PyGrunn is the "Python and friends" developer conference
with a local footprint and global mindset. Firmly rooted
in the open source culture, it aims to provide the
leaders in advanced internet technologies a
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Speakers 2019

Òscar Vilaplana

Advanced Pytest

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Òscar Vilaplana Òscar Vilaplana

Advanced Pytest

Unit tests. At days you can't live without them, at days you can't live with them. Often considered write once, read never, panic later. And yet unit tests can break every build, stop every deploy, ruin every productive day.

I'll share the techniques we use at Tiqets to keep our tests simple, maintainable, and powerful using Pytest.

BIO information

I help tech teams. I work as lead developer for internal tooling at Tiqets. I write short fiction and sometimes make music.

Niek Hoekstra and Jean-Paul van Oosten

Lessons from using GraphQL in production

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Niek Hoekstra and Jean-Paul van Oosten Niek Hoekstra and Jean-Paul van Oosten

Lessons from using GraphQL in production

In 2017, GraphQL was introduced to the PyGrunn community, we will expand on that presentation with some lessons from using GraphQL in production. We will touch some more advanced GraphQL features: Using Fragments, how to combine this with uploading files, authentication, and more. We will also have a quick demonstration and recap of GraphQL and the pros and cons.

BIO information

Niek is a backend software engineer at Target Holding, Jean-Paul a machine learning engineer with experience in both frontend and backend. At Target Holding we build end-to-end AI applications.

Rik Huijzer

Deep learning and natural language processing

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Rik Huijzer Rik Huijzer

Deep learning and natural language processing

Like many fields in computer science, natural language processing (NLP) has changed due to the ever increasing accuracy of deep learning systems. During my graduation at Spindle I have focused my thesis on NLP. In this talk I will show how grammar from a language used to be captured, and how it is done by current approaches. The goal is to provide attendees with some intuition about what is currently possible and what is not.

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Graduated computer science at Spindle

Teake Nutma

Managing environments and deploying code with conda

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Teake Nutma Teake Nutma

Managing environments and deploying code with conda

Anaconda is a popular data science driven Python distribution. Its package manager, conda, can however be used for much more than just installing Anaconda. It can for example manage independent environments for different Python versions, or even C libraries. And its tooling makes it fairly easy to build and distribute cross-platform packages. In fact, we recently migrated from a homegrown solution to conda for the deployment of our astronomical data reduction software.

In this talk I'll survey the current state of Python package managers, explain why we chose conda, and describe our development and deploy workflow with conda. I'll also highlight some of its lesser known nice features and pain points.

BIO information

Teake Nutma is a software engineer at the Kapteyn Astronomical Institute in Groningen. He obtained his PhD in theoretical physics at the University of Groningen and was a post-doctoral researcher at the Max-Planck Institute for Gravitational Physics in Potsdam. After a brief stint in industry, he joined the ESA Euclid mission in 2016 and has since been working enthusiastically at the intersection of fundamental science, big data, and IT architecture.

Peter C Kroon

Python as a scientist's playground

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Peter C Kroon Peter C Kroon

Python as a scientist's playground

Python is the perfect tool for scientific programming since there are SO MANY different modules with pre-packaged solutions available on PyPI and conda-forge. However, there are SO MANY different modules available, with many either providing the exact same solutions with a different coating, or at least having overlapping functionality. This can make it difficult to choose one package over another.

In my talk I will discuss some specific features I take into consideration as a scientist when choosing one package over another. As an example I will look at some packages providing implementations for graphs, and why I chose one over the others. I hope that from my talk you can learn what you can do to make your package as useful as possible to me and help me choose your package.

BIO information

I'm a PhD student at the university of Groningen working on Molecular Dynamics. Most of the time however, I'm busy creating new tools and research possibilities using Python rather than doing what I was hired to do. Science presents its own unique set of programming challenges, some of which I encounter every day. I hope to highlight a few of those in my talk.

Gerard Lutterop

Provide a scalable and secure REST based backend with Python

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Gerard Lutterop Gerard Lutterop

Provide a scalable and secure REST based backend with Python

REST based backends are a boon for front end developers, since connecting the front end to the business logic and data layer is very straightforward using standard internet technologies.

Providing a REST backend turns out to be very difficult, when you want to adhere to the 'real' REST standards.

We used Python to develop a framework which is agnostic to the web framework and to the database. The interface can be specified declaratively using a standard language, to which our framework is also agnostic. All non-standard logic is implemented using Python on pre-defined events.

