Science

Computational Musicology For Python Programmers - Episode 198

Summary

Music is a part of every culture around the world and throughout history. Musicology is the study of that music from a structural and sociological perspective. Traditionally this research has been done in a manual and painstaking manner, but the advent of the computer age has enabled an increase of many orders of magnitude in the scope and scale of analysis that we can perform. The music21 project is a Python library for computer aided musicology that is written and used by MIT professor Michael Scott Cuthbert. In this episode he explains how the project was started, how he is using it personally, professionally, and in his lectures, as well as how you can use it for your own exploration of musical analysis.

Announcements

  • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
  • When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!
  • And to keep track of how your team is progressing on building new features and squashing bugs, you need a project management system designed by software engineers, for software engineers. Clubhouse lets you craft a workflow that fits your style, including per-team tasks, cross-project epics, a large suite of pre-built integrations, and a simple API for crafting your own. Podcast.__init__ listeners get 2 months free on any plan by going to pythonpodcast.com/clubhouse today and signing up for a trial.
  • Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email [email protected])
  • To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.
  • Join the community in the new Zulip chat workspace at pythonpodcast.com/chat
  • Your host as usual is Tobias Macey and today I’m interviewing Michael Cuthbert about music21, a toolkit for computer aided musicology

Interview

  • Introductions
  • How did you get introduced to Python?
  • Can you start by explaining what computational musicology is?
  • What is music21 and what motivated you to create it?
    • What are some of the use cases that music21 supports, and what are some common requests that you purposefully don’t support?
  • How much knowledge of musical notation, structure, and theory is necessary to be able to work with music21?
  • Can you talk through a typical workflow for doing analysis of one or more pieces of existing music?
    • What are some of the common challenges that users encounter when working with it (either on the side of Python or musicology/musical theory)?
    • What about for doing exploration of new musical works?
  • As a professor at MIT, what are some of the ways that music21 has been incorporated into your classroom?
    • What have they enjoyed most about it?
  • How is music21 implemented, and how has its structure evolved since you first started it?
    • What have been the most challenging aspects of building and maintaining the music21 project and community?
  • What are some of the most interesting, unusual, or unexpected ways that you have seen music21 used?
    • What are some analyses that you have performed which yielded unexpected results?
  • What do you have planned for the future of music21?
  • Beyond computational analysis of musical theory, what are some of the other ways that you are using Python in your academic and professional pursuits?

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

Teaching Digital Archaeology With Jupyter Notebooks - Episode 194

Summary

Computers have found their way into virtually every area of human endeavor, and archaeology is no exception. To aid his students in their exploration of digital archaeology Shawn Graham helped to create an online, digital textbook with accompanying interactive notebooks. In this episode he explains how computational practices are being applied to archaeological research, how the Online Digital Archaeology Textbook was created, and how you can use it to get involved in this fascinating area of research.

Introduction

  • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
  • When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!
  • And to keep track of how your team is progressing on building new features and squashing bugs, you need a project management system designed by software engineers, for software engineers. Clubhouse lets you craft a workflow that fits your style, including per-team tasks, cross-project epics, a large suite of pre-built integrations, and a simple API for crafting your own. Podcast.__init__ listeners get 2 months free on any plan by going to pythonpodcast.com/clubhouse today and signing up for a trial.
  • Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email [email protected])
  • To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.
  • Join the community in the new Zulip chat workspace at pythonpodcast.com/chat
  • Your host as usual is Tobias Macey and today I’m interviewing Shawn Graham about his work on the Online Digital Archaeology Textbook

Interview

  • Introductions
  • How did you get introduced to Python?
  • Can you start by explaining what digital archaeology is?
  • To facilitate your teaching you have collaborated on the O-DATE textbook and associated Jupyter notebooks. Can you describe what that resource covers and how the project got started?
  • What have you found to be the most critical lessons for your students to help them be effective archaeologists?
    • What are the most useful aspects of leveraging computational techniques in an archaeological context?
  • Can you describe some of the sources and formats of data that would commonly be encountered by digital archaeologists?
  • The notebooks that accompany the text have a mixture of R and Python code. What are your personal guidelines for when to use each language?
  • How have the skills and tools of software engineering influenced your views and approach to research and education in the realm of archaeology?
  • What are some of the most novel or engaging ways that you have seen computers applied to the field of archaeology?
  • What are your goals and aspirations for the O-DATE project?

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

Analyzing Satellite Image Data Using PyTroll - Episode 193

Summary

Every day there are satellites collecting sensor readings and imagery of our Earth. To help make sense of that information, developers at the meteorological institutes of Sweden and Denmark worked together to build a collection of Python packages that simplify the work of downloading and processing satellite image data. In this episode one of the core developers of PyTroll explains how the project got started, how that data is being used by the scientific community, and how citizen scientists like you are getting involved.

