semvec/pypi-packages huggingface dataset:
better-stamp-processing 0.1.dev46
Stamp processing package
Requires Python: >=3.8

# Stamp Processing
 > A package develop by Sun* AI Research Team as part of the PDF Converter project. 

 This is a Deep Learning based package for detecting and removing stamp from document images.
 This package uses  [Yolov5](https://github.com/ultralytics/yolov5) for the stamp detection model and [fastai](https://github.com/fastai/fastai) Unet for stamp removal model

 ## Install
 Due to the requirements of the used libraries, stamp-processing requires **Python 3.8 or higher**.

 `stamp-processing` is published on [Pypi](https://pypi.org/project/stamp-processing/). To install the package, use `pip`:

 ```bash
 pip install stamp_processing
 ```

 **Note**: This package uses PyTorch. If you need GPU support, you may want to install PyTorch with CUDA support first:
 ```bash
 pip install torch torchvision --index-url https://download.pytorch.org/whl/cu118
 pip install stamp_processing
 ```

 ## How to use
 Check out [example](https://github.com/sun-asterisk-research/stamp_processing/blob/master/example/example.md) for basic usage or run  `getting_started.ipynb` in the `example` folder for example usage.

 ## Documentation
 Documentation will be available soon.

 ## Changelog
 See [CHANGELOG.md](CHANGELOG.md) for version history and breaking changes.

 ## Contact 
 Create an issue if you run into any bug or want to suggest a feature on [Github](https://github.com/sun-asterisk-research/stamp_processing)
custom-function 1.0.3
自用常用函数
Requires Python: >=3.10

# custom_function

这是一个自定义的 Python 库。


## 自用教程

### 上传到 PyPI
1. 注册 PyPI 账号：访问 PyPI 注册页面，注册一个新账号。

2. 安装 twine：twine 是用于上传包到 PyPI 的工具，可使用以下命令安装：
`pip install twine`

3. 打包项目：在项目根目录下运行以下命令打包项目：
`python setup.py sdist bdist_wheel`
此命令会生成一个源代码分发包（.tar.gz）和一个二进制分发包（.whl），存于 dist 目录。

4. 上传包：使用 twine 上传生成的包到 PyPI：
`twine upload dist/*`

执行该命令后，会提示输入 PyPI 的用户名和密码，输入正确信息即可完成上传。
mfd-serial 1.7.0
Module for handling communication via serial port like Universal asynchronous receiver-transmitter (UART)
Requires Python: <3.14,>=3.10

> [!IMPORTANT]  
> This project is under development. All source code and features on the main branch are for the purpose of testing or evaluation and not production ready.

# MFD SERIAL
Tool for communication via serial port (e.g UART). 

## Usage
Open serial connection put serial port and baud rate:  
port can be e.g. COM1 if windows or /dev/ttyUSB0 if linux 
```python 
serial = SerialConnection(serial_port='/dev/ttyUSB0', baud=115200)  
```

Read data from serial:  
```python
serial.read()
```

Send text msg via serial port  
```python
serial.send('Text to send')
```

Read until some expression appear:   
```python
serial.read_until('Module Error') 
 ```
 
Close serial connection:  
```python
serial.close()  
```

Read in background (e.g. if we want to run tests and log everything what happen on serial port during the test)  
```python
serial.read_in_background()  
(... test steps ...)  
serial_log = serial.get_result()  #get_result will automatically close the connection
```

SerialConnection can be used as context manager:  
```python
with SerialConnection(serial_port='/dev/ttyUSB0', baud=115200) as serial:  
	log = serial.read()  
```

## Issue reporting

If you encounter any bugs or have suggestions for improvements, you're welcome to contribute directly or open an issue [here](https://github.com/intel/mfd-serial/issues).
framezip 0.1.0
Utilities to pack and unpack pandas DataFrames
Requires Python: >=3.7

# framezip

A lightweight Python module for **packing** and **unpacking** pandas DataFrames by grouping rows into list-aggregated columns and exploding them back into individual rows.

---

## Features

- **Pack** rows sharing common keys into a single row with list-aggregated columns.
- **Unpack** those list columns back into multiple rows.
- Flexible column referencing by name or integer index.

---

## Installation

Make sure you have pandas installed:

```
pip install framezip
```

## Use Cases

* Aggregate user sessions or grouped events into compact representations.
* Compress DataFrame rows for efficient storage or transfer.
* Switch easily between grouped (packed) and row-wise (unpacked) views.
* Prepare data for modeling or reporting workflows requiring different granularities.

