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Sample records for zachary wasserman python

  1. Python import replacement

    Energy Science and Technology Software Center (ESTSC)

    2011-10-01

    SmartImport.py is a Python source-code file that implements a replacement for the standard Python module importer. The code is derived from knee.py, a file in the standard Python diestribution , and adds functionality to improve the performance of Python module imports in massively parallel contexts.

  2. Fast retreat of Zachariæ Isstrøm, northeast Greenland.

    PubMed

    Mouginot, J; Rignot, E; Scheuchl, B; Fenty, I; Khazendar, A; Morlighem, M; Buzzi, A; Paden, J

    2015-12-11

    After 8 years of decay of its ice shelf, Zachariæ Isstrøm, a major glacier of northeast Greenland that holds a 0.5-meter sea-level rise equivalent, entered a phase of accelerated retreat in fall 2012. The acceleration rate of its ice velocity tripled, melting of its residual ice shelf and thinning of its grounded portion doubled, and calving is now occurring at its grounding line. Warmer air and ocean temperatures have caused the glacier to detach from a stabilizing sill and retreat rapidly along a downward-sloping, marine-based bed. Its equal-ice-volume neighbor, Nioghalvfjerdsfjorden, is also melting rapidly but retreating slowly along an upward-sloping bed. The destabilization of this marine-based sector will increase sea-level rise from the Greenland Ice Sheet for decades to come. PMID:26563135

  3. Fast retreat of Zachariæ Isstrøm, northeast Greenland

    NASA Astrophysics Data System (ADS)

    Mouginot, J.; Rignot, E.; Scheuchl, B.; Fenty, I.; Khazendar, A.; Morlighem, M.; Buzzi, A.; Paden, J.

    2015-12-01

    After 8 years of decay of its ice shelf, Zachariæ Isstrøm, a major glacier of northeast Greenland that holds a 0.5-meter sea-level rise equivalent, entered a phase of accelerated retreat in fall 2012. The acceleration rate of its ice velocity tripled, melting of its residual ice shelf and thinning of its grounded portion doubled, and calving is now occurring at its grounding line. Warmer air and ocean temperatures have caused the glacier to detach from a stabilizing sill and retreat rapidly along a downward-sloping, marine-based bed. Its equal-ice-volume neighbor, Nioghalvfjerdsfjorden, is also melting rapidly but retreating slowly along an upward-sloping bed. The destabilization of this marine-based sector will increase sea-level rise from the Greenland Ice Sheet for decades to come.

  4. Python for Ecology

    EPA Science Inventory

    Python is a high-level scripting language that is becoming increasingly popular for scientific computing. This all-day workshop is designed to introduce the basics of Python programming to ecologists. Some scripting/programming experience is recommended (e.g. familiarity with R)....

  5. Python to learn programming

    NASA Astrophysics Data System (ADS)

    Bogdanchikov, A.; Zhaparov, M.; Suliyev, R.

    2013-04-01

    Today we have a lot of programming languages that can realize our needs, but the most important question is how to teach programming to beginner students. In this paper we suggest using Python for this purpose, because it is a programming language that has neatly organized syntax and powerful tools to solve any task. Moreover it is very close to simple math thinking. Python is chosen as a primary programming language for freshmen in most of leading universities. Writing code in python is easy. In this paper we give some examples of program codes written in Java, C++ and Python language, and we make a comparison between them. Firstly, this paper proposes advantages of Python language in relation to C++ and JAVA. Then it shows the results of a comparison of short program codes written in three different languages, followed by a discussion on how students understand programming. Finally experimental results of students' success in programming courses are shown.

  6. Python in Astronomy 2016

    NASA Astrophysics Data System (ADS)

    Jenness, Tim; Robitaille, Thomas; Tollerud, Erik; Mumford, Stuart; Cruz, Kelle

    2016-04-01

    The second Python in Astronomy conference will be held from 21-25 March 2016 at the University of Washington eScience Institute in Seattle, WA, USA. Similarly to the 2015 meeting (which was held at the Lorentz Center), we are aiming to bring together researchers, Python developers, users, and educators. The conference will include presentations, tutorials, unconference sessions, and coding sprints. In addition to sharing information about state-of-the art Python Astronomy packages, the workshop will focus on improving interoperability between astronomical Python packages, providing training for new open-source contributors, and developing educational materials for Python in Astronomy. The meeting is therefore not only aimed at current developers, but also users and educators who are interested in being involved in these efforts.

  7. Python Data Visualization

    SciTech Connect

    2011-08-15

    PDV is a Python module that reads in ULTRA data files and allows the user to perform data analysis and to create high-quality plots of the data. Features include the ability to perform integrals, take derivatives, perform arithmetic on data-sets, and to produce publication-quality plots. PDV is deliberaltely designed so that ULTRA users will be able to start running PDV with minimal training. PDV is easily customizable and extendible by anyone who knows a little Python.

  8. Developing Sherpa with Python

    NASA Astrophysics Data System (ADS)

    Doe, S.; Nguyen, D.; Stawarz, C.; Refsdal, B.; Siemiginowska, A.; Burke, D.; Evans, I.; Evans, J.; McDowell, J.; Houck, J.; Nowak, M.

    2007-10-01

    Sherpa is the general purpose fitting and modeling application for CIAO, the Chandra Interactive Analysis of Observations system. We have modified the original design and implemented a new version in Python. This version will be part of the upcoming CIAO4.0 release. We have previously presented a modular, flexible design for CIAO4.0 with the goal of packaging many models, fitting methods and statistics for analysis of astronomical data. The new design promised to be more robust than the previous Sherpa, and more easily extensible with user-written scripts. (We already see some sign of this, in that there were 50,000 lines of code in the CIAO3.0 implementation; with our new, cleaner design, implemented in Python, only half that number of lines were required.) We present the latest updates to our design, and our progress developing Sherpa. A major feature of this work has been the use of Python to implement the data structures from our design. Each part of Sherpa---models, fitting methods, statistics, and so on---has been implemented as a Python module. We have also developed application code to bind together data, models, statistics, and fitting methods for performing fits to data, as well as a high-level UI that makes it simple for users to read in data, define models, and perform fits. Working in Python has been a great aid in speeding development of Sherpa. We expect that Python will also simplify extending and maintaining the Sherpa code base, as well as making it possible to interoperate with other Python-based astronomy packages. To make Sherpa fully accessible to S--Lang users, we use PySL, a new package that is an interface between Python and S--Lang. Users are now able to import other Python or S--Lang modules to extend Sherpa; in addition, users may write and use scripts of their own, written in either Python or S--Lang.

  9. Repast for Python Scripting.

    SciTech Connect

    Collier, N.; North, M. J.; Decision and Information Sciences

    2005-01-01

    Repast for Python Scripting (RepastPy) is a rapid application development (RAD) tool for producing simulations from the Repast agent simulation framework. Using a point-and-click component based interface, users can easily construct a simulation and then use a special subset of the Python programming language to define agent behaviors. RepastPy is the next generation of Repast visual development tools superceding the older SimBuilder tool and incorporating a streamlined user interface, improved Python language support, and the latest improvements to Repast. RepastPy now provides the ability to export RepastPy models to Java, allowing users to then work in the traditional Repast for Java environment.

  10. Python Data Visualization

    Energy Science and Technology Software Center (ESTSC)

    2011-08-15

    PDV is a Python module that reads in ULTRA data files and allows the user to perform data analysis and to create high-quality plots of the data. Features include the ability to perform integrals, take derivatives, perform arithmetic on data-sets, and to produce publication-quality plots. PDV is deliberaltely designed so that ULTRA users will be able to start running PDV with minimal training. PDV is easily customizable and extendible by anyone who knows a littlemore » Python.« less

  11. Python/Lua Benchmarks

    SciTech Connect

    Busby, L.

    2014-08-01

    This is an adaptation of the pre-existing Scimark benchmark code to a variety of Python and Lua implementations. It also measures performance of the Fparser expression parser and C and C++ code on a variety of simple scientific expressions.

  12. Python and computer vision

    SciTech Connect

    Doak, J. E.; Prasad, Lakshman

    2002-01-01

    This paper discusses the use of Python in a computer vision (CV) project. We begin by providing background information on the specific approach to CV employed by the project. This includes a brief discussion of Constrained Delaunay Triangulation (CDT), the Chordal Axis Transform (CAT), shape feature extraction and syntactic characterization, and normalization of strings representing objects. (The terms 'object' and 'blob' are used interchangeably, both referring to an entity extracted from an image.) The rest of the paper focuses on the use of Python in three critical areas: (1) interactions with a MySQL database, (2) rapid prototyping of algorithms, and (3) gluing together all components of the project including existing C and C++ modules. For (l), we provide a schema definition and discuss how the various tables interact to represent objects in the database as tree structures. (2) focuses on an algorithm to create a hierarchical representation of an object, given its string representation, and an algorithm to match unknown objects against objects in a database. And finally, (3) discusses the use of Boost Python to interact with the pre-existing C and C++ code that creates the CDTs and CATS, performs shape feature extraction and syntactic characterization, and normalizes object strings. The paper concludes with a vision of the future use of Python for the CV project.

  13. Pythons in Burma: Short-tailed python (Reptilia: Squamata)

    USGS Publications Warehouse

    Zug, George R.; Gotte, Steve W.; Jacobs, Jeremy F.

    2011-01-01

    Short-tailed pythons, Python curtus species group, occur predominantly in the Malayan Peninsula, Sumatra, and Borneo. The discovery of an adult female in Mon State, Myanmar, led to a review of the distribution of all group members (spot-mapping of all localities of confirmed occurrence) and an examination of morphological variation in P. brongersmai. The resulting maps demonstrate a limited occurrence of these pythons within peninsular Malaya, Sumatra, and Borneo with broad absences in these regions. Our small samples limit the recognition of regional differentiation in the morphology of P. brongersmai populations; however, the presence of unique traits in the Myanmar python and its strong allopatry indicate that it is a unique genetic lineage, and it is described as Python kyaiktiyo new species.

  14. Learning Scientific Programming with Python

    NASA Astrophysics Data System (ADS)

    Hill, Christian

    2016-02-01

    1. Introduction; 2. The core Python language I; 3. Interlude: simple plotting with pylab; 4. The core Python language II; 5. IPython and IPython notebook; 6. NumPy; 7. Matplotlib; 8. SciPy; 9. General scientific programming; Appendix A; Solutions; Index.

  15. Python fiber optic seal

    SciTech Connect

    Ystesund, K.; Bartberger, J.; Brusseau, C.; Fleming, P.; Insch, K.; Tolk, K.

    1993-08-01

    Sandia National Laboratories has developed a high security fiber optic seal that incorporates tamper resistance features that are not available in commercial fiber optic seals. The Python Seal is a passive fiber optic loop seal designed to give indication of unauthorized entry. The seal includes a fingerprint feature that provides seal identity information in addition to the unique fiber optic pattern created when the seal is installed. The fiber optic cable used for the seal loop is produced with tamper resistant features that increase the difficulty of attacking that component of a seal. A Seal Reader has been developed that will record the seal signature and the fingerprint feature of the seal. A Correlator software program then compares seal images to establish a match or mismatch. SNL is also developing a Polaroid reader to permit hard copies of the seal patterns to be obtained directly from the seal.

  16. Pynamic: the Python Dynamic Benchmark

    SciTech Connect

    Lee, G L; Ahn, D H; de Supinksi, B R; Gyllenhaal, J C; Miller, P J

    2007-07-10

    Python is widely used in scientific computing to facilitate application development and to support features such as computational steering. Making full use of some of Python's popular features, which improve programmer productivity, leads to applications that access extremely high numbers of dynamically linked libraries (DLLs). As a result, some important Python-based applications severely stress a system's dynamic linking and loading capabilities and also cause significant difficulties for most development environment tools, such as debuggers. Furthermore, using the Python paradigm for large scale MPI-based applications can create significant file IO and further stress tools and operating systems. In this paper, we present Pynamic, the first benchmark program to support configurable emulation of a wide-range of the DLL usage of Python-based applications for large scale systems. Pynamic has already accurately reproduced system software and tool issues encountered by important large Python-based scientific applications on our supercomputers. Pynamic provided insight for our system software and tool vendors, and our application developers, into the impact of several design decisions. As we describe the Pynamic benchmark, we will highlight some of the issues discovered in our large scale system software and tools using Pynamic.

  17. Extension Modules for the Python Interpretive language

    Energy Science and Technology Software Center (ESTSC)

    2006-12-29

    Python is an interpreted computer language, freely available to all, which may be extended by user developed "modules". These modules ay be written in a complied language such as 'C', and then linked into the Python program

  18. Accessing the VO with Python

    NASA Astrophysics Data System (ADS)

    Plante, R.; Fitzpatrick, M.; Graham, M.; Tody, D.; Young, W.

    2014-05-01

    We introduce two products for accessing the VO from Python: PyVO and VOClient. PyVO is built on the widely-used Astropy package and is well suited for integrating automated access to astronomical data into highly customizable scripts and applications for data analysis in Python. VOClient is built on a collection of C-libraries and is well suited for integrating with multi-language analysis packages. It also provides a framework for integrating legacy software into the Python environment. In this demo, we will run through several examples demonstrate basic data discovery and retrieval of data. This includes finding archives containing data of interest (VO registry), retrieving datasets (SIA, SSA), and exploring (Cone Search, SLAP). VOClient features some extended capabilities including the ability to communicate to other desktop applications from a script using the SAMP protocol.

  19. An Array Module for Python

    NASA Astrophysics Data System (ADS)

    Greenfield, Perry; Miller, Todd; Hsu, Jin-Chung; White, Richard L.

    Although Python has long had a module for numeric array manipulations, it has had some shortcomings that prevent it from being as useful for astronomy applications as it could be. We have re-implemented the module to handle large arrays in a more memory-efficient manner, and to support direct access of data in tables and non-native data formats. The new module has been implemented mostly in Python although the core computational loops are performed in C for efficiency. The new approach allows arrays to be sub-classed as well as new kinds of array objects, such as record arrays, to be created. This paper discusses the design and implementation issues that were addressed in the development of the new array module for Python and gives examples of its use.

  20. Algorithmic synthesis using Python compiler

    NASA Astrophysics Data System (ADS)

    Cieszewski, Radoslaw; Romaniuk, Ryszard; Pozniak, Krzysztof; Linczuk, Maciej

    2015-09-01

    This paper presents a python to VHDL compiler. The compiler interprets an algorithmic description of a desired behavior written in Python and translate it to VHDL. FPGA combines many benefits of both software and ASIC implementations. Like software, the programmed circuit is flexible, and can be reconfigured over the lifetime of the system. FPGAs have the potential to achieve far greater performance than software as a result of bypassing the fetch-decode-execute operations of traditional processors, and possibly exploiting a greater level of parallelism. This can be achieved by using many computational resources at the same time. Creating parallel programs implemented in FPGAs in pure HDL is difficult and time consuming. Using higher level of abstraction and High-Level Synthesis compiler implementation time can be reduced. The compiler has been implemented using the Python language. This article describes design, implementation and results of created tools.

  1. Python-ARM Radar Toolkit

    SciTech Connect

    Jonathan Helmus, Scott Collis

    2013-03-17

    The Python-ARM Radar Toolkit (Py-ART) is a collection of radar quality control and retrieval codes which all work on two unifying Python objects: the PyRadar and PyGrid objects. By building ingests to several popular radar formats and then abstracting the interface Py-ART greatly simplifies data processing over several other available utilities. In addition Py-ART makes use of Numpy arrays as its primary storage mechanism enabling use of existing and extensive community software tools.

  2. Python in Astronomy 2016 Unproceedings

    NASA Astrophysics Data System (ADS)

    Robitaille, Thomas; Cruz, Kelle; Greenfield, Perry; Jeschke, Eric; Juric, Mario; Mumford, Stuart; Prescod-Weinstein, Chanda; Sosey, Megan; Tollerud, Erik; VanderPlas, Jake; Ford, Jes; Foreman-Mackey, Dan; Jenness, Tim; Aldcroft, Tom; Alexandersen, Mike; Bannister, Michele; Barbary, Kyle; Barentsen, Geert; Bennett, Samuel; Boquien, Médéric; Campos Rozo, Jose Ivan; Christe, Steven; Corrales, Lia; Craig, Matthew; Deil, Christoph; Dencheva, Nadia; Donath, Axel; Douglas, Stephanie; Ferreira, Leonardo; Ginsburg, Adam; Goldbaum, Nathan; Gordon, Karl; Hearin, Andrew; Hummels, Cameron; Huppenkothen, Daniela; Jennings, Elise; King, Johannes; Lawler, Samantha; Leonard, Andrew; Lim, Pey Lian; McBride, Lisa; Morris, Brett; Nunez, Carolina; Owen, Russell; Parejko, John; Patel, Ekta; Price-Whelan, Adrian; Ruggiero, Rafael; Sipocz, Brigitta; Stevens, Abigail; Turner, James; Tuttle, Sarah; Yanchulova Merica-Jones, Petia; Yoachim, Peter

    2016-03-01

    This document provides proceedings for unconference sessions as well as hacks/sprints which took place at the Python in Astronomy 2016 workshop, which was held at the University of Washington eScience Institute in Seattle from March 21st to 25th 2016.

  3. Imagining a Stata / Python Combination

    NASA Technical Reports Server (NTRS)

    Fiedler, James

    2012-01-01

    There are occasions when a task is difficult in Stata, but fairly easy in a more general programming language. Python is a popular language for a range of uses. It is easy to use, has many high ]quality packages, and programs can be written relatively quickly. Is there any advantage in combining Stata and Python within a single interface? Stata already offers support for user-written programs, which allow extensive control over calculations, but somewhat less control over graphics. Also, except for specifying output, the user has minimal programmatic control over the user interface. Python can be used in a way that allows more control over the interface and graphics, and in so doing provide a roundabout method for satisfying some user requests (e.g., transparency levels in graphics and the ability to clear the results window). My talk will explore these ideas, present a possible method for combining Stata and Python, and give examples to demonstrate how this combination might be useful.

  4. Advanced Python Scripting Using Sherpa

    NASA Astrophysics Data System (ADS)

    Refsdal, R.; Doe, S.; Nguyen, D.; Siemiginowska, A.; Burke, D.; Evans, J.; Evans, I.

    2011-07-01

    Sherpa is a general purpose modeling and fitting application written in Python. The dynamism of Python allows Sherpa to be a powerful and extensible software package ready for the modern challenges of data analysis. Primarily developed for the Chandra Interactive Analysis of Observations (CIAO) package, it provides a flexible environment for resolving spectral and image properties, analyzing time series, and modeling generic types of data. Complex model expressions are supported using Sherpa's general purpose definition syntax. Sherpa's parameterized data modeling is achieved using robust optimization methods implementing the forward fitting technique. Sherpa includes functions to calculate goodness-of-fit and parameter confidence limits. CPU intensive routines are written in C++/FORTRAN. But since all other data structures are contained in Python modules, users can easily add their own data structures, models, statistics or optimization methods to Sherpa. We will introduce a scripted example that highlights Sherpa's ability to estimate energy and photon flux errors using simulations. The draws from these simulations, accessible as NumPy ndarrays, can be sampled from uni-variate and multi-variate normal distributions and can be binned and visualized with simple high level functions. We will demonstrate how Sherpa can be extended with user-defined model and statistic classes written in Python. Sherpa's open design even allows users to incorporate prior statistics derived from the source model.

