**Structure tensor tutorials** for the implementation in https://github.com/Skielex/structure-tensor. This implementation is compatible with the python package `cupy`. Therefore, if you have large volumes to process and a graphical processing unit (GPU), you can speed up your computations simply by having a working installation of [cupy](https://docs.cupy.dev/en/stable/install.html).
Run the tutorials online on [](https://mybinder.org/v2/gl/https%3A%2F%2Flab.compute.dtu.dk%2FQIM%2Ftutorials%2Fstructuretensor/HEAD) by pressing on the binder icon. If you want to run the tutorials locally, check our instructions for [installing Jupyter Notebooks](https://lab.compute.dtu.dk/QIM/tutorials/getting-started).
Run the tutorials online on [](https://mybinder.org/v2/git/https%3A%2F%2Flab.compute.dtu.dk%2FQIM%2Ftutorials%2Fstructuretensor/HEAD) by pressing on the binder icon. If you want to run the tutorials locally, check our instructions for [installing Jupyter Notebooks](https://lab.compute.dtu.dk/QIM/tutorials/getting-started).
The tutorials demonstrate the use of the structure tensor tool for the analysis of 2D and 3D data. The tutorials come with a set of utils (helper functions) to inspect 2D and 3D data, and analyse the structure tensor output. The 2D and 3D examples come as python scripts (.py) and Jupyter Notebooks (.ipynb), the latter is more complete and pedagogical, as it comes with explanations. With these tutorials we would like you to 1) learn how to choose the parameters to obtain desirable results, 2) see different options for visualising the output of the structure tensor, and 3) get inspiration for scientific questions that you could answer with the structure tensor tool.
