axiom-t2

Create a Python-based mini-application named 'ToroidalVisualizer' that leverages the 'axiom-t2' library to visualize and manipulate data on toroidal manifolds. This application will serve as a tool for researchers and students interested in understanding the geometric properties of toroidal spaces through machine learning techniques.

**Features to Include:**
1. **Data Input:** Users should be able to upload their own datasets or use pre-defined datasets included in the application. These datasets can be multidimensional arrays representing points in space.
2. **Visualization:** Implement a visualization module that plots the uploaded data onto a toroidal manifold using 'axiom-t2'. This could include both static and interactive visualizations allowing users to rotate and zoom in/out.
3. **Machine Learning Operations:** Use 'axiom-t2' to perform basic machine learning operations such as clustering or classification directly on the toroidal manifold. Provide options for different algorithms like K-means or DBSCAN.
4. **Customization Options:** Allow users to customize parameters related to the visualization and machine learning operations, such as color schemes, algorithm settings, etc.
5. **Documentation and Help:** Ensure there is comprehensive documentation available within the application explaining each feature and how 'axiom-t2' is utilized.

**Steps to Build the Application:**
1. Set up a Python environment with all necessary dependencies including 'axiom-t2'.
2. Design the user interface for uploading data and selecting visualization options.
3. Implement the backend logic using 'axiom-t2' to process and visualize the data on a toroidal manifold.
4. Integrate machine learning functionalities to allow for analysis directly on the toroidal space.
5. Add customization options and ensure all components work seamlessly together.
6. Write thorough documentation and provide examples for each feature.
7. Test the application thoroughly to ensure reliability and usability.