uk: Комп'ютерні науки

description: Juan Manuel Santos - Salting things up in the sysadmin's world SaltStack is a thriving configuration management system written in Python that leverages YAML and Jinja2 which, by now, probably needs no introduction. This talk will cover a brief summary of why we need configuration management tools, followed by a full dive into SaltStack, its features, pros and cons, how to use it and how to extend it. By the end of this talk you will have gone from knowing little or nothing about SaltStack, to being able to deploy your own setup. This talk will be targeted to either seasoned Python developers who are taking their first steps in the system administration world, or established system administrators who secretly love Python and prefer to stay away of configuration management systems based on other languages. Its advisable that attendees have some familiarity with Python as well as with system administration concepts. Also, this presentation will be focused on GNU/Linux systems, so it is expected that attendees are comfortable with some of its concepts.

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Dive into Scrapy

Juan Riaza

type: Person

keywords:
EuroPython Conference
EP 2015
EuroPython 2015

cs: Informatika

de: Informatik

fi: tietojenkäsittelytiede

uk: Комп'ютерні науки

en: Computer Science

fr: Informatique

da: Datalogi

es: Ciencias de la Computación

nl: Informatica

cs: Technické vědy

de: Ingenieurwissenschaften

fi: tekniikka

uk: Інженерія

en: Engineering Sciences

fr: Ingénierie

da: Ingeniørvidenskab

es: Ingeniería

nl: Engineering

description: Juan Riaza - Dive into Scrapy Scrapy is a fast high-level screen scraping and web crawling framework, used to crawl websites and extract structured data from their pages. It can be used for a wide range of purposes, from data mining to monitoring and automated testing. In this talk some advanced techniques will be shown based on how Scrapy is used at Scrapinghub. Goals: - Understand why its necessary to _Scrapy-ify_ early on. - Anatomy of a Scrapy Spider. - Using the interactive shell. - What are items and how to use item loaders. - Examples of pipelines and middlewares. - Techniques to avoid getting banned. - How to deploy Scrapy projects.

TIB AV-Portal

type: Service

type: WebContent

type: MediaObject

@language: en

cs: přihlášení není vyžadováno

de: keine Anmeldung erforderlich

uk: вхід без авторизації

en: no login required

nl: geen login vereist

EuroPython

type: Organization

cs: Video

de: Video

fi: Video

uk: Відео

en: Video

fr: Vidéo

da: Video

nl: Video

es: Video


uk: Комп'ютерні науки

description: Julian Berman - Just Because You Can, Doesn't Mean You Should Python is a powerful language that provides many tools for creating highly dynamic programs. It offers tools all across the complexity spectrum that library authors can use to make their libraries seem convenient to use for users. While it's true that there are a wealth of techniques with huge positive benefits, there are a number of common antipatterns which can deceptively cause a net-loss in flexibility, readability, and predictability for users. We'll explore a few specific commonalities in this area of library and object API design, and talk about the ramifications they have on each of these programmer concerns.

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How-To: Build a local Python community

uk: Комп'ютерні науки

description: Jyrki Pulliainen - How-To: Build a local Python community Do you like visiting Python conferences like the EuroPython? Does it make you to want something similar where you live too? This talk looks into the effort, practical things and some good tips on how to bootstrap your own Python community where you live! If you already run a local Python community, join this talk to share your views and give your comments to those interested in building their first Python community. After the talk you have a good idea of what it takes to run your local Python community (spoiler: not much!) and how can you take it even further!

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The Python Compiler

uk: Комп'ютерні науки

description: Kay Hayen - The Python Compiler The Python compiler Nuitka has evolved from an absurdly compatible Python to C++ translator into a **statically optimizing Python compiler**. The mere peephole optimization is now accompanied by full function/module level optimization, with more to come, and only increased compatibility. Witness local and module **variable value propagation**, **function in-lining** with suitable code, and graceful degradation with code that uses the full Python power. (This is considered kind of the break through for Nuitka, to be finished for EP.) No compromises need to be made, full language support, all modules work, including extension modules, e.g. PyQt just works. Also new is a plugin framework that allows the user to provide workarounds for the standalone mode (create self contained distributions), do his own type hinting to Nuitka based on e.g. coding conventions, provide his own optimization based on specific knowledge. Ultimately, Nuitka is intended to grow the Python base into fields, where performance is an issue, it will need your help. Scientific Python could largely benefit from future Nuitka. Join us now.

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uk: Комп'ютерні науки

description: Kyle Knapp - It Works on My Machine: Writing Python Code for Any Environment Have you ever developed a nice, well-working python program on one environment, only to have it blow up with exceptions and tracebacks when you run it on a different environment? Have no fear! This talk will show you how to write and maintain python code that is compatible across environments that may differ by python versions and/or operating systems. Techniques and tips will be drawn from lessons and experiences gained from making the AWS CLI, a python-based command line tool to manage AWS resources, compatible across a wide range of environments. In a case-study-like format, real-life compatibility issues encountered while developing the AWS CLI will be presented along with how we resolved each of them. These real-life examples will encompass, but will not be limited to, the following topics: • How to use functions and classes that may differ across python versions and/or operating systems • How to handle version-specific bugs • How to handle strings, bytes, and Unicode across python versions • How to handle differing locale settings • How to handle file operations across operating systems • How and when to vendor dependencies • How to write tests that are compatible across python versions and operating systems • How to create a testing environment that monitors compatibility of code across various environments Ultimately, the goal of these examples is introduce to you some effective, real-world programming practices to overcome your current or next compatibility issue.

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Python's Infamous GIL

uk: Комп'ютерні науки

description: Larry Hastings - Python's Infamous GIL You've heard about Python's GIL. But what is it really? What does it do, both good and bad? Come learn all about the Python GIL. You'll learn about its history, all the problems it solves, all the problems it causes (that we know about!), and what it would take to remove the GIL. Attendees should be familiar with the terrors inherent in multithreaded programming, and be comfortable with a little C code in the slides.

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Odoo: The underdog python killer app

uk: Комп'ютерні науки

description: Antony Lesuisse - Odoo the underdog python killer app. A python framework for web based business apps. Odoo is used by 2 millions of users, although relatively unknown in the python community, it has a vibrant community and is one of the most active python open source project. I will present you the Odoo framework and how it can help to be more productive when building web based business apps. I will highlight its advantages compared to more popular framework such as django.

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uk: Комп'ютерні науки

description: Lluís Esquerda - CityBikes: bike sharing networks around the world CityBikes started on 2010 as a FOSS alternative endpoint (and Android client) to gather information for Barcelona's Bicing bike sharing service. Later evolved as an open API providing bike sharing data of any (mostly) service worldwide. Fast forward today and after some C&D letters, there's support for more than 200 cities, more than 170M historical entries have been gathered for analysis (in approx. a year) and the CityBikes API is the main source for open bike share data worldwide. This talk will tour about how we got there with the help of python and the community.

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Diversity: We are not done yet

uk: Комп'ютерні науки

description: Lynn Root - Diversity: We are not done yet The past few years, we have made large strides to welcome more diverse people into our community. You see better gender ratios in attendance numbers at Python conferences, the billed speakers, the amount of women-centric programs. We can see the benefits of outreach. But we're not done yet. While a lot of the Python community embraces the importance of being diverse, we haven't taken that mindset to our workplace. From recruiting, we still hear, "sure, we wanted to recruit women, but we couldn't find them" and "we only focus on quality here, not gender!" Within company cultures, we hear "gender equality isn't a problem here!" or "women don't ask for a higher salary" and to "just lean in!" This talk will recount the diversity efforts of the past few years and quantify the effects on the Python community. But this talk will also address the not-so-low-hanging fruit; the deeper-rooted problems that still plague the community from inside where we work. And it will talk the audience through actionable items to improve one's work place that welcomes more diversity.

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Metrics-driven development

uk: Комп'ютерні науки

description: Lynn Root - Metrics-driven development At Spotify, my team struggled to be awesome. We had a very loose understanding of what product/service our squad was responsible for, and even less so of the expectations our internal and external customers had for those services. Other than “does our Facebook login work?”, we had no understanding of how our services we’re responsible for were doing. How many users actually sign up or log in with Facebook? How many users have connected their Spotify account with their Uber account? Do folks even use Spotify with Uber? With a 2-month challenge period, my squad and I focused inward to establish those unanswered questions and to establish feedback loops and always-on dashboards. This talk will tell the story of how we chose which metrics are important for us to focus on, what technologies we have used and are using, and how we’ve iterated over our feedback loops to fine-tune what metrics we care about.

uk: вхід без авторизації


uk: Комп'ютерні науки

description: Maciej Kula - Speeding up search with locality sensitive hashing Locality sensitive hashing (LSH) is a technique for reducing complex data down to a simple hash code. If two hash codes are similar than the original data is similar. Typically, they are used for speeding up search and other similarity comparisons. In this presentation I will discuss two ways of implementing LSH in python; the first method is completely stateless but only works on certain forms of data; the second is stateful but does not make any assumptions about the distribution of the underlying data. I will conclude the presentation by describing how we apply LSH to search at Lyst.

uk: вхід без авторизації


uk: Комп'ютерні науки

description: Mandy Waite - Keynote: So, I have all these Docker containers, now what? You've solved the issue of process-level reproducibility by packaging up your apps and execution environments into a number of Docker containers. But once you have a lot of containers running, you'll probably need to coordinate them across a cluster of machines while keeping them healthy and making sure they can find each other. Trying to do this imperatively can quickly turn into an unmanageable mess! Wouldn't it be helpful if you could declare to your cluster what you want it to do, and then have the cluster assign the resources to get it done and to recover from failures and scale on demand? Kubernetes is an open source, cross platform cluster management and container orchestration platform that simplifies the complex tasks of deploying and managing your applications in Docker containers. You declare a desired state, and Kubernetes does all the work needed to create and maintain it. In this talk, we’ll look at the basics of Kubernetes and at how to map common applications to these concepts. This will include a hands-on demonstration and visualization of the steps involved in getting an application up and running on Kubernetes.

uk: вхід без авторизації


uk: Комп'ютерні науки

description: Marc-André Lemburg - Python idioms to help you write good code Python focuses a lot on writing readable code and also tries to make solutions obvious, but this doesn't necessarily mean that you cannot write unreadable code or design your code in ways which makes it hard to extend or maintain. This talk will show some useful idioms to apply when writing Python code, how to structure your modules and also goes into details on which techniques to use and which to think about twice, based on 20 years of experience writing Python.

