Data gathered by personal GPS trackers are becoming a major source of information pertaining to human activity and behaviour. This presentation will include python work flows which aim to accurately and efficiently carry out the necessary computation required to process volunteered GPS trajectories into useful spatial information.

3D reflection seismic data collected as a part of the NanTroSEIZE project revealed complex interactions between active sedimentation and tectonics in the Nankai Trough, Japan. We implemented co-rendering of multiple attributes and stratal slicing in python to better visualize the structural and stratigraphic relationships within the piggyback slope basins of the accretionary prism.

Presenting "airspeed velocity", a new tool for benchmarking Python software projects over their lifetime.

This tutorial will be the introduction to matplotlib. Users will learn the types of plots and experiment with them. Then the fundamental concepts and terminologies of matplotlib are introduced. Next, we will learn how to change the "look and feel" of their plots. Finally, users will be introduced to other toolkits that extends matplotlib.

This tutorial will be the introduction to matplotlib. Users will learn the types of plots and experiment with them. Then the fundamental concepts and terminologies of matplotlib are introduced. Next, we will learn how to change the "look and feel" of their plots. Finally, users will be introduced to other toolkits that extends matplotlib.

This tutorial will be the introduction to matplotlib. Users will learn the types of plots and experiment with them. Then the fundamental concepts and terminologies of matplotlib are introduced. Next, we will learn how to change the "look and feel" of their plots. Finally, users will be introduced to other toolkits that extends matplotlib.

The introductory session will start with an overview of the astropy project and the goals of the tutorial, followed by the basics on accessing astronomical data and the associated attributes of such data, including the units and coordinates. Also covered is how to use the new and powerful quantities facility, which allows physical quantities to be explicitly bound to the units they are defined in.

We report on the progress made on the Astropy Project in the past year highlighting the new capabilities added as well as the near-term development plans.

This hands-on tutorial will introduce statistical analysis in Python using Bayesian methods. Bayesian statistics offer a flexible & powerful way of analyzing data, but are computationally-intensive, for which Python is ideal. As a gentle introduction, we will solve simple problems using NumPy and SciPy, before moving on to Markov chain Monte Carlo methods to build more complex models using PyMC.

This hands-on tutorial will introduce statistical analysis in Python using Bayesian methods. Bayesian statistics offer a flexible & powerful way of analyzing data, but are computationally-intensive, for which Python is ideal. As a gentle introduction, we will solve simple problems using NumPy and SciPy, before moving on to Markov chain Monte Carlo methods to build more complex models using PyMC.

This hands-on tutorial will introduce statistical analysis in Python using Bayesian methods. Bayesian statistics offer a flexible & powerful way of analyzing data, but are computationally-intensive, for which Python is ideal. As a gentle introduction, we will solve simple problems using NumPy and SciPy, before moving on to Markov chain Monte Carlo methods to build more complex models using PyMC.

The University of California, Berkeley and San Francisco combined are one of the largest buyers in the Bay Area. Historically, it has been a time-consuming process to analyze suppliers' proposed price files and ensure the University is not paying more than contracted. Through the use of Pandas and Python, this once tedious and manual process can routinely be done in a matter of a few seconds.

I will describe the new Berkeley Institute for Data Science (BIDS), part of a collaboration with UW and NYU funded by the Moore and Sloan Foundations. It will be a space for the open and interdisciplinary work that is typical of the SciPy community. In the creation of BIDS, the role of open source scientific tools for Data Science, and specifically the SciPy ecosystem, played an important role.

The Blaze project is a collection of libraries being built towards the goal of generalizing NumPy's data model and working on distributed data. This talk covers each of these libraries, and how they work together to accomplish this goal.

Bokeh is a Python visualization library for large datasets that natively uses the latest web technologies. Its goal is to provide concise construction of novel graphics, while delivering high-performance interactivity over large data to thin clients. This talk will cover the motivation and architecture behind Bokeh, demonstrate interesting uses and capability, and discuss future plans.

