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, v_T, and bulk effective thermal diffusivity, k_e. 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 v_T. Available software packages were developed to quantify q and k_e 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.

中文翻译：

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iFLOW：一个框架和 GUI，用于根据温度时间序列量化可渗透材料中的有效热扩散率和平流


iFLOW 是一个免费的开源、基于 Python 的框架和图形用户界面，用于可视化和分析温度时间序列，并提取一维热速度 v_T 和体有效热扩散率 k_e。沉积物-水混合物（块体）和水的热特性信息允许从 v_T 量化一维达西通量 q 和渗流速度 v。开发的可用软件包仅基于特定的数学模型或专注于特定的数据处理或参数估计技术来量化 q 和 k_e，并且所有这些步骤都集中在一起，防止用户识别潜在的错误来源。iFLOW 提出了一种新颖的组织理念，该理念具有模块化框架，可将分析过程解析为三个基本步骤：（a） 数学模型，（b） 信号处理，以及 （c） 参数估计。iFLOW 包含一套模型和分析技术。该套件可以通过其模块化框架轻松添加和扩展。iFLOW 包含一个向导，用于指导用户完成三个基本步骤的选择过程。用户可以分析和可视化中间结果，以识别时间序列数据中的问题并改进数据解释。在这里，我们介绍了 iFLOW，并使用一组一维合成热传递仿真总结了它的性能。