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Lessons learned about testing and TDD

uk: Комп'ютерні науки

description: Marco Buttu - Lessons learned about testing and TDD One day our software will go in production, and so shortly we will pay dearly for our youthful mistakes. Without regression tests, we will be in deep trouble. If we have regression tests, but we did not have performed TDD, we should probably increase the effort in bug fixing and maintenance, since we do not have enough code coverage and our tests come out complex. By retracing the author youthful mistakes, we will see a complete development workflow, from the user story to the low-level tests, in order to highlight the differences between functional, integration and unit tests, the best practices, and the lessons learned by the author during the development of the [Sardinia Radio Telescope] control software.

uk: вхід без авторизації


Nameko for Microservices

uk: Комп'ютерні науки

description: Matt Bennett - Nameko for Microservices Microservices are popping up everywhere. This talk will explain what this fashionable new architecture is, including the pros and cons of adopting it, and then discuss an open-source framework that can help you do so. Nameko assists you in writing services with well-defined boundaries that are easy to test. By leveraging some neat design patterns and providing test helpers, it also encourages good service structure and clean code.

uk: вхід без авторизації


uk: Комп'ютерні науки

description: Matthieu Rigal - Bringing PostgreSQL towards zero downtime migration with Python Using an SQL database offers a bunch of advantages; first of all its maturity and that it is understood by almost every software developer. But it has at least one main disadvantage. As the data is structured, if you want to modify the structure, for example on a long-running project, you need a migration and therefore almost for sure, a downtime. When you have to make a migration, to modify the structure of data for a small amount of records, it is so fast that it never gets problematic. But if you think to modify the structure of tables containing millions or billions of records, the time required to simply apply the structural change is problematic. Here are some changes we are working on at orderbird to go towards zero downtime migrations using some of the latest improvements of PostgreSQL 9.4, mainly logical replication and mixing in a little magic of some python scripting with psycopg.

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TDD for APIs

uk: Комп'ютерні науки

description: Michael Kuehne - TDD for APIs It is always tough to test a complex API comprehensively. The additional level of complexity brings us to the question "How can we validate that our API is working as intended?" In this talk I will explain how to use test driven development for APIs to solve this problem and even further how TDD can drive an API Design towards a more usable design. I will outline my practical approach with an implementation example based on django. And finally I will give you a brief summary of my lessons learned using this approach in customer projects.

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uk: Комп'ютерні науки

description: Max Brauer - Stop trying to glue your services together; import lymph What if you could focus on functionality rather than the glue code between services? Lymph is an opinionated framework for writing services in Python. It features pluggable service discovery, request-reply messaging and pluggable pub-sub messaging. As our development teams are growing, we're moving away from our monolithic architecture. We want to write services and not worry about the infrastructure's needs. We want development to be fast, quick and simply work.

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Big Data with Python & Hadoop

uk: Комп'ютерні науки

description: Max Tepkeev - Big Data with Python & Hadoop Big Data - these two words are heard so often nowadays. But what exactly is Big Data ? Can we, Pythonistas, enter the wonder world of Big Data ? The answer is definitely "Yes". This talk is an introduction to the big data processing using Apache Hadoop and Python. We'll talk about Apache Hadoop, it's concepts, infrastructure and how one can use Python with it. We'll compare the speed of Python jobs under different Python implementations, including CPython, PyPy and Jython and also discuss what Python libraries are available out there to work with Apache Hadoop. This talk is intended for beginners who want to know about Hadoop and Python or those who are already working with Hadoop but are wondering how to use it with Python or how to speed up their Python jobs.

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Python microservices on PaaS done right

uk: Комп'ютерні науки

description: Michał Bultrowicz - Python microservices on PaaS done right Lately, there's a lot of talk about microservices but not enough concrete examples and case studies. I want to change that by showing: - how thinking in PaaS terms can lead to robust and scalable designs; - what infrastructure and automation you need to set up to go along smoothly; - how to get real time metrics of your apps; - what makes Python good for microservices; - what is Python's performance relative to some alternatives. **Prerequisites for the talk:** - some experience with web development in Python; - basic knowledge of RESTful web services.

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uk: Комп'ютерні науки

description: Michael Foord - To the Clouds: Why you should deploy to the cloud even if you don't want to Do you deploy your Python services to Amazon EC2, or to Openstack, or even to HP cloud, joyent or Azure? Do you want to - without being tied into any one of them? What about local full stack deployments with lxc or kvm containers? Even if you're convinced you don't need "the cloud" because you manage your own servers, amazing technologies like Private clouds and MaaS, for dynamic server management on bare metal, may change your mind. Fed up with the cloud hype? Let us rehabilitate the buzzword! (A bit anyway.) A fully automated cloud deployment system is essential for rapid scaling, but it's also invaluable for full stack testing on continuous integration systems. Even better, your service deployment and infrastructure can be managed with Python code? (Devops distilled) Treat your servers as cattle not as pets, for service oriented repeatable deployments on your choice of back-end. Learn how service orchestration is a powerful new approach to deployment management, and do it with Python! If any of this sounds interesting then Juju maybe for you! In this talk we'll see a demo deployment for a Django application and related infrastructure. We'll be looking at the key benefits of cloud deployments and how service orchestration is different from the "machine provisioning" approach of most existing cloud deployment solutions.

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Testing web apps with Selenium

uk: Комп'ютерні науки

description: Michal Hořejšek - Testing web apps with Selenium „Selenium automates browser.“ Selenium can be used as tool for testing web applications. At first it can be pretty hard to start testing with Selenium, but later on it can be even harder. I want to show you that it doesn't have to be true. That it can be easy, actually. But you have to know few things which you have to be careful about and that there is tool webdriverwrapper which can make it easy for you. I will speak about handling pages with JavaScript and which common problem can you have, how to run Selenium on servers without X server, how to deal with tabs, how to test with UnitTest or pytest, and how can webdriverwrapper make things easier for you and more.

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Functional Python with Mochi

uk: Комп'ютерні науки

description: Mike Müller - Functional Python with Mochi While Python supports procedural, object-oriented, and functional programming, its functional features are not fully developed. Mochi is a Python-like functional language that compiles to Python 3 and PyPy 3 bytecode. It can use Python libraries and can be used from Python. Mochi adds functional features such as tail recursion optimization, no re-assignments in function definitions, persistent data structures, pattern matching, algebraic data types, a pipeline operator, better anonymous functions, Erlang-style actors, Lisp-style macros as well as many useful builtin functions. This talk presents what Mochi is, how it works, and what you can do with it. Functional programming can help to solve certain kind of problems elegantly. Done right, functional programs can be easily tested and provide more confidence that you program is really doing what you want. Mochi could be another tool in your toolbox. Functional programming can expand your horizon and can be a lot of fun. Mochi offers easy access to this new world because you can leverage your existing Python knowledge and libraries whenever needed.

uk: вхід без авторизації


uk: Комп'ютерні науки

description: Mikel Larreategi - Python gure etxean: (r)eboluzioa atzo, gaur eta bihar 2000 urtean CodeSyntax sortu zenetik Python erabili dugu gure lan ia guztiak egiteko. Lan horiek egitean izandako (r)eboluzioa azalduko dugu hitzaldi honetan: python script arruntetatik, Zope aplikazioen zerbitzarian nabigatzaile baten programatzetik, fitxategi sisteman programatzera pasatu gara, Turbogears ere ikutu dugu eta orain Plone, Django eta Pyramid darabilgu. Since the beginning of our company in year 2000 we have been using Python to do our work. We will explain the (r)evolution we faced working with python during this 15 years: small python scripts, browser-based-development using Zope Application Server, we touched Turbogears and now Plone, Django and Pyramid applications.

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uk: Комп'ютерні науки

description: Mikey Ariel - FOSS DOCS 101 (keep it simple, present!) Does your open source project need better documentation? Do you wish that new users could get started with your software more easily? Do you feel that your code contribution workflow isn't documented well enough, or that contributors are discouraged from documenting their code? How can you give your project docs the love they deserve? This high-level talk aims to introduce the main principles of technical communication in the context of FOSS projects. It is intended for anyone who interacts with docs, whether your project is fresh off the dev environment or has been around since the dawn of Git. Topics include tone, style, process management, structure, and contribution workflow.

uk: вхід без авторизації


uk: Комп'ютерні науки

description: Moritz Gronbach - What's the fuzz all about? Randomized data generation for robust unit testing In static unit testing, the output of a function is compared to a precomputed result. Even though such unit tests may apparently cover all the code in a function, they might cover only a small subset of behaviours of the function. This potentially allows bugs such as heartbleed to stay undetected. Dynamic unit tests using fuzzing, which allows you to specify a data generation template, can make your test suite more robust. In this talk, we demonstrate fuzzing using the hypothesis library. Hypothesis is a Python library to automatically generate test data based on a template. Data is generated using a strategy. A strategy specifies how data is generated, and how falsifying examples can be simplified. Hypothesis provides strategies for Python's built-in data types, and is easily customizable.Since test data is generated automatically, we can not compare against pre-computed results. Instead, tests are usually done on invariants of functions. We give an overview of such invariants. Finally, we demonstrate how we use fuzzing to test machine learning algorithms at Blue Yonder.

