Python Implementation of Plum (PyPlum)

PyPlum is a comprehensive Python implementation of the Plum model, which generates age-depth models using 210Pb data through the autoregressive gamma process, as detailed in Blaauw and Christen (2011). This implementation, based on Aquino-López et al. (2018), extends 210Pb models by integrating radiocarbon and calendar dates.

Citation: For detailed methodology, refer to Aquino-López et al., 2018.

Note: PyPlum is the Python variant of Plum. For the official R version, see rplum.

Prerequisites:

To use PyPlum, ensure you have the following installed:

• Python 3
• Numpy
• Scipy
• Matplotlib
• PyTwalk (Installation guide available at PyTwalk)
• Sklearn (Required for estimating 210Pb samples; not needed for 226Ra or pre-provided core file values)

Setting up the Environment

1. Module Setup: PyPlum can be used as a Python module. Place the PyPlum.py file in your project's core directory. We recommend creating a PyPlum folder in ~/Documents (default location).
2. File Location: Ensure PyPlum.py is located in the directory containing Cores and Calibration curves. Set this directory as your working directory in Python.

File Structure

Core data should be stored in a csv format as follows:

ID	Depth	Density	Total 210Pb	SD Total 210Pb	Thickness	Total 226Ra	SD Total 226Ra	Info

• ID:
  Sample's ID
• Depth:
  Lower depth of sample
• Density:
  Density of sample (g/cm^3)
• Total 210Pb:
  Total 210Pb measurements (Bq/kg)
• SD Total 210Pb: Standard deviation of Total 210Pb measurements
• Thickness: Samples thickness (upper - lower depth)
• Total 226Ra: Total 226Ra measurements (Bq/kg)
• SD Total 226Ra: Standard deviation of Total 226Ra measurements
• Info:
  Core's information: First cell should contain sampling date and second cell can contain the number of samples to be used for only supported activity.

Ensure the core data file (Core_name.csv) is placed in a dedicated Core folder, which is in the same directory as PyPlum.py and Calibration Curves.

For radiocarbon or calendar data, add a Core_name-C.csv file with the format:

ID	BP Age	Age error	Depth	cc

• ID:
  Sample's ID
• BP Age: Sample's age on Before Present format
• Age error: Standar deviation of sample's age
• Depth: Sample's depth
• cc: Calibration curve: 0. Calendar dates, 1. IntCal13.14C, 2. Marine13.14C, 3. SHCal13.14C

PyPlum will automatically detect the presence of Core_name-C.csv and adjust the model accordingly.

Running Instructions

1. Loading the Package:

   import PyPlum
   

2. Preparing for Analysis:

   Core_name = PyPlum.Plum('Core_name')
   

   This loads the data and prepares PyPlum for execution.

3. Running the Analysis:

   Core_name.runPlum()
   

   This executes PyPlum with default settings.

Settings

The following settings can be adjusted in PyPlum:

• Core: Core name
• dirt: Core folder location
• Dircc: Calibration curve folder
• thick: length of the autoregressive gamma process sections
• n_supp: Number samples to be used exclusively for inferring supported 210Pb
• mean_m: Prior mean of memory parameter (used by the autoregressive gamma process)
• shape_m: Prior shape of memory parameter (used by the autoregressive gamma process)
• mean_acc: Prior mean of alpha parameters (used by the autoregressive gamma process)
• shape_acc: Prior shape of alpha parameters (used by the autoregressive gamma process)
• fi_mean: Prior mean of prior 210Pb influx
• fi_shape: Prior shape of 210Pb influx
• s_mean: Prior mean of supported 210Pb
• s_shape: Prior shape of supported 210Pb
• intv: Length of credible interval
• Ts_mod: If True radiocarbon likelihood will be constructed using the T model, if False the normal model will be used
• iterations: Bumber of final MCMC iteration
• burnin: Size of burn-in
• thi: Thinning of the MCMC
• cc: Calibration Curve (name of file containing the calibration curve)
• ccpb: Postbomb Calibration curve
• showchrono: If True the chronology will be display after the running the model, if False chronology will not be display. In both cases chronology will be save as a pdf file
• g_thi: Size of radiocarbon globes in plot
• Al: Detection limit, this variable limits the chronology (see: Aquino-López et al. 2018 for details)
• seed: Seed for the random variables (used to replicate results). If not changed random seed will be used.
• d_by: Length of sections used to create age file.
• reservoir_eff: Assumes a constant reservoir effect in all radiocarbon dates and it calculates it as a parameter of the model.

rplum, R's Official Version

For the R version of Plum, install rplum from CRAN:

install.packages('rplum')
library(rplum)
Plum()

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This revised README provides a clear, organized, and user-friendly guide for PyPlum users.