Edward O. Pyzer-Knapp - Deep Learning for Physical Scientists

$> conda install notebook

Standard Python scripts have their advantages – the code can be contained within a single file that can easily be run and debugged, without the need to step through and run multiple cells of code. Comments can be added to describe blocks of code, and docstrings can be used to provide more comprehensive descriptions of the script's functionality. So why use a notebook?

While Python scripts have a number of advantages while writing code, there are several shortfalls for their use in sharing your work. Comments and docstrings easily become overwhelming when detailed descriptions are required – turning a clean script into an awkward mess of comments and code blocks. This can make it difficult for others to read and understand what's going on. Furthermore, it does not allow you to incorporate plotting or other visualisations alongside your text. This is where notebooks come in.

Notebooks allow you to create a well‐structured document that incorporates executable code. The structuring allows you to create a comprehensive, flowing document – in the same way that you would for a paper or report. Unlike a traditional document, notebooks are enriched by their ability to incorporate executable code; code that can be used to run real experiments alongside the text and/or to produce interactive data visualisations. This is particularly important for sharing code and facilitating usability – providing a far more intuitive method of sharing code than a collection of Python scripts.

To use Jupyter notebooks, you will need to run the Jupyter notebook server. This can be done simply by executing the following:

$> jupyter notebook

It is recommended that you execute this from the directory you wish to work from, as this makes accessing existing project content and creating new content easy. Once the above command is executed, Jupyter should prompt your browser to open Jupyter's web User interface (UI), taking you to a page that looks like this:

You can then click “new,” which will give the option of creating a new file.

To create a new Python notebook, simple click on the Python option (in this case, Python 3). You will then be presented with a new empty notebook.

The Jupyter notebook is connected to an iPython kernel, which will allow you to execute Python code in code blocks, such as the block above. Once you have written the code you would like to execute, simply click “run” in the Jupyter UI, or hit shift + enter on your keyboard to execute the cell. Jupyter also has another type of block – markdown blocks – which we will explore in the next section.

Markdown helps to add structure to documents and improve readability. As with typical markdown, you can easily specify headings and subheadings, as demonstrated in the following example:

# Heading 1 ## Heading 2 ### Heading 3

This code produces the following:

You can also embolden or italicise your text:

**Here is an example of emboldening** and *italicization*.

Bullet points or numbered lists can also easily be used:

* Here are * some bullet points 1. Here is 2. a list 1. and sublist

Markdown in Jupyter notebooks also allows you to drop in non‐executable code, in cases where code is being used illustratively, rather than being intended for execution. This is done using backticks:

`code = example`

As well as being able to incorporate illustrative code, the markdown also makes it very easy to incorporate mathematical symbols and equations, as it supports LaTeX:

$y = \sum:{i=0}^{10}{x^i}$

Plotting in Jupyter notebooks is an easy and powerful way to visualise data and interact with the visualisations. Below we show a simple plotting example:

Note the “%matplotlib inline” line in the above code. This will produce an in‐line plot within the notebook, as follows:

But Jupyter also provides tools for creating interactive plots. This can be achieved by replacing the “%matplotlib inline” instruction to “%matplotlib notebook,” producing a plot as follows:

This interactive plot allows the user to easily pan, zoom, and download the plot.

In this section, we have seen how Jupyter notebooks can be used to create dynamic documents with embedded code. These notebooks facilitate intuitive knowledge transfer, helping you to convey complex scientific and mathematical concepts with the aid of executable code and interactive visualisations. For more information, and to see further examples of what can be achieved with Jupyter notebooks, please visit the Jupyter website at: jupyter.org

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