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Antipatterns for Diversity

uk: Комп'ютерні науки

description: Naomi Ceder - Antipatterns for Diversity **Stop doing the same thing but expecting different results** As developers we put considerable effort into optimisation. We are always tinkering, trying to make things better, and striving to remove antipatterns from our code and our development processes. Yet for some reason we have not been as good at applying this spirit of optimisation to the problem of increasing diversity, even though most people these days agree that, like good tests, agile methodologies, and virtual environments, diversity is a "good thing". My position is that just as there is no single easy way to write good code there is no single easy way to increasing diversity. There are, however, several things that companies and organisations do which actually work against diversity. This talk will explore these antipatterns for diversity, including uncritical belief in meritocracy, lack of understanding of the realities of marginalisation, null processes, misunderstanding of "culture fit", and an unwillingness to change, as well as some ways that teams, companies, and organisations might work to combat them.

uk: вхід без авторизації


uk: Комп'ютерні науки

description: Francesco Nazzaro - Image recognition and camera positioning with OpenCV. A tourist guide application. OpenCV Python bindings provide several ready to use tools for camera calibration, image recognition and camera position estimation. This talk will show how to recognize a picture, from a library of known paintings, and compute the camera position with respect to the recognized picture using OpenCV and numpy. This is applied to a tourist guide application for Google Glass through the recognition of the paintings exposed in the museum.

uk: вхід без авторизації


uk: Комп'ютерні науки

description: Nicholas Tollervey - Lessons learned with asyncio ("Look ma, I wrote a distributed hash table!") This talk introduces the asyncio module. I'll cover what it's for, how it works and describe how I used it to write a real-world networked application (a distributed hash table). We'll explore the event loop, coroutines, futures and networking with examples from my code. This won't be an exhaustive exposition. Rather, attendees will grasp enough of asyncio to continue with their own studies. By the end of this introductory talk I hope attendees will want to learn more about asyncio and perhaps give it a try in their own projects.

uk: вхід без авторизації


uk: Комп'ютерні науки

description: Núria Pujol/Ignasi Fosch - What dojos are and how we run them at pyBCN Coding dojos are a very good way to share coding knowledge among members in a community, and, at the same time, introduce people into the language and community. Sometimes, though, the typical approach to set coding dojos may prevent expert coders to join the session. This is the story of the pyBCN's dojos, so far.

uk: вхід без авторизації


uk: Комп'ютерні науки

description: oier etxaniz - Karakate magaletik EuroPythoneko tontorrera Orain dela urte batzuk asi genuen bidea azalduko dut, Python San Sebastian elkartea nola sotu genuen eta hortik pixkanaka pixkanaka nola sortzen joan den EuroPython sortzeko grina. Gendeari nahi izan eskero eta lan egin eskero EuroPython bezelako kongresu bat antolatzea posible dela erakustea du helburu hitzaldi honek.

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uk: Комп'ютерні науки

description: Ola Sitarska/Ola Sendecka - Keynote: It's Dangerous To Go Alone, Take This: The Power of a Community In this keynote, Ola and Ola will take you on a fantastic journey to the magical world of little Liz, who is totally enchanted by technology. The story of Liz will show that with a little bit of magic, curiosity, courage and hard work, you can defeat all the obstacles standing in your way. You'll discover with her that making big and scary things is easier when you're not doing them alone. Because sometimes, one magical spell, the helpful hand of a friend or this shiny sparkle is all it takes to make a dent in one's universe.

uk: вхід без авторизації


uk: Комп'ютерні науки

description: Oliver Braun - Multibody Simulation using sympy, scipy and vpython The talk is about the implementation of multibody simulation in the scientific python world on the way to a stage usefull for engineering and educational purposes. Multibody simulation (MBS) requires two major steps: first the formulation of the specific mechanical problem. Second step is the integration of the resulting equations. For the first step we use the package sympy which is on a very advanced level to perform symbolic calculation and which supports already Lagrange's and Kane's formalism. The extensions we made are such that a complex mechanical setup can be formulated easily with several lines of python code. The functionality is analogous to well known MBS-tools, with that you can assemble bodies, joints, forces and constraints. Also external forces even in a cosimulation model can be added on top. The second step, the integration is done via ode- integrators implemented in scipy. Finally for visual validation the results are visualized with the vpython package and for further analytics with matplotlib. Conclusion: not only highly constrained pendulums with many rods and springs but also driving simulation of passenger cars an be performed with our new extension using python packages off the shelf.

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Decorators demystified

uk: Комп'ютерні науки

description: Pablo Enfedaque - Decorators demystified Do you know what happens every time you use the **@** symbol in Python? In this talk we will see the magic behind the _syntactic sugar_ of the decorators. To understand how they internally work we will see in detail Python's **scopes**, **namespaces** and **closures**, and finally we will manually apply our own handcrafted decorator. Level: Intermediate. Attendees must have previous knowledge of Python and should be somehow familiar with the **'@'** notation to decorate a function

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The Salt Route

uk: Комп'ютерні науки

description: Pablo Seminario - The Salt Route An introduction to the devops culture by sharing our experience at PeopleDoc Inc. a successfully French start-up. The salt route talk presents some best practices and common mistakes that arise in everyday teamwork between developers and sysadmins using SaltStack for configuration management, server provisioning, orchestration and Django web applications deployment. As an introductory talk there is no prerequisites required.

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Panel Discussion: Code Review Tools

uk: Комп'ютерні науки

description: Gautier Hayoun (org) - Panel Discussion: Code Review Tools This was a last minute replacement for a cancelled talk.

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uk: Комп'ютерні науки

description: Patrick Mühlbauer - Building nice command line interfaces - a look beyond the stdlib One of the problems programmers are most often faced with is the parsing and validation of command-line arguments. If you're new to Python or programming in general, you might start by parsing sys.argv. Or perhaps you might've already come across standard library solutions such as getopt, optparse or argparse in the official documentation. While these modules are probably preferable to parsing sys.argv yourself, you might wonder if there are more satisfactory solutions outside of the standard library. Well, yes there are! This talk will give you an overview of some popular alternatives to the standard library solutions (e.g. click, docopt and cliff), explain their basic concepts and differences and show how you can test your CLIs.

uk: вхід без авторизації


uk: Комп'ютерні науки

description: Paul Hallett - What it's really like building RESTful APIs with Django At the beginning of this year I started working at Lyst and I was tasked with helping to replace their old and outdated web API with a modern RESTful replacement. Along the way we encountered some interesting design decisions and now I’m going to share what we learned about building a real RESTful API with Django and Django REST framework. I've been talking about how to build great RESTful APIs for the past year at various Python and Django conferences in Europe. Now I'd like to take some real world experiences from creating Lyst's new web API and share what I've learned along the way.

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description: Peter Hoffmann - PySpark - Data processing in Python on top of Apache Spark. [Apache Spark] is a computational engine for large-scale data processing. It is responsible for scheduling, distribution and monitoring applications which consist of many computational task across many worker machines on a computing cluster. This Talk will give an overview of PySpark with a focus on Resilient Distributed Datasets and the DataFrame API. While Spark Core itself is written in Scala and runs on the JVM, PySpark exposes the Spark programming model to Python. It defines an API for Resilient Distributed Datasets (RDDs). RDDs are a distributed memory abstraction that lets programmers perform in-memory computations on large clusters in a fault-tolerant manner. RDDs are immutable, partitioned collections of objects. Transformations construct a new RDD from a previous one. Actions compute a result based on an RDD. Multiple computation steps are expressed as directed acyclic graph (DAG). The DAG execution model is a generalization of the Hadoop MapReduce computation model. The Spark DataFrame API was introduced in Spark 1.3. DataFrames envolve Spark's RDD model and are inspired by Pandas and R data frames. The API provides simplified operators for filtering, aggregating, and projecting over large datasets. The DataFrame API supports diffferent data sources like JSON datasources, Parquet files, Hive tables and JDBC database connections.

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Import Deep Dive

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description: Petr Viktorin - Import Deep Dive Whatever you need to do with Python, you can probably import a library for it. But what exactly happens when you use that import statement? How does a source file that you've installed or written become a Python module object, providing functions or classes for you to play with? While the import mechanism is relatively well-documented in the reference and dozens of PEPs, sometimes even Python veterans are caught by surprise. And some details are little-known: did you know you can import from zip archives? Write CPython modules in C, or even a dialect of Lisp? Or import from URLs (which might not be a good idea)? This talk explains exactly what can happen when you use the import statement – from the mundane machinery of searching PYTHONPATH through subtle details of packages and import loops, to deep internals of custom importers and C extension loading.

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description: Philipp Mack - Python in the world of retail and mail order At Blue Yonder a lot of different python packages, provided by the community, as well as our own self-written ackages, are used in order to provide flexible solutions to our problems. In this talk I'll present a walkthrough of a generic python application example for demand and purchase order quantity calculations, putting together those packages in an orderly way. The example will feature real world problems derived from hands-on experience with our retail and mail order customers. Additionally the talk addresses the subjects of testing, configuring, parallelising and deploying the code.

