Jupyterlab features install#
Jupyterlab features code#

If you are an Anaconda user, then you can use conda for installation purposes by using the following command:

Jupyterlab features install#

You can use conda, pip or pipenv to install JupyterLab. This includes theming support, file editors and more. You can add new behavior to your JupyterLab instance via extensions. You can also display rich output from these formats using various visualizations or Markdown.įor navigation, you can use customizable keyboard shortcuts or key maps from vim, emacs and even SublimeText. JupyterLab will allow you to view and handle multiple types of data. Multiple views of the same document - Gives you the ability to live edit documents and see the results in real-time.Mirrored Notebook cell outputs - This let's you create simple dashboards.

Jupyterlab features code#

Kernel-backed documents - These allow you to enable code in any text file (Markdown, Python, R, etc) that can then be run in the Jupyter kernel.Code Consoles - These are coding scratchpads that you can use for running code interactively, kind of like Python's IDLE.You might say JupyterLab is a more powerful version of Jupyter Notebook.Īnyway, here are a few of the things that JupyterLab is capable of: I like to think of JupyterLab as a kind of web-based Integrated Development Environment that you an use to to work with Jupyter Notebooks as well as using terminals, text editors and code consoles. However the Jupyter Notebook is a separate project from JupyterLab. In some ways, it is kind of a replacement for Jupyter Notebook. Department of Energy, Office of Science, Office of Basic Energy Sciences, Small Business Innovation Research (SBIR) program under Award Number DE-SC0017193.JupyterLab is the latest package from Project Jupyter. This material is based upon work supported by the U.S. The code for the extensions has been developed here, and here, using Docker and Ansible to support deployment of a deep stack that is summarized in the architecture diagram above. Many of these structures have also been mapped to the MongoDB-based document store underpinning the Girder data server component, and the chemistry specific RESTful endpoints added in support of the work described above. This work has been supported by development of JSON-based data structures, extending it to support communication of vibrational modes, volumetric data, and chemical structure to support the web-based components. This was complemented with D3-based charting, enabling the display, selection, and animation of calculated vibrational modes in the browser and JupyterLab notebook. The 3D molecular visualization was developed using 3DMol.js, taking advantage of WebGL accelerated 3D rendering, isosurface calculations on the client-side, and cached cubes calculated on the server-side to support the visualization of electronic structure. The JupyterLab widgets are added using the extension API, and the core components are reused in a single-page web application for sharing data more widely. We recently published a research paper on the underpinnings of the Girder integration and single-page web application, and presented an early version of the JupyterLab prototype at the American Chemical Society national meeting in Washington, DC earlier this year. The functionality from Cumulus has been enhanced by new code that integrates with input generation for NWChem, along with indexing of calculation parameters, molecular identifiers, and search of the calculations to support the Open Chemistry notebook interface. This takes advantage of a Girder plugin, Cumulus, that adds integration of HPC/cloud resources for batch execution and monitoring of command-line codes. The screencast above captures some of the core functionality we have been working on, which includes triggering quantum mechanical calculations, after searching the calculation cache for existing results.

It also features inline visualization of the electronic structure, vibrational modes, and summarization of multiple calculations in higher level analytics. It currently features the ability to search for chemical structures by name (using NCI/CADD’s chemical identifier resolver service), perform geometry optimizations, energy calculations, and vibrational analyses using the NWChem quantum code running on HPC resources. We have been working on a DoE Phase I SBIR this year as part of the Open Chemistry project, looking at the best way to combine our Girder data server project with a single-page web application and JupyterLab to offer an integrated solution for knowledge discovery in chemistry.