  5. Pybus -- A Python Software Bus

    SciTech Connect

    Lavrijsen, Wim T.L.P.

    2004-10-14

    A software bus, just like its hardware equivalent, allows for the discovery, installation, configuration, loading, unloading, and run-time replacement of software components, as well as channeling of inter-component communication. Python, a popular open-source programming language, encourages a modular design on software written in it, but it offers little or no component functionality. However, the language and its interpreter provide sufficient hooks to implement a thin, integral layer of component support. This functionality can be presented to the developer in the form of a module, making it very easy to use. This paper describes a Pythonmodule, PyBus, with which the concept of a ''software bus'' can be realized in Python. It demonstrates, within the context of the ATLAS software framework Athena, how PyBus can be used for the installation and (run-time) configuration of software, not necessarily Python modules, from a Python application in a way that is transparent to the end-user.

  6. 77 FR 3329 - Injurious Wildlife Species; Listing Three Python Species and One Anaconda Species as Injurious...

    Federal Register 2010, 2011, 2012, 2013, 2014

    2012-01-23

    ...The U.S. Fish and Wildlife Service (Service) is amending its regulations under the Lacey Act to add Python molurus (which includes Burmese python Python molurus bivittatus and Indian python Python molurus molurus), Northern African python (Python sebae), Southern African python (Python natalensis), and yellow anaconda (Eunectes notaeus) to the list of injurious reptiles. By this action, the......

  7. Pyomo : Python Optimization Modeling Objects.

    SciTech Connect

    Siirola, John; Laird, Carl Damon; Hart, William Eugene; Watson, Jean-Paul

    2010-11-01

    The Python Optimization Modeling Objects (Pyomo) package [1] is an open source tool for modeling optimization applications within Python. Pyomo provides an objected-oriented approach to optimization modeling, and it can be used to define symbolic problems, create concrete problem instances, and solve these instances with standard solvers. While Pyomo provides a capability that is commonly associated with algebraic modeling languages such as AMPL, AIMMS, and GAMS, Pyomo's modeling objects are embedded within a full-featured high-level programming language with a rich set of supporting libraries. Pyomo leverages the capabilities of the Coopr software library [2], which integrates Python packages (including Pyomo) for defining optimizers, modeling optimization applications, and managing computational experiments. A central design principle within Pyomo is extensibility. Pyomo is built upon a flexible component architecture [3] that allows users and developers to readily extend the core Pyomo functionality. Through these interface points, extensions and applications can have direct access to an optimization model's expression objects. This facilitates the rapid development and implementation of new modeling constructs and as well as high-level solution strategies (e.g. using decomposition- and reformulation-based techniques). In this presentation, we will give an overview of the Pyomo modeling environment and model syntax, and present several extensions to the core Pyomo environment, including support for Generalized Disjunctive Programming (Coopr GDP), Stochastic Programming (PySP), a generic Progressive Hedging solver [4], and a tailored implementation of Bender's Decomposition.

  8. Steering object-oriented computations with Python

    SciTech Connect

    Yang, T.-Y.B.; Dubois, P.F.; Furnish, G.; Beazley, D.M.

    1996-10-01

    We have described current approaches and future plans for steering C++ application, running Python on parallel platforms, and combination of Tk interface and Python interpreter in steering computations. In addition, there has been significant enhancement in the Gist module. Tk mega widgets has been implemented for a few physics applications. We have also written Python interface to SIJLO, a data storage package used as an interface to a visualization system named MeshTv. Python is being used to control large-scale simulations (molecular dynamics in particular) running on the CM-5 and T3D at LANL as well. A few other code development projects at LLNL are either using or considering Python as their steering shells. In summary, the merits of Python have been appreciated by more and more people in the scientific computation community.

  9. BoF - Python in Astronomy

    NASA Astrophysics Data System (ADS)

    Barrett, P. E.

    This BoF will be chaired by Paul Barrett and will begin with an introduction to Python in astronomy, be followed by reports of current Python projects, and conclude with a discussion about the current state of Python in astronomy. The introduction will give a brief overview of the language, highlighting modules, resources, and aspects of the language that are important to scientific programming and astronomical data analysis. The closing discussion will provide an opportunity for questions and comments.

  10. Destabilization of marine-based Zachariæ Isstrøm, northeast Greenland since 2012 from a combination of interferometry data and Operation IceBridge observations.

    NASA Astrophysics Data System (ADS)

    Mouginot, J.; Rignot, E. J.; Scheuchl, B.; Morlighem, M.; An, L.; Cai, C.; Fenty, I. G.; van den Broeke, M. R.

    2014-12-01

    We study the 79north (Nioghalvfjerdsbræ) and Zachariæ Isstrøm sector of the Greenland Ice Sheet using a combination of satellite and Operation IceBridge (OIB) remote sensing data and numerical ocean modeling. Around 2004, the southern portion of the floating ice tongue of Zachariæ Isstrøm began to break up, presumably due to changes in sea-ice/ocean conditions in the northeast. While the floating portion of the glacier started to speed up in response to the reduction in ice shelf buttressing, it was not until 2012 that the velocity of the grounded portion increased significantly. In 2014, the glacier is calving at its grounding line, which retreated 5 km in 1996-2011. The southern ice tongue is gone, and the northern ice tongue is detached from the glacier. We use satellite-derived ice velocity and refined ice thickness based on mass conservation to estimate discharge of these glaciers. We compare the results with RACMO surface mass balance and reconstruct the mass balance of this sector for the period 1992-2014 to show that most mass loss picked up after 2012, not in the earlier 2000's as stated in a recent study. We reconstruct the sea floor bathymetry beneath the former ice shelves for the first time using OIB gravity data. The results reveal the natural passages of subsurface warm waters and help constrain the simulation of ice shelf melt rates. The grounding line of 79north also retreated in 1996-2011, but its flow speed has remained constant. We attribute this contrasting evolution of the two glaciers to a difference in sea floor bathymetry, which allows an intrusion of larger amounts of warm subsurface waters for Zachariæ since the early 2000s, but limits access of such currents for 79north. The latter will likely undergo a fast recession until its grounding reaches a topographic barrier about 30 km upstream, where ice retreat will temporarily slow down. We conclude that since about 2012, major change in glacier dynamics have started to hit north

  11. Computed tomography of ball pythons (Python regius) in curled recumbency.

    PubMed

    Hedley, Joanna; Eatwell, Kevin; Schwarz, Tobias

    2014-01-01

    Anesthesia and tube restraint methods are often required for computed tomography (CT) of snakes due to their natural tendency to curl up. However, these restraint methods may cause animal stress. The aim of this study was to determine whether the CT appearance of the lungs differs for ball pythons in a curled position vs. tube restraint. Whole body CT was performed on ten clinically healthy ball pythons, first in curled and then in straight positions restrained in a tube. Curved multiplanar reformatted (MPR) lung images from curled position scans were compared with standard MPR lung images from straight position scans. Lung attenuation and thickness were measured at three locations for each scan. Time for positioning and scanning was 12 ± 5 min shorter for curled snakes compared to tube restraint. Lung parenchyma thickness and attenuation declined from cranial to caudal on both straight and curled position images. Mean lung parenchyma thickness was greater in curled images at locations 1 (P = 0.048) and 3 (P = 0.044). Mean lung parenchyma thickness decreased between location 1 and 2 by 86-87% (straight: curled) and between location 1 and 3 by 51-50% (straight: curled). Mean lung attenuation at location 1 was significantly greater on curled position images than tube restraint images (P = 0.043). Findings indicated that CT evaluation of the lungs is feasible for ball pythons positioned in curled recumbency if curved MPR is available. However, lung parenchyma thickness and attenuation in some locations may vary from those acquired using tube restraint. PMID:24438485

  12. A field test of attractant traps for invasive Burmese pythons (Python molurus bivittatus) in southern Florida

    USGS Publications Warehouse

    Reed, R.N.; Hart, K.M.; Rodda, G.H.; Mazzotti, F.J.; Snow, R.W.; Cherkiss, M.; Rozar, R.; Goetz, S.

    2011-01-01

    Context. Invasive Burmese pythons (Python molurus bivittatus) are established over thousands of square kilometres of southern Florida, USA, and consume a wide range of native vertebrates. Few tools are available to control the python population, and none of the available tools have been validated in the field to assess capture success as a proportion of pythons available to be captured. Aims. Our primary aim was to conduct a trap trial for capturing invasive pythons in an area east of Everglades National Park, where many pythons had been captured in previous years, to assess the efficacy of traps for population control.Wealso aimed to compare results of visual surveys with trap capture rates, to determine capture rates of non-target species, and to assess capture rates as a proportion of resident pythons in the study area. Methods.Weconducted a medium-scale (6053 trap nights) experiment using two types of attractant traps baited with live rats in the Frog Pond area east of Everglades National Park.Wealso conducted standardised and opportunistic visual surveys in the trapping area. Following the trap trial, the area was disc harrowed to expose pythons and allow calculation of an index of the number of resident pythons. Key results. We captured three pythons and 69 individuals of various rodent, amphibian, and reptile species in traps. Eleven pythons were discovered during disc harrowing operations, as were large numbers of rodents. Conclusions. The trap trial captured a relatively small proportion of the pythons that appeared to be present in the study area, although previous research suggests that trap capture rates improve with additional testing of alternative trap designs. Potential negative impacts to non-target species were minimal. Low python capture rates may have been associated with extremely high local prey abundances during the trap experiment. Implications. Results of this trial illustrate many of the challenges in implementing and interpreting results

  13. Parallel, Distributed Scripting with Python

    SciTech Connect

    Miller, P J

    2002-05-24

    Parallel computers used to be, for the most part, one-of-a-kind systems which were extremely difficult to program portably. With SMP architectures, the advent of the POSIX thread API and OpenMP gave developers ways to portably exploit on-the-box shared memory parallelism. Since these architectures didn't scale cost-effectively, distributed memory clusters were developed. The associated MPI message passing libraries gave these systems a portable paradigm too. Having programmers effectively use this paradigm is a somewhat different question. Distributed data has to be explicitly transported via the messaging system in order for it to be useful. In high level languages, the MPI library gives access to data distribution routines in C, C++, and FORTRAN. But we need more than that. Many reasonable and common tasks are best done in (or as extensions to) scripting languages. Consider sysadm tools such as password crackers, file purgers, etc ... These are simple to write in a scripting language such as Python (an open source, portable, and freely available interpreter). But these tasks beg to be done in parallel. Consider the a password checker that checks an encrypted password against a 25,000 word dictionary. This can take around 10 seconds in Python (6 seconds in C). It is trivial to parallelize if you can distribute the information and co-ordinate the work.

  14. Development of hemipenes in the ball python snake Python regius.

    PubMed

    Leal, Francisca; Cohn, Martin J

    2015-01-01

    Within amniotes, external copulatory organs have undergone extensive morphological diversification. One of the most extreme examples is squamate (lizards and snakes) hemipenes, which are paired copulatory organs that extend from the lateral margins of the cloaca. Here, we describe the development of hemipenes in a basal snake, the ball python (Python regius). Snake hemipenes arise as a pair of lateral swellings on either side of the caudal part of the cloaca, and these paired outgrowths persist to form the left and right hemipenes. In non-squamate amniotes, external genitalia form from paired swellings that arise on the anterior side of the cloaca, which then fuse medially to form a single genital tubercle, the anlagen of the penis or clitoris. Whereas in non-squamate amniotes, Sonic hedgehog (Shh)-expressing cells of the cloacal endoderm form the urethral or sulcus epithelium and are required for phallus outgrowth, the hemipenes of squamates lack an endodermal contribution, and the sulcus does not express Shh. Thus, snake hemipenes differ from the genital tubercles of non-squamate amniotes both in their embryonic origins and in at least part of patterning mechanisms, which raises the possibility that hemipenes may not be direct homologs of the unpaired amniote penis. Nonetheless, we find that some developmental genes show similar expression patterns in snake hemipenes buds and non-squamate genital tubercles, suggesting that homologous developmental mechanisms are involved in aspects of external genital development across amniotes, even when these structures may have different developmental origins and may have arisen independently during evolution. PMID:24970309

  15. Reflection-Based Python-C++ Bindings

    SciTech Connect

    Generowicz, Jacek; Lavrijsen, Wim T.L.P.; Marino, Massimo; Mato, Pere

    2004-10-14

    Python is a flexible, powerful, high-level language with excellent interactive and introspective capabilities and a very clean syntax. As such, it can be a very effective tool for driving physics analysis. Python is designed to be extensible in low-level C-like languages, and its use as a scientific steering language has become quite widespread. To this end, existing and custom-written C or C++ libraries are bound to the Python environment as so-called extension modules. A number of tools for easing the process of creating such bindings exist, such as SWIG and Boost. Python. Yet, the process still requires a considerable amount of effort and expertise. The C++ language has few built-in introspective capabilities, but tools such as LCGDict and CINT add this by providing so-called dictionaries: libraries that contain information about the names, entry points, argument types, etc. of other libraries. The reflection information from these dictionaries can be used for the creation of bindings and so the process can be fully automated, as dictionaries are already provided for many end-user libraries for other purposes, such as object persistency. PyLCGDict is a Python extension module that uses LCG dictionaries, as PyROOT uses CINT reflection information, to allow /cwPython users to access C++ libraries with essentially no preparation on the users' behalf. In addition, and in a similar way, PyROOT gives ROOT users access to Python libraries.

  16. Python fiber-optic seal

    SciTech Connect

    Ystesund, K.; Bartberger, J.; Brusseau, C.; Fleming, P.; Insch, K.; Tolk, K.

    1993-12-31

    Sandia National Laboratories (SNL) has developed a high-security fiber-optic seal that incorporates tamper-resistance features not available in commercial fiber-optic seals. The Python Seal is a passive fiber-optic loop seal designed to give indication of unauthorized entry. The seal includes a fingerprint feature that provides seal identity information in addition to the unique fiber-optic pattern created when the seal is installed. The fiber-optic cable used for the seal loop is produced with tamper-resistant features that increase the difficulty of attacking this component of a seal. A Seal Reader has been developed that records the seal signature and the fingerprint feature of the seal. A Correlator software program compares seal images to establish a match or mismatch. SNL also is developing a Polaroid Reader to permit hard copies of the seal patterns to be obtained directly from the seal.

  17. Python-Based Applications for Hydrogeological Modeling

    NASA Astrophysics Data System (ADS)

    Khambhammettu, P.

    2013-12-01

    Python is a general-purpose, high-level programming language whose design philosophy emphasizes code readability. Add-on packages supporting fast array computation (numpy), plotting (matplotlib), scientific /mathematical Functions (scipy), have resulted in a powerful ecosystem for scientists interested in exploratory data analysis, high-performance computing and data visualization. Three examples are provided to demonstrate the applicability of the Python environment in hydrogeological applications. Python programs were used to model an aquifer test and estimate aquifer parameters at a Superfund site. The aquifer test conducted at a Groundwater Circulation Well was modeled with the Python/FORTRAN-based TTIM Analytic Element Code. The aquifer parameters were estimated with PEST such that a good match was produced between the simulated and observed drawdowns. Python scripts were written to interface with PEST and visualize the results. A convolution-based approach was used to estimate source concentration histories based on observed concentrations at receptor locations. Unit Response Functions (URFs) that relate the receptor concentrations to a unit release at the source were derived with the ATRANS code. The impact of any releases at the source could then be estimated by convolving the source release history with the URFs. Python scripts were written to compute and visualize receptor concentrations for user-specified source histories. The framework provided a simple and elegant way to test various hypotheses about the site. A Python/FORTRAN-based program TYPECURVEGRID-Py was developed to compute and visualize groundwater elevations and drawdown through time in response to a regional uniform hydraulic gradient and the influence of pumping wells using either the Theis solution for a fully-confined aquifer or the Hantush-Jacob solution for a leaky confined aquifer. The program supports an arbitrary number of wells that can operate according to arbitrary schedules. The

  18. Julia and Python in Astronomy: Better Together

    NASA Astrophysics Data System (ADS)

    Barbary, Kyle

    2016-03-01

    Astronomers love Python because it is open source, easy to learn, and has a tremendous ecosystem for scientific computing. The Julia programming language has many of those same characteristics. In this talk, I'll discuss the use of Julia in astronomy and the growing ecosystem of astronomy packages, particularly those managed by the JuliaAstro organization (http://JuliaAstro.github.io). Most importantly, I will highlight some areas ripe for collaboration between Python and Julia developers in astronomy.

  19. Identification and characterization of two closely related unclassifiable endogenous retroviruses in pythons (Python molurus and Python curtus).

    PubMed

    Huder, Jon B; Böni, Jürg; Hatt, Jean-Michel; Soldati, Guido; Lutz, Hans; Schüpbach, Jörg

    2002-08-01

    Boid inclusion body disease (BIBD) is a fatal disorder of boid snakes that is suspected to be caused by a retrovirus. In order to identify this agent, leukocyte cultures (established from Python molurus specimens with symptoms of BIBD or kept together with such diseased animals) were assessed for reverse transcriptase (RT) activity. Virus from cultures exhibiting high RT activity was banded on sucrose density gradients, and the RT peak fraction was subjected to highly efficient procedures for the identification of unknown particle-associated retroviral RNA. A 7-kb full retroviral sequence was identified, cloned, and sequenced. This virus contained intact open reading frames (ORFs) for gag, pro, pol, and env, as well as another ORF of unknown function within pol. Phylogenetic analysis showed that the virus is distantly related to viruses from both the B and D types and the mammalian C type but cannot be classified. It is present as a highly expressed endogenous retrovirus in all P. molurus individuals; a closely related, but much less expressed virus was found in all tested Python curtus individuals. All other boid snakes tested, including Python regius, Python reticulatus, Boa constrictor, Eunectes notaeus, and Morelia spilota, were virus negative, independent of whether they had BIBD or not. Virus isolated from P. molurus could not be transmitted to the peripheral blood mononuclear cells of B. constrictor or P. regius. Thus, there is no indication that this novel virus, which we propose to name python endogenous retrovirus (PyERV), is causally linked with BIBD. PMID:12097574

  20. Gist: A scientific graphics package for Python

    SciTech Connect

    Busby, L.E.

    1996-05-08

    {open_quotes}Gist{close_quotes} is a scientific graphics library written by David H. Munro of Lawrence Livermore National Laboratory (LLNL). It features support for three common graphics output devices: X Windows, (Color) PostScript, and ANSI/ISO Standard Computer Graphics Metafiles (CGM). The library is small (written directly to Xlib), portable, efficient, and full-featured. It produces X versus Y plots with {open_quotes}good{close_quotes} tick marks and tick labels, 2-dimensional quadrilateral mesh plots with contours, vector fields, or pseudo color maps on such meshes, with 3-dimensional plots on the way. The Python Gist module utilizes the new {open_quotes}Numeric{close_quotes} module due to J. Hugunin and others. It is therefore fast and able to handle large datasets. The Gist module includes an X Windows event dispatcher which can be dynamically added (e.g., via importing a dynamically loaded module) to the Python interpreter after a simple two-line modification to the Python core. This makes fast mouse-controlled zoom, pan, and other graphic operations available to the researcher while maintaining the usual Python command-line interface. Munro`s Gist library is already freely available. The Python Gist module is currently under review and is also expected to qualify for unlimited release.