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Writing quality code

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description: Radosław Jankiewicz - Writing quality code The quality of written code is an important factor in a final success of a software project. Perhaps there is no universal definition of high quality code, however usually it's characterized as clear and readable, well-designed, well tested and documented, easier to debug, maintain and extend, etc. Python was designed to be a highly readable language that would make it easier to develop high quality code. Nevertheless, programming language is only a tool in a software development process and in the end the quality of code depends mostly on its author's concept and decisions he make. In this talk I would like to present some of ideas, techniques and tools for improving the quality of written code, tried out with a good result in everyday work on developing software in Python.

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description: Piotr Dyba - with modern_peripherals: Python and Flask Auto-scrolling sites, glance-following ads, and gesture friendly web pages are coming! Over the last few years three products emerged that enable interaction with computer in a new way: Myo Armband, Leap Motion Controller and EyeTribe. The Myo Armband is a device that uses the electrical activity in your muscles to wirelessly control your computer, phone, and tablet, which is especially useful when your hands are "tied" or dirty. This device will be used to navigate through the presentation. The Leap Motion Controller tracks both hands in front of the screen. From a web developer’s perspective, both devices allows us to use gestures, previously restricted to touch devices, on desktops. EyeTribe is an affordable eye-tracking device. The talk will briefly cover setting up SDKs and python wrappers, and then focus on possible uses in daily life, business and, of course, web app development. Code examples will be included. In addition, the trade-offs between processing this new type of input data in the client versus processing input on the server will be discussed.

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name: With modern_peripherals: Python and Flask


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description: Radosław Jan Ganczarek - Code Quality in Python - tools and reasons Beginner's guide to Python code quality. I'll talk about the tools for code analysis, differences between them, extending them with new features and ways to running them automatically. In the end, I'll talk about reasons behind all of these tools and try to convince you to using them in your projects (but if you are against it - I'll gladly listen to your arguments).

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BDD: You’re doing it wrong!

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description: Rafał Nowicki - BDD: You’re doing it wrong! Talk about mistakes we made and best practises we have elaborated while implementation Behave Driven Development into one of the projects. Great idea to coverage whole application with functional tests fall down in development chaos and reborn on new better foundations. Project referred is web-based big data management which main features are transcoding and file sharing. Thanks to Django and many Python frameworks we have web interface for it and we are able to run automation tests with Selenium.

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description: Ramon Navarro Bosch - Plone 5 and how to use machine learning with it. Plone is a Document Management System and Content Management System that has been in the Python scope for more than 10 years. Plone 5’s features allow us to manage content, define various kinds of content and provide a nice useful UI to work on semantic web technologies. In this talk we are going to explain our approach for using Plone with the Python machine learning toolkit sklearn to enable clusterization and classification of content using a scalable content management system. We will also add some sophisticated front-end gloss using the new functionalities on frontend development added on Plone 5 and some real use cases of CMS/DMS with machine learning using sklean and solr.

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description: Raphael Pierzina - Come to the Dark Side! We have a whole bunch of Cookiecutters! *(This talk is intended for intermediate-level participants who have a basic understanding of the Python language and contains quotes from Darth Vader that some attendees may find hilarious)* Writing a Python script from scratch is fairly easy and you get on with very little boilerplate code in general. However starting a new Python project can be tiring if you decide to stick to best practices and plan on submitting it to PyPI. It requires great diligence and occasionally gets pretty cumbersome if you start new tools on a regular basis. You underestimate the power of a good template ---------------------------------------------- Why not just use a template for it? Cookiecutter is a CLI tool written in pure Python that enables you to do so. Not only is it working for Python code, but also markdown formats and even other programming languages. We will talk about the ideas behind Cookiecutter and go over how you can create your very own template, so you and others can benefit from your experience. I would like to briefly go into the technologies used and how you can get involved in the Cookiecutter GitHub project. There are already plenty of Cookiecutter templates, or Cookiecutters as we call them, available online. Most of them target Python projects, but others can be used to create C++, LaTeX or Javascript projects. The ability to destroy a planet is insignificant next to the power of Cookiecutter. ------------------------------------------------- I will show you how to use Cookiecutter and highlight some of the amazing templates created by the community. More importantly we will create a Cookiecutter template from scratch using the example of a simple Kivy app and make use of advanced features such as post-gen hooks, copy-without-render and templates in context values. Finally I will recommend resources on how to follow up on this talk and how to get in touch in case of any queries.

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Metaprogramación en Python

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description: Raúl Cumplido - Metaprogramación en Python Según wikipedia: "La metaprogramación consiste en escribir programas que escriben o manipulan otros programas (o a sí mismos) como datos, o que hacen en tiempo de compilación parte del trabajo que, de otra forma, se haría en tiempo de ejecución. Esto permite al programador ahorrar tiempo en la producción de código." En esta charla veremos diferentes mecanismos que Python proporciona como: - Decoradores - Metaclasses - Descriptors A través de varios ejemplos veremos como reutilizar código en varias funciones y clases, como modificar como nuestras clases se generan, como se genera una clase (que funciones se llaman cuando una clase se crea) o como se genera una instancia. Veremos también que fácilmente se nos puede ir de las manos y como utilizar con cuidado las herramientas que Python nos proporciona.

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Python & Internet of Things

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description: Ravi Vagadia - Python & Internet of Things There is a lot buzz about the Internet of things and how it's going to be the next big thing in computing. Python can power "things" and is used extensively in network applications, however there isn't much information out there about where Python can be used to build end-to- end IoT products. Goals : To put into perspective the usefulness of Python in building IoT products. Spread awareness on possibilities of using Python on embedded hardware.

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description: Richard Wall - Faking It - The Art of Testing Using Verified Fakes Have you ever worried that your tests aren't as good because they're running against a fake or mock instead of the real thing? Verified fakes solve this problem. Verified fakes allow for simplified testing using fakes while still providing the assurance that code tested using a fake implementation will behave the same way when used with a real implementation. The talk will begin with a case-study, demonstrating what it means to write a "verified fake" implementation of a public API. I will show how to write tests that verify a fake implementation of a well defined API and I will show how those same tests can be re-used to verify and test real implementations of the same API. The talk will end with a proposal that more libraries should include verified fakes. I will show, with real-world examples, how verified fakes can be used by integrators and discuss how they are superior to ad-hoc, unverified, mocking. During the talk I will refer to various real world, Open Source examples. Including: * Flocker's Pluggable "Block Device Backend" This API allows Flocker to manipulate file systems on OpenStack Cinder Blocks and AWS EBS devices. It also makes it easy for third parties to implement their own Flocker block device backends. * Eliot's Memory Logger - and its use in testing and verifying logged messages. * LibCloud's DummyNodeDriver - and its limitations. * Boto - as an example of a library that could benefit from a verified, introspectable fake. * Docker-py - as an example of a library for which we have written a verified fake. There will be at least 5 minutes for discussion at the end of the talk.

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description: Rob Collins - DumbDev -- eight rules for dumb development So often, we've been encouraged to be smart in our development. "Work smarter not harder!" say the encouraging posters. But the desire to be smart, and be seen to be smart, is hurting. The design suffers, the code suffers, and it's hard to recruit developers smart enough to cope with the problems caused. In this talk, I'm proposing an alternative to being smart: **_DumbDev_**. Let's use our brains for enjoyable, interesting things, rather than wrestling with code written for smart developers. **So what do I mean by _dumb_?** Well, I don't mean 'ignorant'. A clever person can be ignorant of some important information, and learn it. With ignorance, there is hope. I'm also not talking about its opposite, 'stupidity'. This occurs when someone is given the information or advice, and chooses to ignore it. Dumb isn't stupid. Nor is it silent, as in someone who can't speak. Instead, the picture I have is of one of the early computers: very small RAM, disk space measured in KB, and a woefully inadequate CPU. In other words, slow, with very little working memory and limited persistent storage. Hey, this describes my brain -- and I realise that's an asset! I will write better software if I take this into account. So, I'm a **_DumbDev_**, which means I can't hold in my mind the infamous [Plone Site class hierarchy] (see [Michele Simionato's article]). Rather than beat myself up about this, I can say, "Hold on, maybe deep inheritance is a bad idea..." There is some debate about the number of things we can think about simultaneously: it may be 7, 9, 5, 4 or even only 3. We can learn some tricks to appear to cope with more, but most of us can't easily do 38. Here's the first **_DumbDev_** rule, putting a sensible limit on complexity: 1. All diagrams must fit on a Noughts and Crosses (Tic-tac-toe) board**. There are seven further rules for me to explain in this talk. I will demonstrate the benefits of the **_DumbDev_** approach, with good and bad examples. At the end of the presentation, I hope you will realise that you're a better developer than you thought at the start. The next time it takes you two hours to debug a simple exception, you'll know that it's not you. It's because the system wasn't written using **_DumbDev_** rules.

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Scaling MySQL with Python

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description: Roberto Polli - Scaling MySQL with Python Python is the language of choice for the orchestration part of MySQL 5.6. After a brief introduction of MySQL replication architecture, the talk presents the python utilities released by MySQL: - a set of drivers in pure-python - mysql-utilites for replication, management and failover - fabric, a tool for scaling, sharding and provisioning new servers You will see how to: - create resilient configurations in minutes - use mysql-fabric to create high available infrastructures As a plus, we'll show how we: - implemented a fabric provider for provisioning new databases via docker

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description: Romain Guillebert - PyPy and the future of the Python ecosystem Python has a great versatile ecosystem but the competition is getting better, this talk is about how Python can keep up with these new languages and where PyPy fits into this. Recently we've seen the rise of new technologies like Go, Node.js and Julia, those have the ability to build an ecosystem on a clean slate and thus be better than Python in some aspects. What would it take to be as good as those technologies on those aspects without loosing all the things we love about Python ? This talk will describe my perfect future where Python keeps getting better, gets to keep it's great set of libraries and where PyPy fits in that future.

