semvec/pypi-packages huggingface dataset:
gpalib 0.1.5
A library to convert scores using many arithmetics
Requires Python: >=3.5, <4
Keywords: GPA

# GPA

A library used to calculate your GPA. Now the GPA algorithm varies slightly from school to school, and calculating your GPA score one by one when applying to schools is a complicated and time-consuming process. This library is used to implement some common GPA calculation methods, which is convenient for you to calculate your GPA.

一个用来计算你GPA的库。现在各个学校的GPA算法略有不同，在申请学校时逐个计算你的GPA分数是很复杂且耗费时间的过程。这个库用来实现一些常见的GPA计算方法，方便你计算你的GPA。

# How to install

```bash
pip install gpalib
```

# How to use

```python
import gpalib
a = ['A', 'B', 'A', 'A']
r = gpalib.convert_to_4(a, 'rank')
print(r)
```

```
[4, 3, 4, 4]
```


```python
import gpalib
a = [99, 87, 89, 83, 96]
r = gpalib.convert_to_4(a, 'hundred', 'Beida_4')
print(r)
```
```
[4, 3.7, 3.7, 3.3, 4]
```
rotic 1.0.0
Python SDK let you call Rotic Intelligent Solutions API

# RoticSDK-Python
Python SDK let you call Rotic Intelligent Solutions API

For Chat:

```angular2html
rotic = Connect()
rotic.token = "{YOUR_TOKEN}"
rotic.api = "{YOUR_API_TOKEN}"
rotic.unique_token="{Random_Generated_String}"
print(rotic.chat("Hello world"));
```
taichi-glsl 0.0.12
A Taichi extension library providing a set of GLSL-alike helper functions
Requires Python: >=3.6


Taichi GLSL
===========

Taichi GLSL is an extension library of the `Taichi Programming Language <https://github.com/taichi-dev/taichi>`_, which provides a set of useful helper functions including but not limited to:

1. Handy scalar functions like ``clamp``, ``smoothstep``, ``mix``, ``round``.
2. GLSL-alike vector functions like ``normalize``, ``distance``, ``reflect``.
3. Well-behaved random generators including ``randUnit3D``, ``randNDRange``.
4. Handy vector and matrix initializer: ``vec`` and ``mat``.
5. Handy vector component shuffle accessor like ``v.xy``.
6. Handy field sampler including ``bilerp`` and ``sample``.
7. Useful physics helper functions like ``boundReflect``.
8. Shadertoy-alike inputed GUI base class ``Animation``.

`[Clike me for documentation] <https://taichi-glsl.readthedocs.io>`_

`[Clike me for GitHub repo] <https://github.com/taichi-dev/taichi_glsl>`_
hitutil-en 0.1.2
hit.edu.cn utils with magic modification
Requires Python: >=3.5, <4
Keywords: Harbin Institute of Technology,HIT,requests

# hitutil

`hitutil` 基于 `requests`，是一个与哈尔滨工业大学网络业务相关的支持库。

## ids

统一身份认证相关服务

`ids.idslogin()` 模拟统一身份认证登陆, 返回一个已登陆的 `requests.Session`.

```python
from hit import ids
session = ids.idslogin('USERNAME', 'PASSWORD')
session.get('http://jwes.hit.edu.cn/queryWsyyIndex') # 登陆本科生网上服务系统
response = session.get('http://jwes.hit.edu.cn/cjcx/queryQmcj') # 查询成绩
```


## timetable

课表相关服务

```python
from hit import timetable

def main():
    print(timetable.parse_to_df(timetable.query_by_gxh('学号', '学期', '''周次，整数'''))) # 学期类似：'2021-2022;1' 表示秋季学期

if __name__ == '__main__':
    main()
```

### 学期参数说明

| 接口参数 | 对应实际学期 |
| ----------- | ------------ |
| 2020-2021;1 | 2020秋季   |
| 2020-2021;2 | 2021春季   |
| 2020-2021;3 | 2021夏季   |
| 2020-2021;4 | 2021暑假   |
| 2020-2021;5 | 2021寒假   |
| 2021-2022;1 | 2021秋季   |
| ...          | ...          |


## score

成绩查询服务

```python
from hit import score
from hit import ids


def main():
    s = ids.idslogin('学号', 'ids密码')
    score.query(s).to_csv('我的成绩.csv') # 保存结果到csv


if __name__ == '__main__':
    main()
```
matthewflegg-mbti 0.0.2
Predicting MBTI types based on Twitter posts
Keywords: MBTI,LSTM,machine learning,neural network,BERT,keras

# MBTI Predictor
Predicting someone's MBTI type based on their online posts, using AI.

### Approach
I originally attempted to use a simple LSTM, using nltk and Keras. However, this approach led to the model badly overfitting.

I then decided to re-write the program and use a bidirectional LSTM. This was slightly better and, somewhat, prevented overfitting. However, the model trained slowly due to a low learning rate.

I then decided to use a TF BERT Model as a layer within a Keras model. I thought this would improve the performance of the predictor but my GPU (NVIDIA GTX 1650) couldn't run it. I decided to publish this project as a package so that others could attempt to train the BERT model.

### Findings
I could not find a significant correlation between a post and the MBTI type of its poster.
Feel free to download the source code and try for yourself.
wsnasa 0.0.2b0
Library for use nasa.api

# Library for use services of NASA


Install package
>pip install wsnasa  

After installed package need does settings it for work. 

>from wsnasa.config import Config  
>Config(token='TOKENNASA',storage=ClassStorage, connection_string='conn_str')  

ClassStorage - wsnasa.entity.storage*

## Token
Get keys can here https://api.nasa.gov/
or use DEMO_KEY  

## Storage
Class <Storage*> implementes rule stores caches
Has second of classes: StorageMemory, StorageDatabase.  
If need use another class for storage use for implement of the interface  <AbcStorage>

>from wsnasa.entity.abclass.abcstorage import AbcStorage