Science

MetPy: Taming The Weather With Python - Episode 100

Summary

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.

Preface

  • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
  • I would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable.
  • When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app.
  • Visit our site to subscribe to our show, sign up for our newsletter, read the show notes, and get in touch.
  • To help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers
  • Your host as usual is Tobias Macey and today I’m interviewing Ryan May, Sean Arms, and John Leeman about MetPy, a collection of tools and notebooks for analyzing meteorological data in Python.

Interview

  • Introductions
  • How did you get introduced to Python?
  • What is MetPy and what is the problem that prompted you to create it?
  • Can you explain the problem domain for Meteorology and how it compares to other domains such as the physical sciences?
  • How do you deal with the inherent uncertainty of atmospheric and weather data?
  • What are some of the data sources and data formats that a meteorologist works with?
  • To what degree is machine learning or artificial intelligence employed when modelling climate and local weather patterns?
  • The MetPy documentation has a number of examples of how to use the library and a number of them produce some fairly complex plots and graphs. How prevalent is the need to interact with meteorological data visually to properly understand what it is trying to tell you?
  • I read through your developer guide and watched your SciPy talk about development automation in MetPy. My understanding is that individuals with a pure science background tend to eschew formal code styles and software engineering practices so I’m curious what your experience has been when interacting with your user community.
  • What are some of the interesting innovations in weather science that you are looking forward to?

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

Python for GIS with Sean Gillies - Episode 80

Summary

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.

Brief Introduction

  • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
  • I would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable.
  • When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app.
  • You’ll want to make sure that your users don’t have to put up with bugs, so you should use Rollbar for tracking and aggregating your application errors to find and fix the bugs in your application before your users notice they exist. Use the link rollbar.com/podcastinit to get 90 days and 300,000 errors for free on their bootstrap plan.
  • Visit our site to subscribe to our show, sign up for our newsletter, read the show notes, and get in touch.
  • To help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers
  • Join our community! Visit discourse.pythonpodcast.com for your opportunity to find out about upcoming guests, suggest questions, and propose show ideas.
  • Your host as usual is Tobias Macey
  • Today I’m interviewing Sean Gillies about writing Geographic Information Systems in Python.

Interview with Sean Gillies

  • Introductions
  • How did you get introduced to Python?
  • Can you start by describing what Geographic Information Systems are and what kinds of projects might take advantage of them?
  • How did you first get involved in the area of GIS and location-based computation?
  • What was the state of the Python ecosystem like for writing these kinds of applications?
  • You have created and contributed to a number of the canonical tools for building GIS systems in Python. Can you list at least some of them and describe how they fit together for different applications?
  • What are some of the unique challenges associated with trying to model geographical features in a manner that allows for effective computation?
    • How does the complexity of modeling and computation scale with increasing land area?
  • Mapping and cartography have an incredibly long history with an ever-evolving set of tools. What does our digital age bring to this time-honored discipline that was previously impossible or impractical?
  • To build accurate and effective representations of our physical world there are a number of domains involved, such as geometry and geography. What advice do you have for someone who is interested in getting started in this particular niche?
  • What level of expertise would you advise for someone who simply wants to add some location-aware features to their application?
  • I know that you joined Mapbox a little while ago. Which parts of their stack are written in Python?
  • What are the areas where Python still falls short and which languages or tools do you turn to in those cases?

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

PsychoPy with Jonathan Peirce - Episode 76

Summary

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.

