List of videos

Floris Bruynooghe - Build your Microservices with ZeroMQ [EuroPython 2016] [21 July 2016] [Bilbao, Euskadi, Spain] (https://ep2016.europython.eu//conference/talks/build-your-microservices-with-zeromq) While microservices are rather commonly implemented using JSON over HTTP this is merely an implementation choice. This talk will cover the reasons why you might want to choose ZeroMQ as communication transport between your microservices instead. It will show how ZeroMQ is used from within Python and the common patterns which can simplify the overal architecture while at the same time providing reliable and low-latency communications between your services. ----- Microservices is the popular term for the trend to build backend architectures as a number of smaller independent processes. As an evolution from the Service Oriented Architecture the core aims are to create independent services which are easy to operate and even replace while all of them together compose into providing the business logic required for your application. While it is rather common for microservices to choose JSON over HTTP to communicate with each other, this is purely an implementation choice. HTTP is a protocol using a strict request-response format, this can become a little burdensome when needing to deal with asynchronous requests and forces some architectural descisions to be not as ideal as they could be. ZeroMQ has more flexible communication patterns allowing for easier mapping of real-life interactions between services. Coupled with an easy to use asynchronous user-level API and very fast underlying communication on persistent TCP connections ZeroMQ is a rather attractive transport to build your microservices based applications in. This talk will show how to use ZeroMQ within Python to build your microservices. It will show the benefits of ZeroMQ's asynchronous API, common usage patterns and how to handle backpressure. Furthermore different communication patterns will be explored and the impact this has on how to simplify the overall architecture using these patterns.

Anna Wszeborowska - Music transcription with Python [EuroPython 2016] [21 July 2016] [Bilbao, Euskadi, Spain] (https://ep2016.europython.eu//conference/talks/music-transcription-with-python) Music transcription allows to convert an audio recording to musical notation through mathematical analysis. In the talk we will focus on transcribing a monophonic audio input and see how we can modify it on the fly. To achieve that, we need to determine pitch and duration of each note, and then use these parameters to create a sequence of MIDI events. ----- Music transcription allows to convert an audio recording to musical notation through mathematical analysis. It is a very complex problem, especially for polyphonic music - currently existing solutions yield results with approx. 70% or less accuracy. In the talk we will focus on transcribing a monophonic audio input and see how we can modify it on the fly. To achieve that, we need to determine pitch and duration of each note, and then use these parameters to create a sequence of MIDI events. MIDI stands for _Musical Instrument Digital Interface_ and it encodes commands used to generate sounds by musical hardware or software. Let's see how to play around with sounds using Python and a handful of its powerful libraries. And let's do it in real-time!

Fabio Pliger - OMG, Bokeh is better than ever! [EuroPython 2016] [21 July 2016] [Bilbao, Euskadi, Spain] (https://ep2016.europython.eu//conference/talks/omg-bokeh-is-better-then-ever) Bokeh is a unique library in its genre that lets users create beautiful and complex visualizations from Python. The talks shows a comprehensive overview of the most powerful and popular Bokeh features, like: the optimized websocket based server for performant python callbacks from actions on the browser, Javascript callbacks written in Python (YES!!), bokeh command that lets target different outputs from the same input, JS transforms from Python, high-level charts, Geo support, ... ----- Bokeh is a unique library in its genre that lets users create beautiful and complex visualizations from Python (and other languages) to the browser without actually writing Javascript or HTML. In the last year the Bokeh team have added a large number of unique features that are extremely powerful. Fully optimized websocket based server that enables performant python callbacks from actions on the browser, Javascript callbacks written in Python (YES!!), bokeh command that lets target different outputs from the same input, JS transforms from Python, high-level charts, Geo support, ... Anyone interested in powerful and easy visualizations should take a look at it. :)

