Astrophysics and cosmology are fields that require working with complex multidimensional data to simulate the workings of our universe. The yt project was created to make working with this data and providing useful visualizations easy and fun. This week Nathan Goldbaum and John Zuhone share the story of how yt got started, how it works, and how it is being used right now.
Computer vision is a complex field that spans industries with varying needs and implementations. Scikit-Image is a library that provides tools and techniques for people working in the sciences to process the visual data that is critical to their research. This week Stefan Van der Walt and Juan Nunez-Iglesias, co-authors of Elegant SciPy, talk about how the project got started, how it works, and how they are using it to power their experiments.
What’s the weather tomorrow? That’s the question that meteorologists are always trying to get better at answering. This week the developers of MetPy discuss how their project is used in that quest and the challenges that are inherent in atmospheric and weather research. It is a fascinating look at dealing with uncertainty and using messy, multidimensional data to model a massively complex system.
Location is an increasingly relevant aspect of software systems as we have more internet connected devices with GPS capabilities. GIS (Geographic Information Systems) are used for processing and analyzing this data, and fortunately Python has a suite of libraries to facilitate these endeavors. This week Sean Gillies, an author and contributor of many of these tools, shares the story of his career and contributions, and the work that he is doing at MapBox.
We’re delving into the complex workings of your mind this week on Podcast.__init__ with Jonathan Peirce. He tells us about how he started the PsychoPy project and how it has grown in utility and popularity over the years. We discussed the ways that it has been put to use in myriad psychological experiments, the inner workings of how to design and execute those experiments, and what is in store for its future.
MP3 Audio [65 MB]DownloadShow URL Summary Dave Beazley has been using and teaching Python since the early days of the language. He has also been instrumental in spreading the gospel of asynchronous programming and the many ways that it can improve the performance of your programs. This week I had …
Being able to understand the context of a piece of text is generally thought to be the domain of human intelligence. However, topic modeling and semantic analysis can be used to allow a computer to determine whether different messages and articles are about the same thing. This week we spoke with Radim Řehůřek about his work on GenSim, which is a Python library for performing unsupervised analysis of unstructured text and applying machine learning models to the problem of natural language understanding.
Wouldn’t it be nice to be able to generate interactive 3D visualizations of physical systems in a declarative manner with Python? In this episode we spoke with Ruth Chabay and Bruce Sherwood about the VPython project which does just that. They tell us about how the use VPython in their classrooms, how the project got started, and the work they have done to bring it into the browser.
Looking for an open source alternative to Mathematica or MatLab for solving algebraic equations? Look no further than the excellent SymPy project. It is a well built and easy to use Computer Algebra System (CAS) and in this episode we spoke with the current project maintainer Aaron Meurer about its capabilities and when you might want to use it.
What is Solar Physics? How does it differ from AstroPhysics? What does this all have to do with Python? In this episode we answer all of those questions when we interview Stuart Mumford about his work on SunPy. So put on your sunglasses and learn about how to use Python to decipher the secrets of our closest star.