iFLOW is a resource in the realm of thermal analysis, offering a sophisticated yet accessible platform for visualizing and dissecting temperature time series data. It not only provides a dynamic graphical user interface but also operates as a Python-based framework, emphasizing accessibility and versatility. Its primary functions include the visualization and analysis of temperature fluctuations over time, with a particular focus on extracting vital metrics such as one-dimensional seepage flux (q) and bulk effective thermal diffusivity (ke).
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Table of Contents
Download the lastest install release from here.
You can whatch the video tutorial on the youtube iFlow Channel.
Install the following python packages
- matplotlib (used version 3.5.1)
pip install matplotlib
- numpy (used version 1.21.5)
pip install numpy
- pandas (used version 1.3.5)
pip install pandas
- PyQt5 (used version 5.15.6)
pip install PyQt5
- scipy (used version 1.7.3)
pip install scipy
- loguru (used version 0.7.2)
pip install loguru
Clone the repo
git clone https://github.com/iFlowCode/iFlow.gitContributions are greatly appreciated. You can contibute to this project, forking the repository and then creating a "pull request".
- Fork the Project
- Create your Feature Branch (
git checkout -b feature/AmazingFeature) - Commit your Changes (
git commit -m 'Add some AmazingFeature') - Push to the Branch (
git push origin feature/AmazingFeature) - Open a Pull Request
Or you send an e-mail to discuss about your inclusion as a contributor to this project.
Distributed under the GPL3 License. See [LICENSE] for more information.
Project Link: iFlow Project
| M. van Berkel | ||
| A. Bertagnoli | ||
| C. Luce | ||
| R. van Kampen | ||
| S. Krause | ||
| S. Schneidewind | ||
| D. Tonina | ||
| U. Schneidewind | ||
| G. Vandersteen |