Brief Introduction

  • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
  • I would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable.
  • Hired is sponsoring us this week. If you’re looking for a job as a developer or designer then Hired will bring the opportunities to you. Sign up at hired.com/podcastinit to double your signing bonus.
  • Once you land a job you can check out our other sponsor Linode for running your awesome new Python apps. Check them out at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for your next project
  • You want to make sure your apps are error-free so give our last sponsor, Rollbar, a look. Rollbar is a service for tracking and aggregating your application errors so that you can find and fix the bugs in your application before your users notice they exist. Use the link rollbar.com/podcastinit to get 90 days and 300,000 errors for free on their bootstrap plan.
  • Visit our site to subscribe to our show, sign up for our newsletter, read the show notes, and get in touch.
  • By leaving a review on iTunes, or Google Play Music it becomes easier for other people to find us.
  • Join our community! Visit discourse.pythonpodcast.com to help us grow and connect our wonderful audience.
  • Your hosts as usual are Tobias Macey and Chris Patti
  • Today we’re interviewing Jonathan Peirce about PsychoPy, an open source application for the presentation and collection of stimuli for psychological experimentation

Interview with Jonathan Peirce

  • Introductions
  • How did you get introduced to Python? – Chris
  • Can you start by telling us what PsychoPy is and how the project got started? – Tobias
  • How does PsychoPy compare feature wise against some of the proprietary alternatives? – Chris
  • In the documentation you mention that this project is useful for the fields of psychophysics, cognitive neuroscience and experimental psychology. Can you provide some insight into how those disciplines differ and what constitutes an experiment? – Tobias
  • Do you find that your users who have no previous formal programming training come up to speed with PsychoPy quickly? What are some of the challenges there? -Chris
  • Can you describe the internal architecture of PsychoPy and how you approached the design? – Tobias
  • How easy is it to extend PsychoPy with new types of stimulus? – Chris
  • What are some interesting challenges you faced when implementing PsychoPy? – Chris
  • I noticed that you support a number of output data formats, including pickle. What are some of the most popular analysis tools for users of PsychoPy? – Tobias
    • Have you investigated the use of the new Feather library? – Tobias
  • How is data input typically managed? Does PsychoPy support automated readings from test equipment or is that the responsibility of those conducting the experiment? – Tobias
  • What are some of the most interesting experiments that you are aware of having been conducted using PsychoPy? – Chris
  • While reading the docs I found the page describing the integration with the OSF (Open Science Framework) for sharing and validating an experiment and the collected data with other members of the field. Can you explain why that is beneficial to the researchers and compare it with other options such as GitHub for use within the sciences? – Tobias
  • Do you have a roadmap of features that you would like to add to PsychoPy or is it largely driven by contributions from practitioners who are extending it to suit their needs? – Tobias

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

Dave Beazley - Episode 72

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 the pleasure of speaking with him about his history with the language and some of his favorite presentations and projects.

Brief Introduction

  • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
  • I would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable. For details on how to support the show you can visit our site at pythonpodcast.com
  • Linode is sponsoring us this week. Check them out at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for your next project
  • We are also sponsored by Sentry this week. Stop hoping your users will report bugs. Sentry’s real-time tracking gives you insight into production deployments and information to reproduce and fix crashes. Check them out at getsentry.com and use the code podcastinit at signup to get a $50 credit!
  • Hired has also returned as a sponsor this week. If you’re looking for a job as a developer or designer then Hired will bring the opportunities to you. Sign up at hired.com/podcastinit to double your signing bonus.
  • Visit our site to subscribe to our show, sign up for our newsletter, read the show notes, and get in touch.
  • To help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers
  • Join our community! Visit discourse.pythonpodcast.com for your opportunity to find out about upcoming guests, suggest questions, and propose show ideas.
  • Your hosts as usual are Tobias Macey and Chris Patti
  • Today we’re interviewing Dave Beazley about his career with Python