  1. SunPy—Python for solar physics

    NASA Astrophysics Data System (ADS)

    SunPy Community; Mumford, Stuart J.; Christe, Steven; Pérez-Suárez, David; Ireland, Jack; Shih, Albert Y.; Inglis, Andrew R.; Liedtke, Simon; Hewett, Russell J.; Mayer, Florian; Hughitt, Keith; Freij, Nabil; Meszaros, Tomas; Bennett, Samuel M.; Malocha, Michael; Evans, John; Agrawal, Ankit; Leonard, Andrew J.; Robitaille, Thomas P.; Mampaey, Benjamin; Campos-Rozo, Jose Iván; Kirk, Michael S.

    2015-01-01

    This paper presents SunPy (version 0.5), a community-developed Python package for solar physics. Python, a free, cross-platform, general-purpose, high-level programming language, has seen widespread adoption among the scientific community, resulting in the availability of a large number of software packages, from numerical computation (NumPy, SciPy) and machine learning (scikit-learn) to visualization and plotting (matplotlib). SunPy is a data-analysis environment specializing in providing the software necessary to analyse solar and heliospheric data in Python. SunPy is open-source software (BSD licence) and has an open and transparent development workflow that anyone can contribute to. SunPy provides access to solar data through integration with the Virtual Solar Observatory (VSO), the Heliophysics Event Knowledgebase (HEK), and the HELiophysics Integrated Observatory (HELIO) webservices. It currently supports image data from major solar missions (e.g., SDO, SOHO, STEREO, and IRIS), time-series data from missions such as GOES, SDO/EVE, and PROBA2/LYRA, and radio spectra from e-Callisto and STEREO/SWAVES. We describe SunPy's functionality, provide examples of solar data analysis in SunPy, and show how Python-based solar data-analysis can leverage the many existing tools already available in Python. We discuss the future goals of the project and encourage interested users to become involved in the planning and development of SunPy.

  2. The Python Interface to Antelope and Applications

    NASA Astrophysics Data System (ADS)

    Lindquist, K. G.; Clemesha, A.; Newman, R. L.; Vernon, F. L.

    2008-12-01

    The Antelope Environmental Monitoring System from Boulder Real-Time Technologies, Inc. (http://www.brtt.com) is widely used for acquiring, processing, distributing, and archiving near-real-time monitoring data, especially in seismological networks. We have contributed a new Python interface to the Antelope toolkit, paralleling other commercial and open-source language interfaces in Matlab, PHP, TCL/Tk, and C. The Python programming language (http://www.python.org) is well suited both to scientific computing applications and to interactive web-based applications. In the latter, Python serves as the programming interface through which to connect to standardized open-source frameworks. Community development of these frameworks has advanced in parallel with cross-browser standardization and increasing broadband data transfer rates, making web-based applications the defacto standard for platform-agnostic access to large, heterogeneous datasets. These web-based solutions are starting to mirror some of the capabilities of standard desktop-based applications. We describe the functionality of the new Python interface to Antelope, applications of the interface to the interactive exploration of time-series data on the web using the Twisted open-source framework, and web-based prototype tools developed for the Earthscope Array Network Facility to provide community access to network monitoring and seismic event datasets.

  3. Charming Users into Scripting CIAO with Python

    NASA Astrophysics Data System (ADS)

    Burke, D. J.

    2011-07-01

    The Science Data Systems group of the Chandra X-ray Center provides a number of scripts and Python modules that extend the capabilities of CIAO. Experience in converting the existing scripts—written in a variety of languages such as bash, csh/tcsh, Perl and S-Lang—to Python, and conversations with users, led to the development of the ciao_contrib.runtool module. This allows users to easily run CIAO tools from Python scripts, and utilizes the metadata provided by the parameter-file system to create an API that provides the flexibility and safety guarantees of the command-line. The module is provided to the user community and is being used within our group to create new scripts.

  4. Python for Large-Scale Electrophysiology

    PubMed Central

    Spacek, Martin; Blanche, Tim; Swindale, Nicholas

    2008-01-01

    Electrophysiology is increasingly moving towards highly parallel recording techniques which generate large data sets. We record extracellularly in vivo in cat and rat visual cortex with 54-channel silicon polytrodes, under time-locked visual stimulation, from localized neuronal populations within a cortical column. To help deal with the complexity of generating and analysing these data, we used the Python programming language to develop three software projects: one for temporally precise visual stimulus generation (“dimstim”); one for electrophysiological waveform visualization and spike sorting (“spyke”); and one for spike train and stimulus analysis (“neuropy”). All three are open source and available for download (http://swindale.ecc.ubc.ca/code). The requirements and solutions for these projects differed greatly, yet we found Python to be well suited for all three. Here we present our software as a showcase of the extensive capabilities of Python in neuroscience. PMID:19198646

  5. Building a programmable interface for physics codes using numeric python

    SciTech Connect

    Yang, T.-Y.B.; Dubois, P.F.; Motteler, Z.C.

    1996-04-16

    With its portability, ease to add built-in functions and objects in C, and fast array facility among many other features, Python proved to be an excellent language for creating programmable scientific applications. In addition to the two modules presented, there are also other progresses at LLNL in using Python. For example, Python interfaces are being developed for at least three graphics packages, and Python interpreter and applications have been built on distributed platforms such as meiko and Cray T3D.

  6. A field test of attractant traps for invasive Burmese pythons (Python molurus bivittatus) in southern Florida

    USGS Publications Warehouse

    Reed, Robert N.; Hart, Kristen M.; Rodda, Gordon H.; Mazzotti, Frank J.; Snow, Ray W.; Cherkiss, Michael; Rozar, Rondald; Goetz, Scott

    2011-01-01

    Conclusions: The trap trial captured a relatively small proportion of the pythons that appeared to be present in the study area, although previous research suggests that trap capture rates improve with additional testing of alternative trap designs. Potential negative impacts to non-target species were minimal. Low python capture rates may have been associated with extremely high local prey abundances during the trap experiment. Implications: Results of this trial illustrate many of the challenges in implementing and interpreting results from tests of control tools for large cryptic predators such as Burmese pythons.

  7. Python Ephemeris Module for Radio Astronomy

    NASA Astrophysics Data System (ADS)

    Kuiper, T. B.

    2013-05-01

    An extension of the Python pyephem module was developed for Deep Space Network (DSN) radio astronomy. The class DSS( ) provides the geodetic coordinates of the DSN stations as well as other properties such as antenna diameter. The class Quasar( ) provides positional data for the sources in the National Radio Astronomy Observatory Very Large Array (NRAO VLA) Calibrator Handbook and flux estimates based the University of Michigan Radio Astronomy Observatory (UMRAO) Database or the VLA Calibrator Handbook. Flux calibration data are also available for the bright planets. Class Pulsar( ) provides the data from the Australia Telescope National Facility (ATNF) Pulsar Catalogue in Python format.

  8. THE PYTHON SHELL FOR THE ORBIT CODE

    SciTech Connect

    Shishlo, Andrei P; Gorlov, Timofey V; Holmes, Jeffrey A

    2009-01-01

    A development of a Python driver shell for the ORBIT simulation code is presented. The original ORBIT code uses the SuperCode shell to organize accelerator-related simulations. It is outdated, unsupported, and it is an obstacle to future code development. The necessity and consequences of replacing the old shell language are discussed. A set of core modules and extensions that are currently in PyORBIT are presented. They include particle containers, parsers for MAD and SAD lattice files, a Python wrapper for MPI libraries, space charge calculators, TEAPOT trackers, and a laser stripping extension module.

  9. Python Bindings for the Common Pipeline Library

    NASA Astrophysics Data System (ADS)

    Streicher, O.; Weilbacher, P. M.

    2012-09-01

    The Common Pipeline Library is a set of routines written by ESO to provide a standard interface for VLT instrument data reduction tasks (“pipelines”). To control these pipelines from Python, we developed a wrapper called PYTHON-CPL that allows one to conveniently work interactively and to process data as part of an automated data reduction system. The package will be used to implement the MUSE pipeline in the AstroWISE data management system. We describe the features and design of the package.

  10. Consumption of bird eggs by invasive Burmese Pythons in Florida

    USGS Publications Warehouse

    Dove, Carla J.; Reed, Robert N.; Snow, Ray W.

    2012-01-01

    Burmese Pythons (Python molurus bivittatus or P. bivittatus) have been reported to consume 25 species of adult birds in Everglades National Park, Florida (Dove et al. 2011), but until now no records documented this species eating bird eggs. Here we report three recent cases of bird-egg consumption by Burmese Pythons and discuss egg-eating in basal snakes.

  11. Python Scripting in the Nengo Simulator

    PubMed Central

    Stewart, Terrence C.; Tripp, Bryan; Eliasmith, Chris

    2008-01-01

    Nengo (http://nengo.ca) is an open-source neural simulator that has been greatly enhanced by the recent addition of a Python script interface. Nengo provides a wide range of features that are useful for physiological simulations, including unique features that facilitate development of population-coding models using the neural engineering framework (NEF). This framework uses information theory, signal processing, and control theory to formalize the development of large-scale neural circuit models. Notably, it can also be used to determine the synaptic weights that underlie observed network dynamics and transformations of represented variables. Nengo provides rich NEF support, and includes customizable models of spike generation, muscle dynamics, synaptic plasticity, and synaptic integration, as well as an intuitive graphical user interface. All aspects of Nengo models are accessible via the Python interface, allowing for programmatic creation of models, inspection and modification of neural parameters, and automation of model evaluation. Since Nengo combines Python and Java, it can also be integrated with any existing Java or 100% Python code libraries. Current work includes connecting neural models in Nengo with existing symbolic cognitive models, creating hybrid systems that combine detailed neural models of specific brain regions with higher-level models of remaining brain areas. Such hybrid models can provide (1) more realistic boundary conditions for the neural components, and (2) more realistic sub-components for the larger cognitive models. PMID:19352442

  12. Python scripting in the nengo simulator.

    PubMed

    Stewart, Terrence C; Tripp, Bryan; Eliasmith, Chris

    2009-01-01

    Nengo (http://nengo.ca) is an open-source neural simulator that has been greatly enhanced by the recent addition of a Python script interface. Nengo provides a wide range of features that are useful for physiological simulations, including unique features that facilitate development of population-coding models using the neural engineering framework (NEF). This framework uses information theory, signal processing, and control theory to formalize the development of large-scale neural circuit models. Notably, it can also be used to determine the synaptic weights that underlie observed network dynamics and transformations of represented variables. Nengo provides rich NEF support, and includes customizable models of spike generation, muscle dynamics, synaptic plasticity, and synaptic integration, as well as an intuitive graphical user interface. All aspects of Nengo models are accessible via the Python interface, allowing for programmatic creation of models, inspection and modification of neural parameters, and automation of model evaluation. Since Nengo combines Python and Java, it can also be integrated with any existing Java or 100% Python code libraries. Current work includes connecting neural models in Nengo with existing symbolic cognitive models, creating hybrid systems that combine detailed neural models of specific brain regions with higher-level models of remaining brain areas. Such hybrid models can provide (1) more realistic boundary conditions for the neural components, and (2) more realistic sub-components for the larger cognitive models. PMID:19352442

  13. PyXNAT: XNAT in Python

    PubMed Central

    Schwartz, Yannick; Barbot, Alexis; Thyreau, Benjamin; Frouin, Vincent; Varoquaux, Gaël; Siram, Aditya; Marcus, Daniel S.; Poline, Jean-Baptiste

    2012-01-01

    As neuroimaging databases grow in size and complexity, the time researchers spend investigating and managing the data increases to the expense of data analysis. As a result, investigators rely more and more heavily on scripting using high-level languages to automate data management and processing tasks. For this, a structured and programmatic access to the data store is necessary. Web services are a first step toward this goal. They however lack in functionality and ease of use because they provide only low-level interfaces to databases. We introduce here PyXNAT, a Python module that interacts with The Extensible Neuroimaging Archive Toolkit (XNAT) through native Python calls across multiple operating systems. The choice of Python enables PyXNAT to expose the XNAT Web Services and unify their features with a higher level and more expressive language. PyXNAT provides XNAT users direct access to all the scientific packages in Python. Finally PyXNAT aims to be efficient and easy to use, both as a back-end library to build XNAT clients and as an alternative front-end from the command line. PMID:22654752

  14. A New Python Library for Spectroscopic Analysis with MIDAS Style

    NASA Astrophysics Data System (ADS)

    Song, Y.; Luo, A.; Zhao, Y.

    2013-10-01

    The ESO MIDAS is a system for astronomers to analyze data which many astronomers are using. Python is a high level script language and there are many applications for astronomical data process. We are releasing a new Python library which realizes some MIDAS commands in Python. People can use it to write a MIDAS style Python code. We call it PydasLib. It is a Python library based on ESO MIDAS functions, which is easily used by astronomers who are familiar with the usage of MIDAS.

  15. Challenges to a molecular approach to prey identification in the Burmese python, Python molurus bivittatus

    USGS Publications Warehouse

    Falk, Bryan; Reed, Robert N.

    2015-01-01

    Molecular approaches to prey identification are increasingly useful in elucidating predator–prey relationships, and we aimed to investigate the feasibility of these methods to document the species identities of prey consumed by invasive Burmese pythons in Florida. We were particularly interested in the diet of young snakes, because visual identification of prey from this size class has proven difficult. We successfully extracted DNA from the gastrointestinal contents of 43 young pythons, as well as from several control samples, and attempted amplification of DNA mini-barcodes, a 130-bp region of COX1. Using a PNA clamp to exclude python DNA, we found that prey DNA was not present in sufficient quality for amplification of this locus in 86% of our samples. All samples from the GI tracts of young pythons contained only hair, and the six samples we were able to identify to species were hispid cotton rats. This suggests that young Burmese pythons prey predominantly on small mammals and that prey diversity among snakes of this size class is low. We discuss prolonged gastrointestinal transit times and extreme gastric breakdown as possible causes of DNA degradation that limit the success of a molecular approach to prey identification in Burmese pythons

  16. Challenges to a molecular approach to prey identification in the Burmese python, Python molurus bivittatus

    PubMed Central

    Reed, Robert N.

    2015-01-01

    Molecular approaches to prey identification are increasingly useful in elucidating predator–prey relationships, and we aimed to investigate the feasibility of these methods to document the species identities of prey consumed by invasive Burmese pythons in Florida. We were particularly interested in the diet of young snakes, because visual identification of prey from this size class has proven difficult. We successfully extracted DNA from the gastrointestinal contents of 43 young pythons, as well as from several control samples, and attempted amplification of DNA mini-barcodes, a 130-bp region of COX1. Using a PNA clamp to exclude python DNA, we found that prey DNA was not present in sufficient quality for amplification of this locus in 86% of our samples. All samples from the GI tracts of young pythons contained only hair, and the six samples we were able to identify to species were hispid cotton rats. This suggests that young Burmese pythons prey predominantly on small mammals and that prey diversity among snakes of this size class is low. We discuss prolonged gastrointestinal transit times and extreme gastric breakdown as possible causes of DNA degradation that limit the success of a molecular approach to prey identification in Burmese pythons. PMID:26623196

  17. PythonTeX: reproducible documents with LaTeX, Python, and more

    NASA Astrophysics Data System (ADS)

    Poore, Geoffrey M.

    2015-01-01

    PythonTeX is a LaTeX package that allows Python code in LaTeX documents to be executed and provides access to the output. This makes possible reproducible documents that combine results with the code required to generate them. Calculations and figures may be next to the code that created them. Since code is adjacent to its output in the document, editing may be more efficient. Since code output may be accessed programmatically in the document, copy-and-paste errors are avoided and output is always guaranteed to be in sync with the code that generated it. This paper provides an introduction to PythonTeX and an overview of major features, including performance optimizations, debugging tools, and dependency tracking. Several complete examples are presented. Finally, advanced features are summarized. Though PythonTeX was designed for Python, it may be extended to support additional languages; support for the Ruby and Julia languages is already included. PythonTeX contains a utility for converting documents into plain LaTeX, suitable for format conversion, sharing, and journal submission.

  18. Size, but not experience, affects the ontogeny of constriction performance in ball pythons (Python regius).

    PubMed

    Penning, David A; Dartez, Schuyler F

    2016-03-01

    Constriction is a prey-immobilization technique used by many snakes and is hypothesized to have been important to the evolution and diversification of snakes. However, very few studies have examined the factors that affect constriction performance. We investigated constriction performance in ball pythons (Python regius) by evaluating how peak constriction pressure is affected by snake size, sex, and experience. In one experiment, we tested the ontogenetic scaling of constriction performance and found that snake diameter was the only significant factor determining peak constriction pressure. The number of loops applied in a coil and its interaction with snake diameter did not significantly affect constriction performance. Constriction performance in ball pythons scaled differently than in other snakes that have been studied, and medium to large ball pythons are capable of exerting significantly higher pressures than those shown to cause circulatory arrest in prey. In a second experiment, we tested the effects of experience on constriction performance in hatchling ball pythons over 10 feeding events. By allowing snakes in one test group to gain constriction experience, and manually feeding snakes under sedation in another test group, we showed that experience did not affect constriction performance. During their final (10th) feedings, all pythons constricted similarly and with sufficiently high pressures to kill prey rapidly. At the end of the 10 feeding trials, snakes that were allowed to constrict were significantly smaller than their non-constricting counterparts. J. Exp. Zool. 9999A:XX-XX, 2016. © 2016 Wiley Periodicals, Inc. PMID:26847931

  19. Challenges to a molecular approach to prey identification in the Burmese python, Python molurus bivittatus.

    PubMed

    Falk, Bryan G; Reed, Robert N

    2015-01-01

    Molecular approaches to prey identification are increasingly useful in elucidating predator-prey relationships, and we aimed to investigate the feasibility of these methods to document the species identities of prey consumed by invasive Burmese pythons in Florida. We were particularly interested in the diet of young snakes, because visual identification of prey from this size class has proven difficult. We successfully extracted DNA from the gastrointestinal contents of 43 young pythons, as well as from several control samples, and attempted amplification of DNA mini-barcodes, a 130-bp region of COX1. Using a PNA clamp to exclude python DNA, we found that prey DNA was not present in sufficient quality for amplification of this locus in 86% of our samples. All samples from the GI tracts of young pythons contained only hair, and the six samples we were able to identify to species were hispid cotton rats. This suggests that young Burmese pythons prey predominantly on small mammals and that prey diversity among snakes of this size class is low. We discuss prolonged gastrointestinal transit times and extreme gastric breakdown as possible causes of DNA degradation that limit the success of a molecular approach to prey identification in Burmese pythons. PMID:26623196

  20. Stimfit: quantifying electrophysiological data with Python

    PubMed Central

    Guzman, Segundo J.; Schlögl, Alois; Schmidt-Hieber, Christoph

    2013-01-01

    Intracellular electrophysiological recordings provide crucial insights into elementary neuronal signals such as action potentials and synaptic currents. Analyzing and interpreting these signals is essential for a quantitative understanding of neuronal information processing, and requires both fast data visualization and ready access to complex analysis routines. To achieve this goal, we have developed Stimfit, a free software package for cellular neurophysiology with a Python scripting interface and a built-in Python shell. The program supports most standard file formats for cellular neurophysiology and other biomedical signals through the Biosig library. To quantify and interpret the activity of single neurons and communication between neurons, the program includes algorithms to characterize the kinetics of presynaptic action potentials and postsynaptic currents, estimate latencies between pre- and postsynaptic events, and detect spontaneously occurring events. We validate and benchmark these algorithms, give estimation errors, and provide sample use cases, showing that Stimfit represents an efficient, accessible and extensible way to accurately analyze and interpret neuronal signals. PMID:24600389

  1. Simulation of Planetary Formation using Python

    NASA Astrophysics Data System (ADS)

    Bufkin, James; Bixler, David

    2015-03-01

    A program to simulate planetary formation was developed in the Python programming language. The program consists of randomly placed and massed bodies surrounding a central massive object in order to approximate a protoplanetary disk. The orbits of these bodies are time-stepped, with accelerations, velocities and new positions calculated in each step. Bodies are allowed to merge if their disks intersect. Numerous parameters (orbital distance, masses, number of particles, etc.) were varied in order to optimize the program. The program uses an iterative difference equation approach to solve the equations of motion using a kinematic model. Conservation of energy and angular momentum are not specifically forced, but conservation of momentum is forced during the merging of bodies. The initial program was created in Visual Python (VPython) but the current intention is to allow for higher particle count and faster processing by utilizing PyOpenCl and PyOpenGl. Current results and progress will be reported.