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Preparing Apps for Dynamic Scaling

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description: Roy Simkes - Preparing Apps for Dynamic Scaling Scalability is a big problem for everyone who wants to grow. In order to handle the demand, appropriate infrastructure both in terms of software and hardware should be met. What if hardware was as dynamic as a service where CPU and RAM could have been acquired when only it's needed. Is there such an environment? How can you work with it? What you should be careful of? How your applications should evolve?

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Introduction to Baserock

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description: Sam Thursfield - Introduction to Baserock The Baserock project is about creating system images from source code in a clean, reproducible way. All of the tooling is written in Python. In this talk I'll explain a bit about the core idea of Baserock: declarative system definitions (expressed in YAML) that can be built and deployed in various ways. Then I'll go into more detail about the tools available, and some of the cool things that they can do: distributed building, atomic system updates, creating custom container images, and more.

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Distributed locks with Python and Redis

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description: Sebastian Buczyński - Distributed locks with Python and Redis Traditional methods of coping with concurrent programming problems are well-known and described in literature. Many programming languages, including Python, contain in their standard libraries tools and primitives such as semaphores and can spawn threads or subprocesses. However, in the face of increasing interest in service oriented architecture and building distributed systems, that span across many independent server nodes, emerges a need to adapt traditional solutions, so they can be applied in the new environment. In this talk I will share my experiences gathered during building a modern contact center - highly concurrent system, which requires certain resources to be accessed exclusively by several self-contained components.

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description: Sebastian Neubauer - A Pythonic Approach to Continuous Delivery Software development is all about writing code that delivers additional value to a customer. Following the agile and lean approach this value created by code changes should be continuously delivered as fast, as early and as often as possible without any compromise on the quality. Remarkably, there is a huge gap between the development of the application code and the reliable and scalable operation of the application. As an example, most of the tutorials about web development with Flask or Django end by starting a local “dummy” server, missing out all the steps needed for production ready operation of the web service. Furthermore, as there is no “rocket science” in-between, many proposals to bridge that gap from both sides, operations and developers start with sentences like: “you just have to...”, a clear indication that it will cause problems later on and also a symptom of a cultural gap between developers and operations staff. In this talk I will go through the complete delivery pipeline from application development to the industrial grade operation, clearly biased towards the “DevOps” mindset. Instead of presenting a sophisticated enterprise solution, I will outline the necessary building blocks for continuous delivery and fill them up with simple but working poor man's solutions, so that it is equally useful for professional and non-professional developers and operations engineers. After the talk you will know how to build such a continuous delivery pipeline with open-source tools like “Ansible”, “Devpi” and “Jenkins” and I will share some of my day-to-day experiences with automation in general. Although many of the concepts are language agnostic I will focus on the ins and outs in a python universe and outline the pythonic way of “get this thing running”.

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Distributed Workflows with Flowy

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description: Sever Banesiu - Distributed Workflows with Flowy This presentation introduces Flowy, a library for building and running distributed, asynchronous workflows built on top of different backends (such as Amazon’s SWF). Flowy deals away with the spaghetti code that often crops up from orchestrating complex workflows. It is ideal for applications that do multi-phased batch processing, media encoding, long-running tasks, and/or background processing. We'll start by discussing Flowy's unique execution model and see how different execution topologies can be implemented on top of it. During the talk we'll run and visualize workflows using a local backend. We'll then take a look at what it takes to scale beyond a single machine by using an external service like SWF.

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description: Shahriar Tajbakhsh - Parallelism Shootout: threads vs asyncio vs multiple processes You need to download data from lots and lots of URLs stored in a text file and then save them on your machine. Sure, you could write a loop and get each URL in sequence, but imagine that there are so many URLs that the sun may burn out before that loop is finished; or, you're just too impatient. For the sake of making this instructive, pretend you can only use one box. So, what do you do? Here are some typical solutions: Use a single process that creates lots of threads. Use many processes. Use a single process and a library like asyncio, gevent or eventlet to yield between coroutines when the OS blocks on IO. The talk will walk through the mechanics of each approach, and then show benchmarks of the three different approaches.

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Web Scraping Best Practises

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description: Shane Evans - Web Scraping Best Practises Python is a fantastic language for writing web scrapers. There is a large ecosystem of useful projects and a great developer community. However, it can be confusing once you go beyond the simpler scrapers typically covered in tutorials. In this talk, we will explore some common real-world scraping tasks. You will learn best practises and get a deeper understanding of what tools and techniques can be used and how to deal with the most challenging of web scraping projects! We will cover crawling and extracting data at different scales - from small websites to large focussed crawls. This will include an overview of automated extraction techniques. We'll touch on common difficulties like rendering pages in browsers, proxy management, and crawl architecture.

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A Deep Look at Logging

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description: Stefan Baerisch - A Deep Look at Logging Do you know what your application did last night? Python logging can help you. This talk you will show you how to implement a systematic logging approach without boilerplate code and how to set up the Python logging module for different needs in production systems. We will see how to work with log files and other logging endpoints. We will address the data protection concerns that come up when logging from application with personal information. We will also look at the performance implications of logging. We will then cover best practices - how to structure logging, what to include in a log message, and how to configure logging for different use cases. We will use the Python standard logging module to implement logging. This talk is useful to beginners with some experience. An understanding of decorators is useful, but not required. Some experience in web programming is a plus.

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Get native with Cython

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description: Stefan Behnel - Get native with Cython [Cython] is not only an excellent and widely used tool to speed up computational Python code, it's also a very comfortable way to talk to native code and libraries. The Cython compiler translates Python code to C or C++ code, and supports static type annotations to allow direct use of C/C++ data types and functions. The tight integration of all three languages makes it possible to freely mix Python features like generators and comprehensions with C/C++ features like native data types, pointer arithmetic or manually tuned memory management in the same code. This talk by a core developer introduces the Cython compiler by interactive code examples and presents recent enhancements in the language that continue to make Cython the best choice for the development of fast and portable Python extensions.

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Release Management with Devpi

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description: Stephan Erb - Release Management with Devpi Devpi is an open source PyPi-compatible package server. Its versatile features make it the Swiss Army knife of Python package and release management, enabling anyone to shape a custom release workflow. In this talk, I will detail how we use our company-wide Devpi installation in order to share a large set of packages across teams, deploy binary packages to our application servers, and mix and mash open source packages with our own. With Devpi being a critical part of our release and deployment infrastructure, I will also cover our high- availability setup and how we perform major version updates with minimal downtime. While this talk is not meant to be an exhaustive introduction of all available Devpi features, it can offer insights on how Devpi can be used at a larger scale.

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description: Stephan Jaensch - Building mobile APIs with services at Yelp At Yelp, we ship code multiple times a day and have maintained this pace as our team has grown to 300+ and our codebase to several million lines of Python code. This talk explores the pain points we experienced along the ways, how our service-oriented architecture alleviates them, and the infrastructure we built to develop, test, and deploy in this highly-distributed environment. As a case study, we’ll be looking at the backend powering the new Yelp Business Owner Android and iOS apps. At the start, most of the development at Yelp occurred in a single, monolithic web application, creatively named “yelp-main” (naming is hard!). As the company grew, our developers were spending increasing amounts of time trying to ship code. After recognizing this pain point, we started experimenting with a service oriented architecture to scale the development process, and so far it’s been a resounding success. Over the course of the last three years, we’ve gone from writing our first service to having over seventy production services. Along the way, we’ve dabbled with Docker containers, Pyramid, SQLAlchemy, uWSGI, gevent, and virtualenv in an effort to build the next-generation service platform for our engineers.

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description: Takayuki Shimizukawa - Sphinx autodoc: automated API documentation Using the automated documentation feature of Sphinx, you can make with ease the extensive documentation of Python program. You just write python function documents (docstrings), Sphinx organizes them into the document, can be converted to a variety of formats. In this session, I'll explain a documentation procedure that uses with sphinx autodoc and autosummary extensions. Description: Sphinx provides autodoc feature that generate document from docstring in your python sources. The docstring that contains description and example of the use of function written near the program, makes doc easy to update. In addition, the output of the Sphinx will make you understand what to write in docstring. As a result, this will improve your motivation of doc writing. To use the autodoc, you must specify python modules to automodule directive one by one. This is a tedious task, hoswever autosummary extension automate this task. In most cases, once developers have developed the API, you only need to run the make html of Sphinx, you get a nicely formatted document. Sphinx also has coverage and doctest extentions. These support writing the documentation to work with autodoc. This allow you to check the APIs that have not been documented or you can verify each doctest part is correct or not. If you use such autodoc-related extensions, you can create a Sphinx API documentation in the following procedure. 1. make coverage; you can get the APIs that have not been documented. 2. Write docstrings that includes the doctest format how to use the API. 3. make doctest; you can verify each doctest part is correct or not. 4. make html; you can generate the HTML or your favorite format. In this session, I'll explain a documentation procedure that uses with sphinx autodoc, autosummary, coverage and doctest extensions. Target: - Python programmer who is struggling with documentation. - Python library author who want to generate API docs automatically. - Python library author who want to create a clear documentation which contains python snippets. Outline: * Self introduction (2 min) * Sphinx introduction (2 min) * What is Sphinx? * Sphinx examples * Have you written API docs for your code? (2 min) * I don't know what/where should I write. * Docstrings is needed? Are there some specific format? * Getting start Sphinx (2 min) * How to install Sphinx * How to start a Sphinx project * Generate API docs from your python code (5 min) * setup autodoc extension * write docstrings for yuor python module * "automodule & make html" will generate API docs from python code * autodoc pros & cons: docs for many modules * Listing APIs automatically (5 mins) * setup autosummary extension * how to use autosummary directive * no more autodoc directive * Discovering undocumented APIs (5 min) * setup coverage extension * make coverage * Detect deviations of the impl and doc (5 min) * setup doctest extension * make doctest * Overall picture, tips, Q&A (10 min) * Overall picture of the process * Options for autodoc * translate them into other langs

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description: Teodor Dima - Use Python to process 12mil events per minute and still keep it simple (Talk) Creating a large-scale event processing system can be a daunting task. Especially if you want it “stupid simple” and wrapped around each client’s needs. We built a straightforward solution for this using Python 3 and other open-source tools. Main issues to solve for a system that needs to be both performant and scalable: - handling a throughput of 1 million events per minute in a 4 cores AWS instance; - following the principle of least astonishment; - data aggregation and how Python's standard libraries and data structures can help; - failsafe and profiling mechanisms that can be applied to any Linux service in production; - addressing unexpected behaviors of Python’s Standard Library; like reading from a file while it is written; - tackling a sudden spectacular cloud instance failure; The alternative to this system would be to adopt existing technology stacks that might be too general, add more complexity, bloat, costs and which need extensive work to solve your specific problem. Moreover, our approach resulted in over 85% drop on hardware utilisation.