This talk will educate the audience about Python tools and best practices for creating reproducible petabyte-scale pipelines. This is done within the context of demonstrating a new grammar-based approach to comparative genomics. The genome grammars are produced using public data from the National Institutes of Health, streamed over a high-throughput Internet2 connection to Amazon Web Services.

Textbook Companion (TBC) has code for solved problems of textbooks, coordinated by FOSSEE (http://fossee.in). We explain a collaborative method that helped create 500 Scilab TBCs (http://cloud.scilab.in) and over 100 Python TBCs (http://tbc-python.fossee.in/). We also explain a self learning method that trained 250,000 students on FOSS systems using spoken tutorials (http://spoken-tutorial.org).

Cartopy is a Python package which builds on Proj.4 to define coordinate reference systems for the transformation and visualisation of geospatial data. It has a simple matplotlib interface for publication quality visualisation. This talk will outline some of cartopy's functionality and demonstrate some practical applications within the realm of scientific presentation of geospatial data.

Understanding environmental and climate change requires data fusion, format conversions, processing and visualization to gain insight into the data. Our open source scientific Python and JavaScript based tools makes it easy to manipulate geo-spatial and climate data, create and execute workflows, and produce visualizations over the web for scientific and decision making tools.

In high content imaging screens, cells are subjected to various treatments (usually shutting down specific genes) in high throughput, imaged, and a phenotype of interest measured. We argue that there is a wealth of information to be found in off-target phenotypes, and present an image clustering approach to discover these and infer gene function.

Learn, Share and Collaborate with CodaLab -- A new open source platform which lets communities create and explore experiments together and engage in benchmarking and competitions to enable true reproducibility and advance the state of the art in data-driven research

I provide an overview of the challenges we’ve tackled at UC Berkeley deploying scientific compute environments in both educational and research contexts. After a discussion of how these needs can be served by devops tools like Docker and Ansible, I argue that a coherent, easy-to-understand philosophy around reproducible compute environments is fundamental.

Conda is an open source package manager, which can be used to manage binary packages and virtual environments on any platform. It is the package manager of the Anaconda Python distribution, although it can be used independently of Anaconda. We will look at how conda solves many of the problems that have plagued Python packaging in the past, followed by a demonstration of its features.

With laptops and tablets becoming more powerful and more ubiquitous in the classroom, traditional computer labs with rows of expensive desktops are beginning to lose their relevance. This presentation will discuss browser-based virtual computer labs for teaching Python, using a notebook interface, as an alternative approach to classroom instruction.

The geospatial community has coalesced around Python, both in the commercial and open source spaces. In this talk, I'll show how Python tools can be shared with users of ArcGIS, a commercial GIS system which uses Python as its primary development environment. By constructing small Python wrappers, code can be shared in graphical tools which enable non-programmers to use what you've built.

We describe recent developments in the Ginga package, a open-source astronomical image viewer and toolkit written in python and hosted on Github. The package was introduced to the scientific python community at SciPy 2013 and has received a number of enhancements since then based on user feedback. The talk includes an image mosaicing demo of a wide-field camera exposure with 116 4Kx2K CCDs.

We introduce two new plots for visualizing infectious disease outbreaks. Case tree plots depict the emergence and growth of clusters of zoonotic disease over time. Checkerboard plots also represent temporal case clusters, but do not construct transmission trees. These plots visualize outbreak dynamics and allow for analyses like case fatality risk stratified by generation.

Parallel and asynchronous computing in python is crippled by pickle's poor object serialization. Dill, a more robust serialization package, strives to serialize all of python. Dill has been used to enable state persistence and recovery, global caching, and the coordination of distributed parallel calculations across a network of the world's largest computers.

Spatial weights matrices, $W$, represent potential interaction between all $i,j$ in a study area and play an essential role in many spatial analysis tasks. Commonly applied binary adjacency algorithms using decomposition and tree representations scale quadratically and are ill suited for large data sets. We present a linearly scaling, adjacency algorithm with significantly improved performance.

Statistical analysis comes in two main flavors: frequentist and Bayesian. The subtle differences between the two can lead to widely divergent approaches to common data analysis tasks. After a brief discussion of the philosophical distinctions between the views, I’ll utilize well-known Python libraries to demonstrate how this philosophy affects practical approaches to several common analysis tasks.

