Thu, Apr 11 2024 at 06:00 PM at Slalom Build

Intro to Property-Based Testing with Hypothesis
(30 Minutes)
By: Paul Zuradzki
Experience Level: Intermediate

One shortfall of example-based unit tests is that they only test known examples. Property-based testing lets you test against randomized inputs if you can specify properties that must be true of the code's behavior ("invariants"). You also test your function against extreme-values (aka, fuzzing).

In this talk, will review some examples of property-based tests using the Hypothesis library. We will demo automated test generation ("ghostwriting" tests) to make writing tests easier. We will demo stateful testing to confirm that all possible states are valid in a program.  Lastly, we will end with parting thoughts on how to specify properties. Often, the tricky part with PBT is knowing what to test! Since we are using randomized input, we need to specify properties that should hold true across all outputs.

Exploring Cellular Automata in Python using Golly
(45 Minutes)
By: Joshua Herman
Experience Level: Intermediate

Golly is an open source, multiplatform tool for exploring various cellular automata (such as the game of life) that allows Python scripts to study and interact with the cellular automata. First we will look at very basic operation of the rule by studying the game of life and also inputting a new initial conditions such as gliders, still lifes and spaceships. Then we will switch over to study my cellular automata I created which I call and use Python scripts to analyze my cellular automata.

73 Python enthusiasts attended this meeting.

Thu, Apr 04 2024 at 06:30 PM at Tegus

12 Python enthusiasts attended this meeting.

Thu, Mar 14 2024 at 06:00 PM at Tegus

ChiPy celebrates Pi Day
(10 Minutes)
By: Phil Robare
Experience Level: Intermediate

The calculation of π to an insane number of digits is something that has an interesting history.  This talk will look at historic algorithms for the calculation π and implementation of the algorithms in Python.  And we will meditate upon how lucky we are to have computers to do the calculation. In doing this we will see things in the Python standard library that make it possible to calculate the crazy values needed in modern algorithms (e.g. one over a factorial cubed). The final demonstration is the calculation of π to 100 significant digits.

Headless CMS with Wagtail and Nextjs
(30 Minutes)
By: Josh Martin
Experience Level: Novice

This talk will cover how to host a Wagtail/Django backend running on Digital Ocean with Dokku. And a Next.js frontend running on Vercel. This combination leads to an ultra-cheap solution for a scaleable and fault-tolerant solution for personal projects or startups.

Getting Started with Software Testing in Python
(30 Minutes)
By: Paul Zuradzki
Experience Level: Novice

The goal of this talk is to give you a roadmap on a journey to writing stronger code with software testing. How do you actually know if your code really works? How do you know that you didn't break something "over there" when you changed something "over here"? In this talk, we'll demonstrate common problems and solutions with respect to verifying code correctness and improving maintainability.

Maybe you've already started trying to learn testing and some things are still unclear: 
- "What is the point of a mock?",  
- "What is the difference between patching with pytest vs unittest or using a with-block vs a decorator?"
- "I get stuck writing a test as soon as I go to a nontrivial example."

48 Python enthusiasts attended this meeting.

Thu, Mar 07 2024 at 06:30 PM at SpotHero

13 Python enthusiasts attended this meeting.

Thu, Feb 08 2024 at 06:00 PM at SpotHero

Boosting Neuroimaging Analysis and Results Digestibility 🧠
(30 Minutes)
By: Carlos A Aranibar
Experience Level: Intermediate

Make neuroimaging results easier to digest for patients and equip technicians with an enhanced toolkit featuring improved visualization and statistical analysis capabilities through MNE, an open-source Python Library.

From Cron to Airflow - Understanding the need for Schedulers
(40 Minutes)
By: Raymond Berg
Experience Level: Novice

Help! I want to run a simple task on a schedule how do I do that? Wait, remember when I said "a simple task", well it just got more complicated. And why isn't the data ready when my job runs?! Oh no, yesterdays job failed...what do I do?

These are all common problems lots of data analysts/engineers and scripters encounter. How do you solve them in a sustainable way? This intro talk is helpful for all who think about job scheduling.

76 Python enthusiasts attended this meeting.