Valerio Maggio - Data Formats for Data Science [EuroPython 2016] [21 July 2016] [Bilbao, Euskadi, Spain] (https://ep2016.europython.eu//conference/talks/data-science-formats-beyond-csv-and-hdfs) The CSV is the most widely adopted data format. It used to store and share *not-so-big* scientific data. However, this format is not particularly suited in case data require any sort of internal hierarchical structure, or if data are too big. To this end, other data formats must be considered. In this talk, the different data formats will be presented and compared w.r.t. their usage for scientific computations along with corresponding Python libraries. ----- The *plain text* is one of the simplest yet most intuitive format in which data could be stored. It is easy to create, human and machine readable, *storage-friendly* (i.e. highly compressible), and quite fast to process. Textual data can also be easily *structured*; in fact to date the CSV (*Comma Separated Values*) is the most common data format among data scientists. However, this format is not properly suited in case data require any sort of internal hierarchical structure, or if data are too big to fit in a single disk. In these cases other formats must be considered, according to the shape of data, and the specific constraints imposed by the context. These formats may leverage *general purpose* solutions, e.g. [No]SQL databases, HDFS (Hadoop File System); or may be specifically designed for scientific data, e.g. hdf5, ROOT, NetCDF. In this talk, the strength and flaws of each solution will be discussed, focusing on their usage for scientific computations. The goal is to provide some practical guidelines for data scientists, derived from the the comparison of the different Pythonic solutions presented for the case study analysed. These will include `xarray`, `pyROOT` *vs* `rootpy`, `h5py` *vs* `PyTables`, `bcolz`, and `blaze`. Finally, few notes about the new trends for **columnar databases** (e.g. *MonetDB*) will be also presented, for very fast in-memory analytics.

Sławomir Piasecki - Python in Astronomy [EuroPython 2016] [21 July 2016] [Bilbao, Euskadi, Spain] (https://ep2016.europython.eu//conference/talks/python-in-astronomy) I would like to talk about modern Astronomy where I would give a brief history of Astronomy. I will answer some question: What do we use computers for today in astronomy? Where is Python’s place in today’s science? Is Python is the best language for scientific computation? I would like to give a short introduction into AstroPy module. Finally I would like presents some result of my research where Python was used to create data. ----- For ages people have been watching the sky, and tried to learn something about all those mysterious lights. In ancient times, scientist used mostly their naked eyes to watch what happened in the night sky. Astronomy is one of the oldest fields in science. Everything changed when Galileo invented his lunette. Thanks to thi, we were able to proof Copernicus’ new model of the solar system with the sun in the center. The next big step in Astronomy was using computers. Where there are computers and Astronomy, there is a place for programming. For many years astronomers were mostly using Fortran and C/C++. Both are suited to numeric computation and scientific computing. Since they are structured programming language, that makes them very valuable for science. Over the past decade, Python has started to be used by more and more people in astronomy. But is there a place in Astronomy for Python, as it is not as fast as Fortran or C/C++? In Python there is a module called AstroPy which helps astronomers in their work. MatPlotLib is one of the most popular library use in astronomy. This tool helps created very sophisticated plots and graphs. Finally I would like talk about some research I did using Python. For research, we decided to use AUTO. It is a hybrid of Fortran and Python, to compute bifurcation points in mathematical models. In Python we introduce mathematical model, ODE and initial parameters. Fortran does all the computation.

Joe Drumgoole - A deep dive into the Pymongo MongoDB driver [EuroPython 2016] [21 July 2016] [Bilbao, Euskadi, Spain] (https://ep2016.europython.eu//conference/talks/a-deep-dive-into-the-pymongo-mongodb-driver) The Pymongo driver is one of MongoDB’s most popular driver interfaces for connecting to MongoDB. But developers rarely look under the cover to see what’s happening inside the driver. By having a deeper insight into how the driver constructs server requests and responds, developers will be able to write more effective MongoDB applications in Python. ----- *The Pymongo driver is one of MongoDB’s most popular driver interfaces for connecting to MongoDB. But developers rarely look under the cover to see what’s happening inside the driver. * *By having a deeper insight into how the driver constructs server requests and responds, developers will be able to write more effective MongoDB applications in Python.* *We will look at :* - *Initial connection* - *A query* - *A simple write operation* - *A bulk write operation* - *How the driver responds when we have a node failure* *We will also give insight into the driver’s approach to server selection when connecting to a replicas set (a multi-node instance of MongoDB).*

