pyDiffusion combines tools like diffusion simulation, diffusion data smooth, forward simulation analysis (FSA), etc. to help people analyze diffusion data efficiently.
- Python 3.5+
- numpy, matplotlib, scipy, pandas
Via pip(recommend)
pip install pydiffusion
Based on Ni-Mo interdiffusion coefficients data at 1100C, simulate the diffusion process for 800 hours. See Diffusion Simulation Example.
Calculate interdiffusion coefficients of Ni-Mo at 1100C based on raw diffusion data (1000 hours). See FSA Example.
The interdiffusion coefficients in Ti-Zr system at 1000C are calculated using FSA. The error bounds of the diffusivity data are estimated using error analysis tool. See Error Analysis Example.
If you use pydiffusion in your research, please consider citing the following article published in JORS:
Chen, Z., Zhang, Q. and Zhao, J.-C., 2019. pydiffusion: A Python Library for Diffusion Simulation and Data Analysis. Journal of Open Research Software, 7(1), p.13. DOI: http://doi.org/10.5334/jors.255