semvec/pypi-packages huggingface dataset:
pejmanai-data-analysis 0.1.2
A package for data analysis including data description, data preprocessing, data visualization, and modeling
Requires Python: >=3.8

# Pejmanai Data Analysis Package

![Package Banner](https://i.postimg.cc/s2K9BMDh/design.png)


## Overview

* `pejmanai_data_analysis` is a Python package for comprehensive data analysis, including read data in csv format, data preprocessing, data visualization, and machine learning modeling for both regression and classification problems. It provides tools to streamline the process of understanding and modeling datasets with ease.

## Features

- **Data Reading:** Load datasets from CSV files with error handling.
- **Data Description:** View basic statistics, data information, and missing values.
- **Data Preprocessing:** Handle missing values and encode categorical variables.
- **Data Visualization:** Generate scatter plots, histograms, KDE plots, and heatmaps.
- **Regression Models:** Evaluate multiple regression models including Linear Regression, Ridge Regression, Decision Trees, Random Forests, and K-Nearest Neighbors.
- **Classification Models:** Compare various classification models such as Decision Trees, Random Forests, Support Vector Machines, K-Nearest Neighbors, and MLP Classifiers.

## Installation

* You can install the package using pip:
`pip install pejmanai_data_analysis`

## Usage
* Data Reading
- `from pejmanai_data_analysis.app import read_csv`
- `df = read_csv('path/to/your/data.csv')`
- `print(df)`

* Data Description
- `from pejmanai_data_analysis.app import data_description`
- `data_description('path/to/your/data.csv')`

* Data Preprocessing
- `from pejmanai_data_analysis.app import data_preprocessing`
- `df_preprocessed = data_preprocessing('path/to/your/data.csv')`
- `print(df_preprocessed)`

* Data Visualization
- `from pejmanai_data_analysis.app import data_visualization`
- `data_visualization('path/to/your/data.csv', 'x_column', 'y_column')`

* Data Prediction (Regression)
- `from pejmanai_data_analysis.app import data_prediction`
- `data_prediction('path/to/your/data.csv', 'target_column')`

* Data Classification
- `from pejmanai_data_analysis.app import data_classification`
- `data_classification('path/to/your/data.csv', 'target_column')`

## License
* This project is licensed under the MIT License

## Contact
For any questions or feedback, please reach out to [Pejman Ebrahimi](https://www.giltech-megoldasok.com/).
vulp 2.5.1
Robot/simulation switch for the mjbots stack.
Requires Python: >=3.7
Keywords: motion control, real time, robotics

# Vulp – Robot/simulation switch

> [!IMPORTANT]\
> Vulp has been merged into the [Upkie wheeled biped software](https://github.com/upkie/upkie) and is not developed as a standalone library any more. If you need similar features for your own robot, we recommend you rather start from the latest [moteus](https://pypi.org/project/moteus/) C++ bindings.

Vulp provides an action-observation loop to control robots from a standalone "agent" process, like this:

<p align="center">
<img src="https://raw.githubusercontent.com/upkie/vulp/main/docs/figures/action-observation-loop-full.svg" alt="Action-observation loop with Vulp" class="center"/>
</p>

Vulp is designed for robots built with the mjbots stack ([moteus](https://github.com/mjbots/moteus/) servo controllers and [pi3hat](https://github.com/mjbots/pi3hat/) communication board). It provides a robot/simulation switch to train or test agents in [Bullet](https://github.com/bulletphysics/bullet3) before running them on the real system. Vulp supports Linux and macOS for development, and Raspberry Pi OS for robot deployment.

## See also

* [moteus](https://pypi.org/project/moteus/): bindings for moteus brushless controllers.
* [odri_control_interface](https://github.com/open-dynamic-robot-initiative/odri_control_interface): interface to control robots built with the [ODRI](https://github.com/open-dynamic-robot-initiative) Master Board.
* [kodlab_mjbots_sdk](https://github.com/KodlabPenn/kodlab_mjbots_sdk): C++-only framework integrated with [LCM](https://lcm-proj.github.io/lcm/) for logging and remote I/O.
* [mc\_rtc](https://github.com/jrl-umi3218/mc_rtc/): C++ real-time control framework with QP-based whole-body control.
* [robot\_interfaces](https://github.com/open-dynamic-robot-initiative/robot_interfaces): IPC between non-realtime Python and real-time C++ processes with Python bindings.
* [ros2_control](https://github.com/ros-controls/ros2_control): C++ framework for real-time control using ROS2 (still a work in progress).

## Why the name "Vulp"?

[Vulp](https://en.wiktionary.org/wiki/vulp#Noun_2) means "fox" in Romansh, a language spoken in the Swiss canton of the Grisons. Foxes are arguably quite reliable in their reaction times 🦊
OldHangeul 1.2.1
Program with functions for manipulation of old Korean script, including Unicode normalization and jamo separation.
Requires Python: >=3

# OldHangeul

`OldHangeul`은 Python에서 옛한글을 편리하게 다루기 위해 개발한 패키지입니다. 

