detritalpy 1.3.14

A Python-based toolset for visualizing and analyzing detrital geo-thermochronologic data

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.

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.

Data Import and Selection

One or more spreadsheets can be simultaneously imported using a number of different ways of specifying the file path.

# 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:

# 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

figMDS, stress = dFunc.MDS(ages, errors, labels, sampleList, metric=False, plotWidth=10, plotHeight=8, plotPie=True, pieSize=0.05, agebins=[0, 23, 65, 85, 100, 135, 200, 300, 500, 4500], agebinsc=['slategray','royalblue','gold','red','darkred','purple','navy','gray','saddlebrown'], criteria='Dmax')

(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.