In this tutorial, attendees will learn how to use the IPython Notebook’s display architecture and interactive widgets. As we cover these topics, attendees will learn about the underlying architecture, how to use IPython’s existing APIs, and how to extend them for their own purposes. This tutorial will not cover the basics of the IPython Notebook.

GeoPandas extends the pandas data analysis library to work with geographic objects.

There are a growing number of Python packages (fiona, geopandas, pysal, shapely, etc.) addressing various types of spatial data, as well as the geoprocessing of that data and its statistical analysis. This session explore ways to best collaborate between and strengthen these efforts.

Using the wide range of tools and libraries available for working with geospatial data, it is now possible to transport geospatial data from a database to a web-interface in only a few lines of code. In this tutorial, we explore some of these libraries and work through examples which showcase the power of Python for geospatial data.

Using the wide range of tools and libraries available for working with geospatial data, it is now possible to transport geospatial data from a database to a web-interface in only a few lines of code. In this tutorial, we explore some of these libraries and work through examples which showcase the power of Python for geospatial data.

Using the wide range of tools and libraries available for working with geospatial data, it is now possible to transport geospatial data from a database to a web-interface in only a few lines of code. In this tutorial, we explore some of these libraries and work through examples which showcase the power of Python for geospatial data.

HDF5 is a hierarchical, binary database format that has become the de facto standard for scientific computing. While the spec may be used in a relatively simple way it also supports several high-level features that prove invaluable. HDF5 bindings exist for almost every language - including two Python libraries (PyTables and h5py). This tutorial will cover HDF5 through the lens of PyTables.

HDF5 is a hierarchical, binary database format that has become the de facto standard for scientific computing. While the spec may be used in a relatively simple way it also supports several high-level features that prove invaluable. HDF5 bindings exist for almost every language - including two Python libraries (PyTables and h5py). This tutorial will cover HDF5 through the lens of PyTables.

The development of geophysical software by individual scientists is achievable through languages such as Python. All goals behind developing a geophysical potential field interpretation and modelling software have been achieved to date. The implication of this is that innovation can be a driving force in projects, rather than waiting for commercial vendors to provide appropriate scientific tools.

Digital holography microscopy is a powerful tool for fast 3D imaging of soft matter systems. However, making measurements from holograms requires special computation. HoloPy is a set of tools for reconstructing and fitting to holograms. It also includes tools for computing light scattering, setting up inverse problems, and working with images and metadata.

Digital holographic microscopy is a fast 3D imaging technique. A camera records a time series of light scattering patterns as standard 2D images and then post-processing routines extract 3D information. By creating a GPU-accelerated GUI on top of the Holopy package, we noticed unexpected discrepancies between the different models used during post-processing.

Representing data through colours is a very common approach to conveying important information to an audience. We suggest some best practices scientists should consider when deciding how they should present their results.

From telescopes to satellite cameras to electron microscopes, scientists are producing more images than they can manually inspect. This tutorial will introduce automated image analysis using the "images as numpy arrays" abstraction, run through various fundamental image analysis operations (filters, morphology, segmentation), and finally complete one or two more advanced real-world examples.

Python and C++ can be powerful complements to one another. C++ is great for performance-critical calculations, while Python is great for everything else. In this tutorial we’ll look at how to integrate Python and C++ using the Boost.Python library. You’ll learn techniques for easily developing hybrid systems that use the right language for the right task, resulting in better software.

Python and C++ can be powerful complements to one another. C++ is great for performance-critical calculations, while Python is great for everything else. In this tutorial we’ll look at how to integrate Python and C++ using the Boost.Python library. You’ll learn techniques for easily developing hybrid systems that use the right language for the right task, resulting in better software.

Python and C++ can be powerful complements to one another. C++ is great for performance-critical calculations, while Python is great for everything else. In this tutorial we’ll look at how to integrate Python and C++ using the Boost.Python library. You’ll learn techniques for easily developing hybrid systems that use the right language for the right task, resulting in better software.

