MinexPy 0.1.1

A python toolkit for geoscience researchers and students

MinexPy

MinexPy is a practical toolkit for geoscience researchers and students who are learning how to solve real geoscientific problems with Python—or who want a deeper, more structured understanding of which Python tools fit which geoscience tasks.

Even though modern AI tools can help you prototype quickly, it’s still hard for beginners to answer questions like:

• Which library should I use for this exact problem?
• What’s the “standard” workflow in geoscience when using Python?
• How do I discover reliable tools without getting lost in hundreds of packages?

MinexPy aims to reduce that confusion by providing curated, beginner-friendly building blocks and sensible combinations of widely-used libraries, so you can focus more on the science and less on setup and guesswork.

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Installation

Install with pip:

pip install minexpy

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How to use

MinexPy provides both a Python API and a command-line interface.

Python API

Like most Python packages, you install it and import what you need:

import minexpy.stats as mstats
from minexpy import (
    StatisticalAnalyzer,
    describe,
    pearson_correlation,
    spearman_correlation,
    plot_histogram,
)

Example: Correlation + Visualization

import numpy as np
from minexpy.correlation import pearson_correlation, spearman_correlation
from minexpy.statviz import plot_histogram

zn = np.array([45.2, 52.3, 38.7, 61.2, 49.8, 55.1])
cu = np.array([12.5, 15.3, 11.2, 18.4, 14.1, 16.0])

print(pearson_correlation(zn, cu))
print(spearman_correlation(zn, cu))

fig, ax = plot_histogram(zn, bins=10, scale="linear", xlabel="Zn (ppm)")

Command-Line Interface

After installation, use the minexpy command for quick access to documentation and examples:

# Show help and available functions
minexpy

# Show practical code examples you can copy
minexpy demo

# Open documentation in browser
minexpy docs

# Show package information
minexpy info

Documentation and examples will expand over time as modules are added.

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Roadmap / TODO

Want to contribute? Here are some high-impact areas to work on:

• Mapping module for geological, geochemical, and geophysical data

• Interpolation methods commonly used in geosciences (e.g., minimum curvature, triangulation, and others)

• AOI extraction from large satellite imagery (selecting/cropping a region of interest from big scenes)

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Contributing

Contributions are welcome—especially examples, notebooks/markdown tutorials, and new modules that follow the project’s goals:

• beginner-friendly

• practical workflows

• clear and detailed documentation using NumPy's docstrings guidelines