Research Platform Services Blog
Month
by Emilie Walsh
If you looking for a hard copy of that little comic on 3D printing, come over at Colab to pick up one, and join a training in 3D modelling and 3D printing with Eric Jong!
Next training the 10th of October (cake included) :
https://www.eventbrite.com.au/e/introduction-to-3d-printing-with-tinkercad-tickets-50681449580
Have you ever wanted to type some mathematics in a document?
During a meet-up on the 5th of September, 2018 we discussed how to typeset mathematics, using LaTeX and JavaScript. Keep reading to learn how, and to view some beautiful examples (including animated rainbows!)…
Do you want to use mathematics in your documents? Join us tomorrow afternoon to learn how, using LaTeX and JavaScript!
Register for free: https://t.co/2w4Ec1CnMS
…see examples, learn tips & tricks, and ask questions while enjoying light snacks and beverages with the community! pic.twitter.com/YIv2nzVN1C— Meirian (@MeirianLT)
September 4, 2018
LaTeX is a programming language for high-quality, beautiful typesetting. TeX was originally released by Donald Knuth in 1978. As a mathematician himself, Knuth was motivated to create something which allowed one to type complicated mathematical formulas in a professional-looking manner. As a result of his efforts, the ability to typeset mathematics in LaTeX is a major strength.
Anyone who has studied mathematics or statistics will know that the notation is plentiful. LaTeX allows for the many symbols you may desire to use, whilst presenting them in a clear and beautiful way. So many symbols are available that there is a 338 page Comprehensive LaTeX Symbol List. But when it comes to finding the symbols you need, I recommend using Detexify by Kirelabs. This tool allows you to simply draw the symbol you’re looking for, then it will identify it and tell you which command and package to use!
During the meet-up we created a LaTeX document which was abundant in examples of mathematics notation and formulas. You can view the document on Overleaf.
Once LaTeX had been introduced, Errol then gave an excellent presentation on the inclusion of mathematics on the web. Read on to learn more and see his animated rainbow equation!
Thanks to @maegul for your presentation on #JavaScript during our meet-up today on Mathematics in your Documents at @ResPlat! Your use of emoticons, rainbows and animation captured everyone’s attention! pic.twitter.com/h5B33B7ZdM
— Meirian (@MeirianLT)
September 5, 2018
One of the great things LaTeX has given to the world is its mathematics or equation typesetting syntax. Not just because LaTeX is great and powerful, but, because this typesetting syntax is now a standard across many different kind of software.
This is especially the case when it comes to web pages. Go to the wikipedia page on the normal distribution, and see all of the mathematics typesetting. Copy one of the equations and paste it into a text file … what do you see? (Spoiler alert … LaTeX).
All of this works because when it comes to the programming language called javascript, which powers all of the interactivity and animation of the web, there’s a principle which has shown to be true again and again: ‘anything that can be made in javascript will be made in javascript’. The web is everywhere and everyone uses it all of the time. Which means javascript has to do its best to be everything to everyone. And so, LaTeX mathematics can be done with javascript.
If ever you find yourself wanting to write a blog, create a simple web page (which is easier than you might think), or share your Jupyter or RMarkdown notebook or create an attractive interactive data visualisation or publication for your research, know that javascript has got your back when you need to show some mathematics.
Beyond blog writing, you may be wondering why you should be interested in javascript and making web pages. There are two responses to this. First, with the Observable Notebook, it is now easy to learn and get started. Check out the tutorials at the home page and just start playing. Second, unlike any programming language you’ve learnt, javascript is built to normal programming things like crunching numbers, as well as graphics. This means you can make interactive publications, like this notebook on predator and prey population dynamics.
Or, you can just make rainbow equations:
#javascript + LaTeX = Awesomeness @observablehq https://t.co/V7KTWx3IlL pic.twitter.com/b1VfXhJxXV
— Errol Lloyd (@maegul)
September 6, 2018
If you’re interested to learn more, take a look at this notebook of mine, which demonstrates LaTeX, interactivity, using Observable, and making interactive graphics with javascript.
This blogpost was created by Meirian Lovelace-Tozer (@MeirianLT), and Errol Lloyd (@maegul) who are Research Community Co-ordinator and trainers at Research Platforms Services (@ResPlat).
Why would you want to learn Numpy? Well I guess I can start by telling you why I learned it!
I don’t often admit this, But I started as a Matlab user (gasp!). Needless to say it is an excellent product, but sadly in many professions, it is quite a bit too expensive. So I found myself looking for an open source replacement. Also, I wanted to learn Python because it got along well with the GIS programs I often use.
However there is a bit of a learning curve: In Matlab you can easily do vectorized mathematics or matrix mathematics on two vectors of data like so:
And that is hella convenient! The maths look like maths!!!
Now, when I first started using Python, I was told that it was basically like Matlab so I tried to do some basic vectorized operations:
That is not what I wanted!!
Python will actually concatenate, or splice, the two lists together into one list, instead of adding up the items. This is because lists can store any type of data or python object, so Python wouldn’t necessarily know how to add up each item in the list like we saw up there… hmmm A bit frustrating!
Thank you for your realism Borimir. Now a Python Purist would use a list comprehension, which is basically a quick one liner loop creating a new list
**Opinion: **There is a lot of riff raff around the math up above, which makes it a bit harder to read and comprehend, plus, if we accidentally have a non number in our inputs, python will flag an error right?
Well luckily people wayyyy smarter than I have addressed my concerns, and have created the Numpy Library in order to make maths look like maths again in Python:
As you can see above, we can turn any list we want into a numpy array for analysis, it just follows one major rule:
Numpy Array Rule 0 - All data in it are coerced into the same data type,
that way python knows how to add the elements together (and do other mathematical operations)
But wait there is more!