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Terminal Whispering

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description: Thomas Ballinger - Terminal Whispering The terminal emulators we run so many of our programming tools in are more powerful than we remember to give them credit for, and the key to that power is understanding the interface. This talk will cover terminal colors and styles, writing to arbitrary portions of the screen, handling signals from the terminal, determining the terminal's dimensions and scrollback buffer behavior. Terminal programming can get hairy; along the way we'll deal with encoding issues, consider cross platform concerns, acknowledge 4 decades' worth of standards for terminal communication, and consider that humans at interactive terminals may not be the only users of our interfaces. By gaining an understanding of these issues, we'll be able choose from the abstractions over them offered by Python libraries Urwid, Blessings, and Python Prompt Toolkit. This talk requires minimal Python knowledge, but does assume familiarity with command line tools in a unix environment. An abbreviated version of this talk was presented at PyCon 2015 in Montréal: With the additional time I'd hope to present more code examples, a more in- depth tour of existing libraries and more practical advice about writing programs that use the terminal, and an additional example of a difficult terminal details: dealing with reflowing of text in modern terminal emulators like GNOME Terminal and iTerm.

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description: Tibor Arpas - Mashing up py.test, coverage.py and ast.py to take TDD to a new level Users and developers especially, hate waiting. Computing has adapted and we almost never wait for the computer for more then 10 seconds. One big exception is runnig a test suite which takes MINUTES on many projects. That is incredibly distracting, frustrating and dragging the whole concept of automated tests down. I present a technique and a tool (py.test plugin called "testmon") which automatically selects only tests affected by recent changes. Does it sound too good to be true? Python developers rightfully have a suspecting attitude towards any tool which tries to be too clever about their source code. Code completion and symbol searching doesn't need to be 100% reliable but messing with the test suite execution? I show that we can cut test suite execution time significantly but maintain it's reliability.

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uk: Комп'ютерні науки

description: Timo Stollenwerk - The Butler and the Snake - Continuous Integration for Python Continuous Integration is a software development practice where members of a team integrate their work frequently, leading to multiple integrations per day. Each integration is verified by an automated process (including tests) to detect integration errors as quickly as possible. This talk will introduce the basic principles for building an effective Continuous Integration system for Python-based projects. It will present the lessons learned from building a Jenkins-based CI system for an Open Source project with a distributed team of more than 340 core developers that ranks among the top 2% of all open source projects worldwide (Plone).

uk: вхід без авторизації


uk: Комп'ютерні науки

description: Tom Viner - Testing with two failure seeking missiles: fuzzing and property based testing Testing with purely random data on it's own doesn't get you very far. But two approaches that have been around for a while have new libraries that help you generate random input, that homes in on failing testcases. First **[Hypothesis]**, a Python implementation and update of the Haskell library QuickCheck. Known as property based testing, you specify a property of your code that must hold, and Hypothesis does its best to find a counterexample. It then shrinks this to find the minimal input that contradicts your property. Second, **[American fuzzy lop]** (AFL), is a young fuzzing library that's already achieved an impressive trophy case of bug discoveries. Using instrumentation and genetic algorithms, it generates test input that carefully searches out as many code paths as it can find, seeking greater functional coverage and ultimately locating crashes and hangs that no other method has found. I'll be showing how with **[Python-AFL]** we can apply this tool to our Python code.

uk: вхід без авторизації


uk: Комп'ютерні науки

description: Holger Peters - Using Scikit-Learn's interface for turning Spaghetti Data Science into Maintainable Software Finding a good structure for number-crunching code can be a problem, this especially applies to routines preceding the core algorithms: transformations such as data processing and cleanup, as well as feature construction. With such code, the programmer faces the problem, that their code easily turns into a sequence of highly interdependent operations, which are hard to separate. It can be challenging to test, maintain and reuse such "Data Science Spaghetti code". Scikit-Learn offers a simple yet powerful interface for data science algorithms: the estimator and composite classes (called meta- estimators). By example, I show how clever usage of meta-estimators can encapsulate elaborate machine learning models into a maintainable tree of objects that is both handy to use and simple to test. Looking at examples, I will show how this approach simplifies model development, testing and validation and how this brings together best practices from software engineering as well as data science. _Knowledge of Scikit-Learn is handy but not necessary to follow this talk._

uk: вхід без авторизації

name: Testable ML Data Science: How to make numeric code testable using Scikit-Learn's interfaces.


Understanding Non-blocking IO

uk: Комп'ютерні науки

description: Vaidik Kapoor - Understanding Non-blocking IO As an engineer working on any web stack, you may have heard about Blocking and Non-Blocking IO. You may as well have used any framework or library that supports Non-Blocking IO. After all, they are very useful as you don't want to block execution of other tasks while one task is waiting to complete a network call to another service (like HTTP call to an API or may be a TCP call to your database). Non-Blocking IO while doing tasks and not wait for IO. This also helps us handle a lot many connections than we possibly could with Blocking IO. Python supports Non-Blocking IO, but we always use some existing 3rd party library that hides all the gory details and makes it all look like black magic to the uninitiated. But there is nothing like black magic. This presentation will be an introductory talk focused at explaining how Non-Blocking IO works, which is the basis of libraries like Gevent, Tornado and Twisted. We will learn about how Non-Blocking IO can be implemented using the most basic modules that form the base for the above mentioned libraries. Hopefully after this talk, Non-Blocking IO will not be an unsolved mystery for you anymore.

uk: вхід без авторизації


Machine Learning Under Test

uk: Комп'ютерні науки

description: Valerio Maggio - Machine Learning Under Test One point usually underestimated or omitted when dealing with machine learning algorithms is how to write *good quality* code. The obvious way to face this issue is to apply automated testing, which aims at implementing (likely) less-buggy and higher quality code. However, testing machine learning code introduces additional concerns that has to be considered. On the one hand, some constraints are imposed by the domain, and the risks intrinsically related to machine learning methods, such as handling unstable data, or avoid under/overfitting. On the other hand, testing scientific code requires additional testing tools (e.g., `numpy.testing`), specifically suited to handle numerical data. In this talk, some of the most famous machine learning techniques will be discudded and analysed from the `testing` point of view, emphasizing that testing would also allow for a better understanding of how the whole learning model works under the hood. The talk is intended for an *intermediate* audience. The content of the talk is intended to be mostly practical, and code oriented. Thus a good proficiency with the Python language is **required**. Conversely, **no prior knowledge** about testing nor Machine Learning algorithms is necessary to attend this talk.

uk: вхід без авторизації


EuroPython 2015: Lightning Talks I

uk: Комп'ютерні науки

description: Various speakers - Lightning Talks Lightning talks, presented by Harry Percival

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Recruiting sponsors presentation

uk: Комп'ютерні науки

description: Various speakers - Recruiting sponsors presentation Recruiting sponsors presentation.

uk: вхід без авторизації


uk: Комп'ютерні науки

description: Vasilij Litvinov - Tuning Python applications can dramatically increase performance Traditional Python profiling tools have limitations. Standard tools like **cProfile** and most all third party tools (like **Python Tools** plugin for Microsoft Visual Studio) suffer from common flaws. First, the profiling overhead is high (up to 50%). Second, the information provided is “function-level” i.e. the tool shows how much time was spent within a function, but not actionable “line-level” information to show which exact lines are _the bottleneck_ in a function. Adding “line-level” information to most tools causes the application to run even slower. Third, some tools require modification of the application source code to enable profiling thus disrupting development. This talk presents an experimental Python profiler. It typically has less than 15% overhead, shows line-level information and does not require modification of application source code. Experiments using it resulted in performance gains of 2x and more. Of course results vary by application, but in a typical application there may be quick optimizations easily identified by this type of profiler. The talk will briefly describe the basics of what, why and how to profile. The profiler‘s use and results will be shown in the presentation with examples based on real-life applications. Previous experience of working with profilers and trying to optimize an application is a plus, but not required, to gain a better appreciation of the work presented.

