Description
detritalPy is a Python module for visualizing and analyzing detrital geo-thermochronologic data. Designed to be implemented via a Jupyter Notebook, detritalPy aims to provide an efficient means of processing and analyzing large detrital mineral isotopic and geochemical datasets. For more information, please refer to Sharman et al., 2018.
Manual
A manual for detritalPy can be found here.
Installation
pip install detritalpy
Upgrading
pip install detritalpy --upgrade
Requirements
Installation of the open data science platform Anaconda by Continuum Analytics will provide most of the required Python modules needed to run detritalPy. The following is a full list of dependencies for all detritalPy functions:
- numpy
- matplotlib
- pandas
- xlrd
- folium
- vincent
- simplekml
- scipy
- sklearn
- statsmodels
- peakutils
Data Formatting
detritalPy requires that input data be organized using a specific format. Example datasets can be found in the example-data folder, and additional information is provided in the detritalPy manual. Dr. Jeff Amato (New Mexico State University) has put together a beginner's guide to detritalPy (geared towards MacOS) that can be found here.
Data Import and Selection
One or more spreadsheets can be simultaneously imported using a number of different ways of specifying the file path. Additional examples are provided in tutorial_dataLoading.ipynb.
# Import relative file pathway(s)
from pathlib import Path
# Specify file paths to data input file(s)
dataToLoad = [Path("example-data/") / "ExampleDataset_1.xlsx",
Path("example-data/") / "ExampleDataset_2.xlsx"]
main_df, main_byid_df, samples_df, analyses_df = dFunc.loadDataExcel(dataToLoad)
Or filepaths can be written this way if using a PC:
# Specify file paths to data input file(s)
dataToLoad = [r'C:\Users\gsharman\Documents\GitHub\detritalPy\example-data\ExampleDataset_1.xlsx',
r'C:\Users\gsharman\Documents\GitHub\detritalPy\example-data\ExampleDataset_2.xlsx']
main_df, main_byid_df, samples_df, analyses_df = dFunc.loadDataExcel(dataToLoad)
Or this way if using a Mac or PC:
# Specify file paths to data input file(s)
dataToLoad = ['/Users/gsharman/Documents/GitHub/detritalPy/example-data/ExampleDataset_1.xlsx',
'/Users/gsharman/Documents/GitHub/detritalPy/example-data/ExampleDataset_1.xlsx']
main_df, main_byid_df, samples_df, analyses_df = dFunc.loadDataExcel(dataToLoad)
Once data is loaded, samples can be selected either as a list of sample names
sampleList = [(['POR-1','POR-2','POR-3','BUT-5','BUT-4','BUT-3','BUT-2','BUT-1'],'All')]
ages, errors, numGrains, labels = dFunc.sampleToData(sampleList, main_byid_df, sigma = '1sigma');
or as groups using a tuple structure.
sampleList = [(['POR-1','POR-2','POR-3'],'Point of Rocks Sandstone'),
(['BUT-5','BUT-4','BUT-3','BUT-2','BUT-1'],'Butano Sandstone')]
ages, errors, numGrains, labels = dFunc.sampleToData(sampleList, main_byid_df, sigma = '1sigma');
Selected Examples
Plot detrital age distributions
fig = dFunc.plotAll(sampleList, ages, errors, numGrains, labels, x1=0, x2=300)
Plot rim age versus core age
sampleList = [(['11-Escanilla','12-Escanilla','10-Sobrarbe','7-Guaso','13-Guaso','5-Morillo','6-Morillo','14AB-M02','14AB-A04','14AB-A05','4-Ainsa','14AB-A06','15AB-352','15AB-118','15AB-150','3-Gerbe','14AB-G07','2-Arro','1-Fosado','14AB-F01'],'All Ainsa Basin')]
ages, errors, numGrains, labels = dFunc.sampleToData(sampleList, main_byid_df, sigma = '1sigma');
rimsVsCores = dFunc.plotRimsVsCores(main_byid_df, sampleList, ages, errors, labels, x1=0, x2=3500, y1=0, y2=3500, plotLog=False, plotError=True, w=8, c=8)
Plot detrital age distributions in comparison to another variable (e.g., Th/U)
figDouble = dFunc.plotDouble(sampleList, main_byid_df, ages, errors, numGrains, labels, variableName='Th_U', plotError=False, variableError=0.05, normPlots=False, plotKDE=False, colorKDE=False, colorKDEbyAge=False, plotPDP=True, colorPDP=False, colorPDPbyAge=True, plotHist=False, x1=0, x2=300, autoScaleY=False, y1=0, y2=2, b=5, bw=10, xdif=1, agebins=[0, 23, 65, 85, 100, 135, 200, 300, 500, 4500], agebinsc=['slategray','royalblue','gold','red','darkred','purple','navy','gray','saddlebrown'], w=10, t=3, l=1, plotLog=False, plotColorBar=False, plotMovingAverage=True, windowSize=25, KDElw=1, PDPlw=1);
Multi-dimensional scaling
Revised and updated in detritalPy version 1.3
model = dFunc.MDS_class(ages, errors, labels, sampleList, criteria='Vmax')
model.MDSplot(figsize=(6,6), savePlot=True, fileName='MDSplot.pdf', plotLabels=True,
plotPie=True, pieType='Age', pieSize=0.02, agebins=[0, 23, 65, 85, 100, 135, 200, 300, 500, 4500],
agebinsc=['slategray','royalblue','gold','red','darkred','purple','navy','gray','saddlebrown'], equalAspect=False)
(U-Th)/He vs U-Pb age "double dating" plot
figDoubleDating = dFunc.plotDoubleDating(main_byid_df, sampleList, x1=0, x2=3500, y1=0, y2=500, plotKDE=False, colorKDE=False, colorKDEbyAge=True, plotPDP=True, colorPDP=True, colorPDPbyAge=False, plotHist=False, b=25, bw=10, xdif=1, width=10, height=10, savePlot=True, agebins=[0, 66, 180, 280, 310, 330, 410, 520, 700, 900, 1200, 1500, 3500], agebinsc=['olivedrab','purple','lightskyblue','lightseagreen','lightsteelblue','gold','sandybrown','orange','darkorange','firebrick','orchid','gray']);
Related publications
If you find this code helpful in your research, please cite the accompanying article published in the Depositional Record.
Sharman G.R., Sharman J.P., and Sylvester Z., 2018, detritalPy: A Python-based toolset for visualizing and analyzing detrital geo-thermochronologic Ddata: The Depositional Record, v. 4, p. 202-215, https://doi.org/10.1002/dep2.45.
Code for maximum depositional age (MDA) calculations was first presented in:
Sharman, G.R., and Malkowski, M.A., 2020, Needles in a haystack: Detrital zircon UPb ages and the maximum depositional age of modern global sediment: Earth-Science Reviews, v. 203, doi:10.1016/j.earscirev.2020.103109.
License
detritalPy is licensed under the Apache License 2.0.
1 maintainer
Metadata
1 maintainer
Release files for detritalpy 1.4.16
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