Nexaris Jupyter Notebook is a Visual Studio Code extension designed to bring the full power of Jupyter Lab to your VS Code environment. Run interactive Python notebooks, visualize data dynamically, embed images seamlessly, and connect directly to Nexaris servers for advanced computation — all without leaving your editor.
Features
Run Jupyter Notebooks: Execute Python code cells interactively with real-time output.
Embed Images: Easily insert images in Markdown or Python code cells.
Interactive Visualizations: Use Matplotlib, Seaborn, and other libraries to create dynamic charts and graphs.
Customizable Settings: Configure your workflow with personalized settings.
Nexaris Server Integration: Connect notebooks directly to Nexaris kernels for remote computation and collaboration.
Workspace Initialization: Quickly set up your workspace with all your files ready to go.
Getting Started
Requirements
Visual Studio Code: Version 1.60 or higher
Python: Version 3.6 or higher
Jupyter Extension for VS Code: Install from the VS Code Marketplace
Pillow Library:
pip install Pillow
IPython:
pip install ipython
Ensure your Python environment is properly configured in VS Code.
Initializing Your Nexaris Workspace
Open VS Code and run the following command in the Command Palette (Ctrl+Shift+P / Cmd+Shift+P):
> Nexaris: Initialize Workspace
This will:
Create a dedicated workspace folder for your notebooks.
Load all available project files automatically.
Prepare the environment to connect seamlessly with Nexaris servers.
Connecting to Nexaris Kernel
Open a notebook (.ipynb).
Click Select Kernel in the top-right corner.
Choose Nexaris Server Kernel.
Authenticate if required, and your notebook will now run on the Nexaris server.
Tip: You can configure a custom server URL in settings if your organization hosts private Nexaris servers.
Extension Settings
Configure Nexaris Jupyter Notebook in Settings > search nexaris:
Setting
Description
Default
nexaris.enable
Enable or disable the extension
true
nexaris.imageDefaultSize
Default width & height for embedded images (e.g., "300,300")