This framework turns out to be extremely productive, not only for the programmer, but especially for the designer of the interface. Implementing a battleproof REST based interface is literally one click away.

BIO information

A veteran in Python, almost 20 years of exclusive Python usage in my projects. Done all kinds of projects: data analysis, migrations, software robots, cloud based services etc. Currently focusing on Sitekick, a service to assist web site owners in managing their web site.

Sitekick is built on the Okapi platform, a modular platform to enable easy development of REST based interfaces.

Guus Klinkenberg

What was its type?

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Guus Klinkenberg Guus Klinkenberg

What was its type?

Coming from Java and a bit of C, the switch to python was not always easy. This was partly because python is a dynamically typed language. With the introduction of type hinting it is possible for developers to provide types for variables. This can help reduce the amount of scope one needs to cope with, while also helping IDEs and other code analysis tools to help you write code. However, in most of the (open source) python code type hinting is not present. This talk will be a short introduction in type hinting, to show its advantages and hopefully make you consider using it sometime.

BIO information

Born and raised in Groningen. While following the Computing Science program at the University, Guus got distracted by what life and programming has to offer.

That is why he is still finishing up his master in data science and systems complexity. This culminated in organising the largest student conference about IT in the Netherlands on disruptive technology. Furthermore, he is participating in Ticketguard, a startup where they offer dynamic, non-resellable access identifier for events, enterprises and public transport.

Ruben Homs

Testing your infrastructure code

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Ruben Homs Ruben Homs

Testing your infrastructure code

Writing code to setup parts of your infrastructure isn't new. Configuration management has been a part of most infrastructure teams toolkits for a while now. However, unfortunately writing your infrastructure code once and forgetting about it is the norm rather than the exception. And, surprise surprise, things eventually break! Repositories stop serving packages, GPG signatures change, and subtle details to the way you implement your infrastructure code can cause bugs. Those bugs are then only discovered when you have to deploy a new instance of a service because it failed in the weekend after you've had one too many drink. This defeats the whole point of having your infrastructure as code. You need it to work always, especially in case of an emergency.

In this talk I will explain how I solved this issue by introducing Python tests for our SaltStack code and how we continuously test this. Note that this technique will work with other configuration management systems besides SaltStack.

BIO information

DevOps engineer at Spindle

Reinout van Rees

Cookiecutter: handy (self-made) templates for starting your projects

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Reinout van Rees Reinout van Rees

Cookiecutter: handy (self-made) templates for starting your projects

Cookiecutter (https://cookiecutter.readthedocs.io) is a handy program to start your project with. You give it a template and it creates the basic structure for your project. There are all sorts of templates online, but you can also create your own. Especially useful inside a company, to ensure projects look more or less the same and to help colleagues get started right away with documentation, testing, packaging, automation, etc.

BIO information

Python/django developer + OPS at Nelen&Schuurmans in Utrecht. I've done everything from sysadmin stuff to python/django/plone to css. I maintain a couple of open source packages like zest.releaser.

Erik-Jan Blanksma

Data processing and visualisation of tractor data

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Erik-Jan Blanksma Erik-Jan Blanksma

Data processing and visualisation of tractor data

Tractors are capable of logging a huge amount of data while working. Things like position, fuel usage and yield are recorded continuously. This data can be an invaluable source of information for farmers about their crop fields. We'll discuss how we leverage tools like postgis, numpy. pandas and matplotlib to process this data and create different types of visualisations and provide usable feedback to farmers to help them make decisions for more efficient and effective farming.

BIO information

Erik-Jan has over 20 years of experience in software design and development, starting from scratching COBOL an RPG in ancient mainframe systems to Java and full stack web development. He has been working for Dacom for the past 4 years, using Python to create an online platform for farmers. He 's been living in or around Groningen for most of his life. He is married and has 2 sons, who are now taller but still somehow weigh less than him.

Berco Beute

Python and AI

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Berco Beute Berco Beute

Python's second youth

How Python lives its second youth due to its popularity in the AI arena.

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Digital polymath. Curious, creative, skilled, enterprising & ambitious. Loves to mix AI, music, 3D & software. Founding father of PyGrunn. Bewonders the mirrorworld!

