**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).
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.
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), named _utilsST_2D.py_- and _utilsST_3D.py_, to inspect the structure tensor output in various ways. The 2D and 3D example tutorials (_ST2D_examples_ and _ST3D_examples_) come as python scripts (.py) and Jupyter Notebooks (.ipynb). While the 2D script covers many examples, the 3D script dives into one example and thus comes with more details. You can also find a set of guided exercises with solution, names _ST2D_exercise_withSolutions.ipynb_. 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.
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) and then follow the instructions below to install the tool.
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**Option 2: Set up environment**
You can run the file called environment.yml to install the structure tensor tool and other required packages via the command line.
You can run the file called environment.yml to install the structure tensor tool and other required packages via the command line, as explained below. If using Anaconda, you can also install the packages using its graphical user interface, just check the packages that are required by opening the environment file with a text reader.
Mac/Linux:
1. Navigate to the tutorial extracted folder.
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3. Type `conda env create -f environment.yml` and press enter.
4. Type `conda activate qim-ST` and press enter.
If using Anaconda, you can also install the packages using its graphical user interface, just check the packages that are required by opening the environment file with a text reader.
To learn more about the computation of structure tensor, the visualization of orientation analysis and other examples of using orientation analysis in volumetric data analysis, we recommend you take a look at the [notes](http://people.compute.dtu.dk/vand/notes/ST_intro.pdf) by Vedrana A. Dahl vand@dtu.dk
To learn more about the computation of structure tensor, the visualisation of orientation analysis and other examples of using orientation analysis in volumetric data analysis, we recommend you take a look at the [notes](http://people.compute.dtu.dk/vand/notes/ST_intro.pdf) by Vedrana A. Dahl vand@dtu.dk
The development of the tutorials is a combined effort from several researchers in the QIM team. The collection of scripts and exercises is in constant development, and actively used to demonstrate the tool and teach at workshops. We would therefore very much appreciate to hear about your experience.
