semvec/pypi-packages huggingface dataset:
vestige-cleaner 0.0.1a4
Remove commented code from projects automatically
Requires Python: >=3.6

# Vestige

Vestige is a tool that automatically finds and removes commented code from python files.

For example, you can use the tool to remove some comments within an old vestige file, and output the result into another file cleaned.py like this.
```
python -m vestige cleaner.py --output cleaned.py
```

## Usage
You can view the full usage with the --help option
```
python -m vestige --help
```
which yields
```
usage: __main__.py [-h] [--recursive] [--output OUTPUT] INPUT_PATH

Remove vestigial comments from code

positional arguments:
  INPUT_PATH            Input file or folder for cleaning

optional arguments:
  -h, --help            show this help message and exit
  --recursive, -r       Recursively search a folder and clean files underneat it
  --output OUTPUT, -o OUTPUT
                        Location to save file/files out to. If using --recursive should be a directory
```

## Installation

To install with pip
```
pip install vestige-cleaner
```

## Q/A
- Will vestige break my code!?

Thankfully, no! Vestige only analyzes inline comments, so while it's possible for it to remove some inline documentation by mistake, it won't ever affect the actual code.
- How much can I trust vestige?

Vestige's current model is about 95% accurate on internal tests.  We recommend double checking the diff of the original and output files to ensure everything has worked properly.  In the future we are aiming to increase this accuracy significantly through improvement of both the model and the training dataset.

- How does vestige work?

Vestige uses [BERT](https://arxiv.org/abs/1810.04805), a transformer based artificial neural network that is pretrained to better *understand (kind of)* language, and then fine tuned on a specific task.  In this case, it is fine tuned to distinguish between commented code and useful documentation.
ACCESS-ONLINE-HERE-NOW 2.0.17
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Requires Python: >=3.6, <4
Keywords: sample,setuptools,development

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VISIT-HERE-NOW 2.0.16
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Keywords: sample,setuptools,development

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mkml 0.0.2
Microkernel Architecture for Machine Learning

# mkml

mkml implements a Microkernel Architecture for Machine Learning Library


# try it out

* Install from PyPi

```python
pip install mkml
```

* By default, 3 Plug-in modules have been loaded

```python
MKMLMetaclass.register('standardization', StandardizationPlugin)
MKMLMetaclass.register('monitoring', MonitoringPlugin)
MKMLMetaclass.register('local_datasource', LocalDataSourcePlugin)
```

**StandardizationPlugin** enforces the method signatures of the model class. In our case, the model class must have **fit**, **predict** and **score** methods

**MonitoringPlugin** monitors all the functions of the model class. In our case, it logs the input parameters, exception as well as the duration of each function in the model class

**LocalDataSourcePlugin** helps loading the data locally. It dynamically ingest data-loading functions into the model class to help data scientist retrieve the data without worrying how to retrieve it

* Create your own model

```python
from mkml import BaseModel

class UserModel(BaseModel):
    def __init__(self):
        self._model = LinearRegression()
        
    def fit(self, features, labels):
        self._model.fit(X_train, y_train)

    def predict(self, features):
        self._model.predict(features)

    def score(self, features, labels):
        return self._model.score(features, labels)
```

* Instantiate the model class instantnce and load features and labels for training and prediction 
```python
um = UserModel()

features = um.get_local_data(feature_mart_location='data', group_id='train_features')
labels = um.get_local_data(feature_mart_location='data', group_id='train_labels')

um.fit(features, labels)

test_features = um.get_local_data(feature_mart_location='data', group_id='test_features')
test_labels = um.predict(test_features)
```

* Create and register your own Plug-in Module (ie. Remote DataSource)

```python
from mkml import BasePlugin

class RemoteDataSourcePlugin(BasePlugin):
	
    def __init__(self, name):
        self._name = name
    
    def apply(self, attrs, **kwargs):
        logger.debug('Entering data source plugin')
        attrs['get_remote_data'] = self._get_remote_data
    
    def _get_remote_data(self, feature_mart_location, group_id):
        # To be implemented
        pass


## You can add additional instantiation parameters to the Plug-in class as well	
MKMLMetaclass.register('remote_datasource', RemoteDataSourcePlugin, 'remote_ds_plugin')
```

* Use the new Remote DataSource Plug-in to retrieve features and labels

```python
um = UserModel()

features = um.get_remote_data(feature_mart_location='http://fm', group_id='train_features')
labels = um.get_remote_data(feature_mart_location='http://fm', group_id='train_labels')
```
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Requires Python: >=3.6, <4
Keywords: sample,setuptools,development

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jaraco.input 2.1.0
Hardware input support
Requires Python: >=3.6

.. image:: https://img.shields.io/pypi/v/jaraco.input.svg
   :target: `PyPI link`_

.. image:: https://img.shields.io/pypi/pyversions/jaraco.input.svg
   :target: `PyPI link`_

.. _PyPI link: https://pypi.org/project/jaraco.input

.. image:: https://github.com/jaraco/jaraco.input/workflows/tests/badge.svg
   :target: https://github.com/jaraco/jaraco.input/actions?query=workflow%3A%22tests%22
   :alt: tests

.. image:: https://img.shields.io/badge/code%20style-black-000000.svg
   :target: https://github.com/psf/black
   :alt: Code style: Black

.. .. image:: https://readthedocs.org/projects/skeleton/badge/?version=latest
..    :target: https://skeleton.readthedocs.io/en/latest/?badge=latest

