mev
Mining analysis toolkit
A toolset for modeling and analyzing the extractable value of mining operations using Python.
Miner extractable value modeling and tools.
125 stars
7 watching
42 forks
Language: Python
last commit: almost 3 years ago
Linked from 1 awesome list
Related projects:
Repository | Description | Stars |
---|---|---|
| A software project providing a set of tools and libraries for building and managing blockchain-related applications | 401 |
| A Python toolkit for classifying and analyzing environmental impacts of surface mining activities on American Lands | 28 |
| Provides a modular framework for assembling image and object analysis workflows in the context of ImageJ/Fiji | 11 |
| Tools and utilities for processing, analyzing, modeling, and visualizing magnetotelluric data in Python | 147 |
| A toolbox to help understand neural networks' predictions by providing different analysis methods and a common interface. | 1,271 |
| A collection of Python tools for analyzing borehole images and assessing reservoir geomechanics. | 58 |
| A Python package implementing an interpretable machine learning model for text classification with visualization tools | 336 |
| Automated machine learning protocols for cheminformatics using Python | 39 |
| A Python-based toolkit for automating computational chemistry tasks and data analysis from quantum chemistry software output files | 13 |
| A Python-based collection of tools for gathering forensic information from Office documents | 26 |
| A PyTorch-based toolkit for creating customized multimedia datasets and handling heterogeneous data for training AI models. | 346 |
| A curated list of resources and articles about MEV (Maximal Extraction Value), with a focus on Ethereum, for developers interested in learning more about the topic. | 15 |
| A Python framework for building deep learning models with optimized encoding layers and batch normalization. | 2,044 |
| A machine learning tool for time series data analysis and modeling | 573 |
| A PyTorch-based toolbox for building and training semantic segmentation models | 408 |