semvec/pypi-packages huggingface dataset:
SSUphysicsTools 2.1.3
숭실대학교 물리학과 실험에 사용되는 데이터 처리 및 시각화 도구
Requires Python: >=3.13.0

# SSUphysics experiment supporting tools
숭실대학교 물리학과 물리계측실험 수업에서 유용하게 사용할 수 있는 python 페키지입니다.

> 향후 다른 실험 수업에서도 사용할 수 있도록 확장할 예정입니다.

## How to install
```zsh
pip install SSUphysicsTools
```

## Example
다음의 repository를 참고하여 사용할 수 있습니다.
[SSUphysicsTools-example](https://github.com/kty1004/SSUphysicsTools_examples)

## How to use
### Setting up
실험 데이터는 data 폴더에 저장되어 있어야 합니다. 이 때 data 폴더는 다음과 같은 구조를 가지고 있어야 합니다.
```
data
├── experiment 1
│   ├── 1.csv
│   ├── 2.csv
├── experiment 2
│   ├── 1.csv
│   ├── 2.csv
└── ...
```
만약 data 폴더가 준비되지 않았더면, 자동으로 data 폴더를 생성하고 `DataDirectoryEmptyError`를 발생시켜 data 폴더에 실험 데이터를 넣도록 알려줍니다.

### getting data
`data` 폴더에 저장된 실험 데이터를 가져오는 방법은 다음과 같습니다.
```python
from SSUphysicsTools import getting_data as gd
from padas import pd
data_paths=gd.get_all_csv_paths_in_data(custom_dir_name=True, flatten=False)()
data_set:list[pd.Dataframe]=[gd.read_csv_Tektronix(path) for path in data_paths]
```
위 코드는 다음과 같은 순서로 작동합니다.
0. SSUphysicsTools 패키지 중 getting_data module을 import 합니다.
1. `get_all_csv_paths_in_data`를 이용하여 실험 데이터의 경로를 가져올 수 있습니다.
2. `read_csv_Tektronix`를 이용하여 실험 데이터를 가져올 수 있습니다. 이 때 list comprehension을 이용하여 가독성 높임과 동시에 여러 실험 데이터를 가져올 수 있습니다.
>[!warning] `data_set`은 `numpy.ndarray`로 두지 마십시오. `numpy.ndarray`는 `pandas.DataFrame`의 내용을 전부 풀어서 저장하기 때문에 데이터를 다루기 어려워집니다.
```
|column1|column2|column3|
|-------|-------|-------|
|data1  |data2  |data3  |
|data4  |data5  |data6  |
...

-> numpy.ndarray를 사용하면 다음과 같이 저장됩니다.
array([data1, data2, data3, data4, data5, data6, ...])
```

### processing data
- Regression
    - cosine_regression
- Delate_offset
### analyzing data
- Phase_shift
    - find_min_x_vector
    - find_ym_y0
    - __call__ : get_phase_shift
- get_error_rate
### plot
- DataPlots
    - data_plots
    - Lissajous_plots
- plot_table
beautifulemail 0.1.2
BeautifulEmail is a python package that makes it easy and quick to save pandas dataframes in beautifully formatted excel files. BeautifulEmail is the Openpyxl for Data Scientists with a deadline.
Requires Python: >=3.7

# BeautifulEmail

> **⚠️🏗️️ Note:**
> This is only the first version - actively working on additional features!

BeautifulEmail is a python package that makes it easy and quick to send beautifully formatted emails with beuatiful tables/dataframes. BeautifulEmail is for Data Scientists with a deadline.
  
> **Email Example:**
> 
> <img src="https://github.com/vanalmsick/beautifulemail/raw/main/docs/docs/imgs/email_preview.jpg" alt="Email Preview" style="width:40%;border: 1px solid #D9D9D9;"/>

## Getting it

```console
$ pip install beautifulemail
```
**Update Package:** *(execute <ins>regularly</ins> to get the latest features)*
```console
$ pip install beautifulemail --upgrade
```
  