Martin Roelfs

Symbolic Fitting using symfit

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Martin Roelfs Martin Roelfs

Symbolic Fitting using symfit

Data fitting problems are very common in disciplines as varied as economics, physics, and psychology, just to name a few. Although very powerful tools for this purpose already exist in the python ecosystem, I found during my own research as a theoretical physicist that I was missing a tool which stays close to mathematics as I would write it on paper, but which could then be used to fit experimental data.

SymPy offers the kind of computer algebra system which is becoming ever more indispensable in modern research, but it speaks a completely different language from commonly used fitting tools such as scipy.minimize. To address this problem I developed symfit, a unifying wrapper around both of these libraries. Using symfit, models can be expressed symbolically and fitted numerically, with very little effort required from the user.

During this talk I will outline some of the data fitting challenges that were overcome using symfit, focusing on the coding aspect. Examples include fitting to ordinary differential equation (ODE) based models, Tikhonov/Ridge regression, global fitting to multiple data sets with shared parameters, global minimization using Differential Evolution, and many more.

BIO information

Physicist by day, python enthusiast by night.

Artur Barseghyan

Find your art twin in the collection of the Rijksmuseum

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Artur Barseghyan Artur Barseghyan

Find your art twin in the collection of the Rijksmuseum

Dive into facial recognition. Brief overview of available solutions. Implementation insights of demq.ai with overview of the client- and server- tooling (Raspberry PI, Docker, etc). Interact with audience (demo). Lessons learned and what's next to come.

BIO information

Senior developer, open source contributor

Miroslav Šedivý

A Day Has Only 24±1 Hours

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Miroslav Šedivý Miroslav Šedivý

A Day Has Only 24±1 Hours

On the last Sunday of October you may get “one more hour of sleep” but also may spend much more time debugging code dealing with the timezones, daylight saving time shifts and datetime stuff in general.

Invention of the geographers in the 19th century, time zones became a true victim of short-sighted political decisions during the 20th and 21st centuries. And it is still our duty to keep track of this whole fiddling with our clocks.

We'll look at a few pitfalls you may encounter when working with datetimes in Python. We'll discover the pytz module and explain why pytz.all_timezones contains over 500 individual timezones, with a slight focus on the Netherlands and surrounding countries. We'll also find the reason why pytz is not part of the standard Python, why it gets updated so often and why even that won't solve all your problems.

Two centuries of propaganda and chaos in thirty minutes. Maybe that will make you want to avoid time zones in your code altogether!

BIO information

Senior Software Developer at solute GmbH. Using Python to get you the best prices online. Born in Czechoslovakia, studied in France, living in Germany. Addicted to foreign languages and the “human” face of computing, such as writing systems, calendar and time zones, and teaching computers to work on the boring tasks. Twitter: @eumiro

Thomas Derksen

The applications and implementation of Generative Adversarial Networks

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Thomas Derksen Thomas Derksen

The applications and implementation of Generative Adversarial Networks

Generative Adversarial Networks (GANs) are systems of two competing artificial neural networks that can generate realistic artificial data in a given domain. Since their introduction in 2014, they have mostly been used to generate realistic looking images, but they can also be applied in other domains like music, or fraud detection. In this talk, I will focus on the inner workings of GANs, their different applications, and how they can be implemented in Python.

BIO information

I'm a backend developer and AI consultant working for Goldmund, Wyldebeast and Wunderliebe in Amsterdam, with a Master's Degree in Artificial Intelligence.

Patrick Vogel & Bogdan Petre

Automatic Monitoring and Profiling Flask-Based Services: The Easy Way

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Patrick Vogel & Bogdan Petre Patrick Vogel & Bogdan Petre

Automatic Monitoring and Profiling Flask-Based Services: The Easy Way

Web-services are becoming more and more used nowadays and many are implemented using Flask, one of the main web-development frameworks for Python. Most existing performance monitoring platforms are heavyweight to configure and treat the subject code as a black box. We present the Flask Monitoring Dashboard (https://github.com/flask-dashboard), our open-source solution for monitoring and profiling Flask-based web-services. It is trivial to install and configure while providing a rich set of features.

BIO information

Second year Master student at the University of Groningen. Together with another student and two assistant professors, we started a project in which we monitor the performance of evolving Flask-applications. This project has gained popularity on Github with over 80 stars and over 30.000 downloads on PyPi. See the project at: https://github.com/flask-dashboard/Flask-MonitoringDashboard

Jovan Veljanoski

Vaex: 1 Billion rows, 1 laptop, serious data science

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Jovan Veljanoski Jovan Veljanoski

Vaex: 1 Billion rows, 1 laptop, serious data science

Working with datasets comprising millions or billions or samples is becoming an increasingly common task, one that is typically tackled with distributed computing. We will demonstrate how one can attack such a problem using just a laptop while bringing extra value by saving time, costs and manpower.

