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Miguel Angel
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Miguel Angel

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WHEN patients are prescribed a drug, they might assume it had been subject to the closest scrutiny. They would be wrong. The results of about half of all clinical trials are never published. Companies are allowed to run many tests and publish only the ones with results they like. Unsurprisingly, negative results are far less likely to appear in public
WHEN patients are prescribed a drug, they might assume it had been subject to the closest scrutiny. They would be wrong. The results of about half of all clinical...
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SciPy 2015
DistArray Distributed Array Computing for Python
Robert Grant

DistArray brings the strength of NumPy to data-parallel high-performance computing. It is an up-and-coming Python package that provides distributed NumPy-like multidimensional arrays, ufuncs, and hdf5-aware IO. DistArray builds on widely-used Python HPC libraries like IPython Parallel, MPI4Py, and h5py. The project also supports the new Distributed Array Protocol to share distributed arrays with external distributed arrays--like Trilinos--without copying.
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SciPy 2015
Circumventing the Linker Using SciPy's BLAS and LAPACK within Cython
Ian Henriksen

In this talk I will discuss SciPy's new Cython API for BLAS and LAPACK; how it provides a model for linking directly against Fortran, and how a similar approach can be used to export low-level APIs that do not require any linking on the part of the user.
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SciPy 2015
PyStruct Structured Prediction in Python
Andreas Mueller

Structured Prediction is a generalization of classification to sequences, graphs and more general output spaces. It is a well-established method in computer vision in natural language processing in a variety of flavors. The talk will introduce basic concepts and show how to perform structure prediction with the PyStruct library.
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SciPy 2015
Astropy in 2015
Erik Tollerud

The Astropy Project is a community effort to develop a single core package for Astronomy in Python and foster interoperability between Python astronomy packages. I will give a status update on the Astropy core package over the last year, which includes the v1.0 release, as well as plans for the core library in the next year. I will also describe some of the "affiliated packages" Python packages that use Astropy and are associated with the community, but are not actually a part of the core library itself. In particular I will focus on the recent growth of the packages involved in this effort, and the tools we have provided to make it easier for working scientists to provide and maintain their own domain-specific packages.
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SciPy 2015
Touch Your Data! Color 3D Printing with Python
Joe Kington

3D printing can be a communication tool, not a gimmick. Here's how to use Python libraries to build color 3D printable models from scientific datasets.

Github repo: https://github.com/joferkington/scipy2015-3d_printing
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SciPy 2015
Rapid Accurate and Simple Segmentation of Objects in Medical Images
Ross Mitchell

The clinically standard approaches to assess cancer treatment response rely upon simplified, diameter-based estimates of lesion size in medical images. These estimates suffer from low accuracy, and poor correlation with treatment response. We recently developed a segmentation algorithm that leverages the massive parallelism of commodity GPUs. Though rapid, the user must still tune image intensity parameters for accurate segmentation. We used Python to implement a classification-based method that only requires users to label foreground and background seed points in the medical image volumes. Our method achieved high accuracy when segmenting complex biological structures. Our algorithm could improve treatment monitoring in cancer clinical trials.
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SciPy 2015
Python in Tidal Energy Three Tools Used in a Collaboration on Array Optimization
Kristen Thyng

Tidal energy is a means of generating electricity by utilizing fast moving currents via rotating turbines. An outstanding question in this field is how to arrange turbines within a given permitted lease area to maximize some goal. Ultimately, the goal is to generate electricity at a low enough rate of cost and a high enough rate of return on investment. These goals may be affected by considerations such as cable costs, depth of the sea water, characteristics of the flow area, array layout, wake interactions, and turbine characteristics. Additional considerations may need to include limiting impact to the environment. A collaboration between the authors aims to improve the methodology and understanding of tidal array optimization. A Python-based tool from each author is used in this study. Dr. Funke runs 2D finite element simulations (using FeNICS) with tidal turbine farms modeled as bottom friction (OpenTidalFarm) in order to optimize the locations of the turbines with respect to some goal (such as power generation). Dr. Roc ties in the economics and engineering side of the problem with his Python-based GUI and function which adds constraints onto the optimization work done in OpenTidalFarm. Finally, Dr. Thyng examines the changes in the system flow fields due to the turbine farm which could have potential environmental consequences. In future work, limiting these environmental impacts will also be incorporated into the OpenTidalFarm farm placement optimization.
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SciPy 2015
Eigenvector Spatial Filtering using NumPy and ArcGIS
Bryan Chastain

