在北京，热爱NLP。，看着
```
### 11.主语多余


```python
from ChineseErrorCorrector.dat import GrammarErrorDat

cged_tool = GrammarErrorDat()
print(cged_tool.red_sub("小明住在北京，热爱NLP。"))
# 输出：小明住在北京，小明热爱NLP。
```


### 12.语序不当


```python
from ChineseErrorCorrector.dat import GrammarErrorDat

cged_tool = GrammarErrorDat()
print(cged_tool.wrong_sentence_order("小明住在北京，热爱NLP。"))
# 输出：热爱NLP。，小明住在北京

```




### 13.动宾搭配不当


```python
from ChineseErrorCorrector.dat import GrammarErrorDat

cged_tool = GrammarErrorDat()
print(cged_tool.wrong_ver_obj("小明住在北京，热爱NLP。"))
# 输出：None ，即无法进行此类错误的增强

```


### 14.其他搭配不当


```python
from ChineseErrorCorrector.dat import GrammarErrorDat

cged_tool = GrammarErrorDat()
print(cged_tool.other_wrong("小明住在北京，热爱NLP。"))
# 输出：None, 即无法进行此类错误的增强

```