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This is a toolbox uses neuronal & behavioral data and visualizes it. The main functionalities include:

• Numerous options to visualize neuronal data and create diagnostic plots
• Creating neural manifolds using sklearn dimensionality reduction algorithms or BunDLeNet
• Make interactive behavioral state diagrams using pyvis
• Cluster behavioral probability trajectories and test them for non-markovianity
• Movies of behavioral/neuronal trajectories saved as .gif-files

1. Installation: Open a terminal window in your Python project directory and run

   pip install ncmcm
   

2. Importing the package: In your Python script or notebook, import the package

   import ncmcm
   

3. Usage: the ncmcm.Database class as a container for your neuronal and behavioral dataset
4. Tutorial: Check out the Demo.ipynb notebook included in the package. It serves as a useful starting point to explore the functionalities of ncmcm

If you're interested in contributing to this project or creating your own versions based on the existing code, follow these steps:

1. Clone the Repository: Clone this repository to your local machine using Git:

   git clone https://github.com/DriftKing1998/NC-MCM-Visualizer.git
   

2. Install Dependencies: Navigate to the project directory and install the required dependencies using pip:

   cd <project_directory>
   pip install -r requirements.txt
   

1. Explore the Code:
   ncmcm.classes.py contains the classes used by ncmcm:

   1. Database is 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.
   2. Visualizer is created by adding a mapping or a BundDLeNet (=default). It allows to create 3D plots and movies.
   3. CustomEnsembleModel is a model that creates an ensemble of models, specializing each model to detect a label.

   
   ncmcm.functions.py contains some auxiliary functions:

   1. Functions to prepare data
   2. Functions to test for stationarity & non-markovianity
   3. Plotting functions for the tests

   
   ncmcm.BundDLeNet.py contains all the parts of BundDLeNet:

   1. Class for creating/training the model
   2. Functions to prep data and get loss

2. Make Changes: Make your desired changes or enhancements to the codebase. Feel free to add new features, fix bugs, or improve documentation.

3. Testing: Ensure that your changes are tested thoroughly. You can run existing tests or write new ones to validate your modifications.

4. Create a Branch: Create a new branch for your changes:

   git checkout -b <new_branch>
   

5. Commit Your Changes: Once you're satisfied with your changes, commit them to your branch:

   git add .
   git commit -m "description of changes"
   

6. Push Changes: Push your changes to your forked repository:

   git push origin <new_branch>
   

Since this project is a first step, any additions are more than welcome.

1. 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.

2. Review: Await feedback from me and address any requested changes. Once approved, your changes will be merged into the main branch.