Open Source

Bringing Africa Online At Ascoderu with Clemens Wolff - Episode 167

Summary

The future is here, it’s just not evenly distributed. One of the places where this is especially true is in sub-Saharan Africa which is a vast region with little to no reliable internet connectivity. To help communities in this region leapfrog infrastructure challenges and gain access to opportunities for education and market information the Ascoderu non-profit has built Lokole. In this episode one of the lead engineers on the project, Clemens Wolff, explains what it is, how it is built, and how the venerable e-mail protocols can continue to provide access cheaply and reliably.

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.
  • 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])
  • 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 Clemens Wolff about how Ascoderu is using Python to help communities in sub-Saharan Africa gain access to the digital age

Interview

  • Introductions
  • How did you get introduced to Python?
  • What is the mission of Ascoderu and how did the organization get started?
    • How did you get involved?
  • The primary project that you build and maintain is Lokole. What is it and how does it help you in achieving the goals of the organization?
    • What are the limitations of using e-mail as the only interface to the broader internet?
    • What are some of the most interesting or unexpected uses of email in isolation have you seen?
  • From the user perspective, can you describe the overall experience of interacting with Lokole?
    • What is happening in the background?
    • Did you consider using a binary message format such as Avro, protocol buffers, or msgpack in place of JSON?
  • What kind of fault tolerance techniques are built into the overall information flow?
  • What are the most challenging or unexpected aspects of building Lokole and interacting with the user communities?
  • What projects do you have planned for the future?

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

Understanding Machine Learning Through Visualizations with Benjamin Bengfort and Rebecca Bilbro - Episode 166

Summary

Machine learning models are often inscrutable and it can be difficult to know whether you are making progress. To improve feedback and speed up iteration cycles Benjamin Bengfort and Rebecca Bilbro built Yellowbrick to easily generate visualizations of model performance. In this episode they explain how to use Yellowbrick in the process of building a machine learning project, how it aids in understanding how different parameters impact the outcome, and the improved understanding among teammates that it creates. They also explain how it integrates with the scikit-learn API, the difficulty of producing effective visualizations, and future plans for improvement and new features.

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 40Gbit 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.
  • 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])
  • 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 Rebecca Bilbro and Benjamin Bengfort about Yellowbrick, a scikit extension to use visualizations for assisting with model selection in your data science projects.

Interview

  • Introductions
  • How did you get introduced to Python?
  • Can you describe the use case for Yellowbrick and how the project got started?
  • What is involved in visualizing scikit-learn models?
    • What kinds of information do the visualizations convey?
    • How do they aid in understanding what is happening in the models?
  • How much direction does yellowbrick provide in terms of knowing which visualizations will be helpful in various circumstances?
  • What does the workflow look like for someone using Yellowbrick while iterating on a data science project?
  • What are some of the common points of confusion that your students encounter when learning data science and how has yellowbrick assisted in achieving understanding?
  • How is Yellowbrick iplemented and how has the design changed over the lifetime of the project?
  • What would be required to integrate with other visualization libraries and what benefits (if any) might that provide?
    • What about other ML frameworks?
  • What are some of the most challenging or unexpected aspects of building and maintaining Yellowbrick?
  • What are the limitations or edge cases for yellowbrick?
  • What do you have planned for the future of yellowbrick?
  • Beyond visualization, what are some of the other areas that you would like to see innovation in how data science is taught and/or conducted to make it more accessible?

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

Modern Database Clients On The Command Line with Amjith Ramanujam - Episode 165

Summary

The command line is a powerful and resilient interface for getting work done, but the user experience is often lacking. This can be especially pronounced in database clients because of the amount of information being transferred and examined. To help improve the utility of these interfaces Amjith Ramanujam built PGCLI, quickly followed by MyCLI with the Prompt Toolkit library. In this episode he describes his motivation for building these projects, how their popularity led him to create even more clients, and how these tools can help you in your command line adventures.

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.
  • 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])
  • 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 Amjith Ramanujam about DBCLI, an umbrella project for command line database clients with autocompletion and syntax highlighting.

Interview

  • Introductions
  • How did you get introduced to Python?
  • What is the DBCLI project?
    • Which of the clients was the first to be created and what was your motivation for starting it?
  • At what point did you decide to create the DBCLI umbrella for the different projects and what benefits does it provide?
  • How much functionality is shared between the different clients?
  • What additional functionality do the different clients provide over those that are distributed with their respective engines?
  • How do you optimize for cases where large volumes of data are returned from a query?
  • What are some of the most interesting or surprising things that you have learned about database engines in the process of building client interfaces for them?
  • What are the most challenging aspects of building the different database clients?
  • What are some unexpected hardships that you encountered through this open source project?
  • What are some unexpected pleasant surprises that you encountered through this project?
  • Why did you hand over the project leadership for pgcli and mycli to other devs? Was it a hard decision?
  • Why do you optimize on being nice over being right?
  • How did Microsoft get involved with dbcli? mssql-cli
  • What’s been the reception for the projects?
  • What are your plans for upcoming releases of the various clients?
  • Which database engines are you planning to target next?

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

Electricity Map: Real Time Visibility of Power Generation with Olivier Corradi - Episode 157

Summary

One of the biggest issues facing us is the availability of sustainable energy sources. As individuals and energy consumers it is often difficult to understand how we can make informed choices about energy use to reduce our impact on the environment. Electricity Map is a project that provides up to date and historical information about the balance of how the energy we are using is being produced. In this episode Olivier Corradi discusses his motivation for creating Electricity Map, how it is built, and his goals for the project and his other work at Tomorrow Co.

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 Olivier Corradi about Electricity Map and using Python to analyze data of global power generation

Interview

  • Introductions
  • How did you get introduced to Python?
  • What was your motivation for creating Electricity Map?
    • How can an average person use or benefit from the information that is available in the map?
  • What sources are you using to gather the information about how electricity is generated and distributed in various geographic regions?
    • Is there any standard format in which this data is produced?
    • What are the biggest difficulties associated with collecting and consuming this data?
    • How much confidence do you have in the accuracy of the data sources?
    • Is there any penalty for misrepresenting the fuel consumption or waste generation for a given plant?
  • Can you describe the architecture of the system and how it has evolved?
  • What are some of the most interesting uses of the data in your database and API that you are aware of?
    • How do you measure the impact or effectiveness of the information that you provide through the different interfaces to the data that you have aggregated?
  • How have you built a community around the project?
    • How has the community helped in building and growing Electricity Map?
  • What are some of the most unexpected things that you have learned in the process of building Electricity Map?
  • What are your plans for the future of Electricity Map?

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

MonkeyType with Carl Meyer and Matt Page - Episode 146

Summary

One of the draws of Python is how dynamic and flexible the language can be. Sometimes, that flexibility can be problematic if the format of variables at various parts of your program is unclear or the descriptions are inaccurate. The growing middle ground is to use type annotations as a way of providing some verification of the format of data as it flows through your application and enforcing gradual typing. To make it simpler to get started with type hinting, Carl Meyer and Matt Page, along with other engineers at Instagram, created MonkeyType to analyze your code as it runs and generate the type annotations. In this episode they explain how that process works, how it has helped them reduce bugs in their code, and how you can start using it today.

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 podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app. And now you can deliver your work to your users even faster with the newly upgraded 200 GBit network in all of their datacenters.
  • If you’re tired of cobbling together your deployment pipeline then it’s time to try out GoCD, the open source continuous delivery platform built by the people at ThoughtWorks who wrote the book about it. With GoCD you get complete visibility into the life-cycle of your software from one location. To download it now go to podcatinit.com/gocd. Professional support and enterprise plugins are available for added piece of mind.
  • 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.
  • A few announcements before we start the show:
    • There’s still time to get your tickets for PyCon Colombia, happening February 9th and 10th. Go to pycon.co to learn more and register.
    • There is also still time to register for the O’Reilly Software Architecture Conference in New York Feb 25-28. Use the link podcastinit.com/sacon-new-york to register and save 20%
    • If you work with data or want to learn more about how the projects you have heard about on the show get used in the real world then join me at the Open Data Science Conference in Boston from May 1st through the 4th. It has become one of the largest events for data scientists, data engineers, and data driven businesses to get together and learn how to be more effective. To save 60% off your tickets go to podcastinit.com/odsc-east-2018 and register.
  • Your host as usual is Tobias Macey and today I’m interviewing Carl Meyer and Matt Page about MonkeyType, a system to collect type information at runtime for your Python 3 code

Interview

  • Introductions
  • How did you get introduced to Python?
  • What is MonkeyType and how did the project get started?
  • How much overhead does the MonkeyType tracing add to the running system, and what techniques have you used to minimize the impact on production systems?
  • Given that the type information is collected from call traces at runtime, and some functions may accept multiple different types for the same arguments (e.g. add), do you have any logic that will allow for combining that information into a higher-order type that gets set as the annotation?
  • How does MonkeyType function internally and how has the implementation evolved over the time that you have been working on it?
  • Once the type annotations are present in your code base, what other tooling are you using to take advantage of that information?
  • It seems as though using MonkeyType to trace your running production systems could be a way to inadvertantly identify dead sections of code that aren’t being executed. Have you investigated ways to use the collected type information perform that analysis?
  • What have been some of the most challenging aspects of building, using, and maintaining MonkeyType?
  • What have been some of the most interesting or noteworthy things that you have learned in the process of working on and with MonkeyType?
  • What have you found to be the most useful and most problematic aspects of the typing capabilities provided in recent versions of Python?
  • For someone who wants to start using MonkeyType today, what is involved in getting it set up and using it in a new or existing codebase?
  • What features or improvements do you have planned for future releases of MonkeyType?

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

Bonobo: Lightweight ETL Toolkit for Python 3 with Romain Dorgueil - Episode 143

Summary

A majority of the work that we do as programmers involves data manipulation in some manner. This can range from large scale collection, aggregation, and statistical analysis across distrbuted systems, or it can be as simple as making a graph in a spreadsheet. In the middle of that range is the general task of ETL (Extract, Transform, and Load) which has its own range of scale. In this episode Romain Dorgueil discusses his experiences building ETL systems and the problems that he routinely encountered that led him to creating Bonobo, a lightweight, easy to use toolkit for data processing in Python 3. He also explains how the system works under the hood, how you can use it for your projects, and what he has planned for the future.

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 podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app. And now you can deliver your work to your users even faster with the newly upgraded 200 GBit network in all of their datacenters.
  • If you’re tired of cobbling together your deployment pipeline then it’s time to try out GoCD, the open source continuous delivery platform built by the people at ThoughtWorks who wrote the book about it. With GoCD you get complete visibility into the life-cycle of your software from one location. To download it now go to podcatinit.com/gocd. Professional support and enterprise plugins are available for added piece of mind.
  • 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.
  • Your host as usual is Tobias Macey and today I’m interviewing Romain Dorgueil about Bonobo, a data processing toolkit for modern Python

Interview

  • Introductions
  • How did you get introduced to Python?
  • What is Bonobo and what was your motivation for creating it?
    • What is the story behind the name?
  • How does Bonobo differ from projects such as Luigi or Airflow?
    [RD] After I explain why that’s totally different things, maybe a good follow up would be to ask about differences from other data streaming solutions, like Apache Beam or Spark.
  • How is Bonobo implemented and how has its architecture evolved since you began working on it?
  • What have been some of the most challenging aspects of building and maintaining Bonobo?
  • What are some extensions that you would like to have but don’t have the time to implement?
  • What are some of the most interesting or creative uses of Bonobo that you are aware of?
  • What do you have planned for the future of Bonobo?

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

Mycroft with Steve Penrod - Episode 82

Summary

Speech is the most natural interface for communication, and yet we force ourselves to conform to the limitations of our tools in our daily tasks. As computation becomes cheaper and more ubiquitous and artificial intelligence becomes more capable, voice becomes a more practical means of controlling our environments. This week Steve Penrod shares the work that is being done on the Mycroft project and the company of the same name. He explains how he met the other members of the team, how the project got started, what it can do right now, and where they are headed in the 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.
  • 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 to talk to previous guests and other listeners of the show.
  • Your host as usual is Tobias Macey and today I’m interviewing Steve Penrod about the company and project Mycroft, a voice controlled, AI powered personal assistant written in Python.

Interview with Steve Penrod

  • Introductions
  • How did you get introduced to Python?
  • Can you start by describing what Mycroft is and how the project and business got started?
  • How is Mycroft architected and what are the biggest challenges that you have encountered while building this project?
  • What are some of the possible applications of Mycroft?
  • Why would someone choose to use Mycroft in place of other platforms such as Amazon’s Alexa or Google’s personal assistant?
  • What kinds of machine learning approaches are being used in Mycroft and do they require a remote system for execution or can they be run locally?
  • What kind of hardware is needed for someone who wants to build their own Mycroft and what does the install process look like?
  • It can be difficult to run a business based on open source. What benefits and challenges are introduced by making the software that powers Mycroft freely available?
  • What are the mechanisms for extending Mycroft to add new capabilities?
  • What are some of the most surprising and innovative uses of Mycroft that you have seen?
  • What are the long term goals for the Mycroft project and the business that you have formed around it?

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

Annapoornima Koppad - Episode 81

Summary

Annapoornima Koppad is a director of the PSF, founder of the Bangalore chapter of PyLadies, and is a Python instructor at the Indian Institute of Science. In this week’s episode she talks about how she got started with Python, her experience running the PyLadies meetup, and working with the PSF.

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 hosts as usual are Tobias Macey and Chris Patti
  • Today we’re interviewing Annapoornima Koppad about her career with Python and her experiences running the PyLadies chapter in Bangalore, India and being a director for the Python Software Foundation.

Interview with Annapoornima Koppad

  • Introductions
  • How did you get introduced to Python? – Tobias
  • I noticed that you have been freelancing for several years now. How much of that has been in Python and how has that fed back into your other activities? – Tobias
  • While preparing for this interview I came across the book that you self-published on Amazon. What was your motivation for writing it and who is the target audience? – Tobias
  • Can you tell us about your experience with starting the PyLadies group in Bangalore? What were some of the biggest challenges that you encountered and how have you approached the task of growing awareness and membership of the group? – Tobias
  • You recently started teaching Python at the Indian Institute of Science. What kinds of subject matter do you cover in your lessons? – Tobias
  • What is it about Python and its community that has inspired you to dedicate so much of your time to contributing back to it? – Tobias
  • In what ways would you like to see the Python ecosystem improve? – Tobias
  • You were voted in as a director of the Python Software Foundation in the most recent election. Can you share what responsibilities that entails? – Tobias
  • What would you like to achieve with your time in the PSF? – Tobias

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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

K Lars Lohn - Episode 79

Summary

K Lars Lohn has had a long and varied career, spending his most recent years at Mozilla. This week he shares some of his stories about getting involved with Python, his work with Mozilla, and his inspiration for the closing keynote at PyCon US 2016. He also elaborates on the intricate mazes that he draws and his life as an organic farmer in Oregon.

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 also have a new sponsor this week. 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.
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  • Your host as usual is Tobias Macey
  • Today we’re interviewing K Lars Lohn about his career, his art, and his work with Mozilla

Interview with K Lars Lohn

  • Introductions
  • How did you get introduced to Python?
  • You have an interesting pair of articles on your website that attempt to detail how you perceive code and why you think that formatting should be configured in a manner analogous to CSS. Can you explain a bit about how your particular perception affects the way that you program?
  • On your website you have some images of incredibly detailed artwork that are actually mazes. Can you describe some of your creation process for those?
  • What is it about mazes that keeps you interested in them and how did you first start using them as a form of visual art?
  • At Mozilla you have helped to create a project called Socorro which utilizes complexity analysis for correlating stacktraces. How did you conceive of that approach to error monitoring?
  • Can you describe how Socorro is architected and how it works under the covers?
  • At this year’s PyCon US you presented the closing keynote and it was one of the most engaging talks that I’ve seen. Where did you get the inspiration for the content and the mixed media approach?
  • For anyone who hasn’t seen it, you managed to weave together a very personal story with a musical performance, and some applications of complexity analysis into a seamless experience. How much did you have to practice before you felt comfortable delivering that in front of an audience?
  • In addition to your technical career you are also very focused on living in a manner that is sustainable and in tune with your environment. What kinds of synergies and conflicts exist between your professional and personal philosophies?

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