Preface

  • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
  • When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so check out Linode. With 200 Gbit/s private networking, scalable shared block storage, node balancers, and a 40 Gbit/s public network, all controlled by a brand new API you’ve got everything you need to scale up. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute.
  • And to keep track of how your team is progressing on building new features and squashing bugs, you need a project management system designed by software engineers, for software engineers. Clubhouse lets you craft a workflow that fits your style, including per-team tasks, cross-project epics, a large suite of pre-built integrations, and a simple API for crafting your own. Podcast.__init__ listeners get 2 months free on any plan by going to pythonpodcast.com/clubhouse today and signing up for a trial.
  • Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email [email protected])
  • To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.
  • Join the community in the new Zulip chat workspace at pythonpodcast.com/chat
  • Your host as usual is Tobias Macey and today I’m interviewing Martin Raspaud about PyTroll, a suite of projects for processing earth observing satellite data

Interview

  • Introductions
  • How did you get introduced to Python?
  • Can you start by explaining what PyTroll is and how the overall project got started?
  • What is the story behind the name?
  • What are the main use cases for PyTroll? (e.g. types of analysis, research domains, etc.)
  • What are the primary types of data that would be processed and analayzed with PyTroll? (e.g. images, sensor readings, etc.)
  • When retrieving the data, are you communicating directly with the satellites, or are there facilities that fetch the information periodically which you can then interface with?
  • How do you locate and select which satellites you wish to retrieve data from?
  • What are the main components of PyTroll and how do they fit together?
  • For someone processing satellite data with PyTroll, can you describe the workflow?
  • What are some of the main data formats that are used by satellites?
  • What tradeoffs are made between data density/expressiveness and bandwidth optimization?
  • What are some of the common issues with data cleanliness or data integration challenges?
  • Once the data has been retrieved, what are some of the types of analysis that would be performed with PyTroll?
  • Are there other tools that would commonly be used in conjunction with PyTroll?
  • What are some of the unique challenges posed by working with satellite observation data?
  • How has the design and capability of the various PyTroll packages evolved since you first began working on it?
  • What are some of the most interesting or unusual ways that you have seen PyTroll used?
  • What are some of the lessons that you have learned while building PyTroll that you have found to be most useful or unexpected?
  • What do you have planned for the future of PyTroll?

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

Exploring Color Theory In Python With Thomas Mansencal - Episode 160

Summary

We take it for granted every day, but creating and displaying vivid colors in our digital media is a complicated and often difficult process. There are different ways to represent color, the ways in which they are displayed can cause them to look different, and translating between systems can cause losses of information. To simplify the process of working with color information in code Thomas Mansencal wrote the Colour project. In this episode we discuss his motiviation for creating and sharing his library, how it works to translate and manage color representations, and how it can be used in your projects.

Preface

  • Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
  • When you’re ready to launch your next app you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 200Gbit network, all controlled by a brand new API you’ve got everything you need to scale up. Go to podcastinit.com/linode to get a $20 credit and launch a new server in under a minute.
  • Finding a bug in production is never a fun experience, especially when your users find it first. Airbrake error monitoring ensures that you will always be the first to know so you can deploy a fix before anyone is impacted. With open source agents for Python 2 and 3 it’s easy to get started, and the automatic aggregations, contextual information, and deployment tracking ensure that you don’t waste time pinpointing what went wrong. Go to podcastinit.com/airbrake today to sign up and get your first 30 days free, and 50% off 3 months of the Startup plan.
  • To get worry-free releases download GoCD, the open source continous delivery server built by Thoughworks. You can use their pipeline modeling and value stream map to build, control and monitor every step from commit to deployment in one place. And with their new Kubernetes integration it’s even easier to deploy and scale your build agents. Go to podcastinit.com/gocd to learn more about their professional support services and enterprise add-ons.
  • Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email [email protected])
  • Your host as usual is Tobias Macey and today I’m interviewing Thomas Mansencal about Colour, a python library for working with algorithms and transformations to explore color theory

Interview

  • Introductions
  • How did you get introduced to Python?
  • What is color theory?
    • How does Colour assist in the process of working with some of the practical applications of colour science?
  • What was your motivation for creating Colour?
  • What are some example use cases for colour?
  • One of the aspects of color in digital environments that is often confusing is the number of different ways that it can be represented. What are the relative benefits of things like RGB, HSV, CMYK, etc.?
  • How is the Colour library architected and how has that evolved over time?
    • Are there new developments in the area of color theory that need to be periodically incorporated into the library?
  • What have you found to be some of the most often misunderstood aspects of color?
  • What have been some of the most difficult or frustrating aspects of building, maintaining, and promoting Colour?
  • What are some of the most interesting or unexpected uses of Colour that you have seen?
  • What are your plans for the future of Colour?

