semvec/pypi-packages huggingface dataset:
ezdxf-shapely 1.0
Convert DXF entities created by ezdxf to shapely geomerty objects.
Requires Python: >=3.10

# EZDXF to Shapely Converter

Convert geometric entities loaded vom DXF files using [`ezdxf`](https://ezdxf.readthedocs.io/) to [`shapely`](https://pypi.org/project/Shapely/) geometry objects.

First use `ezdxf` to load the DXF entities from file and filter them using `query` or similar.

```python
import ezdxf

dxf_doc = ezdxf.readfile("your_file.dxf")
entities = dxf_doc.modelspace().query("*[layer==0]")
```

Then use `ezdxf-shapely` to convert the entities and refine the results.

```python
import ezdxf_shapely

geoms = ezdxf_shapely.convert_all(entities)
polygons = ezdxf_shapely.polygonize(geoms) # optionally merge lines to polygons
```

## License

Licensed under the terms of the [MIT License](LICENSE)

## Acknowledgements

This is a fork of [`cad-to-shapely`](https://github.com/aegis1980/cad-to-shapely) with some simplifications and the provision of more control over the import process to the user.
ssa3021xnoisegui-tspspi 0.0.3
GUI frontend for realtime noise measurements using SSG3021X
Requires Python: >=3.6

# Graphical user interface for noise estimation using SSA3021X

Simple graphical frontend for an SSA3021X spectrum analyzer to
perform simple noise measurements. It assumes one measures a single
peak against background.

![Connect window](https://raw.githubusercontent.com/tspspi/ssa3021xnoisegui/refs/heads/master/doc/wndconnect.png)

![Measurement window (noise measurement)](https://raw.githubusercontent.com/tspspi/ssa3021xnoisegui/refs/heads/master/doc/wndmeasurement_noise.png)

![Measurement window (signal measurement)](https://raw.githubusercontent.com/tspspi/ssa3021xnoisegui/refs/heads/master/doc/wndmeasurement_signal.png)

The utility requires the spectrum analyzer to:

* Be set to linear scale (this will be fixed in later versions)
* The center frequency should be set to the desired signal frequency.
  The program picks the largest peak as signal though.
* The quarter of samples on the left and to the right of the span is
  used as basis to calculate the noise floor of the signal.

## Installation

This package can be installed simply via ```pip```

```
pip install ssa3021xnoisegui-tspspi
```

## Usage

```
ssa3021xnoisegui
```

## Configuration file

One can pre-set some configuration variables in a configuration
file at ```~/.config/ssa3021xrealtimenoisegui.cfg```. All variables
are optional:

```
{
	"ssa" : {
		"ip" : "10.4.1.18"
	},
    "sampling" : {
        "window" : 10,
        "interval" : 0.5,
        "storedsamples" : 1000
    }
}

```
pylabdevs-tspspi 0.0.18
Lab devices base classes
Requires Python: >=3.6

# Lab device base classes

This repository supplies some abstract base classes for various device families
that are used as basis for specific implementations. Those base classes provide
the frontend used by experimental scripts - the implementations are then
done in a device specific way by other packages.

This package emerged from re-implementing different interfaces for different
devices from time to time to provide some kind of common base structure for
different devices.

The base classes just implement basic parameter validation and provide a calling
convention. Changes should never break existing libraries.

Currently implemented base classes:

* Power supplies
   * [KA3005PSerial](https://github.com/tspspi/pyka3005p) for Korad KA3005P power supplies via serial interface
   * [pydp832](https://github.com/tspspi/pydp832) for Rigol DP832 power supplies via Ethernet
* Oscilloscopes
   * [Rigol MSO5000](https://github.com/tspspi/pymso5000) for Rigol MSO5000 oscilloscopes
   * [Rigol DHOxxx](https://github.com/MasterJubei/pydho800) for Rigol DHOxxx oscilloscopes (by [MasterJubei](https://github.com/MasterJubei))
* Function generators / Arbitrary waveform generators
   * [FY6900](https://github.com/tspspi/pyfy6900) for FE FY6900
   * Rigol DG832 (work in progress)
   * [Siglent SSG3021X](https://github.com/tspspi/pyssg3021x/) sinewave 9 kHz - 2.1 GHz generator (work in progress)
   * [Rigol DG1000Z](https://github.com/PMSchueler/pydg100z) by [PMSchueler](https://github.com/PMSchueler)
* Pressure gauges
   * [PYBPG400](https://github.com/tspspi/pybpg400) for the RS232C interface of the INFICON BPG400 pressure gauge
* Vector network analyzers
   * [NanoVNA v2](https://github.com/tspspi/pynanovnav2) (work in progress)
* Scanning Electron Microscopes
   * [XL30 ESEM](https://github.com/tspspi/pyxl30) (work in progress)
* General purpose I/O devices
   * [Rasbperry PI GPIO under FreeBSD](https://github.com/tspspi/fbsdgpio/)
* SPI bus
   * [FreeBSD spigen interface](https://github.com/tspspi/fbsdspiwrapper)
ascii2edf 1.2
A Python package for converting EyeLink .asc files to structured .csv files.
Requires Python: >=3.6


