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Adding copy buttons to code blocks in Sphinx

3 minute read

Published:

NOTE: This is now a sphinx extension! Thanks to some friendly suggestions, I’ve written this up as a super tiny sphinx extension. Check it out here: https://github.com/choldgraf/sphinx-copybutton

Sphinx is a fantastic way to build documentation for your Python package. On the Jupyter project, we use it for almost all of our repositories.

A common use for Sphinx is to step people through a chunk of code. For example, in the Zero to JupyterHub for Kubernetes guide we step users through a number of installation and configuration steps.

A common annoyance is that there is a lot of copy/pasting involved. Sometimes you accidentally miss a character or some whitespace. So, I spent a bit of time figuring out how to automatically embed a copy button into code blocks. It turns out this is pretty easy!

Here’s what the final result will look like (just hover the code block below)

wow = this_text
is_so = much*more*copyable

Adding a copy button to your Sphinx code blocks

To accomplish this we’ll use the excellent clipboard.js which provides the machinery for copying the contents of an HTML element as well as jquery for modifying our built documentation on-demand.

The result will be a Sphinx site with code blocks that display a copy button when you hover over them. You can see how it looks on this very page, which uses a similar method (but is built with Jekyll).

Here’s what you should do:

  1. Add the clipboard.js javascript. Create a javascript script called doc/_static/custom.js. In the file, put the following code (see comments for explanation):

     function addCopyButtonToCode(){
     // get all code elements
     var allCodeBlocksElements = $( "div.highlight pre" );
    
     // For each element, do the following steps
     allCodeBlocksElements.each(function(ii) {
     // define a unique id for this element and add it
     var currentId = "codeblock" + (ii + 1);
     $(this).attr('id', currentId);
    
     // create a button that's configured for clipboard.js
     // point it to the text that's in this code block
     // add the button just after the text in the code block w/ jquery
     var clipButton = '<button class="btn copybtn" data-clipboard-target="#' + currentId + '"><img src="https://clipboardjs.com/assets/images/clippy.svg" width="13" alt="Copy to clipboard"></button>';
        $(this).after(clipButton);
     });
    
     // tell clipboard.js to look for clicks that match this query
     new Clipboard('.btn');
     }
    
     $(document).ready(function () {
     // Once the DOM is loaded for the page, attach clipboard buttons
     addCopyButtonToCode();
     });
    
  2. Add some CSS to make it pretty. Create a custom CSS file called doc/_static/custom.css (or add to one you’ve already got). In the file, put these lines:

    /* Copy buttons */
    button.copybtn {
      webkit-transition: opacity .3s ease-in-out;
      -o-transition: opacity .3s ease-in-out;
      transition: opacity .3s ease-in-out;
      opacity: 0;
      padding: 2px 6px;
      position: absolute;
      right: 4px;
      top: 4px;
    }
    div.highlight:hover .copybtn, div.highlight .copybtn:focus {
        opacity: .3;
    }
    div.highlight .copybtn:hover {
        opacity: 1;
    }
    div.highlight {
        position: relative;
    }
    
  3. Link these scripts in your configuration. You need to link your custom JS and CSS scripts, as well as the clipboard.js script so it ships with your site. In your conf.py file, add the following function/lines (or add to one you’ve already got defined).

    def setup(app):
        app.add_stylesheet('custom.css')
        app.add_javascript("custom.js")
        app.add_javascript("https://cdn.jsdelivr.net/npm/clipboard@1/dist/clipboard.min.js")
    

And that’s it! Once you clear your Sphinx cache and re-build your site, you should now have buttons that appear when you hover over them, and that copy the text inside when you click them.

Thanks

Many thanks to this StackOverflow post for the majority of the code that led to this hack!

Introducing makeitpop, a tool to perceptually warp your data!

15 minute read

Published:

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Note: It should go without saying, but you should never do the stuff that you’re about to read about here. Data is meant to speak for itself, and our visualizations should accurately reflect the data above all else.

Blogging with Jupyter Notebooks and Jekyll using nbconvert templates

3 minute read

Published:

Here’s a quick (and hopefully helpful) post for those wishing to blog in Jekyll using Jupyter notebooks. As some of you may know, nbconvert can easily convert your .ipynb files to markdown, which Jekyll can easily turn into blog posts for you.

An academic scientist goes to DevOps Days

8 minute read

Published:

Last week I took a few days to attend DevOpsDays Silicon Valley. My goal was to learn a bit about how the DevOps culture works, what are the things people are excited about and discuss in this community. I’m also interested in learning a thing or two that could be brought back into the scientific / academic world. Here are a couple of thoughts from the experience.

Dates in python

8 minute read

Published:

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As a part of setting up the website for the Docathon I’ve had to re-learn all of my date string formatting rules. It’s one of those little problems you don’t really think about - turning an arbitrary string into something structured like a date - until you’ve actually got to do it.

Matplotlib Cyclers are Great

1 minute read

Published:

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Every now and then I come across a nifty feature in Matplotlib that I wish I’d known about earlier. The MPL documentation can be a beast to get through, and as a result you miss some cool stuff sometimes.

Brainy Jingle Bells

3 minute read

Published:

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This is a quick demo of how I created this video. Check it out below, or read on to see the code that made it!

5 things I learned at SciPy

4 minute read

Published:

I’ve finally decompressed after my first go-around with Scipy. For those who haven’t heard of this conference before, Scipy is an annual meeting where members of scientific community get together to discuss their love of Python, scientific programming, and open science. It spans both academics and people from industry, making it a unique place in terms of how software interfaces with scientific research. (if you’re interested the full set of Scipy conferences, check out here.