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

QuTiP with Paul Nation - Episode 128

Summary

The future of computation and our understanding of the world around us is driven by the quantum world. This week Paul Nation explains how the Quantum Toolbox in Python (QuTiP) is being used in research projects that are expanding our knowledge of the physical universe.

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 supports us on Patreon. Your contributions help to make the show sustainable.
  • When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at www.podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app.
  • Need to learn more about how to scale your apps or learn new techniques for building them? Pluralsight has the training and mentoring you need to level up your skills. Go to www.podcastinit.com/pluralsight to start your free trial today.
  • Visit the site to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch.
  • To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.
  • If you work with data for your job or want to learn more about how open source is powering the latest innovations in data science then make your way to the Open Data Science Conference, happening in London in October and San Francisco in November. Follow the links in the show notes to register and help support the show in the process.
  • Your host as usual is Tobias Macey and today I’m interviewing Paul Nation about QuTIP, the quantum toolbox in Python.

Interview

  • Introductions
  • How did you get introduced to Python?
  • Before we start talking about QuTiP, can you provide us with a baseline definition of what quantum mechanics is?
  • What is QuTIP and how did the project get started?
  • Is QuTiP used purely in academics, or are there other users?
  • What are some of the practical innovations that have been created as a result of research into different areas of quantum optics?
  • How do you foresee the advent of practical quantum computers impacting the state of quantum mechanical research?
  • Given the inherent complexity of the subject matter that you are dealing with, how do you approach the challenge of trying to present a usable API to users of QuTiP while not inhibiting their ability to operate at a low level when necessary?
  • What is the process for incorporating new understandings of quantum mechanical theory into the QuTiP package?
  • What are some of the most difficult aspects of simulating quantum systems in a standard computational environment?
  • What is the most enjoyable aspect of working on QuTiP, what is the least enjoyable?
  • What are some of the most notable research results that you are aware of which used QuTiP as part of their studies?
  • What are some resources that you can recommend for anyone who wants to learn more about quantum mechanics?

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

Biopython with Peter Cock, Wibowo Arindrarto, and Tiago Antão - Episode 125

Summary

Advances in the techniques used for genome sequencing are providing us with more information to unlock the secrets of biology. But how does that data get processed and analyzed? With Python of course! This week I am joined by some of the core maintainers of Biopython to discuss what bioinformatics is, how Python is used to help power the research in the field, and how Biopython helps to tie everything together.

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 supports us on Patreon. Your contributions help to make the show sustainable.
  • When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at www.podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app.
  • Need to learn more about how to scale your apps or learn new techniques for building them? Pluralsight has the training and mentoring you need to level up your skills. Go to www.podcastinit.com/pluralsight to start your free trial today.
  • Visit the site to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch.
  • To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.
  • If you work with data for your job or want to learn more about how open source is powering the latest innovations in data science then make your way to the Open Data Science Conference, happening in London in October and San Francisco in November. Follow the links in the show notes to register and help support the show in the process.
  • Your host as usual is Tobias Macey and today I’m interviewing Peter Cock, Wibowo Arindrarto, and Tiago Antão about biopython, a suite of python tools for computational molecular biology.

Interview

  • Introductions
  • How did you get introduced to Python?
  • Can you start by explaining what bioinformatics is and highlight some of the different areas of research?
  • What is biopython and how did it get started?
  • Biopython has a long history behind it. How has the project evolved over that time to meet the changing needs in terms of both research amd computation?
  • How does Biopython compare to the sibling Bio* projects in other programming languages?
  • What does a common workflow look like for someone who is working with biological data?
  • What are some of the most interesting or innovative uses of Biopython that you are aware of?
  • What are some of the most challenging aspects of developing and supporting Biopython?
  • What are some of the most exciting developments in bioinformatics, either recently or coming up?
  • How much domain knowledge is necessary for someone who wants to contribute to the project?
  • What are some of the most problematic limitations of Biopython and how do you work around them?

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  • Tobias
  • Peter
    • Jupyter Notebooks (formerly IPython) for producing notebooks combining code, graphical output and descriptive code. Can be seen as a modern take on Donald Knuth’s Literate programming?
  • Wibowo
    • Conda for installing software, including BioConda for community packaged software in bioinformatics.
  • Tiago

Links

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

Data Retriever with Henry Senyondo - Episode 122

Summary

Analyzing and interpreting data is a large portion of the work involved in scientific research. Getting to that point can be a lot of work on its own because of all of the steps required to download, clean, and organize the data prior to analysis. This week Henry Senyondo talks about the work he is doing with Data Retriever to make data preparation as easy as retriever install.

