From 80add44a6f89a6d3c2ed5418718a9d223f2998d4 Mon Sep 17 00:00:00 2001 From: monj <monj@dtu.dk> Date: Thu, 5 Aug 2021 15:05:38 +0000 Subject: [PATCH] Update README.md --- README.md | 19 +++++-------------- 1 file changed, 5 insertions(+), 14 deletions(-) diff --git a/README.md b/README.md index e27c541..31925e4 100644 --- a/README.md +++ b/README.md @@ -1,22 +1,13 @@ -**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). +**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 Binder by pressing this link, if you want to run the tutorials locally, check our instructions for [getting started](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. +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 + 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. Please contact Monica J. Emerson monj@dtu.dk with issues and feedback. -**Structure tensor math background** 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 dos:** - -**1)** Write about how to install Python and run the environment.yml file (where to host this info?) Consider where to host this explanation, as it will be used for tutorials and workshops, like in the readme under the tutorials folde - -- If you cannot install the structure tensor from the environment file, an alternative is to: 1) download the structure tensor repository as a .zip, 2)extract and rename to 'structure_tensor' and 3) place folder in the directory of this exercise. - -**2)** Provide a binder link in case the users don't have a Python installation. - -**3)** Link to this repository from the website - -- GitLab