semvec/pypi-packages huggingface dataset:
cropgbm 1.1.7
Crop Genomic Breeding machine (CropGBM) is a multifunctional program that integrates data preprocessing, population structure analysis, SNP selection, phenotype prediction, and data visualization.
License: MIT
Requires Python: >=3.8

# Welcome to CropGBM！

## Introduction

Crop Genomic Breeding machine (CropGBM) is a multifunctional Python3 program that integrates data preprocessing, population structure analysis, SNP selection, phenotype prediction, and data visualization. Has the following advantages:

* Use LightGBM algorithm to quickly and accurately predict phenotype values and support GPU-accelerated training.
* Supports selection and visualization of SNPs that are strongly related to phenotype.
* Support PCA and t-SNE two dimensionality reduction algorithms to extract SNP information.
* Support Kmeans and OPTICS two clustering algorithms to analyze the sample population structure.
* Plot histograms of heterozygosity rate, deletion rate, and frequency of alleles for genotype data.


## Documentation

*English version documentation*: [https://ibreeding.github.io](https://ibreeding.github.io)

*Chinese version documentation*: [https://ibreeding-ch.github.io](https://ibreeding-ch.github.io)


## Download

Download source code : [https://github.com/YuetongXU/CropGBM/releases/tag/cropgbm-v1.1.2](https://github.com/YuetongXU/CropGBM/releases/tag/cropgbm-v1.1.2)


## Installation

### Install via Conda 

    $ conda install -c xu_cau_cab cropgbm 

### Install via pip

    $ pip install --user cropgbm

### Install via source code

    $ tar -zxf CropGBM.tar.gz

    # Install Python package dependencies of CropGBM: setuptools, wheel, numpy, scipy, pandas, scikit-learn, lightgbm, matplotlib, seaborn
    $ pip install --user setuptools wheel numpy scipy pandas scikit-learn lightgbm matplotlib seaborn
    
    # Install external dependencies of CropGBM: PLINK 1.90 
    $ wget s3.amazonaws.com/plink1-assets/plink_linux_x86_64_20191028.zip
    $ mkdir plink_1.90
    $ unzip plink_linux_x86_64_20191028.zip -d ./plink_1.90
    
    # Add CropGBM, PLINK to the system environment variables for quick use:
    $ vi ~/.bashrc
    export PATH="/userpath/CropGBM:$PATH"
    export PATH="/userpath/plink1.90:$PATH"
    $ source ~/.bashrc


## Test

Run the `run_test.py` to check whether cropgbm can run successfully locally.



## About

**Citation**: Jun Yan, Yuetong Xu, Qian Cheng, Shuqin Jiang, Qian Wang, Yingjie Xiao, Chuang Ma, Jianbing Yan and Xiangfeng Wang. _LightGBM: accelerated genomically-designed crop breeding via ensemble learning._ 

**Supplementary Information**: Support data and materials for the manuscript is available at _https://github.com/YuetongXU/Cropgbm-Paper_

**Contact us**: cropgbm@163.com

**Note**: Academic users can download directly, industrial users first contact us.
beautypix 1.0.1
BeautyPix is a versatile Python tool designed for capturing and managing screenshots of specified domains. It efficiently captures screenshots. Ideal for web developers, testers, and digital archivists, BeautyPix simplifies the process of monitoring and documenting web content
License: MIT
Requires Python: >=3.6

# Bpix

Bpix aka BeautyPix is a Python package that provides functionality for taking screenshots of websites using Selenium WebDriver. This tool reads a list of domains from a file, captures screenshots of each domain,Dns resolution and saves them to a designated directory.

## Features

- Take screenshots of websites specified in a text file.
- Dns Resolution
- Save screenshots in a specified directory.
- Handle different URL formats and provide error messages for invalid URLs.

## Installation

` pip install beautypix `



## USAGE

```
from beautypix import Bpix
from beautypix import DNS

#Initialize Bpix with the path to the file containing domains and a timeout value 
bpix = Bpix(file_path='path/to/domains.txt', time_out=5) 

#Take screenshots of the domains listed in the file 
bpix.s_shot()


#Initialize DNS with the path to the file containing domains and a output file  
dns = DNS(file_path="path-of-file" , output_folder="output.txt")

#Dns resolution 
dns.process_domains()

``` 



## File format

The file_path specified should point to a text file containing one URL per line:
- https://www.google.com
- https://www.facebook.com
- https://www.twitter.com

## License

This project is licensed under the MIT License. See the LICENSE file for details.

## Contributing

Contributions to Bpix are welcome! If you have suggestions, improvements, or bug fixes, please fork the repository and submit a pull request. Ensure your contributions adhere to the coding standards and include appropriate documentation.