<br>
  
## How to use:

```python
from beautifulemail import Connection, DataFrameToHTML

df_styled = ...

df_html = DataFrameToHTML(df=df_styled)

# format numbers automatically 
df_html.col_num_fmt_auto()

# add background styling to columns
df_html.col_styles(column=['last_contact', 'revenue'], classes=['bg_light_blue'])
df_html.col_styles(column=['last_contact'], classes=['text_color_amber'])

# use markdown to write email - more infoss here: www.markdownguide.org/cheat-sheet/
email_body_markdown = f"""
Hi,

This is a test email with **bold text**, *italic text*, ~~strikethrough text~~, <mark>highlited text</mark>, [hyperlink text](https://www.google.com), and text that could be footnoted<note>[1]</note>.

# This would be a Heading 1 of an ordered list

1. First step
2. Second step
3. Third step

{df_html}

Embedded image:
<img src="cid:image1" style="width: 100px;">

Best wishes,
Me
    """

    # connect to email server
    with Connection(host='smtp.gmail.com', port=465, ssl=True, user='myemai@gmail.com', password='my_password') as conn:
        # send email
        status = conn.send_email(
            from_='my_emai@gmail.com',
            to_=['your_emai@gmail.com', 'second_emai@gmail.com'],
            subject='Email Subject',
            body_markdown=email_body_markdown,
            attachments=['./README.md'],
            embedded_imgs=['./email_preview.jpg'],
            dry_run=False
            )
        
        # status contains all the email information including the html body
        print('Email sent:', status)

        # save sent email documntation/summary as excel
        conn.save_sent_email_summary('sent_emails.xlsx')
```

<br><br>
hsrogl 1.0.1
an full package with tools and functions
Keywords: python, math, ui, bool, unicode, windows, mac, linux, unix


# Hsrogl


This is an full package with tools and functions

**Hsrogl** is make for **all people** to use

**Hsrogl** can support all systems


## How to install

Type this code in the console (You must have python):
```shell
pip install hsrogl
```

## Usage

> ## Math
> 
> Example:
> 
> ```python
> # -- Coding by utf-8 --
> 
> import hsrogl.math as mt
> 
> # Calc the circumference of the square
> 
> a=mt.square2(3,3)
> 
> print(a)
> 
> # Program end
> ```
> Output: `12`
> 
> This function can calc the circumference of the square
> 
> <br>
> 
> Another example:
> ```python
> # -- Coding by utf-8 --
> 
> import hsrogl.math as mt
> 
> # Output the 'Phi' value
> 
> a=mt.phi()
> 
> print(a)
> 
> # Program end
> ```
> 
> Output: `1.618`
> 
> This function can output the 'Phi' value
> 
> <br>
>
> You can use `dir(hsrogl.math)` to see the functions

> Ui
> 
> Example:
> 
> ```python
> # -- Coding by utf-8 --
> 
> import hsrogl.ui as ui
>
> 
> a=ui.Select(["=(","=|","=)","=D"],"? ",": ","cancel",True)
> 
> print(a)
> 
> # Program end
> ```
> 
> Input: `=(`
> 
> Output: `=(`
> 
> This is a selection ui function
> 
> You can use `dir(hsrogl.ui)` to see the functions


## Version log
`1.0.1` : Add 'bool','unicode'
`1.0` : First release
rumorpheme 0.1
Проект языковой модели для проведения морфемного анализа и сегментации слов русского языка.
Requires Python: >=3.11

# ruMorpheme - Russian Morphemes Segmentation

Проект языковой модели для проведения морфемного анализа и сегментации слов русского языка.

Обученная модель способна сегментировать слова, выделяя в них:

- приставки (PREF)
- корни (ROOT)
- соединительные гласные (LINK)
- дефисы (HYPH)
- суффиксы (SUFF)
- постфиксы (POSTFIX)
- окончания (END)

Веса модели [evilfreelancer/ruMorpheme-v0.1](https://huggingface.co/evilfreelancer/ruMorpheme-v0.1) на HuggingFace.

Вдохновлён кодовой базой проекта [AlexeySorokin/NeuralMorphemeSegmentation](https://github.com/AlexeySorokin/NeuralMorphemeSegmentation), который
реализован в рамках
публикации "[Deep Convolutional Networks for Supervised Morpheme Segmentation of Russian Language](https://github.com/AlexeySorokin/NeuralMorphemeSegmentation/blob/master/Articles/MorphemeSegmentation_final.pdf)"
за авторством Алексея Сорокина и Анастасии Кравцовой.

