The Python Podcast.init

By Tobias MaceyTechnology Education

The Python Podcast.init episodes

June 18, 2017Industrial Automation with Jonas Neubert
Summary
We all use items that are produced in factories, but do you ever stop to think about the code that powers that production? This week Jonas Neubert takes us behind the scenes and talks about the systems and software that power modern facilities, the development workflows, and how Python gets used 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?utm_source=rss&utm_medium=rss 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 Jonas Neubert about using Python for industrial automation
Interview
Introductions
How did you get introduced to Python?
How did you get involved in factory automation?
What are some of the technical challenges that are unique to a factory environment and the physical computing needs associated with it?
When developing new capabilities for your factory, how do you manage proper testing of your software given the need to interoperate with the hardware?
Which languages are most frequently used for command and control of industrial systems and how does Python interface with them?
How do you manage the problem of interfacing with the various different protocols and data formats that are presented by the different hardware instruments?
In your PyCon presentation you commented on the fact that security in industrial automation systems is lacking. What are some of the most common issues that you have seen?
Why is it that security is such an issue in industrial systems?

How are production releases of your software managed and how does it differ from other types of products such as web applications?

Aside from manufacturing facilities, what are some other types of environments or industries that require similar levels of hardware automation?

What are some of the most interesting or challenging projects that you have worked on?

What are some of the packages on PyPI that you find most useful in your day-to-day work?

For someone who wants to get involved in industrial automation what kind of experience should they have and what are some of the resources that you recommend?

What are some of the innovations in industrial automation that you are most excited about?

Keep In Touch
@jonemo on Twitter
Website
Jobs at Tempo Automation
Picks
Tobias
Opeth

Jonas
Pycon 2017 Talks
Eric Evenchick – Hacking Cars with Python
Building a wireless speedometer with MicroPython
Python from space by Katherine Scott
Łukasz Langa – Unicode what is the big deal
Morgan Wahl – Text is More Complicated Than You Think Comparing and Sorting Unicode
The Prepared Newsletter by Spencer Wright
Long Distance Amtrak rides!

Links
Tempo Automation
Palm webOS
Infinion Technologies
DRAM
Service Oriented Architecture
Singleton
Light Curtain
Factory Acceptance Testing
Site Acceptance Testing
Testing Pyramid
Protocol Analyzer
Multimeter
GCode
IEC-61131
Pascal
Ladder Logic
OPC Standards
OPC DA
C#
Factory Control Systems
Stuxnet
Industroyer
IEC 61850
Industrial Internet of Things
Counsyl
PySerial
FactoryBoy
Parameterized
Freezegun
Struct
XMLRPC
Factory Tours
How It’s Made
McMaster.com
Mass Customization
Life Sciences
CRISPR
PyCon – Reprogramming the human genome
Transcriptic
Autodesk Life Sciences
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
1 hr 3 min
June 11, 2017 Jedi Code Completion with David Halter
When you're writing python code and your editor offers some suggestions, where does that suggestion come from? The most likely answer is Jedi! This week David Halter explains the history of how the Jedi auto completion library was created, how it works under the hood, and where he plans on taking it.
43 min
June 11, 2017Jedi Code Completion with David Halter
Summary
When you’re writing python code and your editor offers some suggestions, where does that suggestion come from? The most likely answer is Jedi! This week David Halter explains the history of how the Jedi auto completion library was created, how it works under the hood, and where he plans on taking it.
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?utm_source=rss&utm_medium=rss 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 David Halter about Jedi, an awesome autocompletion and static analysis library for Python
Interview
Introductions
How did you get introduced to Python?
Can you explain what Jedi is and what problem you were trying to solve when you created it?
What is the story behind the name?

While reading through the documentation I noticed that there is alpha support for linting with Jedi. Can you compare the linting approach and capabilities with those found in other tools such as pylint and flake8?

What does the internal architecture and design look like?

From the research that I did for the show it seems that, rather than use the AST to determine the structure of the code being completed you built your own parser and recursive evaluation of the other methods that you use for determining accurate completion?
What was lacking in existing parsers that led you to build your own?
What are some of the difficulties that you have encountered building and maintaining the grammar definitions and higher level API for parsing multiple versions of Python, including the 2 vs 3 split?

What are some of the biggest challenges associated with introspecting user code?

What are some of the ways that Jedi can be confounded by a user’s project?

What are some of the most difficult technical hurdles that you have been faced with while building Jedi?

What are some unusual or unexpected uses of Jedi that you have seen?

What do you have planned for the future of Jedi?

Keep In Touch
davidhalter on GitHub
@jedidjah_ch on Twitter
Picks
Tobias
Patch utility

David
Bears Den
Soccer
Singing
Dancing
DocOpt
OpenStack

Links
Cloudscale.ch
Vim
Youcompleteme
Neocomplete
pyflakes
pycodestyle
pylint
Parser Generator
Parser Error Recovery
lib2to3
Python grammar file
Finite state automata
Type inference
yapf
AST module
MyPy
IPython
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
43 min
June 04, 2017 Coconut with Evan Hubinger
Functional programming is gaining in popularity as we move to an increasingly parallel world. Sometimes you want access to purely functional syntax and capabilities but you don't want to have to learn an entirely new language. Coconut is here to help! This week Evan Hubinger explains how Coconut is a functional language that compiles to Python and can be mixed and matched with the rest of your program.
34 min
June 04, 2017Coconut with Evan Hubinger
Summary
Functional programming is gaining in popularity as we move to an increasingly parallel world. Sometimes you want access to purely functional syntax and capabilities but you don’t want to have to learn an entirely new language. Coconut is here to help! This week Evan Hubinger explains how Coconut is a functional language that compiles to Python and can be mixed and matched with the rest of your program.
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?utm_source=rss&utm_medium=rss 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 Evan Hubinger about Coconut, a functional language implemented as a superset of Python
Interview
Introductions
How did you get introduced to Python?
Can you start by explaining what Coconut is and what problem you were trying to solve when you created it?
Where did the name come from?