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 supports us on Patreon. Your contributions help to make the show sustainable.
  • When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at www.podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app.
  • Visit the site to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch.
  • To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.
  • Your host as usual is Tobias Macey and today I’m interviewing Henry Senyondo about Data Retriever, the package manager for public data sets.

Interview

  • Introductions
  • How did you get introduced to Python?
  • Can you explain what data retriever is and the problem that it was built to solve?
  • Are there limitations as to the types of data that can be managed by data retriever?
  • What kinds of data sets are currently available and who are the target users?
  • What is involved in preparing a new dataset to be available for installation?
  • How much of the logic for installing the data is shared between the R and Python implementations of Data Retriever and how do you ensure that the two packages evolve in parallel?
  • How is the project designed and what are some of the most difficult technical aspects of building it?
  • What is in store for the future of data retriever?

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

Nuclear Engineering with Dr. Katy Huff - Episode 115

Summary

Access to affordable and consistent electricity is one of the big challenges facing our modern society. Nuclear energy is one answer because of its reliable output and carbon-free operation. To make this energy accessible to a larger portion of the global population further reasearch and innovation in reactor design and fuel sources is necessary, and that is where Python can help. This week Dr. Katy Huff talks about the research that she is doing, the problems facing the nuclear industry, and how she uses Python to make it happen.

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 supports us on Patreon. Your contributions help to make the show sustainable.
  • When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at www.podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app.
  • Visit the site to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch.
  • To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.
  • Your host as usual is Tobias Macey and today I’m interviewing Dr. Katy Huff about using Python for nuclear engineering

Interview

  • Introductions
  • How did you get introduced to Python?
  • Can you start by explaining what nuclear engineering is and give some examples of current research in the field?
  • The most widely used and recognized form of nuclear plant is the light water reactor, which, to my understanding, is also the most susceptible to melt-downs and release of radioactive material carried by escaped steam. What are some of the reactor types that are currently being researched to improve safety and efficiency?
  • One of the major policy and logistics issues regarding nuclear power plants is the problem of how to handle spent fuel rods. What are some of the methods that are being researched to solve this problem?
  • In your PyCon presentation you mentioned the Cyclus and PyNE projects as tools that you use in your research. Can you provide a brief overview of each and explain how you use them?
  • What are some of the most pressing issues in nuclear engineering and how are you leveraging Python to help with addressing them?
  • How does open source software relate to open science, and how do they impact the impact the ways that research is performed?
  • What are some of the current or future developments in nuclear engineering that you are most excited about?

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

yt-project with Nathan Goldbaum and John Zuhone - Episode 106

Summary

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.

Announcements

  • The Open Data Science Conference is coming to Boston May 3rd-5th. Get your ticket now so you don’t miss out on your chance to learn more about the state of the art for data science and data engineering.
  • Now you can get T-shirts, sweatshirts, mugs, and a tote bag to let the world know about Podcast.init, and you can support the show at the same time! Go to teespring.com/podcastinit and load up!

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 supports us on Patreon. Your contributions help to make the show sustainable.
  • When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at www.podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app.
  • Visit the site to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch.
  • To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.
  • Your host as usual is Tobias Macey and today I’m interviewing Nathan Goldbaum and John Zuhone about the YT project for multi-dimensional data analysis.

Interview

  • Introductions
  • How did you get introduced to Python?
  • What is yt and how did it get started?
  • Where does the name come from?
  • How does yt compare to other projects such as AstroPy for astronomical data analysis?
  • What are the domains in which yt is most widely used?
  • One of the main use cases of yt is for visualizing multidimensional data. What are some of the design challenges in trying to represent such complicated domains via a visual model?
  • Some of the sample datasets for the examples are rather large. What are some of the biggest challenges associated with running analyses on such substantial amounts of information?
  • How has the project evolved and what are some of the biggest challenges that it is facing going forward?

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

Scikit-Image with Stefan van der Walt and Juan Nunez-Iglesias - Episode 105

Summary

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.

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 supports us on Patreon. Your contributions help to make the show sustainable.
  • When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at www.podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app.
  • Visit the site to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch.
  • To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.
  • Your host as usual is Tobias Macey and today I am interviewing Stefan van der Walt and Juan Nunez-Iglesias, co-authors of Elegant SciPy, about scikit-image

Interview

  • Introduction
  • How did you get introduced to Python?
  • What is scikit-image and how did the project get started?
  • How does its focus differ from projects like SimpleCV/OpenCV or Pillow?
  • What are some of the common use cases for which the scikit-image package is typically employed?
  • What are some of the ways in which images can exhibit higher dimensionality and what are some of the kinds of operations that scikit-image can perform in those situations?
  • How is scikit designed and what are some of the biggest challenges associated with its development, whether in the past, present, or future?
  • What are some of the most interesting use cases for scikit-image that you have seen?
  • What do you have planned for the future of scikit-image?

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