In this talk, we will analyse the entire public database of the New York City Yellow Cab taxi service, containing the data for well over a billion trips. Our live demonstration will showcase how to find which locations are most lucrative for taxi drivers given a certain time of day, how to identify interesting objects or events in the data, as well as build a machine learning model to predict the expected tip amount for a given trip. We will do the entire exploration and analysis in Python using a single laptop, live!

All this is possible with Vaex, a DataFrame library with an expected API. It leverages simple but efficient out-of-core algorithms for data visualisation, filtering, cleaning, preprocessing and transformation which are lazily evaluated, together with efficient storage (memory mapped arrow or hdf5). This makes Vaex an ideal tool for working with datasets that would otherwise be too large to fit into the memory of a single computer.

BIO information

Jovan is a senior data scientists & researcher at XebiaLabs, where he creates predictive models related to DevOps pipelines. Working mostly with Python in the Jupyter ecosystem, he has considerable experience in clustering analysis and predictive modeling. Jovan has a PhD in Astrophysics, is a co-founder of vaex.io, and is interested in novel machine learning technologies and applications.

Cristian Marocico

Computer vision with Python

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Cristian Marocico Cristian Marocico

Computer vision with Python

In this presentation I will show you how you can use Python for computer vision. This will be done with different examples. First I will start with a short introduction into computer vision. After that I will show a short example of transfer learning where I let a computer recognize the difference between a Sedan and and a SUV. Next, I will talk about one of the projects the data science team at the university of Groningen is working on. In this project we have to recognize budding yeast cells from microscopic images. This is the reproduction of yeast cells. I will show some code for this project. Finally, I will talk about a project we work on together with nuclear physics. The goal of this project is to process a lot of data and filter the data. Through computer vision the right data should be recognized. All scripts will be in Python.

BIO information

I work as a data scientist at the University of Groningen. We support researchers to gain and analyse all sorts of data. We use a combination of skills applied to the fields of statistics, artificial intelligence, computer science and more.

Rolf Berkenbosch

home automation with python and how to contribute to the community

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Rolf Berkenbosch Rolf Berkenbosch

home automation with python and how to contribute to the community

Have you ever contribute to a community? Are you ready for the next step in home automation or afraid of using home automation? Get informed how home-assistant can make you live easier and how you can contribute to a community. In the first part of this talk I’m going to talk about what home-assistant is, how you can run it quickly and start hooking up things. In the second part of my talk I will talk about how you can contribute to the communities.

Home-Assistant is completely written in Python3 and have a large growing community. Thanks by the architecture is it very easy to add custom components, that's why there are more than 1310 build in components. You can hook up your ikea tradfri lights, your toon or nest thermostat to you home-automation.

BIO information

Rolf Berkenbosch, currently working as "specialist infrastructure developer” for Duo (Dienst Uitvoering Onderwijs). He is working for more than 20 years in IT, had several companies, like to automate his whole house, and likes new technologies.

Daniël de Kok

finalfusion: rustic word embeddings for Python

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Daniël de Kok Daniël de Kok

finalfusion: rustic word embeddings for Python

Word embeddings have drastically changed natural language processing throughout the last five years. Word embeddings represent words as vectors rather than as discrete units, allowing machine learning techniques to exploit similarities between words.

In the first half of this talk, I will introduce the finalfusion Python module. finalfusion supports word representations with subword units to account for words that were not seen in the training data. finalfusion's API allows you to do similarity and anology queries, as well as computing word embeddings for downstream use in neural networks.

The finalfusion Python module is a thin, type-safe wrapper around the rust2vec Rust library. In the second half of this talk, I will dive deeper into the implementation of the module as a real-world example of implementing Python modules in Rust.

BIO information

I am a researcher at the University of Tübingen. My interest lies the analysis of natural language using deep learning methods such as convolutional and recurrent neural network. I am particularly interested in part-of-speech tagging, topological field analysis, dependency parsing, and phrasal composition. I primarily use Rust and Tensorflow, with some Python for gluing.

