Abstract
iFLOW is a free, open‐source, and python‐based framework and graphical user interface to visualize and analyze temperature time series, and extract one dimensional thermal velocity, vT, and bulk effective thermal diffusivity, ke. Information of thermal properties of the sediment‐water mixture (bulk) and water allows quantifying the one‐dimensional Darcian flux, q, and seepage velocity, v, from vT. Available software packages were developed to quantify q and ke only based on a specific mathematical model or focused on specific data processing or parameter estimation techniques, and all these steps were lumped together preventing users to identify potential source of errors. iFLOW proposes a novel organizational philosophy with a modular framework that parses the analysis process into three fundamental steps: (a) the mathematical model, (b) signal processing, and (c) parameter estimation. iFLOW houses a suite of models and analysis techniques. This suite can be readily added to and expanded through its modular framework. iFLOW contains a wizard to guide users through the selection process with respect to the three fundamental steps. Users can analyze and visualize intermediate results to identify problematic issues in the time series data and improve data interpretation. Here, we present iFLOW and summarize its performance using a set of one‐dimensional synthetic heat transport simulations.