Interview with Dave Beazley

  • Introductions
  • How did you get introduced to Python? – Tobias
  • How has Python and its community helped to shape your career? – Tobias
  • What are some of the major themes that you have focused on in your work? – Tobias
  • One of the things that you are known for is doing live-coding presentations, many of which are fairly advanced. What is it about that format that appeals to you? – Tobias
    • What are some of your favorite stories about a presentation that didn’t quite go as planned? – Tobias
  • You have given a large number of talks at various conferences. What are some of your favorites? – Tobias
  • What impact do you think that asynchronous programming will have on the future of the Python language and ecosystem? – Tobias
  • Are there any features that you see in other languages that you would like to have incorporated in Python? – Tobias
  • On the about page for your website you talk about some of the low-level code and hardware knowledge that you picked up by working with computers as a kid. Do you think that people who are getting started with programming now are missing out by not getting exposed to the kinds of hardware and software that was present before computing became mainstream?
  • You have had the opportunity to work on a large variety of projects, both on a hobby and professional level. What are some of your favorites? – Tobias
  • What is it about Python that has managed to hold your interest for so many years? – Tobias

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

GenSim with Radim Řehůřek - Episode 71

Summary

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.

Brief Introduction

  • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
  • I would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable. For details on how to support the show you can visit our site at pythonpodcast.com
  • Linode is sponsoring us this week. Check them out at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for your next project
  • We are also sponsored by Sentry this week. Stop hoping your users will report bugs. Sentry’s real-time tracking gives you insight into production deployments and information to reproduce and fix crashes. Check them out at getsentry.com and use the code podcastinit at signup to get a $50 credit on your account.
  • Visit our site to subscribe to our show, sign up for our newsletter, read the show notes, and get in touch.
  • To help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers
  • Join our community! Visit discourse.pythonpodcast.com for your opportunity to find out about upcoming guests, suggest questions, and propose show ideas.
  • Your hosts as usual are Tobias Macey and Chris Patti
  • Today we’re interviewing Radim Řehůřek about Gensim, a library for topic modeling and semantic analysis of natural language.

Interview with Radim Řehůřek

  • Introductions
  • How did you get introduced to Python? – Chris
  • Can you start by giving us an explanation of topic modeling and semantic analysis? – Tobias
  • What is Gensim and what inspired you to create it? – Tobias
  • What facilities does Gensim provide to simplify the work of this kind of language analysis? – Tobias
  • Can you describe the features that set it apart from other projects such as the NLTK or Spacy? – Tobias
  • What are some of the practical applications that Gensim can be used for? – Tobias
  • One of the features that stuck out to me is the fact that Gensim can process corpora on disk that would be too large to fit into memory. Can you explain some of the algorithmic work that was necessary to allow for this streaming process to be possible? – Tobias
    • Given that it can handle streams of data, could it also be used in the context of something like Spark? – Tobias
  • Gensim also supports unsupervised model building. What kinds of limitations does this have and when would you need a human in the loop? – Tobias
    • Once a model has been trained, how does it get saved and reloaded for subsequent use? – Tobias
  • What are some of the more unorthodox or interesting uses people have put Gensim to that you’ve heard about? – Chris
  • In addition to your work on Gensim, and partly due to its popularity, you have started a consultancy for customers who are interested in improving their data analysis capabilities. How does that feed back into Gensim? – Tobias
  • Are there any improvements in Gensim or other libraries that you have made available as a result of issues that have come up during client engagements? – Tobias
  • Is it difficult to find contributors to Gensim because of its advanced nature? – Tobias
  • Are there any resources you’d like to recommend our listeners explore to get a more in depth understanding of topic modeling and related techniques? – Chris

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

VPython with Ruth Chabay and Bruce Sherwood - Episode 49

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Summary

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.