## Примеры

Пример работы модели:

```shell
В	в:ROOT	98.59
воскресенье	воскрес:ROOT/ень:SUFF/е:END	99.30 96.58 100.00
мы	мы:ROOT	99.77
решили	решил:ROOT/и:END	85.80 100.00
перезапланировать	пере:PREF/за:PREF/план:ROOT/ир:SUFF/ова:SUFF/ть:SUFF	100.00 77.91 98.43 100.00 99.98 98.37
```

Или в формате JSONL:

```json lines
{"word": "В", "morphemes": [{"text": "в", "type": "ROOT", "prob": "98.59"}]}
{"word": "воскресенье", "morphemes": [{"text": "воскрес", "type": "ROOT", "prob": "99.3"}, {"text": "ень", "type": "SUFF", "prob": "96.58"}, {"text": "е", "type": "END", "prob": "100.0"}]}
{"word": "мы", "morphemes": [{"text": "мы", "type": "ROOT", "prob": "99.77"}]}
{"word": "решили", "morphemes": [{"text": "решил", "type": "ROOT", "prob": "85.8"}, {"text": "и", "type": "END", "prob": "100.0"}]}
{"word": "перезапланировать", "morphemes": [{"text": "пере", "type": "PREF", "prob": "100.0"}, {"text": "за", "type": "PREF", "prob": "77.91"}, {"text": "план", "type": "ROOT", "prob": "98.43"}, {"text": "ир", "type": "SUFF", "prob": "100.0"}, {"text": "ова", "type": "SUFF", "prob": "99.98"}, {"text": "ть", "type": "SUFF", "prob": "98.37"}]}
```

## Установка и запуск

Склонируем проект и подготовим окружение:

```shell
git clone https://github.com/EvilFreelancer/ruMorpheme.git
cd ruMorpheme
python3 -m venv venv
pip install -r requirements.txt
```

Активируем окружение:

```shell
source venv/bin/activate
```

## Как пользоваться

### Тренировка модели

```shell
python3 train.py config/ruMorpheme.json
```

По завершению тренировки будут созданы:

- `model/pytorch-model.bin` - веса модели
- `model/config.json` - конфигурация модели
- `model/vocab.json` - словарь необходимый для работы предикшена

### Валидация модели

```shell
python3 eval.py config/ruMorpheme.json
```

Отчёт валидации будет в `models/evaluation_report.txt`.

### Использование модели

Запуск тестового предикшена из файла [input_text.txt](./input_text.txt):

```shell
python predict.py input_text.txt --model-path=evilfreelancer/ruMorpheme-v0.1
```

Если не указывать `--model-path` то модель и конфигурация будут прочитаны из директории `./model`.
Polarimetry-PyPOLAR 2.6.4
PyPOLAR is a Python-based app for analyzing polarization-resolved microscopy data to measure molecular orientation and order in biological samples
Requires Python: >=3.8

# [<img src="https://github.com/cchandre/Polarimetry/blob/master/pypolar/main_icon.png" alt=" " width="50"/>](https://www.fresnel.fr/polarimetry) PyPOLAR 

![Version](https://img.shields.io/badge/version-v2.6.4-blue)
![Platform](https://img.shields.io/badge/platform-macOS|Windows-orange)
[![License](https://img.shields.io/badge/license-BSD-lightgray)](https://github.com/cchandre/Polarimetry/blob/master/LICENSE)

PyPOLAR is a Python-based app for analyzing polarization-resolved microscopy data to measure molecular orientation and order in biological samples.  
To install PyPOLAR on your computer, check out the [latest release](https://github.com/cchandre/Polarimetry/releases), and download `PyPOLAR_installer.exe` for Windows or `PyPOLAR.dmg` for macOS.

- Source code: [PyPOLAR.py](https://github.com/cchandre/Polarimetry/blob/master/pypolar/PyPOLAR.py)

- Website: [www.fresnel.fr/polarimetry](https://www.fresnel.fr/polarimetry)

- Wiki: [PyPOLARwiki](https://github.com/cchandre/Polarimetry/wiki)

<div align="center">
<img src="https://github.com/cchandre/Polarimetry/blob/master/pypolar/PyPOLAR_layout.png" alt=" " width="400"/>  
</div>

___
For more information: <cristel.chandre@cnrs.fr>