My open source projects can be found at: https://github.com/danieldk and https://git.sr.ht/danieldk

Marina Papadopoulou

Disentangling scientific fields with topic analysis in Python

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Marina Papadopoulou Marina Papadopoulou

Disentangling scientific fields with topic analysis in Python

‘Science of science’ is an emerging transdisciplinary field of research, which uses new computational tools and big data, to provide insight in the characteristics and dynamics underlying scientific research itself. Under its umbrella fall studies of cultural evolution, when a large amount of text data gathered through years of cultural changes in human societies are being analyzed with an artificial intelligence’s toolset. By ‘reading’ through a vast amount of data impossible to be analyzed by a human mind, these methodological frameworks create ground for gaining quantitative insights on questions that were only theoretically and philosophically discussed in the past. In this talk, I‘ll showcase an analysis on the scientific bibliography of the research fields of ecology and evolutionary biology. I'll go through the methodology of topic analysis in Python, using packages for natural language processing, latent dirichlet allocation models, dimensionality reduction and clustering techniques. By taking advantage of the computational advances of text mining, this is just an example from the vast pool of possibilities that machine learning can offer in the effort of better understanding the world around us.

BIO information

Marina Papadopoulou is a PhD student on Theoretical Biology at the University of Groningen, where she studies computational models of self-organization and complex collective behaviors of social systems. She holds a Bachelor degree of Biology from the Aristotle University of Thessaloniki and a Master of Science on Computational Methods in Ecology and Evolution from Imperial College London. Throughout her studies, she focused on the application of computational tools on natural sciences research, from spatial modelling of wild animal populations, to cultural evolution and birds’ collective escape. Marina is also involved in various side projects, such as a documentary about sustainable agriculture and the organization of the Groningen PhD Day 2019, and supports efforts of promoting women in science and engineering. She mostly speaks in Greek and English, and writes in C++, Python and R.

Daniël de Kok

Tensorflow in Rust with a little help from Python

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Daniël de Kok Daniël de Kok

Tensorflow in Rust with a little help from Python

Tensorflow is a widely-used library for defining and running computation graphs, such as machine learning models. Rust is a safe, strongly-typed, compiled language with zero-cost abstractions. Rust and Tensorflow seem like a natural match for performance-critical machine learning applications. However, the Tensorflow Python module provides the richest API to construct graphs, whereas Rust has to rely on the very limited Tensorflow C API. In this talk, I will show how you can train and run Tensorflow graphs directly from Rust, with a little help from Python. We will use the rich Python Tensorflow API to define a computation graph with the necessary ops. We will then use this graph directly from Rust to train the model parameters and do predictions after training. The result is a Rust program that only has a dependency on the Tensorflow shared library. I will conclude the presentation with a real-world example of a Rust sequence tagger, sticker, that was used to annotate a 27.3 billion token web corpus.

BIO information

I am a researcher at the University of Tübingen. My interest lies the analysis of natural language using deep learning methods such as convolutional and recurrent neural network. I am particularly interested in part-of-speech tagging, topological field analysis, dependency parsing, and phrasal composition. I primarily use Rust and Tensorflow, with some Python for gluing.

My open source projects can be found at: https://github.com/danieldk and https://git.sr.ht/danieldk

Mark Boer

Embedding the Python interpreter

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Mark Boer Mark Boer

Embedding the Python interpreter

More and more applications are adding Python support to offer the user a scripting language to interact with. Even Microsoft is considering to officially add Python to Excel. In this talk we will explore how one would go about doing this.

We dive into the CPtyhon internals and build our own simple REPL using Python's C-API. I'll show you some of the quirks and pitfalls I ran into, how you can wrestle the GIL and how to manage global state.

BIO information

Coming from a background in the hard sciences I found Python relatively late in my career. After working as a C++ dev for a couple of years, Python now offers me all the tools I need as a data scientist / engineer.

André Duarte & Auke Oosterhoff

How you can use Python to charge your electric vehicle.

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André Duarte & Auke Oosterhoff André Duarte & Auke Oosterhoff

How you can use Python to charge your electric vehicle.

We are at the start of a new era of mobility: the combustion engine is being replaced by batteries. The load of Electrical Vehicles (EV) in the electrical grid introduces new challenges.

André and Auke will demonstrate how their company, The Mobility House, uses Python 3.7 in smart charging applications. Their company uses their open source implentation of the protocol OCPP to control chargers while charging EVs.