Brief Introduction

  • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
  • Subscribe on iTunes, Stitcher, TuneIn or RSS
  • Follow us on Twitter or Google+
  • Give us feedback! Leave a review on iTunes, Tweet to us, send us an email or leave us a message on Google+
  • Join our community! Visit discourse.pythonpodcast.com for your opportunity to find out about upcoming guests, suggest questions, and propose show ideas.
  • I would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable. For details on how to support the show you can visit our site at pythonpodcast.com
  • Linode is sponsoring us this week. Check them out at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for your next project
  • I would also like to thank Hired, a job marketplace for developers and designers, for sponsoring this episode of Podcast.__init__. Use the link hired.com/podcastinit to double your signing bonus.
  • Your hosts as usual are Tobias Macey and Chris Patti
  • Today we are interviewing Ruth Chabay and Bruce Sherwood about their work on VPython

Interview

  • Introductions
  • How did you get introduced to Python? – Chris
  • What is VPython and how did it get started? – Tobias
  • What problems inspired you to create VPython? – Chris
  • How do you design an API that allows for such powerful 3D visualization while still making it accessible to students who are focusing on learning new concepts in mathematics and physics so that they don’t get overwhelmed by the tool? – Tobias
  • I know many schools have embraced the open curriculum idea, have any of your physics courses using VPython been made available to the non matriculating public? – Chris
  • How does VPython perform its rendering? If you were to reimplement it would you do anything differently? – Tobias
  • One of the remarkable points about VPython is its ability to execute the simulations in a browser environment. Can you explain the technologies involved to make that work? – Tobias
  • Given the real-time rendering capabilities in VPython I’m sure that performance is a core concern for the project. What are some of the methods that are used to ensure an appropriate level of speed and does the cross-platform nature of the package pose any additional challenges? – Tobias
  • How does collision detection work in VPython, and does it handle more complex assemblies of component objects? – Chris
  • Can you talk a little bit about VPython’s design, and perhaps walk us through how a simple scene is rendered, say the results of the sphere() call? – Chris

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

SymPy With Aaron Meurer - Episode 42

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Summary

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.

Brief Introduction

  • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
  • Subscribe on iTunes, Stitcher, TuneIn or RSS
  • Follow us on Twitter or Google+
  • Give us feedback! Leave a review on iTunes, Tweet to us, send us an email or leave us a message on Google+
  • Join our community at discourse.pythonpodcast.com to follow up with the guests and help us make the show better!
  • nn
  • I would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable. For details on how to support the show you can visit our site at pythonpodcast.com
  • Linode is sponsoring us this week. Check them out at linode.com/podcastinit and get a $20 credit to try out their fast and reliable Linux virtual servers for your next project
  • I would also like to thank Hired, a job marketplace for developers, for sponsoring this episode of Podcast.__init__. Use the link hired.com/podcastinit and double your signing bonus to $4,000.
  • We are recording today on January 18th, 2016 and your hosts as usual are Tobias Macey and Chris Patti
  • Today we are interviewing Aaron Meurer about SymPy

Interview with Aaron Meurer

  • Introductions
  • How did you get introduced to Python? – Chris
  • What is Sympy and what kinds of problems does it aim to solve? – Chris
  • How did the SymPy project get started? – Tobias
  • How did you get started with the SymPy project? – Chris
  • Are there any limits to the complexity of the equations SymPy can model and solve? – Chris
  • How does SymPy compare to similar projects in other languages? – Tobias
  • How does Sympy render results using such beautiful mathematical symbols when the inputs are simple ASCII? – Chris
  • What are some of the challenges in creating documentation for a project like SymPy that is accessible to non-experts while still having the necessary information for professionals in the fields of mathematics? – Tobias
  • Which fields of academia and business seem to be most heavily represented in the users of SymPy? – Tobias
  • What are some of the uses of Sympy in education outside of the obvious like students checking their homework? – Chris
  • How does SymPy integrate with the Jupyter Notebook? – Chris
  • Is SymPy generally used more as an interactive mathematics environment or as a library integrated within a larger application? – Tobias
  • What were the challenges moving SymPy from Python 2 to Python 3? – Chris
  • Are there features of Python 3 that simplify your work on SymPy or that make it possible to add new features that would have been too difficult previously? – Tobias
  • Were there any performance bottlenecks you needed to overcome in creating Sympy? – Chris
  • What are some of the interesting design or implementation challenges you’ve found when creating and maintaining SymPy? – Chris
  • Are there any new features or major updates to SymPy that are planned? – Tobias
  • How is the evolution of SymPy managed from a feature perspective? Have there been any occasions in recent memory where a pull request had to be rejected because it didn’t fit with the vision for the project? – Tobias
  • Which of the features of SymPy do you find yourself using most often? – Tobias

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

Stuart Mumford on SunPy - Episode 34

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Summary

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.