uk: вхід без авторизації


Asyncio community, one year later

uk: Комп'ютерні науки

description: Victor Stinner - asyncio community, one year later The asyncio project was officially launched with the release of Python 3.4 in March 2014. The project was public before that under the name "tulip". asyncio is just a core network library, it requires third party library to be usable for common protocols. One year later, asyncio has a strong community writing libraries on top of it. The most advanced library is aiohttp which includes a complete HTTP client but also a HTTP server. There are also libraries to access asynchronously the file system, resolve names with DNS, have variables local to tasks, read-write locks, etc. There are clients for AMQP, Asterisk, ElasticSearch, IRC, XMPP (Jabber), etc. (and even an IRC server!). There are asynchronous drivers for all common databases, and even for some ORMs. As expected, there are tons of new web frameworks based on asyncio. It's also possible to plug asyncio into Gtk, Qt, gevent, eventlet, gunicorn, tornado, etc. I will also discuss use cases of asyncio in production and benchmarks. Spoiler: asyncio is not slow. The asyncio library also evolved to become more usable: it has a better documentation, is easier to debug and has a few new functions. There is also a port to Python 2: trollius.

uk: вхід без авторизації


PySpark and Warcraft Data

uk: Комп'ютерні науки

description: Vincent Warmerdam - PySpark and Warcraft Data In this talk I will describe how to use Apache Spark (PySpark) with some data from the World of Warcraft API from an iPython notebook. Spark is interesting because it speeds up iterative processes on your hadoop cluster as well as your local machine. I will give basic benchmarks (comparing it to numpy/pandas/scikit), explain the architecture/performance behind the technology and will give a live demo on how I used Spark to analyse an interesting dataset. I'll explain why you might want to use Spark and I'll also go in and explain when you don't want to use it. The dataset I will be using is a 22Gb json blob containing auction house data from all world of warcraft servers over a period of time. The goal of the analysis will be to determine when and if basic economics still applies in a massively online game. I will assume that the everyone knows what the ipython notebook is and I will assume a basic knowledge of numpy/pandas but nothing fancy. The dataset has been chosen such that people who are less interested in Spark can still enjoy the analysis part of the talk. If you know very little about data science but if you love video games then you should like this talk.

uk: вхід без авторизації


Gitfs: Building a filesystem in Python

uk: Комп'ютерні науки

description: Danci Emanuel & Vlad Temian - gitfs - building a filesystem in Python gitfs is an open-source filesystem which was designed to bring the full powers of Git to everyone, no matter how little they know about versioning. A user can mount any repository and all the his changes will be automatically converted into commits. gitfs will also expose the history of the branch you’re currently working on by simulating snapshots of every commit. gitfs is useful in places where you want to keep track of all your files, but at the same time you don’t have the possibility of organizing everything into commits yourself. A FUSE filesystem for git repositories, with local cache. In this talk we will take a look at some of the crucial aspects involved in building a reliable FUSE filesystem, the steps that we took in building gitfs, especially in handling the git objects, what testing methods we have used for it and also we will share the most important lessons learned while building it. The prerequisites for this talk are: A good understanding of how Git works Basic understaning of Operating Systems concepts

uk: вхід без авторизації


uk: Комп'ютерні науки

description: Todd Waits - Incorporando administrado repositorios de información para generar documentación on-demand Generar documentación de forma dinámica es relevante para los ingenieros de software porque ellos interactúan con la data en el mismo donde está localizada. Es también relevante para los clientes porque la documentación se puede presentar en un formato organizado y claro. En esta presentación, hablaremos de cómo usar un proceso unificado para generar dinámicamente la documentación de diversas fuentes de data incluyendo los wikis y los sistemas de rastreo de incidencias. Idealmente, nosotros como ingenieros deberíamos interactuar solamente con una Fuente de información que nos dara como resultado una documentación vigente y correspondiente al estado actual de un sistema. En el Presente, el cliente recibe documentos incompletos y sin actualización dando una incorrecta impresión del estado vigente de un Sistema. Usando un proceso unificado para generar documentación de solo una Fuente de data permite presentar al cliente lo que se merece: artefactos actualizados y completos dando el real y mas reciente estado de un Sistema. El resto de esta presentación se enfocara en cómo lograr este Sistema.

uk: вхід без авторизації


uk: Комп'ютерні науки

description: Florian Wilhelm - "It's about time to take your medication!" or how to write a friendly reminder bot ;-) The author shows how to use the [SleekXMPP] library in order to write a small chatbot that connects to Google Hangouts and reminds you or someone else to take medication for instance. The secure and recommended OAuth2 protocol is used to authorize the bot application in the [Google Developers Console] in order to access the Google+ Hangouts API. The author will elaborate then on how to use an event- driven library to write a bot that sends scheduled messages, waits for a proper reply and repeats the question if need be. Thereby, a primer on event-driven architectures will be given.

uk: вхід без авторизації


uk: Комп'ютерні науки

description: Wojciech Lichota - Continuous Deployment for webapps based on Django When you see users starting to use your feature, you feel very proud and fulfilled. So why feel this only once every few weeks, why not feel it every day? In this talk I will show how we changed our workflow to automate deployment of code changes to production every time a feature is ready - sometimes even few times per day. I will present how to successfully combine open-source tools like Git, Jenkins, Buildout, Fabric, uWSGI, and South, in order to simplify the process and make it more reliable. I will discuss challenges that we faced implementing this workflow in a real project based on Django and how we resolved them. During this talk you will gain the knowledge required to implement Continuous Deployment in your own project.

uk: вхід без авторизації


PIP Internals

uk: Комп'ютерні науки

description: Xavier Fernandez - PIP Internals pip is certainly one of the most used package in the Python ecosystem, but what actually happens when you pip install foo ? The talk will mainly focus on two aspects: - how does it perform an installation and resolve dependencies ? - how does pip find installation candidates and select the 'best' ?

uk: вхід без авторизації


Learnt lessons in a big Django Project

uk: Комп'ютерні науки

description: Yamila Moreno - Learnt lessons in a big Django Project A Django project, developed for 2 years is a valuable source of anecdotes and wisdom. This talk is a review on the decissions, about human and tech, that my team took during the project. I'll point out the good decissions as well as the bad ones, those which made us learn "the hard way". Both good and bad decissions taught us a lot, and here I compile them, together with a handful of tips which can amuse and, hopefully, inspire the audience, specially those who are facing for the first time a big project.

uk: вхід без авторизації


uk: Комп'ютерні науки

description: Yamila Moreno - Lecciones aprendidas en un proyecto grande de Django Un proyecto hecho en Django durante dos años da para muchas anécdotas y mucho aprendizaje. Esta charla es un repaso por las decisiones sobre lo humano y lo técnico que fuimos tomando durante el desarrollo del proyecto. Señalaré las buenas decisiones que tomamos en el equipo, y también las que no nos salieron bien y nos hicieron aprender por las malas. Tanto las buenas como las malas decisiones nos enseñaron muchísimo y aquí las compilo junto con unos cuantos tips que pueden divertir y, ojalá, inspirar a la audiencia, especialmente a aquellas personas que se enfrentan por primera vez a un proyecto grande.

uk: вхід без авторизації


Introduction to Feynman Amplitudes

cs: Matematika

de: Mathematik

fi: matematiikka

uk: Математика

en: Mathematics

fr: Mathématiques

da: Matematik

es: Matemáticas

nl: Wiskunde

de: Mathematik, Naturwissenschaften

uk: Математика, природничі науки

en: Mathematics, Natural Sciences

description: In this talk, aimed at master's and Ph.D students, I will explain how to assign integrals to certain graphs representing physical processes. After discussing the standard integral representations and their underlying geometry, I will give an overview of what is presently known and not known about Feynman integrals, and indicate why they are of interest to mathematicians.

uk: вхід без авторизації

name: Institut des Hautes Études Scientifiques (IHÉS)

addressCountry: FR

addressLocality: Bures-sur-Yvette

addressRegion: Île-de-France


Feynman integrals and hyperlogarithms

uk: Математика, природничі науки

description: Many Feynman integrals evaluate to multiple polylogarithms and their special values like multiple zeta values. One particularly successful approach to understand this phenomenon is due to Francis Brown and uses iterated integrals called hyperlogarithms as a basis for the arising transcendental functions. I will go through the computation of a simple example in detail in order to explain this method, which combines calculus, algebra and combinatorics. The underlying geometric idea of linear reducibility will become clear as well. This hands-on talk aims to be accessible to students of both physics and mathematics.

uk: вхід без авторизації


uk: Математика, природничі науки

description: This talk proposes a complete characterization of the complexity of best-arm identification in one-parameter bandit models. We first give a new, tight lower bound on the sample complexity, that is the total number of draws of the arms needed in order to identify the arm with highest mean with a prescribed accuracy. This lower bound does not take an explicit form, but reveals the existence of a vector of optimal proportions of draws of the arms, that can be computed efficiently. We then propose a 'Track-and-Stop' strategy, whose sample complexity is proved to asymptotically match the lower bound. It consists in a new sampling rule, which tracks the optimal proportions of arm draws, and a stopping rule for which we propose several interpretations and that can be traced back to Chernoff (1959).

uk: вхід без авторизації

name: Optimal Best Arm Identification with Fixed Confidence


uk: Математика, природничі науки

description: Garvesh Raskutti - Algorithmic and statistical perspectives of randomized sketching for ordinary least-squares In large-scale data settings, randomized 'sketching' has become an increasingly popular tool. In the numerical linear algebra literature, randomized sketching based on either random projections or sub-sampling has been shown to achieve optimal worst-case error. In particular the sketched ordinary least-squares (OLS) solution and the CUR decomposition have been shown to achieve optimal approximation error bounds in a worst-case setting. However, until recently there has been limited work on consider the performance of the OLS estimator under a statistical model using statistical metrics. In this talk I present some recent results which address both the performance of sketching in the statistical setting, where we assume an underlying statistical model and show that many of the existing intuitions and results are quite different from the worst-case algorithmic setting.