Python and C++ can be powerful complements to one another. C++ is great for performance-critical calculations, while Python is great for everything else. In this tutorial we’ll look at how to integrate Python and C++ using the Boost.Python library. You’ll learn techniques for easily developing hybrid systems that use the right language for the right task, resulting in better software.

Learn about interactive parallel computing in IPython.parallel, with examples including parallel image processing, machine learning, and physical simulations. IPython provides an easy way to interact with your multicore laptop or compute cluster.

Learn about interactive parallel computing in IPython.parallel, with examples including parallel image processing, machine learning, and physical simulations. IPython provides an easy way to interact with your multicore laptop or compute cluster.

Learn about interactive parallel computing in IPython.parallel, with examples including parallel image processing, machine learning, and physical simulations. IPython provides an easy way to interact with your multicore laptop or compute cluster.

This talk/poster will outline and present recent work in integrating Hyperopt, a package for the optimization of the hyperparameters of machine learning algorithms, with Pylearn2, a machine learning research and prototyping framework focused on "deep learning" algorithms, the technical challenges we faced and how we addressed them.

An introduction to the new Julia language from scratch, emphasising similarities and differences with scientific Python.

An introduction to the new Julia language from scratch, emphasising similarities and differences with scientific Python.

Recently, the IPython Notebook has began to be used for presentations in several conferences. But, there is not a full-featured and executable IPython presentation tool available. So, we developed a new Reveal.js-powered live slideshow extension, designed specifically to be used directly from the notebook and to be as executable as the notebook is, and also powered with great features.

The KBase Narrative builds on the IPython Notebook to provide a multi-user, virtualized Bioinformatics Laboratory Notebook that brings Experimental/Wetlab Biologists, students and the bio-curious into the world of Computational Biology. Tools for genome annotation, visualization, metabolic modeling and more are made available in a collaborative and educational web interface.

Real-time feeds of user activity from various apps such as Twitter, Foursquare, and others are becoming increasingly available. These 'digital footprints' provide new means to understand how individuals utilize the places and spaces of urban environments. We present a light-weight framework for real-time event detection in a city based on existing SciPy libraries and real-time Twitter streams.

A novel approach to both violence prevention and the measurement of propensity to violence is presented. The work is part of the evaluation of Cure Violence's (Ransford, Kane and Slutkin 2009; Slutkin 2012) implementation in NYC. Python libraries such as IPython, PySAL, Numpy, Basemap, Fiona, Shapely, Matplotlib, bNetworkX, Pandas and scikit-learn feature prominently in the work.

Numerical simulations have a broad range of application, from aircraft design to drug discovery. However, any prediction from a computer must be tested to ensure its reliability. Verification ensures the outputs of a computation accurately reflect the underlying model. This talks covers MASA, a tool for the verification of software used in a large class of problems in applied mathematics.

This presentation details how Python is being used to extract geophysical insight from active remote sensing data, namely Radars. By using a common data model our work bridges the gap between the domains of radar engineering and image analysis.

In many scientific contexts it is necessary to identify and track features in video. Several labs with separate projects and priorities collaborated to develop a common, novice-accessible package of standard algorithms. The package manages optional high-performance components, such as numba, and interactive tools to tackle challenging data, while prioritizing testing and easy adoption by novices.

In this tutorial, attendees will learn how to derive, simulate, and visualize the motion of a multibody dynamic system with Python tools. These methods and techniques play an important role in the design and understanding of robots, vehicles, spacecraft, manufacturing machines, human motion, etc. Attendees will develop code to simulate the motion of a human or humanoid robot.

In this tutorial, attendees will learn how to derive, simulate, and visualize the motion of a multibody dynamic system with Python tools. These methods and techniques play an important role in the design and understanding of robots, vehicles, spacecraft, manufacturing machines, human motion, etc. Attendees will develop code to simulate the motion of a human or humanoid robot.

In the paper we compare object-oriented implementations of an advection algorithm written in Python, C++ and modern FORTRAN. The main angles of comparison are code brevity and syntax clarity (and hence maintainability and auditability) as well as performance. A notable performance gain when switching from CPython to PyPy will be exemplified, and the reasons for it will be briefly explained.