  2. ParselTongue: AIPS Talking Python

    NASA Astrophysics Data System (ADS)

    Kettenis, M.; van Langevelde, H. J.; Reynolds, C.; Cotton, B.

    2006-07-01

    After more than 20 years of service, classic AIPS still is the data reduction package of choice for many radio-interferometry projects, especially for VLBI. Its age shows, most prominently in the limited scripting capabilities of its user interface: POPS. ParselTongue is an attempt to make the trusted AIPS algorithms and AIPS data structures available in a modern dynamic programming language: Python. It also provides an environment to do distributed computing to take advantage of modern computing clusters. This makes it suitable for use as a scripting interface for doing complicated data reduction on large data sets. It is also used as a coding platform for the new calibration algorithms that are being developed for the European VLBI Network as part of the ALBUS project. Here we hope to take advantage of Python's extensive support for web-based technologies to automate things like collecting calibration data.

  3. Meteor Shower Identification and Characterization with Python

    NASA Technical Reports Server (NTRS)

    Moorhead, Althea

    2015-01-01

    The short development time associated with Python and the number of astronomical packages available have led to increased usage within NASA. The Meteoroid Environment Office in particular uses the Python language for a number of applications, including daily meteor shower activity reporting, searches for potential parent bodies of meteor showers, and short dynamical simulations. We present our development of a meteor shower identification code that identifies statistically significant groups of meteors on similar orbits. This code overcomes several challenging characteristics of meteor showers such as drastic differences in uncertainties between meteors and between the orbital elements of a single meteor, and the variation of shower characteristics such as duration with age or planetary perturbations. This code has been proven to successfully and quickly identify unusual meteor activity such as the 2014 kappa Cygnid outburst. We present our algorithm along with these successes and discuss our plans for further code development.

  4. matplotlib -- A Portable Python Plotting Package

    NASA Astrophysics Data System (ADS)

    Barrett, P.; Hunter, J.; Miller, J. T.; Hsu, J.-C.; Greenfield, P.

    2005-12-01

    matplotlib is a portable 2D plotting and imaging package aimed primarily at visualization of scientific, engineering, and financial data. matplotlib can be used interactively from the Python shell, called from python scripts, or embedded in a GUI application (GTK, Wx, Tk, Windows). Many popular hardcopy outputs are supported including JPEG, PNG, PostScript and SVG. Features include the creation of multiple axes and figures per page, interactive navigation, many predefined line styles and symbols, images, antialiasing, alpha blending, date and financial plots, W3C compliant font management and FreeType2 support, legends and tables, pseudocolor plots, mathematical text and more. It works with both numarray and Numeric. The goals of the package, basic architecture, current features (illustrated with examples), and planned enhancements will be described.

  5. Writing analytic element programs in Python.

    PubMed

    Bakker, Mark; Kelson, Victor A

    2009-01-01

    The analytic element method is a mesh-free approach for modeling ground water flow at both the local and the regional scale. With the advent of the Python object-oriented programming language, it has become relatively easy to write analytic element programs. In this article, an introduction is given of the basic principles of the analytic element method and of the Python programming language. A simple, yet flexible, object-oriented design is presented for analytic element codes using multiple inheritance. New types of analytic elements may be added without the need for any changes in the existing part of the code. The presented code may be used to model flow to wells (with either a specified discharge or drawdown) and streams (with a specified head). The code may be extended by any hydrogeologist with a healthy appetite for writing computer code to solve more complicated ground water flow problems. PMID:19473273

  6. Experimentally derived salinity tolerance of hatchling Burmese pythons (Python molurus bivittatus) from the Everglades, Florida (USA)

    USGS Publications Warehouse

    Hart, K.M.; Schofield, P.J.; Gregoire, D.R.

    2012-01-01

    In a laboratory setting, we tested the ability of 24 non-native, wild-caught hatchling Burmese pythons (Python molurus bivittatus) collected in the Florida Everglades to survive when given water containing salt to drink. After a one-month acclimation period in the laboratory, we grouped snakes into three treatments, giving them access to water that was fresh (salinity of 0, control), brackish (salinity of 10), or full-strength sea water (salinity of 35). Hatchlings survived about one month at the highest marine salinity and about five months at the brackish-water salinity; no control animals perished during the experiment. These results are indicative of a "worst-case scenario", as in the laboratory we denied access to alternate fresh-water sources that may be accessible in the wild (e.g., through rainfall). Therefore, our results may underestimate the potential of hatchling pythons to persist in saline habitats in the wild. Because of the effect of different salinity regimes on survival, predictions of ultimate geographic expansion by non-native Burmese pythons that consider salt water as barriers to dispersal for pythons may warrant re-evaluation, especially under global climate change and associated sea-level-rise scenarios. ?? 2011.

  7. Experimentally derived salinity tolerance of hatchling Burmese pythons (Python molurus bivittatus) from the Everglades, Florida (USA)

    USGS Publications Warehouse

    Hart, Kristen M.; Schofield, Pamela J.; Gregoire, Denise R.

    2012-01-01

    In a laboratory setting, we tested the ability of 24 non-native, wild-caught hatchling Burmese pythons (Python molurus bivittatus) collected in the Florida Everglades to survive when given water containing salt to drink. After a one-month acclimation period in the laboratory, we grouped snakes into three treatments, giving them access to water that was fresh (salinity of 0, control), brackish (salinity of 10), or full-strength sea water (salinity of 35). Hatchlings survived about one month at the highest marine salinity and about five months at the brackish-water salinity; no control animals perished during the experiment. These results are indicative of a "worst-case scenario", as in the laboratory we denied access to alternate fresh-water sources that may be accessible in the wild (e.g., through rainfall). Therefore, our results may underestimate the potential of hatchling pythons to persist in saline habitats in the wild. Because of the effect of different salinity regimes on survival, predictions of ultimate geographic expansion by non-native Burmese pythons that consider salt water as barriers to dispersal for pythons may warrant re-evaluation, especially under global climate change and associated sea-level-rise scenarios.

  8. Ball Python Nidovirus: a Candidate Etiologic Agent for Severe Respiratory Disease in Python regius

    PubMed Central

    Stenglein, Mark D.; Jacobson, Elliott R.; Wozniak, Edward J.; Wellehan, James F. X.; Kincaid, Anne; Gordon, Marcus; Porter, Brian F.; Baumgartner, Wes; Stahl, Scott; Kelley, Karen; Towner, Jonathan S.

    2014-01-01

    ABSTRACT A severe, sometimes fatal respiratory disease has been observed in captive ball pythons (Python regius) since the late 1990s. In order to better understand this disease and its etiology, we collected case and control samples and performed pathological and diagnostic analyses. Electron micrographs revealed filamentous virus-like particles in lung epithelial cells of sick animals. Diagnostic testing for known pathogens did not identify an etiologic agent, so unbiased metagenomic sequencing was performed. Abundant nidovirus-like sequences were identified in cases and were used to assemble the genome of a previously unknown virus in the order Nidovirales. The nidoviruses, which were not previously known to infect nonavian reptiles, are a diverse order that includes important human and veterinary pathogens. The presence of the viral RNA was confirmed in all diseased animals (n = 8) but was not detected in healthy pythons or other snakes (n = 57). Viral RNA levels were generally highest in the lung and other respiratory tract tissues. The 33.5-kb viral genome is the largest RNA genome yet described and shares canonical characteristics with other nidovirus genomes, although several features distinguish this from related viruses. This virus, which we named ball python nidovirus (BPNV), will likely establish a new genus in Torovirinae subfamily. The identification of a novel nidovirus in reptiles contributes to our understanding of the biology and evolution of related viruses, and its association with lung disease in pythons is a promising step toward elucidating an etiology for this long-standing veterinary disease. PMID:25205093

  9. Python: a language for computational physics

    NASA Astrophysics Data System (ADS)

    Borcherds, P. H.

    2007-07-01

    Python is a relatively new computing language, created by Guido van Rossum [A.S. Tanenbaum, R. van Renesse, H. van Staveren, G.J. Sharp, S.J. Mullender, A.J. Jansen, G. van Rossum, Experiences with the Amoeba distributed operating system, Communications of the ACM 33 (1990) 46-63; also on-line at http://www.cs.vu.nl/pub/amoeba/. [6

  10. POPPY: Physical Optics Propagation in PYthon

    NASA Astrophysics Data System (ADS)

    Perrin, Marshall; Long, Joseph; Douglas, Ewan; Sivaramakrishnan, Anand; Slocum, Christine

    2016-02-01

    POPPY (Physical Optics Propagation in PYthon) simulates physical optical propagation including diffraction. It implements a flexible framework for modeling Fraunhofer and Fresnel diffraction and point spread function formation, particularly in the context of astronomical telescopes. POPPY provides the optical modeling framework for WebbPSF (ascl:1504.007) and was developed as part of a simulation package for JWST, but is available separately and is broadly applicable to many kinds of imaging simulations.

  11. Python in gamma-ray astronomy

    NASA Astrophysics Data System (ADS)

    Deil, Christoph Deil

    2016-03-01

    Gamma-ray astronomy is a relatively new window on the cosmos. The first source detected from the ground was the Crab nebula, seen by the Whipple telescope in Arizona in 1989. Today, about 150 sources have been detected at TeV energies using gamma-ray telescopes from the ground such as H.E.S.S. in Namibia or VERITAS in Arizona, and about 3000 sources at GeV energies using the Fermi Gamma-ray Space Telescope. Soon construction will start for the Cherenkov Telescope Array (CTA), which will be the first ground-based gamma-ray telescope array operated as an open observatory, with a site in the southern and a second site in the northern hemisphere. In this presentation I will give a very brief introduction to gamma-ray astronomy and data analysis, as well as a short overview of the software used for the various missions. The main focus will be on recent attempts to build open-source gamma-ray software on the scientific Python stack and Astropy: ctapipe as a CTA Python pipeline prototype, Fermipy and the Fermi Science Tools for Fermi-LAT analysis, Gammapy as a community-developed gamma-ray Python package and naima as a non-thermal spectral modeling and fitting package.

  12. Saccular lung cannulation in a ball python (Python regius) to treat a tracheal obstruction.

    PubMed

    Myers, Debbie A; Wellehan, James F X; Isaza, Ramiro

    2009-03-01

    An adult male ball python (Python regius) presented in a state of severe dyspnea characterized by open-mouth breathing and vertical positioning of the head and neck. The animal had copious discharge in the tracheal lumen acting as an obstruction. A tube was placed through the body wall into the caudal saccular aspect of the lung to allow the animal to breathe while treatment was initiated. The ball python's dyspnea immediately improved. Diagnostics confirmed a bacterial respiratory infection with predominantly Providencia rettgeri. The saccular lung (air sac) tube was removed after 13 days. Pulmonary endoscopy before closure showed minimal damage with a small amount of hemorrhage in the surrounding muscle tissue. Respiratory disease is a common occurrence in captive snakes and can be associated with significant morbidity and mortality. Saccular lung cannulation is a relatively simple procedure that can alleviate tracheal narrowing or obstruction, similar to air sac cannulation in birds. PMID:19368267

  13. Predators in training: operant conditioning of novel behavior in wild Burmese pythons (Python molurus bivitattus).

    PubMed

    Emer, Sherri A; Mora, Cordula V; Harvey, Mark T; Grace, Michael S

    2015-01-01

    Large pythons and boas comprise a group of animals whose anatomy and physiology are very different from traditional mammalian, avian and other reptilian models typically used in operant conditioning. In the current study, investigators used a modified shaping procedure involving successive approximations to train wild Burmese pythons (Python molurus bivitattus) to approach and depress an illuminated push button in order to gain access to a food reward. Results show that these large, wild snakes can be trained to accept extremely small food items, associate a stimulus with such rewards via operant conditioning and perform a contingent operant response to gain access to a food reward. The shaping procedure produced robust responses and provides a mechanism for investigating complex behavioral phenomena in massive snakes that are rarely studied in learning research. PMID:25139000

  14. The Virtual Observatory for the Python Programmer

    NASA Astrophysics Data System (ADS)

    Plante, Raymond L.; Fitzpatrick, M. J.; Graham, M.; Tody, D.; Virtual Astronomical Observatory, US

    2014-01-01

    The web of astronomical data centers that we refer to as the virtual observatory (VO) has led to the development of a variety of web and desktop applications that can discover and download data from most archives around the world. These are made possible by standard interfaces which archives provide and the applications understand that provide a common way to search for information and retrieve discovered datasets. For some applications, retrieving data through the VO is simply an extra feature that enhances the main purpose of the tool. Despite the accessibility to VO data provided by such tools, the VO offers greater flexibility to developers that access the standard services directly within their own software. This applies not only to those who build tools but also to research astronomers that create highly-customized scripts for data analysis. One of the goals of the US Virtual Astronomical Observatory (VAO) project is to make the VO more accessible to both tool developers and astronomer-programmers. To this end, we announce the release of two products with a special focus on supporting access to the VO via Python. PyVO (http://dev.usvao.org/pyvo) is a pure Python library built on Astropy (astropy.org) that can be used to discover data in the VO. In particular, one can search the registry for archives with data, search archives for images and spectra, and query remote catalogs and spectral line databases. While it provides full support for the VO standards, its API is designed to make processing the most common types of queries simple without requiring knowledge about the underlying standards. It also makes available the full power of Astropy for processing tabular information. VOClient (http://dev.usvao.org/voclient), which provides scripting and programming libraries for a variety of languages, also supports Python programming. While the two products share a common API, VOClient provides higher level interfaces that assist with managing data from many

  15. Feeding a large-scale physics application to Python

    SciTech Connect

    Beazley, D.M.; Lomdahl, P.S.

    1997-10-01

    The authors describe their experiences using Python with the SPaSM molecular dynamics code at Los Alamos National Laboratory. Originally developed as a large monolithic application for massive parallel processing systems, they have used Python to transform their application into a flexible, highly modular, and extremely powerful system for performing simulation, data analysis, and visualization. In addition, they describe how Python has solved a number of important problems related to the development, debugging, deployment, and maintenance of scientific software.

  16. HOPE: Just-in-time Python compiler for astrophysical computations

    NASA Astrophysics Data System (ADS)

    Akeret, Joel; Gamper, Lukas; Amara, Adam; Refregier, Alexandre

    2014-11-01

    HOPE is a specialized Python just-in-time (JIT) compiler designed for numerical astrophysical applications. HOPE focuses on a subset of the language and is able to translate Python code into C++ while performing numerical optimization on mathematical expressions at runtime. To enable the JIT compilation, the user only needs to add a decorator to the function definition. By using HOPE, the user benefits from being able to write common numerical code in Python while getting the performance of compiled implementation.

  17. Subspectacular nematodiasis caused by a novel Serpentirhabdias species in ball pythons (Python regius).

    PubMed

    Hausmann, J C; Mans, C; Dreyfus, J; Reavill, D R; Lucio-Forster, A; Bowman, D D

    2015-01-01

    Subspectacular nematodiasis was diagnosed in three captive-bred juvenile ball pythons (Python regius) from two unrelated facilities within a 6-month period. The snakes were presented with similar lesions, including swelling of facial, periocular and oral tissues. Bilaterally, the subspectacular spaces were distended and filled with an opaque fluid, which contained nematodes and eggs. Histopathology showed nematodes throughout the periocular tissue, subspectacular space and subcutaneous tissue of the head. The nematodes from both facilities were morphologically indistinguishable and most closely resembled Serpentirhabdias species. Morphological characterization and genetic sequencing indicate this is a previously undescribed rhabdiasid nematode. PMID:25670672

  18. Ureka: A Distribution of Python and IRAF Software for Astronomy

    NASA Astrophysics Data System (ADS)

    Hirst, P.; Slocum, C.; Turner, J.; Sienkiewicz, M.; Greenfield, P.; Hogan, E.; Simpson, M.; Labrie, K.

    2014-05-01

    As astronomical data processing expands from our historical platforms into modern Python applications, users are faced with installing and maintaining large numbers of heterogeneous dependencies. A handful of scientific Python distributions make installing key packages easy, but don't cater for specific needs such as integration with IRAF. We have therefore recently released a beta version of a new astronomical software distribution for Linux and OSX, known as Ureka. Ureka is based around STScI Python and dependencies, notably Python, NumPy, IRAF, SciPy, AstroPy, Matplotlib and Tk. It also contains data reduction packages for Gemini, HST, JWST and other observatories, alongside various complementary tools.

  19. Improvement of AMGA Python Client Library for Belle II Experiment

    NASA Astrophysics Data System (ADS)

    Kwak, Jae-Hyuck; Park, Geunchul; Huh, Taesang; Hwang, Soonwook

    2015-12-01

    This paper describes the recent improvement of the AMGA (ARDA Metadata Grid Application) python client library for the Belle II Experiment. We were drawn to the action items related to library improvement after in-depth discussions with the developer of the Belle II distributed computing system. The improvement includes client-side metadata federation support in python, DIRAC SSL library support as well as API refinement for synchronous operation. Some of the improvements have already been applied to the AMGA python client library as bundled with the Belle II distributed computing software. The recent mass Monte- Carlo (MC) production campaign shows that the AMGA python client library is reliably stable.

  20. Re-imagining a Stata/Python Combination

    NASA Technical Reports Server (NTRS)

    Fiedler, James

    2013-01-01

    At last year's Stata Conference, I presented some ideas for combining Stata and the Python programming language within a single interface. Two methods were presented: in one, Python was used to automate Stata; in the other, Python was used to send simulated keystrokes to the Stata GUI. The first method has the drawback of only working in Windows, and the second can be slow and subject to character input limits. In this presentation, I will demonstrate a method for achieving interaction between Stata and Python that does not suffer these drawbacks, and I will present some examples to show how this interaction can be useful.

  1. Leveraging Python Interoperability Tools to Improve Sapphire's Usability

    SciTech Connect

    Gezahegne, A; Love, N S

    2007-12-10

    The Sapphire project at the Center for Applied Scientific Computing (CASC) develops and applies an extensive set of data mining algorithms for the analysis of large data sets. Sapphire's algorithms are currently available as a set of C++ libraries. However many users prefer higher level scripting languages such as Python for their ease of use and flexibility. In this report, we evaluate four interoperability tools for the purpose of wrapping Sapphire's core functionality with Python. Exposing Sapphire's functionality through a Python interface would increase its usability and connect its algorithms to existing Python tools.