## Notes

* Accepts column indices or names for flexibility.
* Uses pandas groupby().agg(list) and explode() for performance.
* Requires pandas version 0.25 or later (for explode() support).
coposet 0.0.1
Collaborative partial orders
Requires Python: <4.0,>=3.13
Keywords: poset, partial-order, mathematics, codesign, consensus, voting

# Coposet

Coposet is a tool for manipulating posets and allowing them to be  and exporting them to various formats.

## Posets (Partially Ordered Sets)

A poset is a set with a partial order relation (≤) that is:
- **Reflexive**: a ≤ a
- **Antisymmetric**: if a ≤ b and b ≤ a, then a = b
- **Transitive**: if a ≤ b and b ≤ c, then a ≤ c

The product of two posets P and Q creates a new poset P × Q where:
  - Elements: All pairs (p, q) where p ∈ P and q ∈ Q
  - Ordering: (p1, q1) ≤ (p2, q2) if and only if p1 ≤ p2 in P AND q1 ≤ q2 in Q

## Schema

### Core

posetspace:
- id: uuid
- name: string
- description: string
- posets: list of posets
- default_poset: poset
- elements: list of elements
- projections?: list of posetspaces
- injections?: list of posetspaces
- consensus(consensus_type) -> poset

poset:
- id: uuid
- name: string
- description: string
- posetspace: posetspace
- relations: list of relations
- enrichment?: posetspace
- product(poset, poset) -> (poset, posetspace)
- coproduct(poset, poset) -> (poset, posetspace)
- export(format: format) -> string

element:
- id: uuid
- name: string
- description: string
- posetspace: posetspace

relation<T: posetspace, E: posetspace>:
- id: uuid
- less: element<where less.posetspace == T>
- greater: element<where greater.posetspace == T>
- enrichment?: element<where enrichment.posetspace == E>

### Helpers

consensus_type:
- derive<T: posetspace>(array of posets<T>, dropEnrichments: boolean) -> poset<T>

format enum:
- mcdp, latex

### Voting
voter:
- poset: poset
- next_antichain(voted_antichain: voted_antichain<T>) -> Either<antichain<T>, poset>

antichain<T: posetspace>:
- id: uuid
- element: element<T>
- comparisons: set of elements<T>

voted_antichain<T: posetspace>:
- id: uuid
- antichain: antichain<T>
- new_less: set of elements<T>
- new_greater: set of elements<T>

### Enricher

enricher:
- poset: poset
- enrichment: posetspace
- enrich(relations: set<(relation<poset>, element<enrichment>)>) -> poset


## Details

We will use the [posets](https://pypi.org/project/posets/) library for the basic poset operations under the hood. This will be connected by a thin interface that converts the posets from the posets library to the coposet types we need to add on our layer of voting and consensus.

We should look into using [networkx](https://networkx.org) for the complex poset operations involved in consensus and voting.
pullhero 0.0.10
pullhero CLI

# (pullhero)

This the pullhero python client.

## How to push a code review

```bash
pullhero --vcs-provider github \
         --vcs-token ghp_B...sw \
         --vcs-repository pullhero/python-client \
         --vcs-change-id 1 \
         --vcs-change-type issue_with_pr \
         --vcs-base-branch unknown \
         --vcs-head-branch unknown \
         --agent review \
         --agent-action comment \
         --llm-api-key sk-ec....e4 \
         --llm-api-host api.deepseek.com \
         --llm-api-model deepseek-chat
```

## How to push a README.md documentation improvement

```bash
pullhero --vcs-provider github \
         --vcs-token ghp_B...sw \
         --vcs-repository pullhero/python-client \
         --vcs-change-id 11111 \
         --vcs-change-type whatever \
         --vcs-base-branch main \
         --vcs-head-branch unknown \
         --agent document \
         --agent-action comment \
         --llm-api-key 3....et \
         --llm-api-host api.mistral.ai \
         --llm-api-model mistral-large-latest
```

## How to push the consult agent

```bash
pullhero --vcs-provider github \
         --vcs-token ghp_B...sw \
         --vcs-repository pullhero/python-client \
         --vcs-change-id 1111 \
         --vcs-change-type whatever \
         --vcs-base-branch whatever \
         --vcs-head-branch whatever \
         --agent consult \
         --agent-action comment \
         --llm-api-key 3....et \
         --llm-api-host api.mistral.ai \
         --llm-api-model mistral-large-latest
```

## How to run the code improvement agent

```bash
pullhero --vcs-provider github \
         --vcs-token ghp_B...sw \
         --vcs-repository pullhero/python-client \
         --vcs-change-id 4 \
         --vcs-change-type whatever \
         --vcs-base-branch "main" \
         --vcs-head-branch "feature_test" \
         --agent code \
         --agent-action comment \
         --llm-api-key 3....et \
         --llm-api-host api.mistral.ai \
         --llm-api-model mistral-large-latest
```