Valeria Pettorino - Unveiling the Universe with python [EuroPython 2016] [21 July 2016] [Bilbao, Euskadi, Spain] (https://ep2016.europython.eu//conference/talks/unveiling-the-universe-with-python) I will describe a scientific application of python in the field of Astrophysics and Cosmology. How the publicly available package Monte Python is used to compare data from space satellite missions with theoretical models that attempt to describe the evolution and content of the Universe. The result is surprising, as it points towards a Universe which is mainly dark. ----- Python is widely used in Cosmology, which is the study of the Universe and all forms of energy in it. A large amount of data has been recently obtained through space satellite missions, such as Planck, financed by ESA/NASA. Planck has observed the radiation emitted about 13 billion years ago (the Cosmic Microwave Background, CMB), which gives us information on the content and space-time geometry of the Universe. Many competitive theoretical models have been proposed that aim at describing the evolution of the species contained in the Universe: therefore, cosmologists need a method to identify which theoretical model better fits the data. In order to compare data with theoretical predictions, cosmologists use Bayesian statistics and Monte Carlo simulations. Among the tools developed for the analysis, the package ‘Monte Python’ is publicly available and uses python to perform Monte Carlo simulations: this allows to determine the theoretical model that maximizes the likelihood to obtain the observed data. Such model is now the standard cosmological model and reveals a Universe that is very different from what scientists had ever expected. A Universe in which the atoms we are made of, constitute only 5% of the total energy budget. The rest is the so-called ‘Dark Universe’. I will illustrate the story of how cosmologists used python to analyse the data of the CMB and unveil the Dark Universe.

Alexander Steffen - Writing unit tests for C code in Python [EuroPython 2016] [21 July 2016] [Bilbao, Euskadi, Spain] (https://ep2016.europython.eu//conference/talks/writing-unit-tests-for-c-code-in-python) There are many unit testing frameworks for C out there, but most of them require you to write your tests in C (or C++). While there might be good reasons to keep your implementation in C, those hardly apply to the tests. So wouldn't it be nice to use all the power of Python and its unit testing capabilities also for your C code? This talk will show you how. ----- There are many unit testing frameworks for C out there, but most of them require you to write your tests in C (or C++). While there might be good reasons to keep your implementation in C (for example execution speed or resource consumption), those hardly apply to the tests. So wouldn't it be nice to use all the power of Python and its unit testing capabilities also for your C code? This talk will show you how to combine CFFI and pycparser to easily create Python unit tests for C code, without a single line of C anywhere in the test cases. It will also cover creating mock functions in Python, that can be used by the C code under test to hide external dependencies. Finally, we will look at some of the challenges you might face when trying to mix Python and C and what to do about them.

Fabien Bochu - Grocker, a Python build chain for Docker [EuroPython 2016] [21 July 2016] [Bilbao, Euskadi, Spain] (https://ep2016.europython.eu//conference/talks/grocker-a-python-build-chain-for-docker) Grocker is a Docker build chain for Python. It transforms your Python package into a self-contained Docker image which can be easily deployed in a Docker infrastructure. Grocker also adds a Docker entry point to easily start your application. ----- At Polyconseil, we build Paris electric car sharing service: Autolib'. This system is based on many services developed using web technologies, Django and our own libraries to handle business logic. Packaging is already a difficult problem, deploying large Python projects is even more difficult. When deploying on a live and user- centric system like Autolib', you cannot rely on Pip and external PyPI servers which might become unavailable and are beyond your control. In the beginning we used classic Debian packaging: it was a maintenance hell. It took hours to build our packages and update their metadata to match our Python packages. So we switched to Docker. Docker allows us to have a unique item that is deployed in production systems: code updates are now atomic and deterministic! But before deploying the Docker image, you need to build it. That's where Grocker comes in. Grocker is a Docker build chain for Python. It will transform your Python package into a self-contained Docker image which can be easily deployed in a Docker Infrastructure. Grocker also adds a Docker entry point to easily start your application.