  2. Rapid web development using AJAX and Python

    NASA Astrophysics Data System (ADS)

    Dolgert, A.; Gibbons, L.; Kuznetsov, V.

    2008-07-01

    We discuss the rapid development of a large scale data discovery service for the CMS experiment using modern AJAX techniques and the Python language. To implement a flexible interface capable of accommodating several different versions of the DBS database, we used a 'stack' approach. Asynchronous JavaScript and XML (AJAX) together with an SQL abstraction layer, template engine, code generation tool and dynamic queries provide powerful tools for constructing interactive interfaces to large amounts of data. We show how the use of these tools, with rapid development in a modern scripting language, improved the scalability and usability of the the search interface for different user communities.

  3. scikit-image: image processing in Python.

    PubMed

    van der Walt, Stéfan; Schönberger, Johannes L; Nunez-Iglesias, Juan; Boulogne, François; Warner, Joshua D; Yager, Neil; Gouillart, Emmanuelle; Yu, Tony

    2014-01-01

    scikit-image is an image processing library that implements algorithms and utilities for use in research, education and industry applications. It is released under the liberal Modified BSD open source license, provides a well-documented API in the Python programming language, and is developed by an active, international team of collaborators. In this paper we highlight the advantages of open source to achieve the goals of the scikit-image library, and we showcase several real-world image processing applications that use scikit-image. More information can be found on the project homepage, http://scikit-image.org. PMID:25024921

  4. TRIPPy: Python-based Trailed Source Photometry

    NASA Astrophysics Data System (ADS)

    Fraser, Wesley C.; Alexandersen, Mike; Schwamb, Megan E.; Marsset, Michael E.; Pike, Rosemary E.; Kavelaars, JJ; Bannister, Michele T.; Benecchi, Susan; Delsanti, Audrey

    2016-05-01

    TRIPPy (TRailed Image Photometry in Python) uses a pill-shaped aperture, a rectangle described by three parameters (trail length, angle, and radius) to improve photometry of moving sources over that done with circular apertures. It can generate accurate model and trailed point-spread functions from stationary background sources in sidereally tracked images. Appropriate aperture correction provides accurate, unbiased flux measurement. TRIPPy requires numpy, scipy, matplotlib, Astropy (ascl:1304.002), and stsci.numdisplay; emcee (ascl:1303.002) and SExtractor (ascl:1010.064) are optional.

  5. scikit-image: image processing in Python

    PubMed Central

    Schönberger, Johannes L.; Nunez-Iglesias, Juan; Boulogne, François; Warner, Joshua D.; Yager, Neil; Gouillart, Emmanuelle; Yu, Tony

    2014-01-01

    scikit-image is an image processing library that implements algorithms and utilities for use in research, education and industry applications. It is released under the liberal Modified BSD open source license, provides a well-documented API in the Python programming language, and is developed by an active, international team of collaborators. In this paper we highlight the advantages of open source to achieve the goals of the scikit-image library, and we showcase several real-world image processing applications that use scikit-image. More information can be found on the project homepage, http://scikit-image.org. PMID:25024921

  6. Pcigale: Porting Code Investigating Galaxy Emission to Python

    NASA Astrophysics Data System (ADS)

    Roehlly, Y.; Burgarella, D.; Buat, V.; Boquien, M.; Ciesla, L.; Heinis, S.

    2014-05-01

    We present pcigale, the port to Python of CIGALE (Code Investigating Galaxy Emission) a Fortran spectral energy distribution (SED) fitting code developed at the Laboratoire d'Astrophysique de Marseille. After recalling the specifics of the SED fitting method, we show the gains in modularity and versatility offered by Python, as well as the drawbacks compared to the compiled code.

  7. A facility for creating Python extensions in C++

    SciTech Connect

    Dubois, P F

    1998-07-14

    Python extensions are usually created by writing the glue that connects Python to the desired new functionality in the C language. While simple extensions do not require much effort, to do the job correctly with full error checking is tedious and prone to errors in reference counting and to memory leaks, especially when errors occur. The resulting program is difficult to read and maintain. By designing suitable C++ classes to wrap the Python C API, we are able to produce extensions that are correct and which clean up after themselves correctly when errors occur. This facility also integrates the C++ and Python exception facilities. This paper briefly describes our package for this purpose, named CXX. The emphasis is on our design choices and the way these contribute to the construction of accurate Python extensions. We also briefly relate the way CXX's facilities for sequence classes allow use of C++'s Standard Template Library (STL) algorithms on C++ sequences.

  8. SunPy: Solar Physics in Python

    NASA Astrophysics Data System (ADS)

    Ryan, Daniel; Christe, Steven; Mumford, Stuart; Perez Suarez, David; Ireland, Jack; Shih, Albert Y.; Inglis, Andrew; Liedtke, Simon; Hewett, Russel

    2015-04-01

    SunPy is a community-developed open-source software library for solar physics. It is written in Python, a free, cross-platform, general-purpose, high-level programming language which is being increasingly adopted throughout the scientific community as well as further afield. This has resulted in a wide array of software packages useful for scientific computing, from numerical computation (NumPy, SciPy, etc.), to machine learning (scifitlearn), to visualization and plotting (matplotlib). SunPy aims to provide required specialised software for analysing solar and heliospheric datasets in Python. The current version is 0.5 with 0.6 expected to be released later this year. SunPy provides solar data access through integration with the Virtual Solar Observatory (VSO), the Heliophysics Event Knowledgebase (HEK), and the HELiophysics Integrated Observatory (HELIO) webservices. It supports common data types from major solar missions such as images (SDO/AIA, STEREO, PROBA2/SWAP etc.), time series (GOES/XRS, SDO/EVE, PROBA2/LYRA), and radio spectra (e-Callisto, STEREO/WAVES). SunPy’s code base is publicly available through github.com and can be contributed to by anyone. In this poster we demonstrate SunPy’s functionality and future goals of the project. We also encourage interested users to become involved in further developing SunPy.

  9. Python Program to Select HII Region Models

    NASA Astrophysics Data System (ADS)

    Miller, Clare; Lamarche, Cody; Vishwas, Amit; Stacey, Gordon J.

    2016-01-01

    HII regions are areas of singly ionized Hydrogen formed by the ionizing radiaiton of upper main sequence stars. The infrared fine-structure line emissions, particularly Oxygen, Nitrogen, and Neon, can give important information about HII regions including gas temperature and density, elemental abundances, and the effective temperature of the stars that form them. The processes involved in calculating this information from observational data are complex. Models, such as those provided in Rubin 1984 and those produced by Cloudy (Ferland et al, 2013) enable one to extract physical parameters from observational data. However, the multitude of search parameters can make sifting through models tedious. I digitized Rubin's models and wrote a Python program that is able to take observed line ratios and their uncertainties and find the Rubin or Cloudy model that best matches the observational data. By creating a Python script that is user friendly and able to quickly sort through models with a high level of accuracy, this work increases efficiency and reduces human error in matching HII region models to observational data.

  10. Report on the observed response of Javan lutungs (Trachypithecus auratus mauritius) upon encountering a reticulated python (Python reticulatus).

    PubMed

    Tsuji, Yamato; Prayitno, Bambang; Suryobroto, Bambang

    2016-04-01

    We observed an encounter between a reticulated python (Python reticulatus) and a group of wild Javan lutungs (Trachypithecus auratus mauritius) at the Pangandaran Nature Reserve, West Java, Indonesia. A python (about 2 m in length) moved toward a group of lutungs in the trees. Upon seeing the python, an adult male and several adult female lutungs began to emit alarm calls. As the python approached, two adult and one sub-adult female jumped onto a branch near the python and began mobbing the python by shaking the branch. During the mobbing, other individuals in the group (including an adult lutung male) remained nearby but did not participate. The python then rolled into a ball-like shape and stopped moving, at which point the lutungs moved away. The total duration of the encounter was about 40 min, during which time the lutungs stopped feeding and grooming. Group cohesiveness during and after the encounter was greater than that before the encounter, indicating that lutungs adjust their daily activity in response to potential predation risk. PMID:26910233

  11. PyMOOSE: Interoperable Scripting in Python for MOOSE

    PubMed Central

    Ray, Subhasis; Bhalla, Upinder S.

    2008-01-01

    Python is emerging as a common scripting language for simulators. This opens up many possibilities for interoperability in the form of analysis, interfaces, and communications between simulators. We report the integration of Python scripting with the Multi-scale Object Oriented Simulation Environment (MOOSE). MOOSE is a general-purpose simulation system for compartmental neuronal models and for models of signaling pathways based on chemical kinetics. We show how the Python-scripting version of MOOSE, PyMOOSE, combines the power of a compiled simulator with the versatility and ease of use of Python. We illustrate this by using Python numerical libraries to analyze MOOSE output online, and by developing a GUI in Python/Qt for a MOOSE simulation. Finally, we build and run a composite neuronal/signaling model that uses both the NEURON and MOOSE numerical engines, and Python as a bridge between the two. Thus PyMOOSE has a high degree of interoperability with analysis routines, with graphical toolkits, and with other simulators. PMID:19129924

  12. DMTCP: bringing interactive checkpoint-restart to Python

    NASA Astrophysics Data System (ADS)

    Arya, Kapil; Cooperman, Gene

    2015-01-01

    DMTCP (Distributed MultiThreaded CheckPointing) is a mature checkpoint-restart package. It operates in user space without kernel privilege, and adapts to application-specific requirements through plugins. While DMTCP has been able to checkpoint Python and IPython ‘from the outside’ for many years, a Python module has recently been created to support DMTCP. IPython support is included through a new DMTCP plugin. A checkpoint can be requested interactively within a Python session or under the control of a specific Python program. Further, the Python program can execute specific Python code prior to checkpoint, upon resuming (within the original process) and upon restarting (from a checkpoint image). Applications of DMTCP are demonstrated for: (i) Python-based graphics using virtual network client, (ii) a fast/slow technique to use multiple hosts or cores to check one (Cython Behnel S et al 2011 Comput. Sci. Eng. 13 31-39) computation in parallel, and (iii) a reversible debugger, FReD, with a novel reverse-expression watchpoint feature for locating the cause of a bug.

  13. Facultative thermogenesis during brooding is not the norm among pythons.

    PubMed

    Brashears, Jake; DeNardo, Dale F

    2015-08-01

    Facultative thermogenesis is often attributed to pythons in general despite limited comparative data available for the family. While all species within Pythonidae brood their eggs, only two species are known to produce heat to enhance embryonic thermal regulation. By contrast, a few python species have been reported to have insignificant thermogenic capabilities. To provide insight into potential phylogenetic, morphological, and ecological factors influencing thermogenic capability among pythons, we measured metabolic rates and clutch-environment temperature differentials at two environmental temperatures-python preferred brooding temperature (31.5 °C) and a sub-optimal temperature (25.5 °C)-in six species of pythons, including members of two major phylogenetic branches currently devoid of data on the subject. We found no evidence of facultative thermogenesis in five species: Aspidites melanocephalus, A. ramsayi, Morelia viridis, M. spilota cheynei, and Python regius. However, we found that Bothrochilus boa had a thermal metabolic sensitivity indicative of facultative thermogenesis (i.e., a higher metabolic rate at the lower temperature). However, its metabolic rate was quite low and technical challenges prevented us from measuring temperature differential to make conclusions about facultative endothermy in this species. Regardless, our data combined with existing literature demonstrate that facultative thermogenesis is not as widespread among pythons as previously thought. PMID:26113382

  14. 75 FR 38069 - Injurious Wildlife Species; Listing the Boa Constrictor, Four Python Species, and Four Anaconda...

    Federal Register 2010, 2011, 2012, 2013, 2014

    2010-07-01

    ... proposed rule (75 FR 11808) to list the Indian python (Python molurus, including Burmese python Python... proposed rule (75 FR 11808; March 12, 2010), draft economic analysis, draft environmental assessment, and U... comments, please refer to the March 12, 2010, proposed rule (75 FR 11808), available online at...

  15. COSMOS: Python library for massively parallel workflows

    PubMed Central

    Gafni, Erik; Luquette, Lovelace J.; Lancaster, Alex K.; Hawkins, Jared B.; Jung, Jae-Yoon; Souilmi, Yassine; Wall, Dennis P.; Tonellato, Peter J.

    2014-01-01

    Summary: Efficient workflows to shepherd clinically generated genomic data through the multiple stages of a next-generation sequencing pipeline are of critical importance in translational biomedical science. Here we present COSMOS, a Python library for workflow management that allows formal description of pipelines and partitioning of jobs. In addition, it includes a user interface for tracking the progress of jobs, abstraction of the queuing system and fine-grained control over the workflow. Workflows can be created on traditional computing clusters as well as cloud-based services. Availability and implementation: Source code is available for academic non-commercial research purposes. Links to code and documentation are provided at http://lpm.hms.harvard.edu and http://wall-lab.stanford.edu. Contact: dpwall@stanford.edu or peter_tonellato@hms.harvard.edu. Supplementary information: Supplementary data are available at Bioinformatics online. PMID:24982428

  16. ScrumPy: metabolic modelling with Python.

    PubMed

    Poolman, M G

    2006-09-01

    ScrumPy is a software package used for the definition and analysis of metabolic models. It is written using the Python programming language that is also used as a user interface. ScrumPy has features for both kinetic and structural modelling, but the emphasis is on structural modelling and those features of most relevance to analysis of large (genome-scale) models. The aim is at describing ScrumPy's functionality to readers with some knowledge of metabolic modelling, but implementation, programming and other computational details are omitted. ScrumPy is released under the Gnu Public Licence, and available for download from http://mudshark.brookes.ac.uk/ ScrumPy. PMID:16986321

  17. galpy: A python LIBRARY FOR GALACTIC DYNAMICS

    SciTech Connect

    Bovy, Jo

    2015-02-01

    I describe the design, implementation, and usage of galpy, a python package for galactic-dynamics calculations. At its core, galpy consists of a general framework for representing galactic potentials both in python and in C (for accelerated computations); galpy functions, objects, and methods can generally take arbitrary combinations of these as arguments. Numerical orbit integration is supported with a variety of Runge-Kutta-type and symplectic integrators. For planar orbits, integration of the phase-space volume is also possible. galpy supports the calculation of action-angle coordinates and orbital frequencies for a given phase-space point for general spherical potentials, using state-of-the-art numerical approximations for axisymmetric potentials, and making use of a recent general approximation for any static potential. A number of different distribution functions (DFs) are also included in the current release; currently, these consist of two-dimensional axisymmetric and non-axisymmetric disk DFs, a three-dimensional disk DF, and a DF framework for tidal streams. I provide several examples to illustrate the use of the code. I present a simple model for the Milky Way's gravitational potential consistent with the latest observations. I also numerically calculate the Oort functions for different tracer populations of stars and compare them to a new analytical approximation. Additionally, I characterize the response of a kinematically warm disk to an elliptical m = 2 perturbation in detail. Overall, galpy consists of about 54,000 lines, including 23,000 lines of code in the module, 11,000 lines of test code, and about 20,000 lines of documentation. The test suite covers 99.6% of the code. galpy is available at http://github.com/jobovy/galpy with extensive documentation available at http://galpy.readthedocs.org/en/latest.

  18. galpy: A python Library for Galactic Dynamics

    NASA Astrophysics Data System (ADS)

    Bovy, Jo

    2015-02-01

    I describe the design, implementation, and usage of galpy, a python package for galactic-dynamics calculations. At its core, galpy consists of a general framework for representing galactic potentials both in python and in C (for accelerated computations); galpy functions, objects, and methods can generally take arbitrary combinations of these as arguments. Numerical orbit integration is supported with a variety of Runge-Kutta-type and symplectic integrators. For planar orbits, integration of the phase-space volume is also possible. galpy supports the calculation of action-angle coordinates and orbital frequencies for a given phase-space point for general spherical potentials, using state-of-the-art numerical approximations for axisymmetric potentials, and making use of a recent general approximation for any static potential. A number of different distribution functions (DFs) are also included in the current release; currently, these consist of two-dimensional axisymmetric and non-axisymmetric disk DFs, a three-dimensional disk DF, and a DF framework for tidal streams. I provide several examples to illustrate the use of the code. I present a simple model for the Milky Way's gravitational potential consistent with the latest observations. I also numerically calculate the Oort functions for different tracer populations of stars and compare them to a new analytical approximation. Additionally, I characterize the response of a kinematically warm disk to an elliptical m = 2 perturbation in detail. Overall, galpy consists of about 54,000 lines, including 23,000 lines of code in the module, 11,000 lines of test code, and about 20,000 lines of documentation. The test suite covers 99.6% of the code. galpy is available at http://github.com/jobovy/galpy with extensive documentation available at http://galpy.readthedocs.org/en/latest.

  19. Neutron Scattering Experiment Automation with Python

    SciTech Connect

    Zolnierczuk, Piotr A; Riedel, Richard A

    2010-01-01

    The Spallation Neutron Source (SNS) at Oak Ridge National Laboratory currently holds the Guinness World Record as the world most powerful pulsed spallation neutron source. Neutrons scattered off atomic nuclei in a sample yield important information about the position, motions, and magnetic properties of atoms in materials. A neutron scattering experiment usually involves sample environment control (temperature, pressure, etc.), mechanical alignment (slits, sample and detector position), magnetic field controllers, neutron velocity selection (choppers) and neutron detectors. The SNS Data Acquisition System (DAS) consists of real-time sub-system (detector read-out with custom electronics, chopper interface), data preprocessing (soft real-time) and a cluster of control and ancillary PCs. The real-time system runs FPGA firmware and programs running on PCs (C++, LabView) typically perform one task such as motor control and communicate via TCP/IP networks. PyDas is a set of Python modules that are used to integrate various components of the SNS DAS system. It enables customized automation of neutron scattering experiments in a rapid and flexible manner. It provides wxPython GUIs for routine experiments as well as IPython command line scripting. Matplotlib and numpy are used for data presentation and simple analysis. We will present an overview of SNS Data Acquisition System and PyDas architectures and implementation along with the examples of use. We will also discuss plans for future development as well as the challenges that have to be met while maintaining PyDas for 20+ different scientific instruments.

  20. drive-casa: Python interface for CASA scripting

    NASA Astrophysics Data System (ADS)

    Staley, Tim D.

    2015-04-01

    drive-casa provides a Python interface for scripting of CASA (ascl.net/1107.013) subroutines from a separate Python process, allowing for utilization alongside other Python packages which may not easily be installed into the CASA environment. This is particularly useful for embedding use of CASA subroutines within a larger pipeline. drive-casa runs plain-text casapy scripts directly; alternatively, the package includes a set of convenience routines which try to adhere to a consistent style and make it easy to chain together successive CASA reduction commands to generate a command-script programmatically.

  1. Using the Scientific Python ecosystem to advance open radar science

    NASA Astrophysics Data System (ADS)

    Collis, S. M.; Helmus, J.

