pyrolite

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.

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.

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.

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.

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.

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.