They'll also show how you can control your charger using OCPP with open source Python solutions.

BIO information

Originated from 2 countries that ruled the seas, Portugal and The Netherlands, André and Python now join forces to try to conquer the world once more...but, using Python, this time.

André is an Electrical and Computers Engineer from Portugal, where he started acquiring experience in the electro-mobility area, working for Siemens, but finally realized that Startups are cooler and decided to embrace a new challenge at TMH.

Auke is an Dutch hobby hacker and electronics enthusiast with a decade of programming experience. He's is author of several open source projects and recently moved to Munich to work in the field of electric mobility.

Do Kester

BayesicFitting. A Toolbox for Bayesian Model Fitting.

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Do Kester Do Kester

BayesicFitting. A Toolbox for Bayesian Model Fitting.

BayesicFitting is a collection of more than 100 python classes that can be used to fit models to data. There are over 50 basic model classes that can be combined in several ways to construct compound models. And over 10 fitters that find least-squares or maximum likelihoods. All fitters can calculate the evidence to discriminate between models. The remainder are error distributions, priors and engines to run NestedSampler, the Bayesian way to solve problems.

The talk will be about the design of the package, the relations between the classes with plenty of examples how to use it.

BayesicFitting is available on GitHub and Pypi.

BIO information

I have over 40 years of programming experience (fortran, pascal, C, java, python) in the area of space based astronomy at SRON (Space Research of the Netherlands). Mostly I was involved in the calibration of instruments on board of satellites. Modelling is an integral part of it.

From 2000 I developed a fitting package in JAVA for the Herschel satellite of ESA. All my past fitting experiences and needs I have tried to address in it. After my retirement in 2014 I have rewritten this package into python. It became BayesicFitting.

Alessio Bogon

File system caching: a backend implementation for dogpile.cache

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Alessio Bogon Alessio Bogon

File system caching: a backend implementation for dogpile.cache

Dogpile.cache is a library that provides caching primitives and it natively supports backends like Memcached, Redis, and a simple file system implementation. In Paylogic, we needed a more advanced file system implementation, and eventually, we wrote our own file system backend.

In this talk, I will briefly introduce the dogpile.cache architecture, discuss the limitations of the native file system backend and why at Paylogic we decided to implement a more advanced backend.

In this journey, we will explore some of the less-used file system locking primitives of *nix systems and how we (ab)used them, the difficulty of testing when concurrency is involved and some lessons learned during the development. We will also see some python WTFs about file objects.

BIO information

Passionate Python software engineer working at Paylogic.

Location

PyGrunn will take place on May 10th at Groninger Forum, the old Forum Images building, in Groningen.

Groninger Forum is a cultural living and working environment. A place where artists, creative people, entrepreneurs and enthusiasts can meet. A perfect spot for PyGrunn! It is a 3-minute walk from the central train station and in the heart of the city.

Address
Hereplein 73, 9711 GD Groningen
Phone
+31 50 312 0433

Tickets

Schedule 2019

Request For Proposals 2019

This year's RFP is closed and all the speaker slots are filled. Please try again next year since we'd love to hear from you! Please mail any questions and/or remarks regarding the RFP to info@pygrunn.org .

Share your tips for 2019

About

PyGrunn is the largest conference in The Netherlands dedicated to Python and Friends. PyGrunn has always been a special gathering for enthousiasts and for those who wish to share their knowledge and passion about Python and related technologies. Its purpose is to provide a pleasant experience to every attendant. We are both excited and proud to present our sponsors, a list of former PyGrunn speakers and (if present) their recorded talk, and a few photographic moments of the latest edition of PyGrunn.

Code of Conduct

At PyGrunn, we don't believe in the need for a formal code of conduct, as there should be no need to codify good behaviour. We expect everyone to behave with common decency and we expect that everyone is treated with equal respect. PyGrunn staff will take any measures necessary to uphold these golden rules of life.

Sponsors

Kryptonite

Your company could be here!

Gold

Devhouse Spindle is on a mission to connect the world using open and free communication. Almost 60 colleagues are working daily on smart tools that make efficient and personal communication possible. With a love for open source software, an experimental organization model, and an international team of coworkers, Spindle is an ever-evolving and inspiring place to be.

