Tutorials

We strongly recommend to read the Getting started section of this documentation to familariaze yourself with the tool. However, if you want to quickly test the tool, you can do so by following the basic usage tutorial available as a jupyter notebook on NeatMS github repository.

You can install jupyter through pip using the following command:

pip install notebook

More information on how to install and run jupyter notebook is available here.

To follow the tutorials more easily, please clone or download the github repository so you have direct access to the example files and model. Links below points to the files on github, once downloaded, your local version will have the same architecture.

Basic usage tutorial

This jupyter notebook will drive you through the basic commands of NeatMS such as loading example data, loading an existing neural network model, run the prediction to label the peaks, and export the results.

You can find the tutorial under the name NeatMS.ipynb here.

Advanced usage tutorial

The advanced tutorial will show you how to use the peak annotation/labelling tool to create a training dataset and train your own model, you will also learn how to save the model for later use.

You will find the notebook in the same folder.

Note: The tutrorials above use default parameters, more advanced use of NeatMS is possible but requires experience in deep learning. The advanced use section of this documentation will help you get started with this.