# ascii2edf

**ascii2edf** is a Python package developed for converting EyeLink `.asc` files into structured `.csv` files, facilitating the analysis of eye-tracking data. It extracts gaze data and event details such as blinks, saccades, and fixations, organizing them into an easy-to-use format.

## Features
- Converts EyeLink `.asc` files to `.csv` format efficiently.
- Extracts and processes blink (`EBLINK`), saccade (`ESACC`), and fixation (`EFIX`) event data.
- Aligns timestamps relative to trial start for consistent temporal analysis.
- Handles missing data by combining event and sample data fields where applicable.

## Installation

To install the package, run:

```bash
pip install ascii2edf
```

## Usage

Import the package and use the `process_asc_file()` function:

```python
import ascii2edf as convert

convert.process_asc_file('input_file.asc', 'output_file.csv', header=True)
```

### Parameters
- **`input_file_path`** (str): Path to the input `.asc` file.
- **`output_csv_path`** (str): Path to save the output `.csv` file.
- **`header`** (bool, optional): Whether to include a header row in the output CSV. Default is `True`.

## Requirements
- Python >= 3.6
- `pandas`

## License

This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for more details.

## Contributing

Contributions are welcome! If you find a bug or have an idea for an improvement, please submit a pull request or open an issue.

## Creator and Maintainer

**Mohammad Ahsan Khodami**  
- Personal Site: [khodami.site](https://khodami.site)  
- Google Scholar: [Profile](https://scholar.google.com/citations?user=WCqPnS4AAAAJ&hl=en)  
- Email: [ahsan.khodami@gmail.com](mailto:ahsan.khodami@gmail.com)

## Acknowledgments
- The EyeLink system and data format are provided by SR Research.
```
chat-shell 0.1.0
An intelligent shell with AI chat capabilities
Requires Python: >=3.7

## smart_shell

基于python库`prompt_toolkit` 实现的一款命令行终端， 再保持现有shell功能的背景下，额外支持以下功能

1. 终端支持chat命令， 可以在命令行和大模型聊天
2. 终端支持chat读取命令行上下文， 支持读取历史shell命令的输入、输出结果
3. 在pipeline管道中, 支持chat命令读取管道命令的输出，输出

### 安装

```shell

pip intall -r requirements.txt

```


### 功能点1

在命令行中输入`chat`命令，进入聊天模式，可以和大模型聊天

```shell
> chat 你是什么模型
```


### 功能点2

在命令行中输入`chat`命令，进入聊天模式，可以和大模型聊天

```shell
chat 刚才git执行，为啥报错
```

### 功能点3

在pipline管道中， 支持chat命令读取管道命令的输出，输出

```shell

> ls -ls |  chat 转换为markdown格式

```
ca5e9fb055b0e9d98f2c2a6bee72c3f9 0.1.0
This package is an example of a Dependency Hallucination. There is no code in this package, it is just a placeholder to prevent exploitation.
Requires Python: >=3.0

This package is an example of a Dependency Hallucination. If you are trying to install it, you have most likely been given this package incorrectly from an AI coding assistant.

There is no code in this package, it is just a placeholder to prevent exploitation.
xingen 0.1.0
An agent framework using LLMs
Requires Python: >=3.7


XinGen
======

![XinGen](src/xingen/coreplugins/chat/static/xingen_logo.png)

[![PyPI](https://img.shields.io/pypi/v/xingen)](https://pypi.org/project/xingen/)

**Note: some of the following is a work in progress and not yet functional. A few of them aren't really started.**

XinGen is a powerful, plugin-based Python framework for creating and managing AI agents. It offers a flexible architecture with indices and a public registry (coming soon) for easily sharing and finding plugins, agents, personas, services, knowledgebases, and apps.

Installation
------------

You can install XinGen using pip:

```bash
pip install xingen
```

For development, you can install the package in editable mode:

```bash
git clone https://github.com/xingen/xingen.git
cd xingen
pip install -e .
```


Key Features:

- Public registry for sharing and finding plugins, agents, personas, models, and knowledgebases
  - https://registry.agenthost.org (work in progress)
  - Customizable and swappable for user-specific registries
- Extensible plugin architecture for adding services, commands, and building arbitrary web apps
- Customizable AI agents with persona definitions
- Intelligent service management based on agent requirements
- Flexible service providers for various AI capabilities
- Plugins can add/use hooks and pipelines such as for modifying prompts, running startup commands, or anything you want
- Easily customizable UI built on Jinja2 and Lit Web Components
- Support for both local and remote AI services
- RAG: easily share, find and use pre-generated embeddings and documents for topic knowledgebases

Core Concepts Overview
----------------------

XinGen revolves around several key concepts:

1. **Open Public Registry**: A flexible system for indexing and sharing plugins, agents, personas, and potentially models. It can be customized or replaced with user-specific registries.

2. **Plugins**: Extend the functionality of XinGen, providing new features, services, or integrations.

3. **Agents and Personas**: AI agents with defined capabilities and customizable personalities.

4. **Services and Providers**: Backend services that power agent capabilities, with support for swapping between local and remote implementations.

5. **Intelligent Service Management**: The system automatically determines and installs required services based on agent definitions.

6. **UI Customization**: Easily modifiable user interface through theme overrides and injections.

7. **RAG and Knowledgebases**: The community can easily share and search for topic embeddings and document sets rather than everyone rebuilding them per topic.
epilepsy2bids 0.0.7
Python library for converting EEG datasets of people with epilepsy to BIDS compatible datasets.
Requires Python: >=3.10

# Epilepsy2Bids

Library for converting EEG datasets of people with epilepsy to [EEG-BIDS](https://doi.org/10.1038/s41597-019-0104-8) compatible datasets. These datasets comply with the [ILAE and IFCN minimum recording standards](https://doi.org/10.1016/j.clinph.2023.01.002). They provide annotations that are [HED-SCORE](http://arxiv.org/pdf/2310.15173) compatible. The datasets are formatted to be operated by the [SzCORE seizure validation framework](https://doi.org/10.1111/epi.18113).

The library provides tools to:

- Convert EEG datasets to BIDS.
- Load and manipulate EDF files.
- Load and manipulate seizure annotation files.

Currently, the following datasets are supported:

- [PhysioNet CHB-MIT Scalp EEG Database v1.0.0](https://doi.org/10.13026/C2K01R)
- [KULeuven SeizeIT1](https://doi.org/10.48804/P5Q0OJ)
- [Siena Scalp EEG Database v1.0.0](https://doi.org/10.13026/s309-a395)
- [TUH EEG Seizure Corpus](https://isip.piconepress.com/projects/nedc/html/tuh_eeg/)

## Installation

The epilepsy2bids package is released for macOS, Windows, and Linux, on [PyPI](https://pypi.org/project/epilepsy2bids/). It can be installed using pip:

```bash
python -m pip install -U pip
python -m pip install -U epilepsy2bids
```

It can also be installed from source with a modern build of `pip` :

```bash
python -m pip install -U pip
git clone https://github.com/esl-epfl/epilepsy2bids.git
cd epilepsy2bids
python -m pip install -e .
```

## Code

The primary function of `epilepsy2bids` is to convert EEG dataset to BIDS by calling the `convert()` for a given dataset.

```python
from epilepsy2bids.bids.chbmit.convert2bids import convert

convert(root: Path, outDir: Path)
```

In addition, the library provides the `Eeg` and `Annotation` classes that be used to manipulate EEG recordings.

### Adding support for a new dataset

All dataset converters should implement the `convert()` method. To assist many helper functions and generic code is already available in `src.epilepsy2bids.bids.convert2bids.py`. Examples of implementation are available in the supported datasets.
learnml 0.11.1
LearnML is a Python module for Machine Learning and Deep Learning.
Requires Python: >=3.11

# LearnML
LearnML is a Python module for Machine Learning and Deep Learning.

# Installation

## Requirements
* Python 3.11+

## Stable release

To install learnml, run this command in your terminal:

```
pip install learnml
```

This is the preferred method to install learnml, as it will always install the most recent stable release.

If you don't have [pip](https://pip.pypa.io) installed, this [Python installation guide](http://docs.python-guide.org/en/latest/starting/installation/) can guide you through the process.

## From sources

The sources for learnml can be downloaded from the [Github repo](https://github.com/Vipul97/learnml).

You can either clone the public repository:

```
git clone git://github.com/Vipul97/learnml
```

Or download the [tarball](https://github.com/Vipul97/learnml/tarball/master):

```
curl -OJL https://github.com/Vipul97/learnml/tarball/master
```

Once you have a copy of the source, you can install it with:

```
python setup.py install
```

# Contributing

Please read [CONTRIBUTING.md](CONTRIBUTING.md) for details on code of conduct, and the process for submitting pull requests.

# License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.