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Computation & Analysis

Build Your Own Weather Station in a Snap with the Wolfram Cloud!

Recently Stephen Wolfram announced the Wolfram Data Drop, which is a great new tool to upload any type of data from any type of device. I'll show how you can use the Wolfram Data Drop with a weather station you build using some basic hardware and a few lines of code. Once completed, your device will take temperature measurements every second for 60 seconds, and upload their average value to the Wolfram Data Drop every minute. This will give you 60 data points per hour and 1,440 data points per day. With this data you can use Wolfram Programming Cloud to understand how the temperature changes over time. You can find the exact times in a given day when the temperature was the highest or lowest, when the temperature changed the fastest, and maybe even use the data to make predictions! Can you beat your local weather station and make a prediction that is better?
Arnoud Buzing, Director of Quality and Release Management
Computation & Analysis

New in the Wolfram Language: TimelinePlot

A few years ago we created a timeline of the history of systematic data and computable knowledge, which you can look at online. I wrote the code that placed events along the timeline, and then our graphic designers did the real work in deciding where to put the labels, choosing fonts and colors, and doing all the other things that go into creating a production-quality poster. Fast-forward a bit, and last year we added NumberLinePlot to the Wolfram Language to visualize points, intervals, and inequalities. Once people started seeing the number lines, we began getting requests for similar plots, but with dates and times, so we decided it was time to tackle TimelinePlot.
Brett Champion, Visualization Manager, Algorithms R&D
Announcements & Events

The Wolfram Data Drop Is Live!

Where should data from the Internet of Things go? We’ve got great technology in the Wolfram Language for interpreting, visualizing, analyzing, querying and otherwise doing interesting things with it. But the question is, how should the data from all those connected devices and everything else actually get to where good things can be done with […]

Stephen Wolfram
Best of Blog

Find Waldo Faster

Martin Handford can spend weeks creating a single Where's Waldo puzzle hiding a tiny red and white striped character wearing Lennon glasses and a bobble hat among an ocean of cartoon figures that are immersed in amusing activities. Finding Waldo is the puzzle's objective, so hiding him well, perhaps, is even more challenging. Martin once said, "As I work my way through a picture, I add Wally when I come to what I feel is a good place to hide him." Aware of this, Ben Blatt from Slate magazine wondered if it's possible "to master Where's Waldo by mapping Handford's patterns?" Ben devised a simple trick to speed up a Waldo search. In a sense, it's the same observation that allowed Jon McLoone to write an algorithm that can beat a human in a Rock-Paper-Scissors game. As Jon puts it, "we can rely on the fact that humans are not very good at being random."
Vitaliy Kaurov, Director of Engagement
Best of Blog

Modeling a Pandemic like Ebola with the Wolfram Language

Data is critical for an objective outlook, but bare data is not a forecast. Scientific models are necessary to predict pandemics, terrorist attacks, natural disasters, market crashes, and other complex aspects of our world. One of the tools for combating the ongoing and tragic Ebola outbreak is to make computer models of the virus's possible spread. By understanding where and how quickly the outbreak is likely to appear, policy makers can put into place effective measures to slow transmissions and ultimately bring the epidemic to a halt. Our goal here is to show how to set up a mathematical model that depicts a global spread of a pandemic, using real-world data. The model would apply to any pandemic, but we will sometimes mention and use current Ebola outbreak data to put the simulation into perspective. The results should not be taken as a realistic quantitative projection of current Ebola pandemic.
Vitaliy Kaurov, Director of Engagement
Education & Academic

Summer Internships

Summer has drawn to a close, and so too have our annual internships. Each year Wolfram welcomes a new group of interns to work on an exciting array of projects ranging all the way from Bell polynomials to food science. It was a season for learning, growth, and making strides across disciplinary and academic divides. The Wolfram interns are an invaluable part of our team, and they couldn't wait to tell us all about their time here. Here are just a few examples of the work that was done.
Jenna Giuffrida, Content Administrator, Technical Communications and Strategy Group
Education & Academic

First Tweet-a-Program Code Challenge: Space Week

In honor of World Space Week and this year’s theme of satellite navigation, “Space: Guiding Your Way,” we’re issuing a Tweet-a-Program Code Challenge focused on anything to do with space and getting there. You tweet us your "space-iest" line(s) of Wolfram Language code, and then we’ll use the Wolfram Language to randomly select three winning tweets (plus a few favorites) to shower with retweets, pin or post to our wall, and receive a free Wolfram T-shirt! Any space-themed submissions tweeted to us @wolframtap all day Thursday and Friday (12am PDT Thursday, October 9 through 11:59pm PDT Friday, October 10) will be eligible to win. To not waste needed code space, no hashtag is required with your original submission, but we encourage you to share your results by retweeting them with hashtag #wsw2014 and #tapspaceweek. In addition to satellite path tracking and real-time analysis, the Wolfram Language gives you access to all sorts of entities, formulas, and other functionality for astronomical computation and coding---from supernovas, comets, and constellations to the Sun, deep space, and other galaxies. Maybe you want to remix the planets and their colors, as Stephen Wolfram did in one of his first Tweet-a-Program tweets:
Wolfram Blog Team
Best of Blog

Which Is Closer: Local Beer or Local Whiskey?

In today's blog post, we will use some of the new features of the Wolfram Language, such as language processing, geometric regions, map-making capabilities, and deploying forms to analyze and visualize the distribution of beer breweries and whiskey distilleries in the US. In particular, we want to answer the core question: for which fraction of the US is the nearest brewery further away than the nearest distillery? Disclaimer: you may read, carry out, and modify inputs in this blog post independent of your age. Hands-on taste tests might require a certain minimal legal age (check your countries' and states' laws). We start by importing two images from Wikipedia to set the theme; later we will use them on maps.
Michael Trott, Chief Scientist, Wolfram|Alpha Scientific Content
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