pyrolite

name: pyrolite description: | Geochemistry data analysis and visualization for igneous, metamorphic, and sedimentary rocks. Use when Claude needs to: (1) Create ternary diagrams for compositional data, (2) Plot REE spider diagrams with normalization, (3) Build TAS or other classification diagrams, (4) Apply log-ratio transforms to compositional data, (5) Calculate CIPW norms, (6) Generate Harker variation diagrams, (7) Compute element ratios and anomalies. version: 1.0.0 author: Geoscience Skills license: MIT tags: [Geochemistry, REE, Spider Diagram, TAS, Compositional Data] dependencies: [pyrolite>=0.3.0, pandas, matplotlib] complements: [] workflow_role: visualization

pyrolite - Geochemistry Analysis

Quick Reference

import pandas as pd
import matplotlib.pyplot as plt
from pyrolite.geochem.norm import get_reference_composition

df = pd.read_csv('samples.csv')
df.pyrochem   # Geochemistry methods
df.pyrocomp   # Compositional methods

# Normalize and plot REE
chondrite = get_reference_composition('Chondrite_McDonough1995')
ax = df.pyrochem.normalize_to(chondrite, units='ppm').pyroplot.REE(unity_line=True)

Key Modules

Essential Operations

Ternary Diagram

ax = df[['SiO2', 'CaO', 'Na2O']].pyroplot.scatter(c='k', s=50)

TAS Diagram

from pyrolite.plot.templates import TAS
df['Na2O_K2O'] = df['Na2O'] + df['K2O']
ax = TAS()
ax.scatter(df['SiO2'], df['Na2O_K2O'], c='red', s=50)

REE Pattern

chondrite = get_reference_composition('Chondrite_McDonough1995')
ax = df.pyrochem.normalize_to(chondrite, units='ppm').pyroplot.REE(unity_line=True)

Trace Element Spider

pm = get_reference_composition('PM_McDonough1995')
ax = df.pyrochem.normalize_to(pm).pyroplot.spider(unity_line=True)

Compositional Transforms

df_closed = df.pyrocomp.renormalise(scale=100)  # Closure
df_clr = df.pyrocomp.CLR()   # Centered log-ratio
df_alr = df.pyrocomp.ALR()   # Additive log-ratio
df_ilr = df.pyrocomp.ILR()   # Isometric log-ratio

Element Ratios and Anomalies

df['La_Yb'] = df['La'] / df['Yb']                        # LREE/HREE
df['Eu_Eu*'] = df['Eu'] / (df['Sm'] * df['Gd']) ** 0.5   # Eu anomaly
lambdas = df.pyrochem.lambda_lnREE()                      # REE shape

CIPW Norm

from pyrolite.mineral.normative import CIPW_norm
norm = CIPW_norm(df)  # df must have major oxides in wt%

Harker Diagrams

fig, axes = plt.subplots(2, 3, figsize=(12, 8))
for ax, elem in zip(axes.flatten(), ['TiO2', 'Al2O3', 'FeO', 'MgO', 'CaO', 'Na2O']):
    ax.scatter(df['SiO2'], df[elem], c='blue', s=50)
    ax.set_xlabel('SiO2 (wt%)'); ax.set_ylabel(f'{elem} (wt%)')

Pearce Discrimination

from pyrolite.plot.templates import pearce_templates
ax = pearce_templates.YNb()
ax.scatter(df['Nb'], df['Y'], c='red', s=50)

Common Normalization References

When to Use vs Alternatives

Use pyrolite when you need geochemistry analysis integrated with pandas workflows, compositional log-ratio transforms, or REE normalization in Python.

Consider alternatives when you need extensive petrographic classification templates (use GCDkit), a quick GUI for classification (use PetroGraph), or only need simple scatter plots without normalization (use matplotlib directly).

Common Workflows

Geochemical classification and REE pattern analysis

• Load sample data into pandas DataFrame
• Close compositions with df.pyrocomp.renormalise(scale=100)
• Plot TAS diagram with TAS() and overlay sample data
• Normalize REE to chondrite with df.pyrochem.normalize_to()
• Plot REE spider diagram with df.pyroplot.REE()
• Calculate Eu anomaly and La/Yb ratio
• Generate Harker variation diagrams for major elements
• Export figures for publication

Tips

1. Close compositions before analysis (ensure sum to 100%)
2. Use log-ratios (CLR/ALR/ILR) for statistical analysis
3. Choose appropriate normalization for spider diagrams
4. Check for Eu anomaly (positive = cumulate, negative = fractionation)

References

• Normalization References - Reference compositions and values
• Classification Schemes - TAS, Pearce, AFM diagrams

Scripts

• scripts/geochemistry_plots.py - Generate common plots