Could Brexit have happened by chance?

11 minute read

Published:

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As a scientist, watching the Brexit vote was a little bit painful. Though probably not for the reason you’re thinking. No, it wasn’t the politics that bothered me, but the method for making such an incredibly important decision. Let me explain…

The beauty of computational efficiency

11 minute read

Published:

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When we discuss “computational efficiency”, you often hear people throw around phrases like $O(n^2)$ or $O(nlogn)$. We talk about them in the abstract, and it can be hard to appreciate what these distinctions mean and how important they are. So let’s take a quick look at what computational efficiency looks like in the context of a very famous algorithm: The Fourier Transform.

NIH grant analysis

9 minute read

Published:

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NIH Fellowship Success Rates

As I’m entering the final years of graduate school, I’ve been applying for a few typical “pre-doc” fellowships. One of these is the NRSA, which is notorious for requiring you to wade through forests of beaurocratic documents (seriously, their “guidelines” for writing an NRSA are over 100 pages!). Doing so ends up taking a LOT of time.

Craigslist data analysis

10 minute read

Published:

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Using Craigslist to compare prices in the Bay Area

In the last post I showed how to use a simple python bot to scrape data from Criagslist. This is a quick follow-up to take a peek at the data.

Scraping craigslist

14 minute read

Published:

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Overview

In this notebook, I’ll show you how to make a simple query on Craigslist using some nifty python modules. You can take advantage of all the structure data that exists on webpages to collect interesting datasets.

opensource

MNE Python

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Neuro electrophysiology visualization and analysis in Python!

The Docathon

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A week-long global sprint to improve documentation in the open-source community.

Binder

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A tool to create interactive and sharable code repositories.

publications

Decoding spectrotemporal features of overt and covert speech from the human cortex

Published in Frontiers in Neuroengineering, 2014 (paper link)

Using machine learning and predictive models to study how the brain uses experience to understand noisy speech.

Recommended citation: Martin, S., Brunner, P., Holdgraf, C. R., Heinze, H.-J., Crone, N. E., Rieger, J. W., … Pasley, B. N. (2014). Decoding spectrotemporal features of overt and covert speech from the human cortex. Frontiers in Neuroengineering, 7(May), 14. http://doi.org/10.3389/fneng.2014.00014 https://www.frontiersin.org/articles/10.3389/fneng.2014.00014/full

Rapid tuning shifts in human auditory cortex enhance speech intelligibility

Published in Nature Communications, 2016 (paper link)

Using machine learning and predictive models to study how the brain uses experience to understand noisy speech.

Recommended citation: Holdgraf, C. R., de Heer, W., Pasley, B. N., Rieger, J. W., Crone, N., Lin, J. J., … Theunissen, F. E. (2016). Rapid tuning shifts in human auditory cortex enhance speech intelligibility. Nature Communications, 7(May), 13654. http://doi.org/10.1038/ncomms13654 https://www.nature.com/articles/ncomms13654

Portable learning environments for hands-on computational instruction using container-and cloud-based technology to teach data science.

Published in ACM International Conference Proceeding Series, 2017 (paper link)

This paper covers recent work at UC Berkeley using cloud resources and open-source tools for teaching data analytics.

Recommended citation: Holdgraf, C. R., Culich, A., Rokem, A., Deniz, F., Alegro, M., & Ushizima, D. (2017). Portable learning environments for hands-on computational instruction using container-and cloud-based technology to teach data science. In ACM International Conference Proceeding Series (Vol. Part F1287). https://arxiv.org/pdf/1703.04900.pdf

Encoding and Decoding Models in Cognitive Electrophysiology

Published in Frontiers in Systems Neuroscience, 2017 (paper link)

A practical methods paper for fitting predictive models of the brain.

Recommended citation: Holdgraf, C. R., Rieger, J. W., Micheli, C., Martin, S., Knight, R. T., & Theunissen, F. E. (2017). Encoding and decoding models in cognitive electrophysiology. Frontiers in Systems Neuroscience, 11. http://doi.org/10.3389/fnsys.2017.00061 https://www.frontiersin.org/articles/10.3389/fnsys.2017.00061/full

The Types, Roles, and Practices of Documentation in Data Analytics Open Source Software Libraries

Published in Computer Supported Cooperative Work, 2018 (paper link)

A Collaborative Ethnography of Documentation Work

Recommended citation: Geiger, R. S., Varoquaux, N., Mazel-Cabasse, C., & Holdgraf, C. (2018). The Types, Roles, and Practices of Documentation in Data Analytics Open Source Software Libraries. Computer Supported Cooperative Work (CSCW). Computer Supported Cooperative Work (CSCW). https://doi.org/10.1007/s10606-018-9333-1 https://rdcu.be/QDdA

talks

From your laptop to the cloud

Published:

How open-source technologies such as Kubernetes and JupyterHub are making it possible to move analytics workflows from a single laptop to shared computational resources. This opens up possibilities in research, education, and publication. The talk discusses several of these pieces of technology, and covers many interesting case-studies in how it has been used.

Visualizing the brain with open source tools

Published:

How open-source tools make it possible to visualize the human brain from many different perspectives. I discuss challenges in visualizing something as complicated as the brain, and cover many modern-day open source tools that make this possible, discussing the lessons we’ve learned along the way.

Zero to JupyterHub on Kubernetes

Published:

A day-long tutorial covering the basics of managing cloud resources, Docker, Kubernetes, and deploying/maintaining a JupyterHub using these tools.

teaching