pyrolite

Name: pyrolite
Availability: InStock
Author: steadfastasart

Analyze geochemical datasets to produce normalized REE patterns, spider diagrams, and TAS classifications.

46|3|Updated Jan 26, 2026

Search Tags:#classification #ternary #spider #geochemistry #pyrolite #ree #tas

Ready to run

npx skills add https://github.com/SteadfastAsArt/geoscience-skills --skill pyrolite

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: pyrolite
Source: https://github.com/SteadfastAsArt/geoscience-skills/tree/main/pyrolite
Command: npx skills add https://github.com/SteadfastAsArt/geoscience-skills --skill pyrolite

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires numpy, pandas, matplotlib, pyrolite, and includes scripts (resource) and references (resource) components.

What problem does it solve?

Geochemistry data analysis and visualization for igneous, metamorphic, and sedimentary rocks. It provides a unified approach to convert oxide major elements into normalized REE patterns, construct ternary diagrams, apply log-ratio transforms, compute CIPW norms, and create classification plots like TAS and Pearce diagrams, all within Python workflows.

Core Features & Use Cases

Ternary diagrams from oxide compositions for budget interpretations.
REE normalization and spider diagrams for geochemical diagnostics.
TAS and other discrimination diagrams for tectonic interpretation.
Log-ratio transforms (CLR/ALR/ILR) and CIPW normative calculations for advanced statistics.
Harker variation diagrams and basic data-visualization pipelines in pandas.

Quick Start

Load a geochemical dataset and generate REE pattern, spider diagram, and TAS classification plots.

Frequently Asked Questions about pyrolite

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I create TAS classification plots from geochemical data in Python?▼

TAS classification plots are generated by analyzing oxide major elements from geochemical datasets to classify igneous rocks. The Skill leverages pandas and matplotlib to produce visual discrimination diagrams for tectonic interpretation directly from your data.

How do I generate normalized REE patterns and spider diagrams for rock samples?▼

Normalized REE patterns and spider diagrams are created by processing trace element data from geochemical datasets. The Skill applies normalization routines and uses matplotlib to visualize multi-element patterns suitable for igneous, metamorphic, and sedimentary rock diagnostics.

Can I compute CIPW norms and apply log-ratio transforms to oxide compositions?▼

CIPW normative calculations and log-ratio transforms like CLR, ALR, and ILR are fully supported. The Skill processes oxide major elements to compute CIPW norms and performs log-ratio transformations for advanced statistical analysis within your Python workflow.

Does this Skill support ternary diagrams for geochemical data visualization?▼

Ternary diagrams are supported for visualizing oxide compositions and budget interpretations. The Skill uses matplotlib to construct ternary plots from your geochemical dataset, enabling quick visual diagnostics alongside spider and Harker variation diagrams.

What format should my geochemical dataset be in for REE normalization and plotting?▼

Your geochemical dataset should be loaded into a pandas DataFrame containing oxide major elements and trace elements. The Skill requires pandas and matplotlib to perform normalization, plotting, and classification tasks on igneous, metamorphic, or sedimentary rock data.

Do I need a specific version of pyrolite to run geochemistry analysis and classification tasks?▼

You need pyrolite version 0.3.0 or higher to run geochemistry analysis and classification tasks. The Skill depends on numpy and pandas alongside pyrolite to execute normalization, plotting, and classification functions on your geochemical datasets.