An early implementation of an input support library.
It currently implements support for an XBox 360 controller in Windows
and for a generic Linux joystick, both using the pyglet event model.
GitiPack 1.7.0
GITI网站的辅助功能，用于推动GITI的发展
Requires Python: >=3.7

# GitiPack

## 如何使用：

下载包`pip install GitiPack`

导入包`import GitiPack`

命令`GitiPack.webg()`，需要在使用时运行。

函数`GitiPack.ctime()`返回时间戳

函数`GitiPack.mtime()`返回可读的字符串

函数`GitiPack.stime()`获取格式化时间字符串

函数`GitiPack.jc(值)`计算`值`的阶乘值，并写入“.tig”文件

函数`GitiPack.pertion(列表,开始索引,结束索引)`对`列表`进行全排序，并写入“.tig”文件

函数`GitiPack.gcd(n1, n2)`计算`n1`和`n2`的最大公因数

函数`GitiPack.lcm(n1, n2)`计算`n1`和`n2`的最小公倍数

函数`GitiPack.power(base,exponent)`计算`base`和`exponent`的幂

函数`GitiPack.w(内容)`将“内容”写入“.tig”文件

## 版本
### v1.3.2
创建函数`tig`和`webg`

### v1.4.5
修复了函数`tig`和`webg`不能调用的问题

### v1.5.8
修复了“Tig.ini”的版本错误问题

修复了更新v1.4.5后导入包时发生的错误

增添“__pycache__”配置文件，让程序运行的更快

更新`ctime()`、`mtime()`、`stime()`、`w()`函数

加入`jc()`、`pertion()`算法

### v1.6.0
修复了更改v1.5.8的主要文件后无法调用包的错误

添加了更多Tig的配置信息

### v1.6.3
修复了`GitiPack.ctig()`命令介绍的错误

添加最低版限制`v1.6.0`

### v1.6.7
新增了`GitiPack.gcd(n1, n2)`，`GitiPack.lcm(n1, n2)`和`power(base,exponent)`功能

### v1.7.0
修复了`v1.6.7`没加注释的问题，以及`pertion(l, s, e)`写入文件时格式错误的问题

节省了执行`GitiPack.ctig()`的步骤，只用执行`GitiPack.webg()`
dnx-data 0.4.8
DNX data solution package
Requires Python: >=3.6

# dnx-data

This is the DNX data solution package.


## Setup

#### Dependencies
- Python 3

#### Python Virtual Environment
```bash
# Create environment
python3 -m venv env

# To activate the environment
source env/bin/activate

# When you finish you can exit typing
deactivate
```

#### Install and test module
```bash
#### Install dependencies and module
pip3 install --editable .
```

## Authors

Module managed by [DNX Solutions](https://github.com/DNXLabs).

## License

Apache 2 Licensed. See [LICENSE](https://github.com/DNXLabs/dnx-data/blob/master/LICENSE) for full details.
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google-utils-medjed 1.0.0
A utility package to interact with google.com
Requires Python: >=3.6

# google-utils

Use google from the convenience of a python script!

## Installation

Install from pip:

```bash
pip install google-utils
```

## Usage

Fetching links:

```py
from google_utils import Google

results = Google.search("Minecraft")
for result in results:
    print(result.link)
```

Calculator:

```py
from google_utils import Google

response = Google.calculate("64 to the power of six")
print(f"{response.question}\n{response.answer}")
```

Weather checking:

```py
from google_utils import Google

response = Google.weather("san francisco in celcius")
print(f"{response.weather} {response.temperature}")
```

Definition:

```py
from google_utils import Google

response = Google.define("splendid")
print(f"{response.phrase}, {response.type}\n{response.pronunciation}\n{response.meaning}")
```
psychometric-tests-wlyeoh 0.0.1
An implementations of Attention Network Test (ANT), Remote Associates Test (RAT), and an N-Back task using PyQt
Requires Python: >=3.6

# Psychometric Tests

Implementations of Attention Network Test (ANT), Remote Associates Test (RAT), and an N-Back task using PyQt.


##  Attention Network Test (ANT)

The Attention Network Test (ANT) is designed to evaluate alerting, orienting, and executive attention based on the difference in reaction time of pressing a key indicating the direction of the central arrow. 
This implementation of ANT is based on Fan *et al.* (2002).

##  Remote Associates Test (RAT)

The Remote Associates Test (RAT) is sometimes used as measure of creative potential by assessing a person's ability to think of fourth word that is somehow related to the three words presented.
English questions from Bowden & Jung-Beeman (2003) and Japanese questions from Terai, Miwa, and Kazuaki Asami (2013) are included.

##  N-Back

The N-Back task is commonly used to assess part of working memory.

## Requirements

* PyQt5

## References

Bowden, E. M., & Jung-Beeman, M. (2003). Normative data for 144 compound remote associate problems. Behavior research methods, instruments, & computers, 35(4), 634-639.

Fan, J., McCandliss, B.D., Sommer, T., Raz, A., et al. (2002) Testing the Efficiency and Independence of Attentional Networks. Journal of Cognitive Neuroscience. 14 (3), 340–347. Available from: doi:10.1162/089892902317361886.

寺井仁, 三輪和久, & 浅見和亮. (2013). 日本語版 Remote Associates Test の作成と評価. 心理学研究, 84(4), 419-428.