What we're about

Meet other local Python programming language enthusiasts! Ask your questions about any aspect of Python development, including "how do I start learning Python?"

You may also join the merged APUG/AWPUG mailing list to participate in more discussions. Visit http://austinpython.org to sign up!

Additionally, you can find us on IRC in the #austinpy channel on Freenode.

Upcoming events (5)

An Evening of Python Coding

Online event

• What we'll do
Learners and complete Python novices are welcome as well as experts. If you want to start something new, please see this as an opportunity to jump start the project. If you need help with an existing coding project, bring it with you and we will have a look at it. If you want to show your complete code to someone else to test it, or if you just want to consult with python experts you are welcome.

The format will be different than regular since this joins the Austin and DC python communities:

6:30 CST = 7:30 EST we will start the meeting. Join the meetup to view the link.
This link may change, so please check this link again before the meeting.

7 CST = 8 EST: Announcement + Demo

Around 7:30 CST = 8:30 EST continue discussions or break into smaller groups.

Although this online meeting is different, for those new to this meetup, please check out previous meetups we had to get an idea of what to expect. Here is a link to previous meetups:
https://github.com/Jacob-Barhak/EveningOfPythonCoding

You will find there demo code and the folk who presented them. Feel free to clone and star demos that were useful to you.

• Important to know
Our focus for this month will be: Blender scripting with python
https://docs.blender.org/api/current/info_quickstart.html#running-scripts

The Austin Python Meetup Monthly Meetup

Online event

We typically have a main presentation or a series of lightning talks, followed by discussion and Q&A. There is a diversity of domains and experience levels represented, so come with your questions and be prepared to talk about how you use Python!

This will be an online meeting - please join the meetup at the link listed. Please note that this link may update and updates may appear in the discussion section below - so scroll down if you have technical difficulties.

The presentations will start after 7, yet feel free to join starting 6:30.

In this meetup we will have the following presentations:

Talk 1: William Waites will present on "Executable storytelling with rule-based models"

Talk 2: Cliff Kerr will present on “Python vs. the pandemic: writing high-performance models in a jiffy”

Details about the presentations below:

-------------------------------------

Talk 1: "Executable storytelling with rule-based models"

Description: Computational models have figured prominently in the SARS-CoV-2 pandemic. Models can have different purposes. Predictive models estimate what will happen in the (usually short term) future. Exploratory models help to understand hypothetical scenarios of either the past or the future. Explanatory models give an account of why things are as they are, or happened as they did. We show a way of constructing models as a narrative with discrete, composable rules implemented in Python with a Domain Specific Language (DSL) made for this purpose. We show how these rule-based stories can be used in exploratory and explanatory ways to help us understand the pandemic and what can be done about it.

Bio: William Waites is an Internet engineer retired to academia. He did PhD work at the intersection of computation and theoretical biology at the University of Edinburgh School of Informatics and is now a Research Fellow at the London School of Hygiene and Tropical Medicine in the Centre for Mathematical Modelling of Infectious Diseases.

Talk 2: “Python vs. the pandemic: writing high-performance models in a jiffy”

Description: When COVID turned the world upside down last year, politicians and public health officials asked academic disease modelers like us for urgent guidance. In this talk, I will discuss how we built Covasim, our agent-based COVID model, by using standard Python libraries like Numpy/Numba along with less common ones like Sciris. Covasim was created in a few weeks, an order of magnitude faster than the typical model development process, and achieves performance comparable to C++ despite being written in pure Python. It is now being used by researchers and policymakers in more than a dozen countries. For more information, see covasim.org

Bio: Dr. Cliff Kerr is a Senior Research Scientist at the Institute for Disease Modeling (IDM), a part of the Bill & Melinda Gates Foundation. Prior to joining IDM, he taught scientific computing at the University of Sydney, co-founded two startups, worked on a DARPA project teaching robots to pick up balls, and wrote an algorithm to translate real-time brain activity into music

An Evening of Python Coding

Online event

• What we'll do
Learners and complete Python novices are welcome as well as experts. If you want to start something new, please see this as an opportunity to jump start the project. If you need help with an existing coding project, bring it with you and we will have a look at it. If you want to show your complete code to someone else to test it, or if you just want to consult with python experts you are welcome.

The format will be different than regular since this joins the Austin and DC python communities:

6:30 CST = 7:30 EST we will start the meeting. Join the meetup to view the link.
This link may change, so please check this link again before the meeting.

7 CST = 8 EST: Announcement + Demo

Around 7:30 CST = 8:30 EST continue discussions or break into smaller groups.

Although this online meeting is different, for those new to this meetup, please check out previous meetups we had to get an idea of what to expect. Here is a link to previous meetups:
https://github.com/Jacob-Barhak/EveningOfPythonCoding

You will find there demo code and the folk who presented them. Feel free to clone and star demos that were useful to you.

• Important to know
Our focus for this month will be: Flake8
https://pypi.org/project/flake8/

The Austin Python Meetup Monthly Meetup

Online event

We typically have a main presentation or a series of lightning talks, followed by discussion and Q&A. There is a diversity of domains and experience levels represented, so come with your questions and be prepared to talk about how you use Python!

This will be an online meeting - please join the meetup at the link listed. Please note that this link may update and updates may appear in the discussion section below - so scroll down if you have technical difficulties.

The presentations will start after 7, yet feel free to join starting 6:30.

In this meetup we will have the following presentations:

Talk 1: Layne Sadler - "AIQC; framework for rapid & reproducible deep learning for open science."

Talk 2: TBD

Details about the presentations below:

-------------------------------------

Talk 1: "AIQC; a framework for rapid & reproducible deep learning for open science."

Description: AIQC is an open source Python package that provides a persistent, relational API for: preprocessing, tuning of models in batches, and automatically generating performance metrics/ plots. It provides a high-level API that reduces the amount of code needed to perform best practice machine learning by 90% so that scientists can easily integrate deep learning into their research.

This talk covers: (1) the origins of AIQC in (a) barriers that prevent scientists from adopting deep learning and (b) sources of bias that are hardcoded into machine learning toolsets, before moving into (2) a live demo of the AIQC high level API.

More specifically, we will see how we can use the AIQC framework to address these chronic problems which are hardcoded into current machine learning toolsets: (1) Data Leakage; when aggregate information about test/ holdout data is used to process training samples. Most encoders are not handling each split/ fold individually, so information about the test data “leaks” into the transformation of the training data itself. (2) Evaluation Bias; when a user makes changes to their topology/ parameters based on how the model performs against the test/ holdout data. Most programs are not using a 3rd validation split, so they are effectively training on their entire dataset when they make adjustments. (3) Partial Reproducibility; it is common for experiment trackers to ignore the sample splits/ folds as generic inputs to or upstream artifacts of the training process (e.g. `X_train, y_train`). Despite the fact that preprocessing can be just as important as hyperparameters (e.g. PowerTransformer vs StandardScaler), most experiment trackers are blind to how the samples were processed, only focusing on the parameters to be tuned.

Bio: An autodidact at heart, Layne began on the business side of technology, but curiosity drew him toward building applications and algorithms alike. This led to cofounding an API-driven startup, athlete.studio, and spearheading product development at a biotech, Genuity Science. While working with pharma and research institutes on national genomic biobank projects, he observed barriers that prevented the adoption of deep learning in scientific research, and gaps in machine learning tools. So he built AIQC to address those problems.

Talk 2: TBD

Past events (239)

The Austin Python Meetup Monthly Meetup

Online event

Photos (143)