Brief Introduction

  • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
  • Subscribe on iTunes, Stitcher, TuneIn or RSS
  • Follow us on Twitter or Google+
  • Give us feedback! Leave a review on iTunes, Tweet to us, send us an email or leave us a message on Google+
  • I would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable. For details on how to support the show you can visit our site at pythonpodcast.com
  • I would also like to thank Hired, a job marketplace for developers, for sponsoring this episode of Podcast.__init__. Use the link hired.com/podcastinit to double your signing bonus.
  • Linode is sponsoring us this week. Check them out at linode.com/podcastinit and get a $10 credit to try out their fast and reliable Linux virtual servers for your next project
  • We are recording today on November 17th, 2015 and your hosts as usual are Tobias Macey and Chris Patti
  • Today we are interviewing Stuart Mumford about SunPy
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Interview with Stuart Mumford

  • Introductions
  • How did you get introduced to Python? – Chris
  • Can you explain what the research and applications of solar physics are and how SunPy facilitates those activities? – Tobias
  • What was your inspiration for the SunPy project and what are you using it for in your research? – Tobias
  • Can you tell us what SunPy’s map and light curve classes are and how they might be used? – Chris
  • Are there any considerations that you need to be aware of when writing software libraries for practitioners of the hard sciences that would be different if the target audience were software engineers? – Tobias
  • Can SunPy consume data directly from telescopes and other observational apparatus? – Chris
  • I noticed on the project site that SunPy leverages AstroPy internally. Can you describe the relationship between the two projects and why someone might want to use SunPy in place of or in addition to AstroPy? – Tobias
  • Looking at the documentation I got the impression that there is a fair amount of visual representation of data for analysis. Can you describe some of the challenges that has posed? Is there integrated support for project Jupyter and are there other graphical environments that SunPy supports? – Tobias
  • What are some of the most interesting applications that SunPy has been used for? – Chris

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

Maneesha Sane on Software and Data Carpentry - Episode 33

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Summary

The Software and Data Carpentry organizations have a mission of making it easier for scientists and data analysts in academia to replicate and review each others work. In order to achieve this goal they conduct training and workshops that teach modern best practices in software and data engineering, including version control and proper data management. In this episode we had the opportunity to speak with Maneesha Sane, the program coordinator for both organizations, so that we could learn more about how these projects are related and how they approach their mission.

Brief Introduction

  • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
  • Subscribe on iTunes, Stitcher, TuneIn or RSS
  • Follow us on Twitter or Google+
  • Give us feedback! Leave a review on iTunes, Tweet to us, send us an email or leave us a message on Google+
  • I would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable. For details on how to support the show you can visit our site at pythonpodcast.com
  • This episode is sponsored by Zato – Microservices, ESB, SOA, REST, API, and Cloud Integrations in Python. Visit zato.io to learn more about how to integrate smarter in the modern world.
  • I would also like to thank Hired, a job marketplace for developers, for sponsoring this episode of Podcast.__init__. Use the link hired.com/podcastinit to double your signing bonus.
  • Linode is sponsoring us this week. Check them out at linode.com/podcastinit and get a $10 credit to try out their fast and reliable Linux virtual servers for your next project
  • We are recording today on November 10th, 2015 and your hosts as usual are Tobias Macey and Chris Patti
  • Today we are interviewing Maneesha Sane about Software Carpentry and Data Carpentry