An end-to-end workflow for assessing storm-driven water levels predicted by coastal ocean models will be discussed which uses OWSLib for CSW Catalog access, Iris for ocean model access and pyoos for Sensor Observation Service data access. Analysis and visualization is done with Pandas and Cartopy, and the entire workflow is shared as in IPython Notebook with custom environment in Wakari.

On several issues related to the perception of colormaps...

PlaceIQ's patented platform analyzes half a trillion diverse data points about location, time, and real-world behavior to define human audiences and allow businesses to understand consumers at scale. It ingests large volumes of mobile activity data and geographic data, calling for creative use machine learning techniques to enable the high-fidelity abstractions insightful to businesses.

We discuss recent advances in the Object Oriented Finite-Element project at NIST, a Python and C++ tool designed to bring sophisticated numerical modeling capabilities to users in the field of Materials Science.

New tools like the IPython notebook can enhance both lectures and textbooks, by making class time and individual study more interactive through the inclusion of executable code and animations. I will demo some materials and activities I've provided for students using the IPython notebook. I will focus on practical issues I've faced that are particular to this teaching approach.

This talk will present an overview of a project-based introductory course in scientific computing using python for physics majors at Cal Poly San Luis Obispo.

Spitz' paper on FX pattern recognition contains a long paragraph that describes a model, an algorithm, and the results of applying the algorithm to the model. The algorithm requires Fourier transforms, convolutional filtering, matrix multiplication, and solving linear equations. I describe how to use numpy, scipy, and mapplotlib to prototype the algorithm and display the processed model.

This talk will explain how to intimately integrate IPython with your favorite text editor, as well as how to customize the IPython Notebook interface to behave in a way that makes sense to you. Though the concrete examples are centered around the world-view of a particular text editor, the content will be valuable to anyone wishing to extend and customize IPython for their own purposes.

PyMC is a Python module that implements Bayesian statistical models and fitting algorithms, including Markov chain Monte Carlo (MCMC). Its flexibility, extensibility, and clean interface make it applicable to a large suite of statistical modeling applications. The upcoming release of PyMC 3 features an expanded set of MCMC samplers, including Hamiltonian Monte Carlo.

Spatial Interaction Modelling is a method for calibrating parameters for components within a system of flows, such as migration or trade, and then using those parameters to estimate new flows. Despite their popularity, a unified Python framework to employ them does not exist. In response, pySI was created as a coherent tool for calibrating models and simulating flows for a variety of models.

We present two web applications: (PICT: Past Interpretation of Climate Tool), a paleo-climates reconstruction tool and CLIDESC, a climate services layer built on top of the Clide database, a database system used widely in the National Meteorological services across the Pacific. Both these tools have been developed at NIWA in New Zealand.

I have developed a curriculum for a three part, graduate level course on computational methods designed to increase the exposure of graduate students and researchers to basic techniques used in computational modeling and simulation using the Python programming language.

A system capable of imaging gamma rays in 3D in near real time has been developed. A flexible software framework has been developed using Python to acquire, analyze, and finally visualize data from multiple sensors, including novel gamma ray imaging detectors and a Microsoft Kinect.

PyViennaCL aims to make powerful GPGPU scientific computing really transparently easy, especially for users already using NumPy for representing matrices, by harnessing the ViennaCL linear algebra and numerical computation library for GPGPU and heterogeneous systems. In this talk, I will discuss PyViennaCL's mathematical features, computational architecture, and current developments.

Learn to read, manipulate, and write georeferenced imagery and other kinds of geospatial raster data using a productive and fun GDAL and Numpy-based library named Rasterio. It's a new open source project from the satellite team at Mapbox and is informed by a decade of experience using Python and GDAL.

Google Earth Engine is a platform designed to enable petabyte-scale scientific analysis and visualization of geospatial datasets. Earth Engine provides a consolidated environment including a massive data catalog co-located with thousands of computers for analysis. This talk will discuss products that Earth Engine has produced, and how to access Earth Engine via its Python API.