    2015-12-01

    The choice of a programming language or environment is rarely made with consideration of its benefits and disadvantages. Often it is something inherited from mentor or enforced by an institution. Python, developed as a "hobby" programming project, has seen increased migration of users from more traditional domain specific environments. This presentation charts our own journey in using the scientific python ecosystem, first as users and then as the developers of a community based toolkit for working with weather radar data, the Python ARM Radar Toolkit, Py-ART. We will highlight how a data model driven design approach can extend the usefulness and reusability of code and act as a bridge between amorphous mathematical algorithms and domain specific data. Finally we will showcase how Python and Py-ART can be used on clusters to tackle pleasantly parallel problems like deriving climatologies swiftly, painlessly and most importantly: reproducibly.

  2. Enrico: Python package to simplify Fermi-LAT analysis

    NASA Astrophysics Data System (ADS)

    Sanchez, David; Deil, Christoph

    2015-01-01

    Enrico analyzes Fermi data. It produces spectra (model fit and flux points), maps and lightcurves for a target by editing a config file and running a python script which executes the Fermi science tool chain.

  3. Adapting the BIMA Image Pipeline for Miriad Using Python

    NASA Astrophysics Data System (ADS)

    Mehringer, D. M.; Plante, R.

    2004-07-01

    Through our experience using AIPS++ in the BIMA Image Pipeline, we found that a sophisticated scripting environment is crucial for supporting an automated pipeline. Miriad V4, now in development, introduces support for calling Miriad programs from a Python environment (referred to as Pyramid). We are creating processing recipes using Miriad through Python that can be used with the BIMA Image Pipeline. As part of this work, we are prototyping tools that could be integrated into Pyramid. These include two Python classes, UVDataset and Image for examining the contents of Miriad datasets. These simple tools have allowed us to recast our Pipeline using Miriad in only a couple of months. Python recipes are used for such things as determining line-free channels for continuum subtraction and determining if data will benefit from self-calibration. We are currently using the Pipeline to do massive processing of hundreds of tracks of archival data using NCSA's Teraflop IA-32 Linux cluster.

  4. CVXPY: A Python-Embedded Modeling Language for Convex Optimization

    PubMed Central

    Diamond, Steven; Boyd, Stephen

    2016-01-01

    CVXPY is a domain-specific language for convex optimization embedded in Python. It allows the user to express convex optimization problems in a natural syntax that follows the math, rather than in the restrictive standard form required by solvers. CVXPY makes it easy to combine convex optimization with high-level features of Python such as parallelism and object-oriented design. CVXPY is available at http://www.cvxpy.org/ under the GPL license, along with documentation and examples. PMID:27375369

  5. MEG and EEG data analysis with MNE-Python

    PubMed Central

    Gramfort, Alexandre; Luessi, Martin; Larson, Eric; Engemann, Denis A.; Strohmeier, Daniel; Brodbeck, Christian; Goj, Roman; Jas, Mainak; Brooks, Teon; Parkkonen, Lauri; Hämäläinen, Matti

    2013-01-01

    Magnetoencephalography and electroencephalography (M/EEG) measure the weak electromagnetic signals generated by neuronal activity in the brain. Using these signals to characterize and locate neural activation in the brain is a challenge that requires expertise in physics, signal processing, statistics, and numerical methods. As part of the MNE software suite, MNE-Python is an open-source software package that addresses this challenge by providing state-of-the-art algorithms implemented in Python that cover multiple methods of data preprocessing, source localization, statistical analysis, and estimation of functional connectivity between distributed brain regions. All algorithms and utility functions are implemented in a consistent manner with well-documented interfaces, enabling users to create M/EEG data analysis pipelines by writing Python scripts. Moreover, MNE-Python is tightly integrated with the core Python libraries for scientific comptutation (NumPy, SciPy) and visualization (matplotlib and Mayavi), as well as the greater neuroimaging ecosystem in Python via the Nibabel package. The code is provided under the new BSD license allowing code reuse, even in commercial products. Although MNE-Python has only been under heavy development for a couple of years, it has rapidly evolved with expanded analysis capabilities and pedagogical tutorials because multiple labs have collaborated during code development to help share best practices. MNE-Python also gives easy access to preprocessed datasets, helping users to get started quickly and facilitating reproducibility of methods by other researchers. Full documentation, including dozens of examples, is available at http://martinos.org/mne. PMID:24431986

  6. MEG and EEG data analysis with MNE-Python.

    PubMed

    Gramfort, Alexandre; Luessi, Martin; Larson, Eric; Engemann, Denis A; Strohmeier, Daniel; Brodbeck, Christian; Goj, Roman; Jas, Mainak; Brooks, Teon; Parkkonen, Lauri; Hämäläinen, Matti

    2013-12-26

    Magnetoencephalography and electroencephalography (M/EEG) measure the weak electromagnetic signals generated by neuronal activity in the brain. Using these signals to characterize and locate neural activation in the brain is a challenge that requires expertise in physics, signal processing, statistics, and numerical methods. As part of the MNE software suite, MNE-Python is an open-source software package that addresses this challenge by providing state-of-the-art algorithms implemented in Python that cover multiple methods of data preprocessing, source localization, statistical analysis, and estimation of functional connectivity between distributed brain regions. All algorithms and utility functions are implemented in a consistent manner with well-documented interfaces, enabling users to create M/EEG data analysis pipelines by writing Python scripts. Moreover, MNE-Python is tightly integrated with the core Python libraries for scientific comptutation (NumPy, SciPy) and visualization (matplotlib and Mayavi), as well as the greater neuroimaging ecosystem in Python via the Nibabel package. The code is provided under the new BSD license allowing code reuse, even in commercial products. Although MNE-Python has only been under heavy development for a couple of years, it has rapidly evolved with expanded analysis capabilities and pedagogical tutorials because multiple labs have collaborated during code development to help share best practices. MNE-Python also gives easy access to preprocessed datasets, helping users to get started quickly and facilitating reproducibility of methods by other researchers. Full documentation, including dozens of examples, is available at http://martinos.org/mne. PMID:24431986

  7. Rapid Development of Interferometric Software Using MIRIAD and Python

    NASA Astrophysics Data System (ADS)

    Williams, Peter K. G.; Law, Casey J.; Bower, Geoffrey C.

    2012-06-01

    State-of-the-art radio interferometers are complex systems that unleash torrents of data. If current and planned instruments are to routinely meet their performance goals, standard analysis techniques must be significantly improved, becoming simultaneously more sophisticated, more automatic, and more scalable. While there is no shortage of ideas for next-generation algorithms, there is a shortage of development resources, so it is vital that programming environments for interferometric software allow for rapid, flexible development. We present an open-source software package, miriad-python, that provides access to the MIRIAD interferometric reduction system in the Python programming language. The modular design of MIRIAD and the high productivity and accessibility of Python provide an excellent foundation for rapid development of interferometric software. Several other projects with similar goals exist, and we describe them and compare miriad-python with them in detail. Along with an overview of the package design, we present sample code and applications, including the detection of millisecond astrophysical transients, determination and application of nonstandard calibration parameters, interactive data visualization, and a reduction pipeline using a directed acyclic graph dependency model analogous to that of the traditional UNIX tool make. The key aspects of the miriad-python software project are documented. We find that miriad-python provides an extremely effective environment for prototyping new interferometric software, though certain existing packages provide far more infrastructure for some applications. While equivalent software written in compiled languages can be much faster than Python, there are many situations in which execution time is profitably exchanged for speed of development, code readability, accessibility to nonexpert programmers, quick interlinking with foreign software packages, and other virtues of the Python language.

  8. PyMultiNest: Python interface for MultiNest

    NASA Astrophysics Data System (ADS)

    Buchner, Johannes

    2016-06-01

    PyMultiNest provides programmatic access to MultiNest (ascl:1109.006) and PyCuba, integration existing Python code (numpy, scipy), and enables writing Prior & LogLikelihood functions in Python. PyMultiNest can plot and visualize MultiNest's progress and allows easy plotting, visualization and summarization of MultiNest results. The plotting can be run on existing MultiNest output, and when not using PyMultiNest for running MultiNest.

  9. Ecological correlates of invasion impact for Burmese pythons in Florida

    USGS Publications Warehouse

    Reed, R.N.; Willson, J.D.; Rodda, G.H.; Dorcas, M.E.

    2012-01-01

    An invasive population of Burmese pythons (Python molurus bivittatus) is established across several thousand square kilometers of southern Florida and appears to have caused precipitous population declines among several species of native mammals. Why has this giant snake had such great success as an invasive species when many established reptiles have failed to spread? We scored the Burmese python for each of 15 literature-based attributes relative to predefined comparison groups from a diverse range of taxa and provide a review of the natural history and ecology of Burmese pythons relevant to each attribute. We focused on attributes linked to spread and magnitude of impacts rather than establishment success. Our results suggest that attributes related to body size and generalism appeared to be particularly applicable to the Burmese python's success in Florida. The attributes with the highest scores were: high reproductive potential, low vulnerability to predation, large adult body size, large offspring size and high dietary breadth. However, attributes of ectotherms in general and pythons in particular (including predatory mode, energetic efficiency and social interactions) might have also contributed to invasion success. Although establishment risk assessments are an important initial step in prevention of new establishments, evaluating species in terms of their potential for spreading widely and negatively impacting ecosystems might become part of the means by which resource managers prioritize control efforts in environments with large numbers of introduced species.

  10. Using Python to Develop Graphical Interfaces to Scientific Data

    SciTech Connect

    MacFarland, L; Streletz, G J

    1999-09-24

    At Lawrence Livermore National Laboratory (LLNL), Python has proven to be a convenient language for the development of graphical user interfaces (GUIs) which allow scientists to view, plot, and analyze scientific data. Two such applications are described in this paper. The first, EOSView, is a browser application for an equation of state data library at LLNL. EOSView is used by scientists throughout the laboratory who use simulation codes that access the data library, or who need equation of state data for other purposes. EOSView provides graphical visualization capabilities, as well as the capability to analyze the data in many different ways. The second application, Zimp, is a GUI that allows interactive use of the Stark Line Shape Database. It is used to access and plot data. The quick construction of Zimp from elements of the EOSView code provides a useful lesson in code reuse, and illustrates how the object-oriented nature of Python facilitates this goal. In general, Python has proven to be an appropriate choice of language for applications of this type for several reasons, including the easy access to GUI functionality provided by Tkinter, the ease with which C functions can be called from Python, and the convenient handling of strings in Python. Moreover, the features of the Python language, combined with the fact that it is interpreted rather than compiled, have allowed for extremely quick prototyping.

  11. Introducing Python tools for magnetotellurics: MTpy

    NASA Astrophysics Data System (ADS)

    Krieger, L.; Peacock, J.; Inverarity, K.; Thiel, S.; Robertson, K.

    2013-12-01

    Within the framework of geophysical exploration techniques, the magnetotelluric method (MT) is relatively immature: It is still not as widely spread as other geophysical methods like seismology, and its processing schemes and data formats are not thoroughly standardized. As a result, the file handling and processing software within the academic community is mainly based on a loose collection of codes, which are sometimes highly adapted to the respective local specifications. Although tools for the estimation of the frequency dependent MT transfer function, as well as inversion and modelling codes, are available, the standards and software for handling MT data are generally not unified throughout the community. To overcome problems that arise from missing standards, and to simplify the general handling of MT data, we have developed the software package "MTpy", which allows the handling, processing, and imaging of magnetotelluric data sets. It is written in Python and the code is open-source. The setup of this package follows the modular approach of successful software packages like GMT or Obspy. It contains sub-packages and modules for various tasks within the standard MT data processing and handling scheme. Besides pure Python classes and functions, MTpy provides wrappers and convenience scripts to call external software, e.g. modelling and inversion codes. Even though still under development, MTpy already contains ca. 250 functions that work on raw and preprocessed data. However, as our aim is not to produce a static collection of software, we rather introduce MTpy as a flexible framework, which will be dynamically extended in the future. It then has the potential to help standardise processing procedures and at same time be a versatile supplement for existing algorithms. We introduce the concept and structure of MTpy, and we illustrate the workflow of MT data processing utilising MTpy on an example data set collected over a geothermal exploration site in South

  12. Surgical management of maxillary and premaxillary osteomyelitis in a reticulated python (Python reticulatus).

    PubMed

    Latney, La'Toya V; McDermott, Colin; Scott, Gregory; Soltero-Rivera, Maria M; Beguesse, Kyla; Sánchez, Melissa D; Lewis, John R

    2016-05-01

    CASE DESCRIPTION A 1-year-old reticulated python (Python reticulatus) was evaluated because of a 2-week history of wheezing and hissing. CLINICAL FINDINGS Rostral facial cellulitis and deep gingival pockets associated with missing rostral maxillary teeth were evident. Tissues of the nares were swollen, resulting in an audible wheeze during respiration. Multiple scars and superficial facial wounds attributed to biting by live prey were apparent. Radiographic examination revealed bilateral, focal, rostral maxillary osteomyelitis. TREATMENT AND OUTCOME Wound irrigation, antimicrobials, and anti-inflammatory drug treatment resulted in reduced cellulitis. A 3-week regimen that included empirical antimicrobial treatment and improved husbandry resulted in resolution of the respiratory sounds and partial healing of bite wounds, but radiographic evaluation revealed progressive maxillary osteomyelitis. Microbial culture of blood yielded scant gram-positive cocci and Bacillus spp, which were suspected sample contaminants. Bilateral partial maxillectomies were performed; microbial culture and histologic examination of resected bone confirmed osteomyelitis with gram-positive cocci. Treatment with trimethoprim-sulfamethoxazole was initiated on the basis of microbial susceptibility tests. Four months later, follow-up radiography revealed premaxillary osteomyelitis; surgery was declined, and treatment with trimethoprim-sulfamethoxazole was reinstituted. Eight months after surgery, the patient was reevaluated because of recurrent clinical signs; premaxillectomy was performed, and treatment with trimethoprim-sulfamethoxazole was prescribed on the basis of microbial culture of bone and microbial susceptibility testing. Resolution of osteomyelitis was confirmed by CT 11 months after the initial surgery. CONCLUSIONS AND CLINICAL RELEVANCE Focal maxillectomies and premaxillectomy were successfully performed in a large python. Surgical management and appropriate antimicrobial treatment

  13. Renal plasticity in response to feeding in the Burmese python, Python molurus bivittatus.

    PubMed

    Esbaugh, A J; Secor, S M; Grosell, M

    2015-10-01

    Burmese pythons are sit-and-wait predators that are well adapted to go long periods without food, yet subsequently consume and digest single meals that can exceed their body weight. These large feeding events result in a dramatic alkaline tide that is compensated by a hypoventilatory response that normalizes plasma pH; however, little is known regarding how plasma HCO3(-) is lowered in the days post-feeding. The current study demonstrated that Burmese pythons contain the cellular machinery for renal acid-base compensation and actively remodel the kidney to limit HCO3(-) reabsorption in the post-feeding period. After being fed a 25% body weight meal plasma total CO2 was elevated by 1.5-fold after 1 day, but returned to control concentrations by 4 days post-feeding (d pf). Gene expression analysis was used to verify the presence of carbonic anhydrase (CA) II, IV and XIII, Na(+) H(+) exchanger 3 (NHE3), the Na(+) HCO3(-) co-transporter (NBC) and V-type ATPase. CA IV expression was significantly down-regulated at 3 dpf versus fasted controls. This was supported by activity analysis that showed a significant decrease in the amount of GPI-linked CA activity in isolated kidney membranes at 3 dpf versus fasted controls. In addition, V-type ATPase activity was significantly up-regulated at 3 dpf; no change in gene expression was observed. Both CA II and NHE3 expression was up-regulated at 3 dpf, which may be related to post-prandial ion balance. These results suggest that Burmese pythons actively remodel their kidney after feeding, which would in part benefit renal HCO3(-) clearance. PMID:26123779

  14. Hydropy: Python package for hydrological time series handling based on Python Pandas

    NASA Astrophysics Data System (ADS)

    Van Hoey, Stijn; Balemans, Sophie; Nopens, Ingmar; Seuntjens, Piet

    2015-04-01

    Most hydrologists are dealing with time series frequently. Reading in time series, transforming them and extracting specific periods for visualisation are part of the daily work. Spreadsheet software is used a lot for these operations, but has some major drawbacks. It is mostly not reproducible, it is prone to errors and not easy to automate, which results in repetitive work when dealing with large amounts of data. Scripting languages like R and Python on the other hand, provide flexibility, enable automation and reproducibility and, hence, increase efficiency. Python has gained popularity over the last years and currently, tools for many aspects of scientific computing are readily available in Python. An increased support in controlling and managing the dependencies between packages (e.g. the Anaconda environment) allows for a wide audience to use the huge variety of available packages. Pandas is a powerful Python package for data analysis and has a lot of functionalities related to time series. As such, the package is of special interest to hydrologists. Some other packages, focussing on hydrology (e.g. Hydroclimpy by Pierre Gerard-Marchant and Hydropy by Javier Rovegno Campos), stopped active development, mainly due to the superior implementation of Pandas. We present a (revised) version of the Hydropy package that is inspired by the aforementioned packages and builds on the power of Pandas. The main idea is to add hydrological domain knowledge to the already existing Pandas functionalities. Besides, the package attempts to make the time series handling intuitive and easy to perform, thus with a clear syntax. Some illustrative examples of the current implementation starting from a Pandas DataFrame named flowdata: Creating the object flow to work with: flow = HydroAnalysis(flowdata) Retrieve only the data during winter (across all years): flow.get_season('winter') Retrieve only the data during summer of 2010: flow.get_season('summer').get_year('2010') which is

  15. Morphological respiratory diffusion capacity of the lungs of ball pythons (Python regius).

    PubMed

    Starck, J Matthias; Aupperle, Heike; Kiefer, Ingmar; Weimer, Isabel; Krautwald-Junghanns, Maria-Elisabeth; Pees, Michael

    2012-08-01

    This study aims at a functional and morphological characterization of the lung of a boid snake. In particular, we were interested to see if the python's lungs are designed with excess capacity as compared to resting and working oxygen demands. Therefore, the morphological respiratory diffusion capacity of ball pythons (Python regius) was examined following a stereological, hierarchically nested approach. The volume of the respiratory exchange tissue was determined using computed tomography. Tissue compartments were quantified using stereological methods on light microscopic images. The tissue diffusion barrier for oxygen transport was characterized and measured using transmission electron micrographs. We found a significant negative correlation between body mass and the volume of respiratory tissue; the lungs of larger snakes had relatively less respiratory tissue. Therefore, mass-specific respiratory tissue was calculated to exclude effects of body mass. The volume of the lung that contains parenchyma was 11.9±5.0mm(3)g(-1). The volume fraction, i.e., the actual pulmonary exchange tissue per lung parenchyma, was 63.22±7.3%; the total respiratory surface was, on average, 0.214±0.129m(2); it was significantly negatively correlated to body mass, with larger snakes having proportionally smaller respiratory surfaces. For the air-blood barrier, a harmonic mean of 0.78±0.05μm was found, with the epithelial layer representing the thickest part of the barrier. Based on these findings, a median diffusion capacity of the tissue barrier ( [Formula: see text] ) of 0.69±0.38ml O(2)min(-1)mmHg(-1) was calculated. Based on published values for blood oxygen concentration, a total oxygen uptake capacity of 61.16mlO(2)min(-1)kg(-1) can be assumed. This value exceeds the maximum demand for oxygen in ball pythons by a factor of 12. We conclude that healthy individuals of P. regius possess a considerable spare capacity for tissue oxygen exchange. PMID:22770588

  16. MTpy: A Python toolbox for magnetotellurics

    NASA Astrophysics Data System (ADS)

    Krieger, Lars; Peacock, Jared R.