uk: вхід без авторизації


uk: Математика, природничі науки

description: PAC-Bayesian bounds are useful tools to control the prediction risk of aggregated estimators. When dealing with the exponentially weightedaggregate (EWA), these bounds lead in some settings to the proof that the predictions are minimax-optimal. EWA is usually computed through Monte Carlo methods. However, in many practical applications, the computational cost of Monte Carlo methods is prohibitive. It is thus tempting to replace these by (faster) optimization algorithms that aim at approximating EWA: we will refer to these methods as variational Bayes (VB) methods. In this talk I will show, thanks to a PAC-Bayesian theorem, that VB approximations are well founded, in the sense that the loss incurred in terms of prevision risk is negligible in some classical settings such as linear classification, ranking... These approximations are implemented in the R package pac-vb (written by James Ridgway) that I will briefly introduce. I will especially insist on the the proof of the PAC-Bayesian theorem in order to explain how this result can be extended to other settings. Joint work with James Ridgway (Bristol) and Nicolas Chopin (ENSAE).

uk: вхід без авторизації

name: On the Properties of Variational Approximations of Gibbs Posteriors


Trade-offs in Statistical Learning

uk: Математика, природничі науки

description: I will explore the notion of constraints on learning procedures, and discuss the impact that they can have on statistical precision. This is inspired by real-life concerns such as limits on time for computation, on reliability of observations, or communication between agents. I will show how these constraints can be shown to have a concrete cost on the statistical performance of these procedures, by describing several examples.

uk: вхід без авторизації


uk: Математика, природничі науки

description: The talk will discuss recent generalizations of sparse recovery guarantees and compressive sensing to the context of machine learning. Assuming some "low-dimensional model" on the probability distribution of the data, we will see that in certain scenarios it is indeed (empirically) possible to compress a large data-collection into a reduced representation, of size driven by the complexity of the learning task, while preserving the essential information necessary to process it. Two case studies will be given: compressive clustering, and compressive Gaussian Mixture Model estimation, with an illustration on large-scale model-based speaker verification. Time allowing, some recent results on compressive spectral clustering will also be discussed.

uk: вхід без авторизації

name: Projections, Learning, and Sparsity for Efficient Data Processing


uk: Математика, природничі науки

description: We consider online convex optimization with noisy zero-th order information, that is noisy function evaluations at any desired point. We focus on problems with high degrees of smoothness, such as online logistic regression. We show that as opposed to gradient-based algorithms, high-order smoothness may be used to improve estimation rates, with a precise dependence on the degree of smoothness and the dimension. In particular, we show that for infinitely differentiable functions, we recover the same dependence on sample size as gradient-based algorithms, with an extra dimension-dependent factor. This is done for convex and strongly-convex functions in constrained or global optimization (with either one point or two points noisy evaluations of the functions). Joint work with F. Bach.

uk: вхід без авторизації

name: Highly-Smooth Zero-th Order Online Optimization


keywords:
Encyclopaedia Cinematographica
Botanik
Physiologie
Biologie
botany
physiology
biology

de: Biologie

uk: Біологія

en: Biology

uk: Математика, природничі науки

description: Die kürzeren Wellenlängen des Ultraviolett-Mikroskops erlauben höhere Auflösung und Lichtabsorption durch Proteine und andere Elemente des Cytoplasmas, was eine detailliertere Analyse des Cytoplasmas und seiner Organelles erlaubt. Dünne Elemente des Endoplasmatischen Reticulums (Durchmesser kleiner als 100 nm) und Bündel von Aktinfilamenten werden sichtbar.

@language: de

uk: вхід без авторизації

name: The cytoplasm of Allium cepa inner epidermal cells observed by UV-microscopy at 310nm

name: Österreichisches Bundesinstitut für den Wissenschaftlichen Film


uk: Математика, природничі науки

description: Holographic View of Singularities in General Relativity I will discuss new features which emerge when one studies several types of singularities present in General Relativity using methods stemming from the AdS/CFT correspondence. Some of the issues involved are the black hole information "paradox", complementarity and the nature and properties of space like singularities. I will attempt to present in each of the lectures problems which I feel need further study.

uk: вхід без авторизації

name: 2/3 Topics in Quantum Field Theory and String Theory


uk: Математика, природничі науки

uk: вхід без авторизації

name: 3/3 Topics in Quantum Field Theory and String Theory


Effective behavior of random media

uk: Математика, природничі науки

uk: вхід без авторизації


uk: Математика, природничі науки

description: Dynamical Systems on Point Processes and Geometric Routing in Stochastic Networks This talk is motivated by the study of geometric routing algorithms used for navigating stationary point processes. The mathematical abstraction for such a navigation is a class of non-measure preserving dynamical systems on counting measures called point-maps. The talk will focus on two objects associated with a point map facting on a stationary point process Φ: * The f-probabilities of Φ, which can be interpreted as the stationary regimes of the routing algorithm f on Φ. These probabilities are defined from the compactification of the action of the semigroup of point-map translations on the space of Palm probabilities. The f-probabilities of Φ are not always Palm distributions. * The f-foliation of Φ, a partition of the support of Φ which is the discrete analogue of the stable manifold of f, i.e., the leaves of the foliation are the points of \Phi with the same asymptotic fate for f. These leaves are not always stationary point processes. There always exists a point-map allowing one to navigate the leaves in a measure-preserving way. Joint work with Mir-Omid Haji-Mirsadeghi, Sharif University, Department of Mathematics.

uk: вхід без авторизації


Where are we 100 years after Shannon?

uk: Математика, природничі науки

description: Panel: Mathematical Theories for Information and Communication Technologies: Where are we 100 years after Shannon?

uk: вхід без авторизації


2nd Huawei-IHES Workshop - Introduction

uk: Математика, природничі науки

uk: вхід без авторизації


Voronoi diagram on a Riemannian surface

uk: Математика, природничі науки

description: On s'intéresse à la mosaïque de Voronoi engendrée par un processus ponctuel de Poisson homogène sur une surface riemannienne. Plus précisément, on montre un lien entre les caractèristiques moyennes et d'une cellule et la courbure gaussienne de la surface. On se concentre sur le nombre moyen de sommets en commançant par donner une formule exacte dans le cas d'une sphère. On généralise ensuite à une surface quelconque en donnant un développement asymptotique à grande intensité. La preuve repose principalement sur des résultats classiques de comparaison issus de la géométrie riemannienne.

@language: fr

uk: вхід без авторизації


uk: Математика, природничі науки

description: Claire Guerrier - Mathematical modeling and multiscale simulations for vesicular release at neuronal synapses Synaptic microdomains are underlying fundamental and yet not completely understood functions, such as learning and memory, breathing, sleeping, and many more. Motivated by understanding and analyzing these neuronal structures, we built a model to study vesicular release at synapses. As a first step, we computed the mean time for a Brownian particle to arrive at a narrow opening defined as the small cylinder joining two tangent spheres. The method relies on Möbius conformal transformation applied to the Laplace equation. We also estimated, when the particle starts inside a boundary layer near the hole, the splitting probability to reach the hole before leaving the boundary layer, which is also expressed using a mixed boundary-value Laplace equation. Using these results, we developed model equations and their corresponding stochastic simulations to study vesicular release at neuronal synapses, taking into account their specific geometry.

uk: вхід без авторизації


Optimal Skorokhod embedding problem

uk: Математика, природничі науки

uk: вхід без авторизації


uk: Математика, природничі науки

description: Dans cet exposé nous présenterons les idées clés de l’article “Liouville Quantum Gravity on the Riemann Sphere” écrit par David, Kupiainen, Rhodes et Vargas. Le but est de construire une surface de Riemann aléatoire canonique tout comme le mouvement brownien nous donne une courbe aléatoire canonique. La construction probabiliste s’effectuera essentiellement grâce au champ libre gaussien. Notons également que ces travaux sont largement inspirés par la physique théorique et en particulier par l’article “Quantum geometry of bosonic strings” dans lequel Polyakov propose une “intégrale de surface” qui correspond à l’équivalent bidimensionnel de l’intégrale de chemin de Feynman.

uk: вхід без авторизації

name: Construction probabiliste de la Liouville Quantum Gravity


La marche aléatoire auto-évitante

uk: Математика, природничі науки

uk: вхід без авторизації


uk: Математика, природничі науки

description: Antibody affinity maturation is a key process in adaptive immunity: it’s a mechanism which allows B-cells to produce high affinity antibodies against a specific antigen. Besides the biological motivations, the analysis of this learning process brought us to build a mathematical model which could be relevant to model other evolutionary systems, but also gossip or virus propagation. Our aim is to propose and analyze a mathematical model of the division-mutation-selection process of B-cells during an immune response. In particular, we investigate how the combination of various mutation models influences the typical time-scales characterizing the efficiency of the state space exploration. Our method is based on the complementarity between probabilistic tools and numerical simulations.

uk: вхід без авторизації

name: Branching random walks applied to antibody affinity maturation


Marche aléatoire linéaire sur le tore

uk: Математика, природничі науки

description: Soit ϱ une mesure de probabilité sur G=SL2(Z) dont le support engendre un sous groupe non élémentaire. L'action de G sur le tore permet de définir une marche aléatoire : partant d'un élément x et d'une suite (gn) iid de loi ϱ, on considère la suite gn ... g1 x. Dans cet exposé nous verrons que si x est irrationnel alors cette suite s'équidistribue ps et puis, nous verrons le théorème central limite pour les points ayant de bonnes propriétés diophantiennes.

uk: вхід без авторизації