I present tools for collecting data generated by Scientific Python community development infrastructure (mailing list archives, pull requests, issue trackers) and analyzing it with Pandas and NetworkX. Showing preliminery results using social network analysis and complex systems modeling, I demonstrate using reflexive data science to enrich our understanding of open source development.

Lessons learned along the bumpy road from Python noob to an open source geophysics web application, with a commercial web service front end.

This talk introduces HashDist, a critical component of the scientific software development workflow. HashDist enables highly customizable, source-driven, and reproducible builds for scientific software stacks. HashDist builds can be made relocatable, allowing the easy redistribution of binaries on all three major operating systems as well as cloud and supercomputing platforms.

This tutorial will train reproducible research warriors on the practices and tools that make experimental verification possible with an end-to-end data analysis workflow. The tutorial will expose attendees to open science methods during data gath ering, storage, analysis up to publication into a reproducible article. Attendees are expected to have basic familiarity with scientific Python and Git.

This tutorial will train reproducible research warriors on the practices and tools that make experimental verification possible with an end-to-end data analysis workflow. The tutorial will expose attendees to open science methods during data gathering, storage, analysis up to publication into a reproducible article. Attendees are expected to have basic familiarity with scientific Python and Git.

We will have an open discussion about current matplotlib enhancement proposals and take calls for new ones. Anyone interested in matplotlib's future development efforts is more than welcome to attend. There will be no presentation. Current MEPs exist here: https://github.com/matplotlib/matplotlib/wiki#matplotlib-enhancement-proposals-meps

Scientific computing is an important aspect of the undergraduate meteorology curriculum at Millersville University. All students take a course in Fortran, and many take additional courses in Python and atmospheric numerical modeling. This presentation discusses how scientific computing is incorporated into the curriculum, and why Fortran and Python were chosen as the languages to be taught.

Do you hate repeating yourself? Want to know when your publication is repeating someone else? The Web of Trails project is a solution to knowledge management that empowers users to quickly find repetition of key phrases. Using syntactic indexing, as opposed to lexical techniques, this approach is capable of representing the literature using less space while providing high value results.

We present scikit-bio, a library based on the Python scientific computing stack implementing core bioinformatics data structures, algorithms and parsers. scikit-bio is useful for students in bioinformatics, who can learn topics such as iterative progressive multiple sequence alignment from the source code and accompanying documentation, and for real-world bioinformatics applications developers.

SimpleITK brings advanced image analysis capabilities to Python. In particular, it provides support for 2D/3D and multi-components images with physical. SimpleITK exposes a large collection of image processing filters from ITK, including image segmentation and registration. SimpleITK is freely available as an open source package under the Apache 2.0 License.

Constructing synthetic X-ray observations from hydrodynamical simulations and other data sources is made possible with a combination of the yt analysis toolkit with a number of other astronomical Python libraries.

SociaLite is a Python-integrated query language for data analysis. It makes scientific data analysis simple, yet achieves fast performance with its compiler optimizations. We support relational tables and operations in SociaLite as well as Python integration, which makes it easy to implement various analysis algorithms, including Blast algorithm and genome assembly algorithm in bioinformatics.

In this presentation, we describe the current status of software for Project Panoptes. Our goal is to build low cost, reliable, robotic telescopes which can be used to detect transiting exoplanets. Panoptes is designed from the ground up to be a citizen science project which will involve the public in all aspects of the science, from data acquisition to data reduction.

In this talk, I\u2019ll show how we apply climate change models to predict shifts in agricultural zones across the western US. I will outline the use of the pyimpute, GDAL and scikit-klearn to perform supervised classification; training a model using current climatic conditions to predict spatially-explicit zones under future climate scenarios.

I recently used Python in a new required graduate level chemical reaction engineering core course. The course was taken by 60 Master's students with a broad set of educational backgrounds and programming experience. Several factors contributed to the success of this course, which I will present and discuss. Based on my experience, it is feasible to use Python in engineering courses.

