View on the Amazon rainforest from above

Software and Data


We produce different software tools for the analysis of ecosystem properties. Some of the open source sofware tools we have developed are:

More information
SoilR SoilR is an R package for modeling soil organic matter dynamics. It can be used to implement any type of soil carbon model and compare output between different model structures or parameterizations. SoilR site Download
from GitHub
LAPM The Linear Pool Models LAPM python package implements a set of system diagnostics for compartment models expressed as linear autonomous systems. The package computes density distributions for system and pool ages as well as transit times using as input a compartmental matrix and a vector of inputs to a system. Read the documentation
and clone from GitHub
CompartmentalSystems CompartmentalSystems is a python package for the analysis of smooth nonautonomous nonlinear compartmental systems. This package can run simulations and also computes time-dependent density distributions for system, pool ages as well as transit time. Read the documentation and clone from GitHub 
BGC-MD We are currently developing the biogeochemistry model database BGC-MD. This is a collection of biogeochemical models expressed in symbolic mathematics and implemented in Python and R. See the Prototype , clone the development repository , or check the static website with the current set of models.
measuRing measuRing is an R package developed by  Wilson Lara  for the detection and control of tree-ring widths from dendrochronological images. It processes the image section and computes luminance data from images producing a matrix of gray values and a time series of smoothed gray values. Luminance data is plotted on segmented images for users to perform both: visual identification of ring borders, or control of automatic detection. The package provides functions to visually include/exclude ring borders on the R graphical device, or automatically detect ring borders using a linear detection algorithm. This algorithm detects ring borders according to negative extreme values in the smoothed time-series of gray values. A paper describing the package is available here. Download from CRAN.


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