Ordinary Least Squares regression techniques are often applied to model spatial phenomena. While these techniques are easy to use and understand, they frequently contain spatially autocorrelated residuals, indicating a misspecification error. Several techniques have been proposed to address this issue, including Geographically Weighted Regression (GWR), Spatial Autoregressive models (SAR/CAR), Bayesian Spatially Varying Coefficients (SVC), and others. However, recent work has shown the Eigenvector Spatial Filtering (ESF) approach to be an unbiased, efficient and consistent estimator for linear regression that often outperforms many of these other techniques (Griffith et al., 2009; Griffith and Chun, 2014). Until now, ESF libraries have only been available for R and SAS (Bivand, 2008). This paper demonstrates the ESF approach in Python, which, through PySAL, streamlines the process of getting GIS data into a NumPy-based regression model.
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SciPy 2015
Examining Malware with Python
Phil Roth

Endgame uses Python extensively for building APIs, constructing data flows, and of course data science. In this talk, I will describe generally how the data science team at Endgame uses Python to build models around security data. I will also describe in detail how Endgame uses Python to process, analyze, and categorize malware.
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SciPy 2015
Efficient Python for High Performance Parallel Computing
Mike McKerns

This tutorial is targeted at the intermediate-to-advanced Python user who wants to extend Python into High-Performance Computing. The tutorial will provide hands-on examples and essential performance tips every developer should know for writing effective parallel Python. The result will be a clear sense of possibilities and best practices using Python in HPC environments. Many of the examples you often find on parallel Python focus on the mechanics of getting the parallel infrastructure working with your code, and not on actually building good portable parallel Python. This tutorial is intended to be a broad introduction to writing high-performance parallel Python that is well suited to both the beginner and the veteran developer. Parallel efficiency starts with the speed of the target code itself, so we will start with how to evolve code from for-loops to Python looping constructs and vector programming. We will also discuss tools and techniques to optimize your code for speed and memory performance. The tutorial will overview working with the common parallel communication technologies (threading, multiprocessing, MPI) and introduce the use of parallel programming models such as blocking and non-blocking pipes, asynchronous and iterative conditional maps, and map-reduce. We will discuss strategies for extending parallel workflow to utilize hierarchical and heterogeneous computing, distributed parallel computing, and job schedulers. We then return our focus to the speeding up our target code by leveraging parallelism within compiled code using Cython. At the end of the tutorial, participants should be able to write simple parallel Python scripts, make use of effective parallel programming techniques, and have a framework in place to leverage the power of Python in High-Performance Computing.

Github repo: https://github.com/mmckerns/tuthpc
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SciPy 2015 Tutorial
Cython: Blend the Best of Python and C++
Kurt Smith

Cython is a flexible and multi-faceted tool that blends C and C++ with Python. With Cython, you can add type information to your Python code to yield dramatic performance improvements. Cython also allows you to wrap C and C++ libraries to work with Python and NumPy. It is used extensively in research environments, in end-user applications, and in leading open-source packages like Pandas, SciPy, scikit-learn, and lxml. This hands-on tutorial will cover Cython from the ground up, and will include the newest Cython features, including typed memoryviews.
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Sciency things:
  • Programming: Scientific computing, algorithms, Data mining, Information retrieval, databases
  • Web development: Front-end development (all that entails, HTML/CSS/Javascript), Python, beginning to be interested in Node.js, Cloud computing
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