Interview with Maneesha Sane

  • Introductions
  • How did you get introduced to Python?
  • Can you explain what the Software and Data Carpentry organizations are and what their respective goals are?
  • What is the history of these organizations and how are they related?
  • What does a typical Software Carpentry or Data Carpentry workshop look like?
  • What is the background of your instructors?
  • Can you explain why Python was chosen as the language for your workshops and why it is such a good language to use for teaching proper software engineering practices to scientists?
  • In what ways do the lessons taught by both groups differ and what parts are common between the two organizations?
  • What are some of the most important tools and lessons that you teach to scientists in academia?
  • Do you tend to focus mostly on procedural development or do you also teach object oriented programming in Software Carpentry?
  • What is the target audience for Data Carpentry and what are some of the most important lessons and tools taught to them?
  • Do you teach any particular method of pre-coding design like flowcharting, pseudocode, or top down decomposition in software carpentry?
  • What scientific domains are most commonly represented among your workshop participants for Software Carpentry?
  • What are some specific things the Python community and the Python core team could do to make it easier to adopt for your students?
  • What are the most common concepts students have trouble with in software & data carpentry?
  • How can our audience help support the goals of these organizations?

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

Erik Tollerud on AstroPy - Episode 32

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Summary

Erik Tollerud is an astronomer with a background in software engineering. He leverages these backgrounds to help build and maintain the AstroPy framework and its associated modules. AstroPy is a set of Python libraries that provide useful mechanisms for astronomers and astrophysicists to perform analyses on the data that they receive from observational equipment such as the mountain observatory that Erik was preparing to visit when we talked to him about his work. If you like Python and space then you should definitely give this episode a listen!

Brief Introduction

  • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
  • Subscribe on iTunes, Stitcher, TuneIn or RSS
  • Follow us on Twitter or Google+
  • Give us feedback! Leave a review on iTunes, Tweet to us, send us an email or leave us a message on Google+
  • I would like to thank everyone who has donated to the show. Your contributions help us make the show sustainable. For details on how to support the show you can visit our site at pythonpodcast.com
  • I would also like to thank Hired, a job marketplace for developers, for sponsoring this episode of Podcast.__init__. Use the link hired.com/podcastinit to double your signing bonus.
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  • We are recording today on November 2nd, 2015 and your hosts as usual are Tobias Macey and Chris Patti
  • Today we are interviewing Erik Tollerud about AstroPy
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Interview with Erik Tollerud

  • Introductions
  • How did you get introduced to Python?
  • What was the inspiration to create AstroPy and what kinds of astronomical research can it be used for?
  • Can you tell us what AstroPy’s modeling functions are and give us examples of where they might be used?
  • Are there any considerations that you need to be aware of when writing software libraries for practitioners of the hard sciences that would be different if the target audience were software engineers?
  • What are some of the most interesting applications that AstroPy has been used for?
  • Are there open data sets that are available for people outside of academia to do analysis of astronomical data using AstroPy?
    • Have there been any useful discoveries made in this way?
  • Could you please tell us about AstroPy’s Virtual Observatory capabilities?
  • What are some interesting use cases for AstroPy’s Cosmological calculations?
  • Are there other libraries available that provide similar capabilities, perhaps in other languages? What makes AstroPy unique among them?
  • Can AstroPy consume data directly from telescopes and other observational apparatus?
  • The amount of data generated from observing astronomical phenomena must be immense. What are some of the tools used to manage that data and how does AstroPy interface with them?
  • How might AstroPy be used to prove or disprove the cold dark matter hypothesis?
  • What are some of the architectural choices that have been made to allow for the AstroPy library to serve as the core for a number of other add-ons?
    • Does AstroPy provide a common data format to allow for easy interoperability between the various addons?
  • I noticed that AstroPy adheres to the PSF code of conduct, as well as having adopted an enhancement proposal process modelled after PEPs. Can you explain why that is important and what kind of an impact it has had on the community around AstroPy?

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The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA