GoCD

Mike Driscoll And His Career In Python - Episode 169

Summary

Mike Driscoll has been writing blogs and books for the Python community for years, including his popular series on the Python Module Of The Week. In his daily work he uses Python to test graphical interfaces written in C++ and QT for embedded platforms. In this episode he explains his work, how he got involved in writing as a regular exercise, and an overview of his recent books.

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 Mike Driscoll about using Python to test QT UIs for embedded platforms, his experience running a popular Python blog, and being a self-published author

Technically, I am testing a C++ Qt app that is deployed to an embedded system

Interview

  • Introductions
  • How did you get introduced to Python?
  • Can you start by describing the way in which you are using Python for your work?
  • What benefits does Python provide for writing and running tests for projects written in other languages?
    • What are the drawbacks or limitations?
  • What are some of the tools or techniques that you have found most useful for your work?
    • How much of that was hard-earned knowledge vs finding it in reference material or prior art?
  • What are some of the most interesting and/or difficult aspects of testing graphical interfaces?
  • What are some of the most surprising or unexpected aspects of the problem space that you have discovered through your work?
  • What are some of the other ways in which you have worked with the Python language and community?
  • What are you most interested in working toward in the future?

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

The Pulp Artifact Repository with Bihan Zhang and Austin Macdonald - Episode 168

Summary

Hosting your own artifact repositories can have a huge impact on the reliability of your production systems. It reduces your reliance on the availability of external services during deployments and ensures that you have access to a consistent set of dependencies with known versions. Many repositories only support one type of package, thereby requiring multiple systems to be maintained, but Pulp is a platform that handles multiple content types and is easily extendable to manage everything you need for running your applications. In this episode maintainers Bihan Zhang and Austin Macdonald explain how the Pulp project works, the exciting new changes coming in version 3, and how you can get it set up to use for your deployments today.

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 Austin Macdonald and Bihan Zhang about Pulp, a platform for hosting and managing software package repositories

Interview

  • Introductions
  • How did you get introduced to Python?
  • What is Pulp and how did the project get started?
  • What are the use cases/benefits for hosting your own artifact repository?
  • What is the high level architecture of the platform?
    • Pulp 3 appears to be a fairly substantial change in architecture and design. What will be involved in migrating an existing installation to the new version when it is released?
  • What is involved in adding support for a new type of artifact/package?
  • How does Pulp compare to other artifact repositories?
  • What are the major pieces of work that are required before releasing Pulp 3?
  • What have been some of the most interesting/unexpected/challenging aspects of building and maintaining Pulp?
  • What are your plans for the future of Pulp?

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

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

Pandas Extension Arrays with Tom Augspurger - Episode 164

Summary

Pandas is a swiss army knife for data processing in Python but it has long been difficult to customize. In the latest release there is now an extension interface for adding custom data types with namespaced APIs. This allows for building and combining domain specific use cases and alternative storage mechanisms. In this episode Tom Augspurger describes how the new ExtensionArray works, how it came to be, and how you can start building your own extensions today.

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 Tom Augspurger about the extension interface for Pandas data frames and the use cases that it enables

Interview

  • Introductions
  • How did you get introduced to Python?
  • Most people are familiar with Pandas, but can you describe at a high level the new extension interface?
    • What is the story behind the implementation of this functionality?
    • Prior to this interface what was the option for anyone who wanted to extend Pandas?
  • What are some of the new data types that are available as external packages?
    • What are some of the unique use cases that they enable?
  • How is the new interface implemented within Pandas?
  • What were the most challenging or difficult aspects of building this new functionality?
  • What are some of the more interesting possibilities that you are aware of for new extension types?
  • What are the limitations of the interface for libraries that add new array functionality?
  • What is the next major change or improvement that you would like to add in Pandas?

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

Making A Difference Through Software With Eric Schles - Episode 163

Summary

Software development is a skill that can create value and reduce drudgery in a wide variety of contexts. Sometimes the causes that are most in need of software expertise are also the least able to pay for it. By volunteering our time and abilities to causes that we believe in, we can help make a tangible difference in the world. In this episode Eric Schles describes his experiences working on social justice initiatives and the types of work that proved to be the most helpful to the groups that he was working with.

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 Eric Schles about how to get involved with social justice causes as an engineer

Interview

  • Introductions
  • How did you get introduced to Python?
  • What are some ways that engineers can create real-world impact with their skills?
  • What are some of the common roadblocks to contribution that people should be aware of?
  • What are some of the types of projects or tools that can provide the most value compared to the amount of effort?
  • Do you have any advice for picking an organization or cause that will benefit the most from technical expertise?
  • Many of the tools and systems that get built for public or non-profit organizations require some amount of data for them to be useful. Do you have any advice on methods for identifying, locating, or collecting the necessary information for feeding into these projects?
  • What are some of the design factors that should be considered when building tools for these organizations to allow them to be maintainable and sustainable in the absense of an experienced engineer?

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

Asking Questions From Data Using Active Learning with Tivadar Danka - Episode 162

Summary

One of the challenges of machine learning is obtaining large enough volumes of well labelled data. An approach to mitigate the effort required for labelling data sets is active learning, in which outliers are identified and labelled by domain experts. In this episode Tivadar Danka describes how he built modAL to bring active learning to bioinformatics. He is using it for doing human in the loop training of models to detect cell phenotypes with massive unlabelled datasets. He explains how the library works, how he designed it to be modular for a broad set of use cases, and how you can use it for training models of your own.

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 Tivadar Danka about modAL, a modular active learning framework for Python3

Interview

  • Introductions
  • How did you get introduced to Python?
  • What is active learning?
    • How does it differ from other approaches to machine learning?
  • What is modAL and what was your motivation for starting the project?
  • For someone who is using modAL, what does a typical workflow look like to train their models?
  • How do you avoid oversampling and causing the human in the loop to become overwhelmed with labeling requirements?
  • What are the most challenging aspects of building and using modAL?
  • What do you have planned for the future of modAL?

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

Great Expectations For Your Data Pipelines with Abe Gong and James Campbell - Episode 161

Summary

Testing is a critical activity in all software projects, but one that is often neglected in data pipelines. The complexities introduced by the inherent statefulness of the problem domain and the interdependencies between systems contribute to make pipeline testing difficult to manage. To make this endeavor more manageable Abe Gong and James Campbell have created Great Expectations. In this episode they discuss how you can use the project to create tests in the exploratory phase of building a pipeline and leverage those to monitor your systems in production. They also discussed how Great Expectations works, the difficulties associated with pipeline testing and managing associated technical debt, and their future plans for the project.

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 James Campbell and Abe Gong about Great Expectations, a tool for testing the data in your analytics pipelines

Interview

  • Introduction
  • How did you first get introduced to Python?
  • What is Great Expectations and what was your motivation for starting it?
  • What are some of the complexities associated with testing analytics pipelines?
    • What types of tests can be executed to ensure data integrity and accuracy?
  • What are some examples of the potential impact of pipeline debt?
  • What is Great Expectations and how does it simplify the process of building and executing pipeline tests?
  • What are some examples of the types of tests that can be built with Great Expectations?
  • For someone getting started with Great Expectations what does the workflow look like?
  • What was your reason for using Python for building it?
    • How does the choice of language benefit or hinder the contexts in which Great Expectations can be used?
  • What are some cases where Great Expectations would not be usable or useful?
  • What have been some of the most challenging aspects of building and using Great Expectations?
  • What are your hopes for Great Expectations going forward?

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The intro and outro music is from The Hug by 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