    2014-11-01

    We present the software package MTpy that allows handling, processing, and imaging of magnetotelluric (MT) data sets. Written in Python, the code is open source, containing sub-packages and modules for various tasks within the standard MT data processing and handling scheme. Besides the independent definition of classes and functions, MTpy provides wrappers and convenience scripts to call standard external data processing and modelling software. In its current state, modules and functions of MTpy work on raw and pre-processed MT data. However, opposite to providing a static compilation of software, we prefer to introduce MTpy as a flexible software toolbox, whose contents can be combined and utilised according to the respective needs of the user. Just as the overall functionality of a mechanical toolbox can be extended by adding new tools, MTpy is a flexible framework, which will be dynamically extended in the future. Furthermore, it can help to unify and extend existing codes and algorithms within the (academic) MT community. In this paper, we introduce the structure and concept of MTpy. Additionally, we show some examples from an everyday work-flow of MT data processing: the generation of standard EDI data files from raw electric (E-) and magnetic flux density (B-) field time series as input, the conversion into MiniSEED data format, as well as the generation of a graphical data representation in the form of a Phase Tensor pseudosection.

  17. Python algorithms in particle tracking microrheology

    PubMed Central

    2012-01-01

    Background Particle tracking passive microrheology relates recorded trajectories of microbeads, embedded in soft samples, to the local mechanical properties of the sample. The method requires intensive numerical data processing and tools allowing control of the calculation errors. Results We report the development of a software package collecting functions and scripts written in Python for automated and manual data processing, to extract viscoelastic information about the sample using recorded particle trajectories. The resulting program package analyzes the fundamental diffusion characteristics of particle trajectories and calculates the frequency dependent complex shear modulus using methods published in the literature. In order to increase conversion accuracy, segmentwise, double step, range-adaptive fitting and dynamic sampling algorithms are introduced to interpolate the data in a splinelike manner. Conclusions The presented set of algorithms allows for flexible data processing for particle tracking microrheology. The package presents improved algorithms for mean square displacement estimation, controlling effects of frame loss during recording, and a novel numerical conversion method using segmentwise interpolation, decreasing the conversion error from about 100% to the order of 1%. PMID:23186362

  18. Bioinformatic pipelines in Python with Leaf

    PubMed Central

    2013-01-01

    Background An incremental, loosely planned development approach is often used in bioinformatic studies when dealing with custom data analysis in a rapidly changing environment. Unfortunately, the lack of a rigorous software structuring can undermine the maintainability, communicability and replicability of the process. To ameliorate this problem we propose the Leaf system, the aim of which is to seamlessly introduce the pipeline formality on top of a dynamical development process with minimum overhead for the programmer, thus providing a simple layer of software structuring. Results Leaf includes a formal language for the definition of pipelines with code that can be transparently inserted into the user’s Python code. Its syntax is designed to visually highlight dependencies in the pipeline structure it defines. While encouraging the developer to think in terms of bioinformatic pipelines, Leaf supports a number of automated features including data and session persistence, consistency checks between steps of the analysis, processing optimization and publication of the analytic protocol in the form of a hypertext. Conclusions Leaf offers a powerful balance between plan-driven and change-driven development environments in the design, management and communication of bioinformatic pipelines. Its unique features make it a valuable alternative to other related tools. PMID:23786315

  19. TRIPPy: Trailed Image Photometry in Python

    NASA Astrophysics Data System (ADS)

    Fraser, Wesley; Alexandersen, Mike; Schwamb, Megan E.; Marsset, Michaël; Pike, Rosemary E.; Kavelaars, J. J.; Bannister, Michele T.; Benecchi, Susan; Delsanti, Audrey

    2016-06-01

    Photometry of moving sources typically suffers from a reduced signal-to-noise ratio (S/N) or flux measurements biased to incorrect low values through the use of circular apertures. To address this issue, we present the software package, TRIPPy: TRailed Image Photometry in Python. TRIPPy introduces the pill aperture, which is the natural extension of the circular aperture appropriate for linearly trailed sources. The pill shape is a rectangle with two semicircular end-caps and is described by three parameters, the trail length and angle, and the radius. The TRIPPy software package also includes a new technique to generate accurate model point-spread functions (PSFs) and trailed PSFs (TSFs) from stationary background sources in sidereally tracked images. The TSF is merely the convolution of the model PSF, which consists of a moffat profile, and super-sampled lookup table. From the TSF, accurate pill aperture corrections can be estimated as a function of pill radius with an accuracy of 10 mmag for highly trailed sources. Analogous to the use of small circular apertures and associated aperture corrections, small radius pill apertures can be used to preserve S/Ns of low flux sources, with appropriate aperture correction applied to provide an accurate, unbiased flux measurement at all S/Ns.

  20. Python-Assisted MODFLOW Application and Code Development

    NASA Astrophysics Data System (ADS)

    Langevin, C.

    2013-12-01

    The U.S. Geological Survey (USGS) has a long history of developing and maintaining free, open-source software for hydrological investigations. The MODFLOW program is one of the most popular hydrologic simulation programs released by the USGS, and it is considered to be the most widely used groundwater flow simulation code. MODFLOW was written using a modular design and a procedural FORTRAN style, which resulted in code that could be understood, modified, and enhanced by many hydrologists. The code is fast, and because it uses standard FORTRAN it can be run on most operating systems. Most MODFLOW users rely on proprietary graphical user interfaces for constructing models and viewing model results. Some recent efforts, however, have focused on construction of MODFLOW models using open-source Python scripts. Customizable Python packages, such as FloPy (https://code.google.com/p/flopy), can be used to generate input files, read simulation results, and visualize results in two and three dimensions. Automating this sequence of steps leads to models that can be reproduced directly from original data and rediscretized in space and time. Python is also being used in the development and testing of new MODFLOW functionality. New packages and numerical formulations can be quickly prototyped and tested first with Python programs before implementation in MODFLOW. This is made possible by the flexible object-oriented design capabilities available in Python, the ability to call FORTRAN code from Python, and the ease with which linear systems of equations can be solved using SciPy, for example. Once new features are added to MODFLOW, Python can then be used to automate comprehensive regression testing and ensure reliability and accuracy of new versions prior to release.

  1. The spectacle of the ball python (Python regius): a morphological description.

    PubMed

    Da Silva, Mari-Ann O; Heegaard, Steffen; Wang, Tobias; Nyengaard, Jens R; Bertelsen, Mads F

    2014-05-01

    A detailed morphological description of the spectacle of the ball python (Python regius) is provided. The eyes of 21 snakes were examined by light microscopy and/or transmission electron microscopy. Additionally, eyes of nine live snakes were examined using optical coherence tomography (OCT) and Scheimpflug scanning (Pentacam). The spectacle consists of three layers: outer epithelium, stroma and inner epithelium. The outer epithelium is made up of flat basal cells overlaid by keratin, the stroma consists of organized layers of collagen fibrils with interweaving nerve fibers and blood vessels, and the inner epithelium holds squamous cells containing vesicles and microvilli. At the rim of the spectacle, there is a transition zone, where the spectacle merges with the epidermis and dermis of the periocular scales. This zone is characterized by a greater height of the basal cells of the outer epithelium and a less orderly organization of the stroma compared with the spectacle proper. The thickness of the spectacle was uniform throughout. It averaged 96 ± 10 µm in histological specimens and 108 ± 13 µm using OCT. The subspectacular space was extremely narrow in the live snakes; however, the space was visible at the periphery of the spectacle with OCT. PMID:24375578

  2. Synthetic seismogram web service and Python tools

    NASA Astrophysics Data System (ADS)

    Heimann, Sebastian; Cesca, Simone; Kriegerowski, Marius; Dahm, Torsten

    2014-05-01

    Many geophysical methods require knowledge of Green's functions (GF) or synthetic seismograms in dependence of ranges of source and receiver coordinates. Examples include synthetic seismogram generation, moment tensor inversion, the modeling of depth phases for regional and teleseismic earthquakes, or the modeling of pressure diffusion induced static displacement and strain. Calculation of Green's functions is a computationally expensive operation and it can be of advantage to calculate them in advance: the same Green's function traces can then be reused several or many times as required in a typical application. Regarding Green's function computation as an independent step in a use-case's processing chain encourages to store these in an application independent form. They can then be shared between different applications and they can also be passed to other researchers, e.g. via a web service. Starting now, we provide such a web service to the seismological community (http://kinherd.org/), where a researcher can share Green's function stores and retrieve synthetic seismograms for various point and extended earthquake source models for many different earth models at local, regional and global scale. This web service is part of a rich new toolset for the creation and handling of Green's functions and synthetic seismograms (http://emolch.github.com/pyrocko/gf). It can be used off-line or in client mode. Its core features are: greatly simplified generation of Green's function stores supports various codes for Green's function computation extensible Green's function storage format flexible spacial indexing of Green's functions integrated travel time computation support for other types of Green's functions; e.g. poro-elastic GFs written in Python

  3. A Python interface with Narcisse graphics

    SciTech Connect

    Motteler, Z.C.

    1996-04-15

    Narcisse is a graphics package developed by our French colleagues at Centre d`Etudes de Limeil Valenton of the Commissariat d`Energie Atomique. Narcisse is quite comprehensive; it can do two-, three-, and four-dimensional plots (the latter meaning that the surface is colored according to the values of an arbitrary function). One can open and send plots to a Narcisse window on a distant machine. Narcisse has a user-friendly graphical user interface (GUI) which, once a graph has appeared, allows the user to change its characteristics interactively. This enables one to find the best appearance for a particular plot without having to graph it repeatedly from the user program. Previously created files in various formats can also be imported directly into the Narcisse GUI and manipulated from there. Narcisse runs independently, as a graphics server. The user program communicates with Narcisse via Unix sockets. This communication is quite low level and very complex. The appearance of a plot is controlled by nearly 150 parameters for determining such things as the color palette, type of shading, axis scales, curve and surface labels, titles, angle and distance of view (for three- and four-dimensional graphs), hidden line removal, etc. Most end users do not wish to spend time learning the tedious details of such interfaces; they would just like to specify data and ask to have it plotted. This paper describes a high level, easy to use graphics interface which hides (as much as possible) the low level details of whatever graphics system is actually being used, so that the low level can be essentially ``plug-and-play.`` Then, whenever a better system becomes available, it should only be necessary to change low level interface routines not normally accessed by ordinary users. Python, with its easy extendability, was ideally suited for this job.

  4. pyam: Python Implementation of YaM

    NASA Technical Reports Server (NTRS)

    Myint, Steven; Jain, Abhinandan

    2012-01-01

    pyam is a software development framework with tools for facilitating the rapid development of software in a concurrent software development environment. pyam provides solutions for development challenges associated with software reuse, managing multiple software configurations, developing software product lines, and multiple platform development and build management. pyam uses release-early, release-often development cycles to allow developers to integrate their changes incrementally into the system on a continual basis. It facilitates the creation and merging of branches to support the isolated development of immature software to avoid impacting the stability of the development effort. It uses modules and packages to organize and share software across multiple software products, and uses the concepts of link and work modules to reduce sandbox setup times even when the code-base is large. One sidebenefit is the enforcement of a strong module-level encapsulation of a module s functionality and interface. This increases design transparency, system stability, and software reuse. pyam is written in Python and is organized as a set of utilities on top of the open source SVN software version control package. All development software is organized into a collection of modules. pyam packages are defined as sub-collections of the available modules. Developers can set up private sandboxes for module/package development. All module/package development takes place on private SVN branches. High-level pyam commands support the setup, update, and release of modules and packages. Released and pre-built versions of modules are available to developers. Developers can tailor the source/link module mix for their sandboxes so that new sandboxes (even large ones) can be built up easily and quickly by pointing to pre-existing module releases. All inter-module interfaces are publicly exported via links. A minimal, but uniform, convention is used for building modules.

  5. Scripting MODFLOW model development using Python and FloPy

    USGS Publications Warehouse

    Bakker, Mark; Post, Vincent E. A.; Langevin, Christian D.; Hughes, Joseph D.; White, Jeremy; Starn, Jeffrey; Fienen, Michael N.

    2016-01-01

    Graphical user interfaces (GUIs) are commonly used to construct and postprocess numerical groundwater flow and transport models. Scripting model development with the programming language Python is presented here as an alternative approach. One advantage of Python is that there are many packages available to facilitate the model development process, including packages for plotting, array manipulation, optimization, and data analysis. For MODFLOW-based models, the FloPy package was developed by the authors to construct model input files, run the model, and read and plot simulation results. Use of Python with the available scientific packages and FloPy facilitates data exploration, alternative model evaluations, and model analyses that can be difficult to perform with GUIs. Furthermore, Python scripts are a complete, transparent, and repeatable record of the modeling process. The approach is introduced with a simple FloPy example to create and postprocess a MODFLOW model. A more complicated capture-fraction analysis with a real-world model is presented to demonstrate the types of analyses that can be performed using Python and FloPy.

  6. Stochastic spatio-temporal modelling with PCRaster Python

    NASA Astrophysics Data System (ADS)

    Karssenberg, D.; Schmitz, O.; de Jong, K.

    2012-04-01

    PCRaster Python is a software framework for building spatio-temporal models of land surface processes (Karssenberg, Schmitz, Salamon, De Jong, & Bierkens, 2010; PCRaster, 2012). Building blocks of models are spatial operations on raster maps, including a large suite of operations for water and sediment routing. These operations, developed in C++, are available to model builders as Python functions. Users create models by combining these functions in a Python script. As construction of large iterative models is often difficult and time consuming for non-specialists in programming, the software comes with a set of Python framework classes that provide control flow for static modelling, temporal modelling, stochastic modelling using Monte Carlo simulation, and data assimilation techniques including the Ensemble Kalman filter and the Particle Filter. A framework for integrating model components with different time steps and spatial discretization is currently available as a prototype (Schmitz, de Jong, & Karssenberg, in review). The software includes routines for visualisation of stochastic spatio-temporal data for prompt, interactive, visualisation of model inputs and outputs. Visualisation techniques include animated maps, time series, probability distributions, and animated maps with exceedance probabilities. The PCRaster Python software is used by researchers from a large range of disciplines, including hydrology, ecology, sedimentology, and land use change studies. Applications include global scale hydrological modelling and error propagation in large-scale land use change models. The software runs on MS Windows and Linux operating systems, and OS X (under development).

  7. Inclusion body disease in two captive Australian pythons (Morelia spilota variegata and Morelia spilota spilota).

    PubMed

    Carlisle-Nowak, M S; Sullivan, N; Carrigan, M; Knight, C; Ryan, C; Jacobson, E R

    1998-02-01

    Two captive Australian pythons, one carpet and one diamond python, presented with signs of central nervous system dysfunction. The carpet python was agitated. Its head was tilting and it was incoordinated and had convulsions. It was treated with antibiotics and anthelmintics but was eventually euthanased after failing to respond to therapy. The diamond python had flaccid paralysis of the caudal half. It was not treated and became disoriented and died. Hepatocytes from both pythons contained irregular 2 to 10 micron eosinophilic intracytoplasmic inclusion bodies. The brain of the diamond python was not available for examination. Occasional neurones in the carpet python brain contained similar inclusion bodies and other changes suggestive of viral infection. The clinical signs and histopathological findings in both pythons were consistent with boid inclusion body disease. PMID:9578777

  8. Leveraging Comparative Genomics to Identify and Functionally Characterize Genes Associated with Sperm Phenotypes in Python bivittatus (Burmese Python)

    PubMed Central

    Rutllant, Josep

    2016-01-01

    Comparative genomics approaches provide a means of leveraging functional genomics information from a highly annotated model organism's genome (such as the mouse genome) in order to make physiological inferences about the role of genes and proteins in a less characterized organism's genome (such as the Burmese python). We employed a comparative genomics approach to produce the functional annotation of Python bivittatus genes encoding proteins associated with sperm phenotypes. We identify 129 gene-phenotype relationships in the python which are implicated in 10 specific sperm phenotypes. Results obtained through our systematic analysis identified subsets of python genes exhibiting associations with gene ontology annotation terms. Functional annotation data was represented in a semantic scatter plot. Together, these newly annotated Python bivittatus genome resources provide a high resolution framework from which the biology relating to reptile spermatogenesis, fertility, and reproduction can be further investigated. Applications of our research include (1) production of genetic diagnostics for assessing fertility in domestic and wild reptiles; (2) enhanced assisted reproduction technology for endangered and captive reptiles; and (3) novel molecular targets for biotechnology-based approaches aimed at reducing fertility and reproduction of invasive reptiles. Additional enhancements to reptile genomic resources will further enhance their value. PMID:27200191

  9. Leveraging Comparative Genomics to Identify and Functionally Characterize Genes Associated with Sperm Phenotypes in Python bivittatus (Burmese Python).

    PubMed

    Irizarry, Kristopher J L; Rutllant, Josep

    2016-01-01

    Comparative genomics approaches provide a means of leveraging functional genomics information from a highly annotated model organism's genome (such as the mouse genome) in order to make physiological inferences about the role of genes and proteins in a less characterized organism's genome (such as the Burmese python). We employed a comparative genomics approach to produce the functional annotation of Python bivittatus genes encoding proteins associated with sperm phenotypes. We identify 129 gene-phenotype relationships in the python which are implicated in 10 specific sperm phenotypes. Results obtained through our systematic analysis identified subsets of python genes exhibiting associations with gene ontology annotation terms. Functional annotation data was represented in a semantic scatter plot. Together, these newly annotated Python bivittatus genome resources provide a high resolution framework from which the biology relating to reptile spermatogenesis, fertility, and reproduction can be further investigated. Applications of our research include (1) production of genetic diagnostics for assessing fertility in domestic and wild reptiles; (2) enhanced assisted reproduction technology for endangered and captive reptiles; and (3) novel molecular targets for biotechnology-based approaches aimed at reducing fertility and reproduction of invasive reptiles. Additional enhancements to reptile genomic resources will further enhance their value. PMID:27200191

  10. A parvovirus isolated from royal python (Python regius) is a member of the genus Dependovirus.

    PubMed

    Farkas, Szilvia L; Zádori, Zoltán; Benko, Mária; Essbauer, Sandra; Harrach, Balázs; Tijssen, Peter

    2004-03-01

    Parvoviruses were isolated from Python regius and Boa constrictor snakes and propagated in viper heart (VH-2) and iguana heart (IgH-2) cells. The full-length genome of a snake parvovirus was cloned and both strands were sequenced. The organization of the 4432-nt-long genome was found to be typical of parvoviruses. This genome was flanked by inverted terminal repeats (ITRs) of 154 nt, containing 122 nt terminal hairpins and contained two large open reading frames, encoding the non-structural and structural proteins. Genes of this new parvovirus were most similar to those from waterfowl parvoviruses and from adeno-associated viruses (AAVs), albeit to a relatively low degree and with some organizational differences. The structure of its ITRs also closely resembled those of AAVs. Based on these data, we propose to classify this virus, the first serpentine parvovirus to be identified, as serpentine adeno-associated virus (SAAV) in the genus Dependovirus. PMID:14993638

  11. High performance Python for direct numerical simulations of turbulent flows

    NASA Astrophysics Data System (ADS)

    Mortensen, Mikael; Langtangen, Hans Petter

    2016-06-01

    Direct Numerical Simulations (DNS) of the Navier Stokes equations is an invaluable research tool in fluid dynamics. Still, there are few publicly available research codes and, due to the heavy number crunching implied, available codes are usually written in low-level languages such as C/C++ or Fortran. In this paper we describe a pure scientific Python pseudo-spectral DNS code that nearly matches the performance of C++ for thousands of processors and billions of unknowns. We also describe a version optimized through Cython, that is found to match the speed of C++. The solvers are written from scratch in Python, both the mesh, the MPI domain decomposition, and the temporal integrators. The solvers have been verified and benchmarked on the Shaheen supercomputer at the KAUST supercomputing laboratory, and we are able to show very good scaling up to several thousand cores. A very important part of the implementation is the mesh decomposition (we implement both slab and pencil decompositions) and 3D parallel Fast Fourier Transforms (FFT). The mesh decomposition and FFT routines have been implemented in Python using serial FFT routines (either NumPy, pyFFTW or any other serial FFT module), NumPy array manipulations and with MPI communications handled by MPI for Python (mpi4py). We show how we are able to execute a 3D parallel FFT in Python for a slab mesh decomposition using 4 lines of compact Python code, for which the parallel performance on Shaheen is found to be slightly better than similar routines provided through the FFTW library. For a pencil mesh decomposition 7 lines of code is required to execute a transform.

