Package: corepytools
CorePytools packageCorePytools (CorePy) applies machine learning cluster algorithms to geological samples of core. 
- The primary focus of CorePy is to classify high resolution X-ray fluoresence data into chemofacies 
- unsupervised and supervised clustering tools are applied
- Folder structures are developed to simplify working on multiple cores and formations
- Visualizations are used to validate clustering results.Installationpip install corepytools
- corepytools includes commonly applied functions that are called by different Python scripts
- corepytools is a dependency 
- Additional notes and tips about GitHub and steps I use are here: https://github.com/Totilarson/MyCheatSheetRunning Corepytools and CorePy1) fork the CorePy repo to your github account
2) make a local clone:
 - command line: `git clone https://github.com/Totilarson/CorePy.git` 
 - if it is necessary to delete the local clone use: 'rm -rf .git*'
3) Navigate to the local repo //CorePy/ and inspect folders 'CoreData' and 'CorePycodes'
4) In //CorePy/CorePycodes open 'settings.py' and 'PCAexample'
- 'settings.py' contains variables for all the Python scripts
 - "CoreOfStudy", "Depth_model", "Formation", and "RockClassification" should match values in Public_XRF.csv datafileData examples- CoreData folder contains an example of a high reoslution XRF dataset and corebox photographs
- Naming patterns for core box sticker location, wireline depths, and elemental concentrations are shownSettings.py and PCAexample- Running PCAexample.py will build additional output folders and run PCA-Kmeans.
- Output files are in output folder. CSV file includes additional columns of data
- Settings.py writes a Run_settings.json file that is accessed by other scriptsCoreBeta file- <corename>.json files are stored for each core in //CorePy/CoreData/CoreBeta
- files provide core-specific data that is referenced in each script
- Wireline scripts also write data to the .jsom fileCorePy_plotting.py1) provides additional elemenal plotting
 - elemental cross plots. Elements are selected from Run_settings['Elements_plotted']
 - elements plotted with respect to depth: Depth model selected by: Run_settings['Depth_model']
 - element box plots. majors and trace
 - pie chart of chemofacies abundance
 - Depth referenced chemostrat column output in a folder //CorePy/CoreOutput/CrossSection/NN_Build.py and NN_apply.py- these scripts build and apply results from supervised chemfoacies classifications
- An example training dataset is included in //CorePy/CoreData/CoreNeuralModel
- model parameters are output _XGB and __NN files in //CorePy/CoreData/CoreNeuralModel
- output .csv file has additional classification columnsCoreimage.py- Designed to overlay chemofacies results on corebox photographs
- Requires coreboxphotographs be converted to 'coretubes'
- Coretubes are created from Corebox_Crop.py
- Coretubes are depth registered and in folder //CorePy/CoreData/CoreTubes/About the authorsCorePy is being developed by Toti Larson at the University of Texas at Austin, Bureau of Economic Geology, Mudrocks Systems Research Laboratory (MSRL) research consortium.Toti E. Larson, Ph.D.- Research Associate at the University of Texas at Austin. PI MSRL research consortiumEsben Pedersen, M.S.- Graduate student (graduated 2020) at the University of Texas at Austin.Priyanka Periwal, Ph.D.- Research Science Associate at the University of Texas at Austin.J. Evan Sivil- Research Science Associate at the University of Texas at Austin.Geoforce students- Ana LetÃ­cia Batista (2020) - Jackson State UniversityPackage InventoryPackage Dependenciesosnumpypandasseabornpickleglobmatplotlib.pyplotseaborn as snssklearn.preprocessing import StandardScalersklearn.decomposition import PCAsklearn.cluster import KMeansmatplotlib.patheffectsjsoncorepytoolsfrom sklearn.model_selection import train_test_splitfrom sklearn.preprocessing import StandardScalerfrom sklearn.neural_network import MLPClassifierfrom sklearn.metrics import classification_report, confusion_matrixfrom sklearn import metricsimport matplotlib.pyplot as pltmatplotlib.patches as patchesNotesInstall corepytools usingpip install corepytoolsFollow over to the authors Github account to download example Python scripts that use corepytoolsFolder structurecorepytools|-LICENSE.txt         **MIT**

|-README.md           **edited in markdown**

|-setup.py            **name=corepy-tools, package=src, python module=corepytools**

|-src

    |-corepytools    **contains functions**

    |-__init__.py     ** empty**