파이썬에서는 한양 PUA로 인코딩된 완성형 옛한글을 지원하지 않습니다. 그래서 옛한글이 포함된 텍스트는 유니코드 정규화(Unicode normalization)가 작동하지 않고, `string index`와 `len()`에서 문제가 발생합니다. `OldHangeul`은 텍스트를 조합형으로 전환하여 이러한 문제를 해결했습니다. 더불어 자음과 모음으로 옛한글을 처리할 수 있는 기능이 있습니다. 




## 설치


```python
pip install OldHangeul
```



## 사용법

`OldHangeul`은 파이썬에서 작동합니다. 

---
### OldTexts

옛한글이 포함된 텍스트를 다루는 클래스입니다. 완성형이 포함된 텍스트를 조합형으로 전환하고, 인덱싱(Indexing)과 슬라이싱(Slicing), `len()`, `get_jamo()`를 지원합니다. 

```python
from OldHangeul import OldTexts
text=OldTexts('스님이 免帖 나 주시고') #완성형이 포함된 텍스트입니다
print(text)
```

OldTexts 사용 예시입니다. 
```python
스스ᇰ님이 免帖 ᄒᆞ나ᄒᆞᆯ 주시고
```

OldTexts는 문자열의 길이를 손쉽게 계산할 수 있으며, 인덱싱(indexing)과 슬라이싱(slicing) 기능도 제공합니다.

```python
len(text)
#15

text[1]
#스ᇰ
```

---
### text_to_jamo

텍스트를 자음과 모음으로 분리합니다. 낱자는 space로 구분되어 있으며, 문서 내의 공백은 _로 나타냅니다. 

compatibility: 초성과 종성을 동일한 유니코드로 통일하여 처리

spacing: 문서 내 공백 표현 

   


```python
from OldHangeul import text_to_jamo
text=text_to_jamo('스스ᇰ님이 免帖 ᄒᆞ나ᄒᆞᆯ 주시고', compatibility=False, spacing=True)
print(text)
```

text_to_jamo는 str타입으로 결과를 반환합니다. 

```python
ᄉ ᅳ ᄉ ᅳ ᇰ ᄂ ᅵ ᆷ ᄋ ᅵ _ 免 帖 _ ᄒ ᆞ ᄂ ᅡ ᄒ ᆞ ᆯ _ ᄌ ᅮ ᄉ ᅵ ᄀ ᅩ
```



```python
from OldHangeul import text_to_jamo
text=text_to_jamo('스스ᇰ님이 免帖 ᄒᆞ나ᄒᆞᆯ 주시고', compatibility=True, spacing=True)
print(text)
```

compatibility와 spacing 옵션을 통해 다양한 자모 분리 및 공백 처리 방식을 설정할 수 있습니다.

```python
ㅅ ㅡ ㅅ ㅡ ㆁ ㄴ ㅣ ㅁ ㅇ ㅣ _ 免 帖 _ ㅎ ㆍ ㄴ ㅏ ㅎ ㆍ ㄹ _ ㅈ ㅜ ㅅ ㅣ ㄱ ㅗ
```


---
### hNFD

[유니코드 정규화](https://ko.wikipedia.org/wiki/%EC%9C%A0%EB%8B%88%EC%BD%94%EB%93%9C_%EB%93%B1%EA%B0%80%EC%84%B1) 중 NFD의 기능입니다. 옛한글이 포함된 텍스트에도 작동하며, 소리마디(한양 PUA)를 첫가끝 코드로 변환합니다. 


```python
from OldHangeul import hNFD
text=hNFD('스님이 免帖 나 주시고')
print(text)
```
```python
스스ᇰ님이 免帖 ᄒᆞ나ᄒᆞᆯ 주시고
```



---
### hNFC

[유니코드 정규화](https://ko.wikipedia.org/wiki/%EC%9C%A0%EB%8B%88%EC%BD%94%EB%93%9C_%EB%93%B1%EA%B0%80%EC%84%B1) 중 NFC의 기능입니다. 옛한글이 포함된 텍스트에도 작동하며, 첫가끝 코드를 소리마디(한양 PUA)로 변환합니다. 변환이 안 된 텍스트는 `activation failed`로 안내됩니다. 


```python
from OldHangeul import hNFC
text=hNFC('스스ᇰ님이 免帖 ᄒᆞ나ᄒᆞᆯ 주시고')
print(text)
```
```python
스님이 免帖 나 주시고
```


---
### old_hNFD

[유니코드 정규화](https://ko.wikipedia.org/wiki/%EC%9C%A0%EB%8B%88%EC%BD%94%EB%93%9C_%EB%93%B1%EA%B0%80%EC%84%B1) 중 NFD의 기능입니다. 옛한글이 포함된 텍스트에도 작동하며, 전체 텍스트 중 옛한글이 포함된 글자만 첫가끝(조합형)으로 변환합니다. 


```python
from OldHangeul import old_hNFD
text=old_hNFD('스스ᇰ님이 免帖 ᄒᆞ나ᄒᆞᆯ 주시고') # 조합형 → 옛한글 글자만 조합형
print(text)
```

```python
스스ᇰ님이 免帖 ᄒᆞ나ᄒᆞᆯ 주시고
```