Because of the above rule, Numpy arrays are much more efficient than lists at storing large data sets of numerical data, and cycling through them than a list
Numpy arrays can be multiple dimensions, representing geographic data, 3-D data, and any sort of multidimensional data set
….AND Numpy arrays are used to feed data into a variety of other data science packages like Pandas, Scipy, Scikit-learn and plotting libraries
So how do I Numpy?
Well I am a Geomorphologist, which is a fancy term for an Earth Scientist who spaces out so much on car rides they wonder why that hill over there is a particular shape.
#realgeomorphologyquestions - answer - It’s very pointy
I literally study the shape of the Earth’s surface. In many cases, the data describing the earth’s surface, and the factors changing it, can be described as a “raster” (basically photograph with pixels)
where we lay an imaginary grid on the earth’s surface to separate it into even sized squares, and store a value (say elevation) representing the value averaged over that square in the grid. Here is some 2 metre by 2 metre resolution LiDAR elevation data as an example:
Note, there is a bridge crossing the river from north to south in the middle,see if you can spot the road fill! The darker red colors represent lower elevations, and therefore old (and current) river channels. I could stare at this stuff all day. That’s why we in the biz call it LiDAR crack… because it is physically addicting to a Geomorphologist!
And with a 2 dimensional Numpy array, i get a lean efficient fast way to to analyse my LiDAR data using open source software, and figure out why that river switched places up above!
But wait there’s more!
if I am feeling really fancy, I can use a third dimension representing time slices of lets say a flood simulation, and make a movie of the water velocities (note matplotlib helped here as well)
SO Are you ready to calculate smarter not harder? Then check out our course material here come on down to our numpy workshops (see calendar) and learn how to crunch the numbers with Python
Cheers,
Jon
At the recent Omeka Meet-Up, we opened up discussion to include all things Digital Humanities.
Over three fantastic presentations and several provocations around data, Humanities research and cultural collections, the group developed a range of responses, provocations and ideas for future projects.
To kick start things we first had a discussion about what we mean when we use the term ‘data’ in the Digital Humanities space. Are we referring to text? Images? Digitised materials? Or simply everything involved in research? Questions around ontologies and silenced voices also came to the fore as did thoughts about the future of infrastructure in Digital HASS.
Julianne Bell then gave a fantastic presentation on a project she is working on as part of the Digital Studio Graduate Internship program, entitled Execution Ballads of Pre-Modern Europe. Developing this project with Dr Una McIlvenna from the School of Historical and Philosophical Studies, Julianne has encountered an array of challenges and discoveries around presenting and narrating cultural materials with Omeka.
The project has developed a database on execution ballads (cheaply printed songs about crime and punishment dating from c. 1550-1900) that includes hundreds, if not thousands of items. These are partly in textual form (transcriptions of lyrics, notes on historical context, dates of publishing, etc), partly pictorial (images of the pamphlets and broadsides, images of historical figures who feature in the ballads, etc), and some audio recordings. Until recently, this has been stored in a MySQL database on Heurist software. The intern project is transferring and setting up the database correctly in the content management software Omeka, as well as adding to the database.
Here’s a breakdown of the project:
And the methods and approaches taken in customising Omeka to fit the parameters and requirements of the project:
Following Julianne, our excellent Omeka ResLead Alex Shermon spoke about his work on an Omeka site that is part of a broader project within the University of Melbourne looking at Sir Redmond Barry.
This project brings together cultural and legal history, biography, philosophy and a wide range of curation and collecting practices.
Here’s the official description from the Lives Lived with Law Journal edition:
“We see ‘Lives Lived with Law’ as drawing into relation the scholarly experiences of disciplinary technique, and the experimentation over time with style and forms that help to show what the conduct of lawful relations can be between peoples, between everyday and official experience of law, as well as between Indigenous and Anglo Australian laws.” (Genovese, Rush, McVeigh. Lives Lived with Law: An Introduction, p.2)
According to Alex, “Jurisography is the hip new rebranding of legal biography.”
Working under Carole Hinchcliff (who is collaborating with several academics from the Melbourne law school), Alex has set out to examine the fragmentary sources and forms of legal theory involved with Sir Redmond Barry.
Title: The trial of Ned Kelly
Subtitle: Newspaper illustration
Date: 1880
Keywords: people, biography, Bushranger, trial, illustration, media
Record creator: Department of Information
National Archives of Australia
Alex writes how, “Sir Redmond Barry (who, by the way, has an apple named after him) got a lot done and left a pretty big legacy. Aside from the apple thing, he helped found the State Library, The National Gallery, and the University of Melbourne (He would have loved the sheer boring monotony evoked in the brown brick monstrosity named after him).”
“In law he was the first standing counsel for Aboriginal people, and famously sentenced Ned Kelly to death just days before he himself died. Barry shattered over Melbourne when he died. The task then for the Jurisographer, is to pick up these pieces and present them in a meaningful way. That’s where Omeka came into our project. It is allowing us to relate contemporary instances of Barry (statues and bookcases) with his own digitised publications, such as the important addresses he made at circuit courts and his annotations in statute volumes.”
Alex presenting his talk:
Mitchell Harrop from SCIP then delivered some timely and useful information about Omeka and Web Archiving - a topic we’ll be coming back to very soon as there is increased need for and interest in researchers being able to maintain and present their work on Omeka into the future.
Stay tuned for the next Omeka event. We have something very exciting in the pipeline which will, once again, consider dynamic scholarly workflows that connect Omeka with other digital research tools.
For more information, get in touch with our Community leader, Tyne!