CorePy: XRF clustering tools to interpret and visualize geological core data
Project description
CorePy package
CorePy is designed to perform machine learning on data collected from geological samples of core.
CorePy bundles a wide range of data analytical tools to interpret multivariate datasets common to geological core characterization
The primary focus of CorePy is to classify high resolution X-ray fluoresence data into chemofacies using unsupervised and supervised clustering tools.
CorePy establishes a folder structure multiple users to work on the same datasets, and also provides visualizations that are useful to validate clustering results.
Installation
pip install corepytools
Example
PCAexample.py shows an example that imports corepy package and an example XRF datafile called 'Public_XRF.csv'
About the authors
CorePy is being developed by Toti Larson at the University of Texas at Austin, Bureau of Economic Geology, Mudrocks Systems Research Laboratory (MSRL) research consortium.
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Toti E. Larson, Ph.D. - Research Associate at the University of Texas at Austin. PI MSRL research consortium
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Esben Pedersen, M.S. - Graduate student (graduated 2020) at the University of Texas at Austin.
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Priyanka Periwal, P.D. - Research Science Associate at the University of Texas at Austin.
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Ana LetÃcia Batista - Undergraduate at Jackson State University (graduated 2020). 2020 Jackson School of Geosciences GeoForce Student
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J. Evan Sivil - Research Science Associate at the University of Texas at Austin.
Package Inventory
CorePy.py
Package Dependencies
os seaborn pickle pandas glob numpy natplotlib.pyplot seaborn as sns sklearn.preprocessing import StandardScaler sklearn.decomposition import PCA sklearn.cluster import KMeans matplotlib.patheffects
Notes
Folder structure
corepy-tools
|-LICENSE.txt **MIT**
|-README.md **edited in markdown**
|-setup.py **name=corepy-tools, package=src, python module=CorePy**
|-src
|-CorePy.py **contains functions**
|-__init__.py ** empty**
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