## Contact
For any inquiries or feedback, please contact:

- Author: Ankush Kumar Rajput (Mr.Horbio)
- GitHub: https://github.com/MrHorbio/bpix/blob/main/bpix_package/README.md
- Youtube: https://www.youtube.com/@Mr-Horbio



Thank you for using Bpix!
cq-electronics 0.2.0
Pure CadQuery models of various electronic boards and components.
License: MIT
Requires Python: <3.14,>=3.9
Keywords: cadquery, cq, cad, occt, electronics

==============
cq-electronics
==============

|pypi-version| |build-status| |lint-status| |test-status| |docs-status|

Pure `CadQuery`_ models of various electronic boards and components.

These models are intended to be representations rather than detailed models.
That is, the major dimensions are accurate and there are enough features to make the component recognisable.


Related projects
----------------

* `KiCad Packages3D Generator`_


.. _`CadQuery`: https://cadquery.readthedocs.io/
.. _`KiCad Packages3D Generator`: https://gitlab.com/kicad/libraries/kicad-packages3D-generator


.. |pypi-version| image:: https://img.shields.io/pypi/v/cq-electronics
    :target: https://pypi.org/project/cq-electronics/
    :alt: PyPi version
.. |build-status| image:: https://github.com/sethfischer/cq-electronics/actions/workflows/build.yml/badge.svg
    :target: https://github.com/sethfischer/cq-electronics/actions/workflows/build.yml
    :alt: Build status
.. |lint-status| image:: https://github.com/sethfischer/cq-electronics/actions/workflows/lint.yml/badge.svg
    :target: https://github.com/sethfischer/cq-electronics/actions/workflows/lint.yml
    :alt: Lint status
.. |test-status| image:: https://github.com/sethfischer/cq-electronics/actions/workflows/test.yml/badge.svg
    :target: https://github.com/sethfischer/cq-electronics/actions/workflows/test.yml
    :alt: Test status
.. |docs-status| image:: https://readthedocs.org/projects/cq-electronics/badge/?version=latest
    :target: https://cq-electronics.readthedocs.io/en/latest/?badge=latest
    :alt: Documentation status
bjpmodel 0.2.2
BJP (Bayesian Joint Probability)
License: MIT
Requires Python: <3.14,>=3.11

# Bayesian Joint Probability (BJP)
[![Publish BJP](https://github.com/csiro-internal/py-bjp/actions/workflows/release.yml/badge.svg)](https://github.com/csiro-internal/py-bjp/actions/workflows/release.yml)
[![Build and Test BJP](https://github.com/csiro-internal/py-bjp/actions/workflows/ci_dev.yml/badge.svg?branch=master)](https://github.com/csiro-internal/py-bjp/actions/workflows/ci_dev.yml)
![license](https://img.shields.io/badge/license-MIT-blue.svg)
![status](https://img.shields.io/badge/status-alpha-blue.svg) 

## Table of Contents
- [Overview](#overview)
  - [Features](#features)
  - [Interactive Demonstration](#interactive-demonstration-of-bjp)
  - [Installation](#installation)
  - [Contributing](#contributing)
  - [License](#license)
  - [Contact](#contact)

---
## Overview

**BJP** is a high-performance Python package (with Cython/C++ backend) for fitting multivariate normal distributions using Gibbs sampling, with support for left and right censoring. 
Typically, BJP is used for modelling with transformed variables using the norm-transform package. Therefore, it is designed for statistical modeling and forecasting in scenarios where data may be treated as partially observed (e.g. rainfall).

---

## Features

- Fast Gibbs sampling for multivariate normal distributions
- Handles left and right censoring (if required)
- Generates ensemble predictions with posterior predictive sampling
- Cython/C++ backend for performance

---

## Interactive Demonstration of BJP

You can explore an interactive demonstration of the normtransform tool [here](https://mybinder.org/v2/gh/csiro-hydroinformatics/hydro-forecasting-demonstrations/HEAD?urlpath=%2Fdoc%2Ftree%2F%2Fnotebooks%2Fbjpmodel-demonstration.ipynb).  This link opens a Jupyter Notebook hosted on the MyBinder platform. Once it loads, click **Run-->Run All Cells** to execute the code and see the demonstration in action.

---
## Installation

A [Python virtual environment](https://packaging.python.org/en/latest/specifications/virtual-environments/) is a self-contained directory that includes a specific Python version and any additional packages you install. It helps you isolate dependencies for different projects, ensuring that changes in one environment don’t affect others. This makes it easier to manage project-specific requirements and maintain a clean development setup.

Use the package manager pip to install bjpmodel.

```bash
python -m venv venv # Create a virtual environment
source venv/bin/activate  # Activate the virtual enviroment, on Windows use `venv\Scripts\activate`
pip install bjpmodel #install bjpmodel
```
Once installed users can start with mimicking the [demonstration](#interactive-demonstration-of-bjp) found above within their own environment.

---

## Contributing

Requests are welcome. Please contact the authors below.

---
## License

[MIT](https://choosealicense.com/licenses/mit/) 

---
## Contact

- Andrew Schepen - andrew.schepen@csiro.au
- David Robertson - david.robertson@csiro.au