Package: ncmcm
NC-MCM-VisualizerSee on GitHubA toolbox to visualize neuronal imaging data and apply the NC-MCM framework to itThis is a toolbox uses neuronal & behavioral data and visualizes it. The main functionalities include:Numerous options to visualize neuronal data and create diagnostic plotsCreating neural manifolds usingsklearndimensionality reduction algorithms orBunDLeNetMake interactive behavioral state diagrams usingpyvisCluster behavioral probability trajectories and test them for non-markovianityMovies of behavioral/neuronal trajectories saved as.gif-filesGetting Started (for end-users)Installation:Open a terminal window in your Python project directory and runpip install ncmcmImporting the package:In your Python script or notebook, import the packageimport ncmcmUsage:thencmcm.Databaseclass as a container for your neuronal and behavioral datasetTutorial:Check out theDemo.ipynbnotebook included in the package. It serves as a useful starting point to explore the functionalities ofncmcmInstallation and usage information (for contributors)If you're interested in contributing to this project or creating your own versions based on the existing code, follow these steps:InstallationClone the Repository:
Clone this repository to your local machine using Git:git clone https://github.com/DriftKing1998/NC-MCM-Visualizer.gitInstall Dependencies:
Navigate to the project directory and install the required dependencies using pip:cd <project_directory>
pip install -r requirements.txtNew BranchesExplore the Code:ncmcm.classes.pycontains the classes used by ncmcm:Databaseis a container for data, which can be used to generate the behavioral probability maps by adding a sklearn-model. It also allows to create different plots and diagnostics.Visualizeris created by adding a mapping or a BundDLeNet (=default). It allows to create 3D plots and movies.CustomEnsembleModelis a model that creates an ensemble of models, specializing each model to detect a label.ncmcm.functions.pycontains some auxiliary functions:Functions to prepare dataFunctions to test for stationarity & non-markovianityPlotting functions for the testsncmcm.BundDLeNet.pycontains all the parts of BundDLeNet:Class for creating/training the modelFunctions to prep data and get lossMake Changes:
Make your desired changes or enhancements to the codebase. Feel free to add new features, fix bugs, or improve documentation.Testing:
Ensure that your changes are tested thoroughly. You can run existing tests or write new ones to validate your modifications.Create a Branch:
Create a new branch for your changes:git checkout -b <new_branch>Commit Your Changes:
Once you're satisfied with your changes, commit them to your branch:git add .
git commit -m "description of changes"Push Changes:
Push your changes to your forked repository:git push origin <new_branch>Add to the CodebaseSince this project is a first step, any additions are more than welcome.Pull request:
Go to the repository and open a pull request from your branch. Provide a clear description of your changes and why they're beneficial.Review:
Await feedback from me and address any requested changes. Once approved, your changes will be merged into the main branch.This project was created as part of the masters project ofHofer Michael