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plotly.js is now open source

plotly

Plot.ly is a fairly comprehensive tool for creating whizzy interactive charts from data. It provides a suite of tools to make a whole range of different types of charts.

Until now, it’s been a web service you send data away to in order to get a chart back. I’d always been wary of that, because I worry about what happens when Plotly the company gets sold off or goes bust, and plot.ly the service gets shut down.

Well, now I can use a little bit of plot.ly, because they’ve released the bit of the chart-drawing code that runs in your browser under the MIT open source licence, meaning anyone can use it independently of Plotly’s servers.

With just the open-source stuff, the process of creating a chart is quite torturous because you have to define what you want by following a fairly illegible JSON schema. That means there’s still a reason to use the proprietary stuff that gives you a nice interface from Python or R, though I suppose people will soon enough start making their own versions of those that just tie into the Javascript stuff.

More information

Plotly.js Open-Source Announcement

plotly.js on GitHub

Beautiful Science at the British Library

farr-cholera-opt

The British Library has an exhibition on at the moment that you might like to see.

Beautiful Science: Picturing Data, Inspiring Insight is all about data visualisation. Here’s the blurb:

Turning numbers into pictures that tell important stories and reveal the meaning held within is an essential part of what it means to be a scientist. This is as true in today’s era of genome sequencing and climate models as it was in the 19th century.

Beautiful Science explores how our understanding of ourselves and our planet has evolved alongside our ability to represent, graph and map the mass data of the time.

From John Snow’s plotting of the 1854 London cholera infections on a map to colourful depictions of the tree of life, discover how picturing scientific data provides new insight into our lives.

Beautiful Science is in the British Library’s Folio Society Gallery until the 26th of May and admission is free.

More information

Beautiful Science at the British Library.

Competition to visualise open government data

Who loves data? If we’re talking about the android from Star Trek: TNG, then I do, and if we’re talking about the thing that’s not the plural of anecdotes, then I’m pretty sure the answer is everyone.

If you love data, then you’ll definitely love visualising data, and Google have teamed up with the Open Knowledge Foundation to launch a data-visualising competition. Nobody has more data than… well, Google, but second in that race is Governments, and the world’s governments are releasing a massive shedload of open data for people to play with.

The London Pie

EDF Energy, one of the pantheon of Olympics sponsors, has opted to share its love for energy through its ‘Energy of the Nation’ project, launched earlier this week. By monitoring the nation’s positive and negative ‘energy’, by which they mean ‘things they are saying on Twitter’, they’ll turn the London Eye into a giant pie chart each evening at 9pm and display the results of the previous 24 hours’ sentiments over the course of 24 minutes. While my approval of such a large act of data representation is practically off the (pie) chart, I’m interested to find out how it works before judging it either way.

WLTM real number. Must be normal and enjoy long walks on the plane

Something that whipped round Twitter over the weekend is an early version of a paper by Francisco Aragón Artacho, David Bailey, Jonathan Borwein and Peter Borwein, investigating the usefulness of planar walks on the digits of real numbers as a way of measuring their randomness.

A million step walk on the concatenation of the base 10 digits of the prime numbers, converted to base 4

A problem with real numbers is to decide whether their digits (in whatever base) are “random” or not. As always, a strict definition of randomness is up to either the individual or the enlightened metaphysicist, but one definition of randomness is normality – every finite string of digits occurs with uniform asymptotic frequency in the decimal (or octal or whatever) representation of the number. Not many results on this subject exist, so people try visual tools to see what randomness looks like, comparing potentially normal numbers like $\pi$ with pseudorandom and non-random numbers. In fact, the (very old) question of whether $\pi$ is normal was one of the main motivators for this study.