How is Coconut implemented and what does the compilation process for Coconut code look like?

How will I be able to debug my Python if I’m not the one writing it?

The documentation mentions that Coconut itself is compatible with both Python 2 and 3, are there any caveats to be aware of in terms of mixing in standard Python syntax?

Are there any performance optimizations that you have had to perform in order to make things like recursion and pattern matching work at reasonable speeds in the Python VM?

Which functional languages have you taken inspiration from during the creation of Coconut?

What are some of the most interesting or unexpected uses of Coconut that you have seen?

What are some resources that you recommend for people who are interested in learning more about functional programming?

Keep In Touch
Coconut
Website
GitHub
Tutorial
Documentation
FAQ
Chat room

Evan
GitHub
LinkedIn

Picks
Tobias
ElementTree

Evan
pyparsing is an awesome PyPI package you should check out

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
34 min
May 28, 2017 Cauldron with Scott Ernst
The notebook format that has been exemplified by the IPython/Jupyter project has gained in popularity among data scientists. While the existing formats have proven their value, they are still susceptible with difficulties in collaboration and maintainability. Scott Ernst created the Cauldron notebook to be testable, production ready, and friendly to version control. This week we explore the capabilities, use cases, and architecture of Cauldron and how you can start using it today!
38 min
May 28, 2017Cauldron with Scott Ernst
Summary
The notebook format that has been exemplified by the IPython/Jupyter project has gained in popularity among data scientists. While the existing formats have proven their value, they are still susceptible with difficulties in collaboration and maintainability. Scott Ernst created the Cauldron notebook to be testable, production ready, and friendly to version control. This week we explore the capabilities, use cases, and architecture of Cauldron 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 www.podastinit.com/linode?utm_source=rss&utm_medium=rss 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 Scott Ernst about Cauldron, a new notebook format built with software engineering best practices in mind.
Interview
Introductions
How did you get introduced to Python?
Can you start by explaining what Cauldron is and what problem you were trying to solve when you created it?
In the documentation it mentions that you can use any editor for creating the content of the notebook. Can you describe a typical workflow of authoring the various files and cells and viewing the output?
How does Cauldron compare to the Jupyter notebook format and what factors would lead someone to choose one over the other?
Does Cauldron support running languages other than Python? If not then what would be involved in adding that capability?
Cauldron notebooks support unit tests of individual cells. How does that process work and what are the limitations?
The option for running the notebook in the context of a task workflow tool appears to be a powerful capability. What are some of the considerations that are necessary when writing a notebook to be run in that manner?
What are some of the most interesting or unexpected projects that you have seen people using Cauldron for?
What do you have planned for the future of Cauldron?
Keep In Touch
@swernst on Twitter
Website
Picks
Tobias
Tiffany Aching Adventures

Scott
Apache Big Data Conference

Links
When I Work
IPython Interview
Spark
R2Py
Bokeh
Website
Podcast.init Interview

Luigi

Airflow
Website
Podcast.init Interview

Digital Paleontology

A16 Project

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
38 min
May 20, 2017 Tech Debt and Refactoring at Yelp! with Andrew Mason
Tech Debt and Refactoring at Yelp! with Andrew Mason - Episode 110
35 min
May 20, 2017Tech Debt and Refactoring at Yelp! with Andrew Mason
Summary
Healthy code makes for happy coders, and there are many ways to measure the health of a project. This week Andrew Mason talks about the Undebt project from Yelp!, as well as some of the other tools and practices that have been developed to make sure that the balance on their technical debt card stays low. Give it a listen to learn how and why to measure and address the painful parts of your software.
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?utm_source=rss&utm_medium=rss 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 Andrew Mason about technical debt and refactoring with Undebt.
Interview
Introductions
How did you get introduced to Python?
How do you define technical debt and why is it an important aspect of a project to keep track of?
How would you characterize refactoring in general and when you might want to do it?
What is Undebt and what was the problem that you were facing at Yelp when it was created?
For someone who wants to get started with using Undebt what does that process look like and how does it work under the covers?
What are some of the other tools and techniques available for refactoring Python code and how do they differ from what is possible in Undebt?
What are some of the other tools and methods that you use to maintain the overall health of your codebase?
What are some of the limitations and edge cases that you have experiemced working with Undebt?
It is often a difficult balancing act when working in a team to determine how much time to spend paying down technical debt and building tools that will act as force multipliers vs doing feature work that will be visible to end-users. In your experience, what are some ways to manage that tension?
Keep In Touch
Andrew
GitHub
Website
@andrew_mason1 on Twitter

Picks
Tobias
Continuous Delivery by Jez Humble and David Farley

Andrew
XI Editor
The Circle by David Eggers

Links
Martin Fowler
“Uncle” Bob Martin
git-code-debt
Undebt
PyParsing
Podcast.init Episode About Parsing
Rope
Pre-Commit
PyLint
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
35 min
May 14, 2017 LBRY with Jeremy Kauffman
Content discovery and delivery and how it works in the digital realm is one of the most critical pieces of our modern economy. The blockchain is one of the most disruptive and transformative technologies to arrive in recent years. This week Jeremy Kauffman explains how the company and platform of LBRY are combining the two in an attempt to redefine how content creators and consumers interact by creating a new distributed marketplace for all kinds of media.
40 min