Running-a-JupyterLab-Notebook

Example Volcano Plot in a JupyterLab Notebook on CAVATICA

Log back in

Within your browser, navigate back to CAVATICA and log back in.

Authorize CAVATICA to act on your behalf.

Arrive at the CAVATICA window - we created a project and an analysis notebook already, you may have to select that project.

Fork the GitHub Repository

We are going to fork the GitHub repository - to do so navigate in your browser to the repository for this course.

Click here Elements of Style Workflow Creation and Maintenance

In the upper righthand side of the screen, select Fork and choose to Fork it into your own personal repository.

If you have already made a fork, it would make sense to fetch any upstream changes that may have occured since you last visted. Your screen should look like this:

Next, navigate to the 🟦Code button on the right and select HTTPS and copy the link.

Clone the repository in the Jupyterlab terminal window

Return to your CAVATICA Window and go back to your JupyterLab notebook. And select the terminal window

Now we want to clone the repository fork we made:

At the prompt type:

git clone https://github.com/adeslat/Elements-of-Style-Workflow-Creation-Maintenance

But, we might get an error.

Authenticating for GitHub in CAVATICA

Because of that, we need to authenticate to our GitHub within CAVATICA.

We need to install GitHub Command Line Interface - more on this in the next lesson but for now type in the terminal window.

conda install -c conda-forge gh -y

After completing, the installation looks like this:

Now we need to authenticate. We do this with our personal GitHub Token.

Generating A GitHub Authentication Token

Generating your own GitHub Authentication Token

Once we have our token, we can now authenticate.

Clone our repository version

Now that we are authenticated, we can clone successfully.

git clone https://github.com/adeslat/Elements-of-Style-Workflow-Creation-Maintenance

And the result should look like this:

Useful Command Line Functions.

First we run the JupyterLab notebook which introduces us to some Command Line Functions that will be useful.

Open the 1-using-the-command-line.ipynb

Now follow the folder and select the first notebook in the directory, and we will execute all the command lines interactively.

Navigate to the folder on the left:

• Double click on Elements-of-Style-Workflow-Creation-Maintenance
• Double click on classes
• Double click on Running-a-JupyterLab-Notebook
• Finally, double click on 1-using-the-command-line.ipynb to open the notebook

Next, clear the outputs and restart the kernel by selecting from the pull down menu under Kernel:

Confirm your desire to restart the Kernel:

And we will walk through all the selected commands.

Note that this notebook had the python kernel running, normally we would have executed this in a bash kernel. This Kernel is not yet available on the CAVATICA platform but it has been added to the Seven Bridges Team Backlog.

Open the 2-reading-data-and-plotting-in-R.ipynb

Next, we will execute the second jupyterlab notebook, this one is running the R kernel.

Look again at the folders on the left and open the second notebook 2-reading-data-and-plotting-in-R.ipynb by double-clicking on the notebook.

Again, Restart Kernel and Clear Outputs.

Confirm your desire to do so:

This is running the R Kernel and as we load the libraries we see that there is a [*] inside the brackets. This indicates that it is executing and looks like this:

When completed, it will look the [*] becomes [1].

Recap

In this lesson:

• We logged into CAVATICA
• We started an interactive analysis with JupyterLab
• We authenticated with GitHub
• We executed and saw some Command Line Functions
• We generated a Volcano Plot in a JupyterLab notebook running an R kernel

Return to the Agenda

Main Agenda

Additional resources:

• CAVATICA documentation for the JupyterLab interface: https://docs.cavatica.org/docs/editor-quick-reference
• Official documentation from project JupyterLab: https://jupyterlab.readthedocs.io/en/stable/