Media2B develops software solutions that allow users to express their wish for an experience in the way they prefer. Using any combination of words, pictures, videos, sketches, music, or whatever they can express what they would like. Our software interprets this message and generates the experience. Whether the experience is purely digital, purely physical, or a mix, that is up to the user. Using the latest advances in AI and software engineering our tools make it super easy and comfortable for end users to turn their wishes into reality. No need for a middleman that interprets the wishes and constructs the experience. It is completely automated. This is more powerful, easier and avoids mistakes and misunderstandings.

Hugo, a Fanbase and (email) Campaign Management platform for Organizers of Events and Festivals to measure expectation, experience and involvement of (potential) event visitors.

Paylogic creates the best possible fan experience to enable a real conversation and relationship between you and your fans.

Silver

Bronze

Former Speakers

2017

  • Kees Hink - The tale of Oscar and the API
  • Marco Vellinga - Creating abstraction between consumer and datastore
  • German Gomez-Herrero - Polku: Serverless Stream Processing with Python
  • Laurens Bosscher - Advanced Django Admin
  • Jaap Bresser - Beyond Role Based Auth: Discretionary Access Control with Postgres & SQLAlchemy
  • Maarten Brugman - Docker do's and don'ts
  • Cees van Wieringen - Django L10N
  • Kilian Evang - Viasock: Automagically Serverize Your Scripts
  • Artur Barseghyan & Job Ganzevoort - Django performance unchained
  • Berco Beute - All you need is less. Rethinking big data
  • Maarten Breddels - A billion stars in the Jupyter Notebook
  • Jonathan Barnoud - Looking at molecules using Python
  • Zakarias Nordfäldt-Laws - HitWizard - Predicting the future Hit Songs
  • Ede Meijer - Deep learning with TensorFlow
  • Òscar Vilaplana - Let's make a GraphQL API in Python
  • Jos van Bakel - Functional Programming with Elm
  • Reinout van Rees - Querying Django models: fabulous & fast filtering
  • Joshua Peper - Find that 🍌 in 10 minutes using Machine Learing
  • Google - Machine Learning APIs for Python Developers

2016

  • Lars de Ridder - MicroPython: The Pythonic Internet of Things (slides)
  • Andrii Mishkovskyi - Vacation from Python
  • Álex González - Python, Kubernetes and friends
  • Gijs Molenaar - Kliko - Compute Container specification and implementation (slides)
  • Oleg Pidsadnyi - Factory injection: Combining PyTest and Factoryboy
  • Emil Loer - Extending C programs with PyPy-powered code
  • Boaz Leskes - Elasticsearch for SQL users
  • Reinout van Rees - Improve your django admin: big gains with little effort
  • Bram Noordzij & Bob Voorneveld - Django Channels (slides)
  • Jelle Feringa - PythonOCC & industrial robotics for the building industry
  • Ben Meijering - Hello, Machine Learning! (slides)
  • Peter Odding & Bart Kroon - Understanding PyPy and using it in production
  • Hugo Buddelmeijer - The orientation of your DAGs matter!
  • Daan Vielen - How to survive your fellow team members and managers
  • Adam Powell & Denis Dallinga - Recommendation systems @ Catawiki
  • Dmitry Chaplinsky - Python superpowers on civic society's secret service.
  • K Rain Leander - Build a Simple Cloud with TripleO Quickstart
  • Bart Wesselink - Processing large quantities of online payments
  • Jasper Spaans - From code to configuration... and back again
  • Theo Wouters - How to create an ideal development team
  • Martijn Faassen - Morepath under the hood (keynote)
  • Steven Pemberton - The future of programming (keynote)

2015

2014

  • Jeff Knupp - Writing idiomatic Python (keynote)
  • Armin Ronacher - SSL, CAs and keeping your stuff safe
  • Kenneth Reitz - Documentation is King
  • Kilian Evang - Produce: Makefiles without the annoying bits
  • Pawel Lewicki - Sphinx + Robot Framework = documentation as result of functional testing
  • Rodrigo Bernardo Pimentel - A first look at async.io
  • Guido Kollerie - Slice & Dice: Data Analysis using Pandas
  • Erik Romijn - Keeping Django chained: top security concerns for Django websites
  • Valerio Basile - Bad habits in academic code
  • Gijs Molenaar - SQLAlchemy and astronomical data
  • Dmitrijs Milajevs - Python for data scientists
  • Oscar Vilaplana - Scaling your system
  • Panel - Dangers of centralization. Options and solutions
  • Saul Ibarra Corretge - asyncio internals
  • Avi Flax - The impedance mismatch of web microframeworks
  • Denis Bilenko - Gevent, threads & async frameworks
  • Berco Beute - Python friends: CoffeeScript & AngularJS
  • Job Ganzevoort & Douwe van der Meij - From zero to hero - Professional Django setup, deploy and maintain
  • Dirk Zittersteyn - Advanced continuous integration
  • Kenneth Reitz - Growing Open Source Seeds
  • Henk Doornbos - Processes, Data and the rest
  • Greg Kowal - Geoprocessing with python
  • Artur Barseghyan - Modern authentication in Python web applications