SymPy is a pure Python library for symbolic mathematics. It aims to become a full-featured computer algebra system (CAS) while keeping the code as simple as possible in order to be comprehensible and easily extensible. SymPy is written entirely in Python and does not require any external libraries.

SymPy is a pure Python library for symbolic mathematics. It aims to become a full-featured computer algebra system (CAS) while keeping the code as simple as possible in order to be comprehensible and easily extensible. SymPy is written entirely in Python and does not require any external libraries.

SymPy is a pure Python library for symbolic mathematics. It aims to become a full-featured computer algebra system (CAS) while keeping the code as simple as possible in order to be comprehensible and easily extensible. SymPy is written entirely in Python and does not require any external libraries.

SymPy is a pure Python library for symbolic mathematics. It aims to become a full-featured computer algebra system (CAS) while keeping the code as simple as possible in order to be comprehensible and easily extensible. SymPy is written entirely in Python and does not require any external libraries.

Building on the new IPython 2.0 widget model and the jsPlumb package we create a schematic capture tool that allows graphically editing a circuit as well as its components' parameters and then instantly updating a domain specific modeling backend. This allows for an integrated circuit modeling workflow and to extend widget-based user interfaces for engineering and research projects.

Good solutions to hard problems require both domain and algorithmic expertise. Domain experts know what to do and computer scientists know how to do it well. This talk discusses challenges and experiences trying to reconcile these two groups, particularly within SymPy. It proposes concrete approaches including multiple dispatch, pattern matching, and programmatic strategies.

This tutorial will give participants an introduction to the use of IPython notebooks in teaching numerical methods or scientific computing, at the level of an undergraduate or graduate university course. Prior familiarity with notebooks is not necessary. Participants will create an interactive notebook that explains and helps students to implement and explore a numerical algorithm.

Teaching undergraduate students in programming is interesting and challenging at the same time, because one has to deal mostly with two types: Those who have already experience and those who have not. I will present two models from Bonn University for Physics students with now much more responsibility for the tutors and would like to initiate discussions about different systems all over the world.

An example of a Python wrapper of a FORTRAN code, applications of a Lagrangian trajectory model, and lessons learned about code development.

TOROS project will be an astronomical survey of the southern hemisphere in search of optical transients counterparts for aLIGO. This project will make extended use of Machine learning techniques to identify interesting transient candidates to aLIGO alerts. It also uses OpenCV library for image aligning and some of the subsequent processing.

The Community Earth System Model produces orders of magnitude more data than earlier models, and the old data handling methods are no longer adequate. We discuss how PyNIO together with MPI for Python has provided the most efficient solution yet tested for the task of converting the raw output of the model to NetCDF files suitable both for archiving and for convenient use by scientists.

I will present WCSAxes, a new framework for plotting astronomical data that seamlessly handles the plotting of ticks, tick labels, and grid lines for arbitrary coordinate systems and projections. While originally written for with astronomical data, it can be used for any kind of map provided that the projection and coordinate system can be represented by a pixel-to-world transformation.

We will give an overview of the basics of the scientific computing ecosystem with Python: what does each of the fundamental packages (numpy, matplotlib, scipy, sympy and pandas) do, and how does it work? We will use the IPython Notebook in our quest to enter this wonderful world.

Reflections on the State of Scientific Python

yt started as a tool for visualizing data from astrophysical simulations, but it has evolved into a method for analyzing generic volumetric data. In this talk we will present yt 3.0, which includes an increased focus on inquiry-driven analysis and visualization, a sympy-powered unit system, a revised user interface, and the ability to scale to petabyte datasets and tens of thousands of cores.

Zeke is an educational platform implemented using Python, SciPy and Django which allows students and researchers interested in the modeling of infectious diseases to learn both modeling and scientific computing skills in an interactive environment, without forcing theoretical and computational skills to be developed simultaneously.

We present a new method for distributing and using Python that requires no dependencies beyond the Google Chrome web browser based on Portable Native Client (PNaCl). We will demonstrate an IPython notebook run completely client side with no out-of-browser components, backed by Google Drive, an HTML5 File System, and able to pass numpy arrays as typed arrays without serialization as JSON.