2013

  • Holger Krekel - Re-inventing Python packaging & testing (keynote)
  • Daniël & Gideon de Kok - What Python can learn from Haskell
  • Luuk van der Velden - Best practices for the lone coder syndrome
  • Peter Odding - Reliable deployment of large Python applications
  • Oscar Vilaplana - Handling massive traffic with Python
  • Álex González - Python and Scala smoke the peace pipe
  • Berco Beute - REST API design
  • Armin Ronacher - A year with MongoDB
  • Oleg Pidsadnyi - Behaviour driven design with PyTest
  • Remco Wendt - Component architectures in Python
  • Mark Vletter - Lean prototyping
  • Emil Loer - Python raytracing
  • Douwe van der Meij - MVC revisited with Diazo
  • Jan-Jaap Driessen - Fan/theme
  • Gijs Molenaar - LOFAR <3 Python
  • Alessandro Molina - High Performance Web Applications with Python and TurboGears
  • Dmitrijs Milajevs - Real Time discussion retrieval from Twitter
  • Kenneth Reitz - Python for humans

2012

  • Michael Bayer - SQLAlchemy (keynote)
  • Bram Noordzij - Amazon Web Services. The good, bad & ugly
  • Alexandros Kanterakis - PyPedia
  • Oleg Pidsadnyi - Large number of markers on Google Maps
  • Emil Loer - Musical Python
  • Douwe van der Meij - AOP in Python API design
  • Remco Wendt - Profiling
  • Miguel Araujo - Django Uni-forms
  • Henk Doornbos & Berco Beute - Chronic Pythonic
  • Ivor Bosloper - GeoDjango
  • Oscar Vilaplana - Tornado in depth
  • Laurence de Jong - Continuous integration
  • Alexander Solovyov - Go: Python + /theme typing?
  • Niels Hageman - Distributed job scheduling
  • Armin Ronacher - A fresh look at HTTP from Python
  • Reinout van Rees - Optimize & automate your Python life
  • Dan Tofan & Spyros Ioakeimidis - Python tools for making architectural decisions
  • Rick Oost - Generalized traversals

2011

  • Armin Ronacher - The state of Python and the web (keynote)
  • Henk Doorbos - Making large, untested code bases testable
  • Reinout van Rees - Practical project automation
  • Jobert Abma - The ten commandments of Security
  • Berco Beute - Growing up Pythonically
  • Alexander Solovyov - hg and complex development processes
  • Òscar Vilaplana - ØMQ
  • Pieter Noordhuis - Redis in practice
  • Duco Dokter - NLTK: natural language processing with Python
  • Gideon de Kok & Tom de Vries - Mobile Architectures
  • Kim Chee Leong - Buildout
  • Emil Loer - Embeddng Python interpreter in Ruby and vice versa
  • Rix Groenboom - MijnOverheid: performance testing in practice

2010

  • Ivan Sagalaev (keynote)
  • Ivan Metzlar
  • Erik Huisman & Aldert Greydanus
  • Michiel Prins & Jobert Abma
  • Tom de Vries & Gideon de Kok
  • Oscar Vilaplana
  • Oleg Pidsadnyi
  • Merijn Terheggen - Minimal Viable Products
  • Henk Doornbos - Python and hardware programming
  • Berco Beute - A Python's Life?
  • Bart jan Wesselink - Advanced Payment Routing
  • Tim Bakker - Green Parking

Photographic Moments of PyGrunn 2015

Movies 2016

Movies 2015

Movies 2014

Jeff Knupp - Keynote: Writing Idiomatic Python

Armin Ronacher - SSL, CAs and keeping your stuff safe

Avi Flax - The impedance mismatch of Web Microframeworks

Saúl Ibarra Corretgé - asyncio internals

Oscar Vilaplana - Scaling your system

Panel Discussion - Dangers of centralization. Options and solutions.