  12. Advanced PANIC quick-look tool using Python

    NASA Astrophysics Data System (ADS)

    Ibáñez, José-Miguel; García Segura, Antonio J.; Storz, Clemens; Fried, Josef W.; Fernández, Matilde; Rodríguez Gómez, Julio F.; Terrón, V.; Cárdenas, M. C.

    2012-09-01

    PANIC, the Panoramic Near Infrared Camera, is an instrument for the Calar Alto Observatory currently being integrated in laboratory and whose first light is foreseen for end 2012 or early 2013. We present here how the PANIC Quick-Look tool (PQL) and pipeline (PAPI) are being implemented, using existing rapid programming Python technologies and packages, together with well-known astronomical software suites (Astromatic, IRAF) and parallel processing techniques. We will briefly describe the structure of the PQL tool, whose main characteristics are the use of the SQLite database and PyQt, a Python binding of the GUI toolkit Qt.

  13. Exomerge user's manual : a lightweight Python interface for manipulating Exodus files.

    SciTech Connect

    Kostka, Timothy D.

    2013-01-01

    Exomerge is a lightweight Python module for reading, manipulating and writing data within ExodusII files. It is built upon a Python wrapper around the ExodusII API functions. This module, the Python wrapper, and the ExodusII libraries are available as part of the standard SIERRA installation.

  14. Record length, mass, and clutch size in the nonindigenous Burmese Python, Python bivittatus Kuhl 1820 (Squamata: Pythonidae), in Florida

    USGS Publications Warehouse

    Krysko, Kenneth L.; Hart, Kristen M.; Smith, Brian J.; Selby, Thomas H.; Cherkiss, Michael S.; Coutu, Nicholas T.; Reichart, Rebecca M.; Nuñez, Leroy P.; Mazzotti, Frank J.; Snow, Ray W.

    2012-01-01

    The Burmese Python, Python bivittatus Kuhl 1820 (Squamata: Pythonidae), is indigenous to northern India,east to southern China, and south to Vietnam and a few islands in Indonesia (Barker and Barker 2008, Reed and Rodda 2009). This species has been introduced since at least 1979 in southern Florida, USA, where it likely began reproducing and became established during the 1980s (Meshaka et al. 2000, Snowet al. 2007b,Kraus 2009, Krysko et al. 2011, Willson et al. 2011). Python bivittatus has been documented in Florida consuming a variety of mammals and birds, and the American Alligator(Alligator mississippiensis) (Snowet al. 2007a, 2007b; Harvey et al. 2008; Rochford et al. 2010b; Holbrook and Chesnes 2011), many of which are protected species. Herein, we provide details on two of the largest known wild P. bivittatus in Florida to date, including current records on length,mass,clutch size, and diet.

  15. PyKrige: Development of a Kriging Toolkit for Python

    NASA Astrophysics Data System (ADS)

    Murphy, B. S.

    2014-12-01

    While Python continues to grow in popularity as a convenient and powerful means of data manipulation and analysis, the language still lacks a package that provides easy access to commonly utilized geostatistical routines. PyKrige is a new contribution that attempts to create a Python library that can be used for basic geostatistical tasks, such as creating water level maps using Ordinary and Universal Kriging. While written in pure Python, the code makes extensive use of NumPy in order to enable fast processing. Supported drift terms for Universal Kriging currently include a regional linear drift (such as would be used to simulate an overall groundwater gradient, as discussed in Tonkin and Larson, Groundwater, 2002), a point-logarithmic drift (such as would be used to simulate wells, as discussed in Tonkin and Larson, Groundwater, 2002), and an external digital elevation model drift (such as would be used to simulate a topographically controlled groundwater surface, as discussed in Desbarats et al., Journal of Hydrology, 2002). The package is intended primarily for kriging of two-dimensional data, but limited support for three-dimensional kriging is currently under development. Though similar tools already exist for other commonly utilized scientific languages, such as R and MATLAB, PyKrige is intended to ease data processing by providing further functionality in Python that can be implemented in a single analysis pipeline. The code will be made available on GitHub.

  16. OMPC: an Open-Source MATLAB®-to-Python Compiler

    PubMed Central

    Jurica, Peter; van Leeuwen, Cees

    2008-01-01

    Free access to scientific information facilitates scientific progress. Open-access scientific journals are a first step in this direction; a further step is to make auxiliary and supplementary materials that accompany scientific publications, such as methodological procedures and data-analysis tools, open and accessible to the scientific community. To this purpose it is instrumental to establish a software base, which will grow toward a comprehensive free and open-source language of technical and scientific computing. Endeavors in this direction are met with an important obstacle. MATLAB®, the predominant computation tool in many fields of research, is a closed-source commercial product. To facilitate the transition to an open computation platform, we propose Open-source MATLAB®-to-Python Compiler (OMPC), a platform that uses syntax adaptation and emulation to allow transparent import of existing MATLAB® functions into Python programs. The imported MATLAB® modules will run independently of MATLAB®, relying on Python's numerical and scientific libraries. Python offers a stable and mature open source platform that, in many respects, surpasses commonly used, expensive commercial closed source packages. The proposed software will therefore facilitate the transparent transition towards a free and general open-source lingua franca for scientific computation, while enabling access to the existing methods and algorithms of technical computing already available in MATLAB®. OMPC is available at http://ompc.juricap.com. PMID:19225577

  17. pypet: A Python Toolkit for Data Management of Parameter Explorations

    PubMed Central

    Meyer, Robert; Obermayer, Klaus

    2016-01-01

    pypet (Python parameter exploration toolkit) is a new multi-platform Python toolkit for managing numerical simulations. Sampling the space of model parameters is a key aspect of simulations and numerical experiments. pypet is designed to allow easy and arbitrary sampling of trajectories through a parameter space beyond simple grid searches. pypet collects and stores both simulation parameters and results in a single HDF5 file. This collective storage allows fast and convenient loading of data for further analyses. pypet provides various additional features such as multiprocessing and parallelization of simulations, dynamic loading of data, integration of git version control, and supervision of experiments via the electronic lab notebook Sumatra. pypet supports a rich set of data formats, including native Python types, Numpy and Scipy data, Pandas DataFrames, and BRIAN(2) quantities. Besides these formats, users can easily extend the toolkit to allow customized data types. pypet is a flexible tool suited for both short Python scripts and large scale projects. pypet's various features, especially the tight link between parameters and results, promote reproducible research in computational neuroscience and simulation-based disciplines. PMID:27610080

  18. OMPC: an Open-Source MATLAB-to-Python Compiler.

    PubMed

    Jurica, Peter; van Leeuwen, Cees

    2009-01-01

    Free access to scientific information facilitates scientific progress. Open-access scientific journals are a first step in this direction; a further step is to make auxiliary and supplementary materials that accompany scientific publications, such as methodological procedures and data-analysis tools, open and accessible to the scientific community. To this purpose it is instrumental to establish a software base, which will grow toward a comprehensive free and open-source language of technical and scientific computing. Endeavors in this direction are met with an important obstacle. MATLAB((R)), the predominant computation tool in many fields of research, is a closed-source commercial product. To facilitate the transition to an open computation platform, we propose Open-source MATLAB((R))-to-Python Compiler (OMPC), a platform that uses syntax adaptation and emulation to allow transparent import of existing MATLAB((R)) functions into Python programs. The imported MATLAB((R)) modules will run independently of MATLAB((R)), relying on Python's numerical and scientific libraries. Python offers a stable and mature open source platform that, in many respects, surpasses commonly used, expensive commercial closed source packages. The proposed software will therefore facilitate the transparent transition towards a free and general open-source lingua franca for scientific computation, while enabling access to the existing methods and algorithms of technical computing already available in MATLAB((R)). OMPC is available at http://ompc.juricap.com. PMID:19225577

  19. pypet: A Python Toolkit for Data Management of Parameter Explorations.

    PubMed

    Meyer, Robert; Obermayer, Klaus

    2016-01-01

    pypet (Python parameter exploration toolkit) is a new multi-platform Python toolkit for managing numerical simulations. Sampling the space of model parameters is a key aspect of simulations and numerical experiments. pypet is designed to allow easy and arbitrary sampling of trajectories through a parameter space beyond simple grid searches. pypet collects and stores both simulation parameters and results in a single HDF5 file. This collective storage allows fast and convenient loading of data for further analyses. pypet provides various additional features such as multiprocessing and parallelization of simulations, dynamic loading of data, integration of git version control, and supervision of experiments via the electronic lab notebook Sumatra. pypet supports a rich set of data formats, including native Python types, Numpy and Scipy data, Pandas DataFrames, and BRIAN(2) quantities. Besides these formats, users can easily extend the toolkit to allow customized data types. pypet is a flexible tool suited for both short Python scripts and large scale projects. pypet's various features, especially the tight link between parameters and results, promote reproducible research in computational neuroscience and simulation-based disciplines. PMID:27610080

  20. pyro: Python-based tutorial for computational methods for hydrodynamics

    NASA Astrophysics Data System (ADS)

    Zingale, Michael

    2015-07-01

    pyro is a simple python-based tutorial on computational methods for hydrodynamics. It includes 2-d solvers for advection, compressible, incompressible, and low Mach number hydrodynamics, diffusion, and multigrid. It is written with ease of understanding in mind. An extensive set of notes that is part of the Open Astrophysics Bookshelf project provides details of the algorithms.

  1. Wyrm: A Brain-Computer Interface Toolbox in Python.

    PubMed

    Venthur, Bastian; Dähne, Sven; Höhne, Johannes; Heller, Hendrik; Blankertz, Benjamin

    2015-10-01

    In the last years Python has gained more and more traction in the scientific community. Projects like NumPy, SciPy, and Matplotlib have created a strong foundation for scientific computing in Python and machine learning packages like scikit-learn or packages for data analysis like Pandas are building on top of it. In this paper we present Wyrm ( https://github.com/bbci/wyrm ), an open source BCI toolbox in Python. Wyrm is applicable to a broad range of neuroscientific problems. It can be used as a toolbox for analysis and visualization of neurophysiological data and in real-time settings, like an online BCI application. In order to prevent software defects, Wyrm makes extensive use of unit testing. We will explain the key aspects of Wyrm's software architecture and design decisions for its data structure, and demonstrate and validate the use of our toolbox by presenting our approach to the classification tasks of two different data sets from the BCI Competition III. Furthermore, we will give a brief analysis of the data sets using our toolbox, and demonstrate how we implemented an online experiment using Wyrm. With Wyrm we add the final piece to our ongoing effort to provide a complete, free and open source BCI system in Python. PMID:26001643

  2. SunPy: Python for Solar Physics Data Analysis

    NASA Astrophysics Data System (ADS)

    Hughitt, V. Keith; Christe, S.; Ireland, J.; Shih, A.; Mayer, F.; Earnshaw, M. D.; Young, C.; Perez-Suarez, D.; Schwartz, R.

    2012-05-01

    In recent years, Python, a free cross platform general purpose high-level programming language, has seen widespread adoption among the scientific community resulting in the availability of wide range of software, from numerical computation and machine learning to spectral analysis and visualization. SunPy is a software suite specializing in providing the tools necessary to analyze solar and heliospheric datasets in Python. It provides a free and open-source alternative to the IDL-based SolarSoft (SSW) solar data analysis environment. We present the current capabilities of SunPy which include WCS-aware map objects that allow simple overplotting of data from multiple image FITS files; time-series objects that allow overplotting of multiple lightcurves, and integration with online services such as The Virtual Solar Observatory (VSO) and The Heliophysics Event Knowledgebase (HEK). SunPy also provides functionality that is not currently available in SSW such as advanced time series manipulation routines and support for working with solar data stored using JPEG 2000. We present examples of solar data analysis in SunPy, and show how Python-based solar data-analysis can leverage the many existing data analysis tools currently available in Python. We discuss the future goals of the project and encourage interested users to become involved in the planning and development of SunPy.

  3. Rabacus: A Python package for analytic cosmological radiative transfer calculations

    NASA Astrophysics Data System (ADS)

    Altay, G.; Wise, J. H.

    2015-04-01

    We describe RABACUS, a Python package for calculating the transfer of hydrogen ionizing radiation in simplified geometries relevant to astronomy and cosmology. We present example solutions for three specific cases: (1) a semi-infinite slab gas distribution in a homogeneous isotropic background, (2) a spherically symmetric gas distribution with a point source at the center, and (3) a spherically symmetric gas distribution in a homogeneous isotropic background. All problems can accommodate arbitrary spectra and density profiles as input. The solutions include a treatment of both hydrogen and helium, a self-consistent calculation of equilibrium temperatures, and the transfer of recombination radiation. The core routines are written in Fortran 90 and then wrapped in Python leading to execution speeds thousands of times faster than equivalent routines written in pure Python. In addition, all variables have associated units for ease of analysis. The software is part of the Python Package Index and the source code is available on Bitbucket at https://bitbucket.org/galtay/rabacus. In addition, installation instructions and a detailed users guide are available at http://pythonhosted.org//rabacus.

  4. Emerge - A Python environment for the modeling of subsurface transfers

    NASA Astrophysics Data System (ADS)

    Lopez, S.; Smai, F.; Sochala, P.

    2014-12-01

    The simulation of subsurface mass and energy transfers often relies on specific codes that were mainly developed using compiled languages which usually ensure computational efficiency at the expense of relatively long development times and relatively rigid software. Even if a very detailed, possibly graphical, user-interface is developed the core numerical aspects are rarely accessible and the smallest modification will always need a compilation step. Thus, user-defined physical laws or alternative numerical schemes may be relatively difficult to use. Over the last decade, Python has emerged as a popular and widely used language in the scientific community. There already exist several libraries for the pre and post-treatment of input and output files for reservoir simulators (e.g. pytough). Development times in Python are considerably reduced compared to compiled languages, and programs can be easily interfaced with libraries written in compiled languages with several comprehensive numerical libraries that provide sequential and parallel solvers (e.g. PETSc, Trilinos…). The core objective of the Emerge project is to explore the possibility to develop a modeling environment in full Python. Consequently, we are developing an open python package with the classes/objects necessary to express, discretize and solve the physical problems encountered in the modeling of subsurface transfers. We heavily relied on Python to have a convenient and concise way of manipulating potentially complex concepts with a few lines of code and a high level of abstraction. Our result aims to be a friendly numerical environment targeting both numerical engineers and physicist or geoscientists with the possibility to quickly specify and handle geometries, arbitrary meshes, spatially or temporally varying properties, PDE formulations, boundary conditions…

  5. Flexible Environmental Modeling with Python and Open - GIS

    NASA Astrophysics Data System (ADS)

    Pryet, Alexandre; Atteia, Olivier; Delottier, Hugo; Cousquer, Yohann

    2015-04-01

    Numerical modeling now represents a prominent task of environmental studies. During the last decades, numerous commercial programs have been made available to environmental modelers. These software applications offer user-friendly graphical user interfaces that allow an efficient management of many case studies. However, they suffer from a lack of flexibility and closed-source policies impede source code reviewing and enhancement for original studies. Advanced modeling studies require flexible tools capable of managing thousands of model runs for parameter optimization, uncertainty and sensitivity analysis. In addition, there is a growing need for the coupling of various numerical models associating, for instance, groundwater flow modeling to multi-species geochemical reactions. Researchers have produced hundreds of open-source powerful command line programs. However, there is a need for a flexible graphical user interface allowing an efficient processing of geospatial data that comes along any environmental study. Here, we present the advantages of using the free and open-source Qgis platform and the Python scripting language for conducting environmental modeling studies. The interactive graphical user interface is first used for the visualization and pre-processing of input geospatial datasets. Python scripting language is then employed for further input data processing, call to one or several models, and post-processing of model outputs. Model results are eventually sent back to the GIS program, processed and visualized. This approach combines the advantages of interactive graphical interfaces and the flexibility of Python scripting language for data processing and model calls. The numerous python modules available facilitate geospatial data processing and numerical analysis of model outputs. Once input data has been prepared with the graphical user interface, models may be run thousands of times from the command line with sequential or parallel calls. We

  6. Cold-induced mortality of invasive Burmese pythons in south Florida

    USGS Publications Warehouse

    Mazzotti, Frank J.; Cherkiss, Michael S.; Hart, Kristen M.; Snow, Ray W.; Rochford, Michael R.; Dorcas, Michael E.; Reed, Robert N.

    2011-01-01

    A recent record cold spell in southern Florida (2-11 January 2010) provided an opportunity to evaluate responses of an established population of Burmese pythons (Python molurus bivittatus) to a prolonged period of unusually cold weather. We observed behavior, characterized thermal biology, determined fate of radio-telemetered (n = 10) and non-telemetered (n = 104) Burmese pythons, and analyzed habitat and environmental conditions experienced by pythons during and after a historic cold spell. Telemetered pythons had been implanted with radio-transmitters and temperature-recording data loggers prior to the cold snap. Only one of 10 telemetered pythons survived the cold snap, whereas 59 of 99 (60%) non-telemetered pythons for which we determined fate survived. Body temperatures of eight dead telemetered pythons fluctuated regularly prior to 9 January 2010, then declined substantially during the cold period (9-11 January) and exhibited no further evidence of active thermoregulation indicating they were likely dead. Unusually cold temperatures in January 2010 were clearly associated with mortality of Burmese pythons in the Everglades. Some radiotelemetered pythons appeared to exhibit maladaptive behavior during the cold spell, including attempting to bask instead of retreating to sheltered refugia. We discuss implications of our findings for persistence and spread of introduced Burmese pythons in the United States and for maximizing their rate of removal.

  7. Rapid microsatellite marker development using next generation pyrosequencing to inform invasive Burmese python -- Python molurus bivittatus -- management

    USGS Publications Warehouse

    Hunter, Margaret E.; Hart, Kristen M.

    2013-01-01

    Invasive species represent an increasing threat to native ecosystems, harming indigenous taxa through predation, habitat modification, cross-species hybridization and alteration of ecosystem processes. Additionally, high economic costs are associated with environmental damage, restoration and control measures. The Burmese python, Python molurus bivittatus, is one of the most notable invasive species in the US, due to the threat it poses to imperiled species and the Greater Everglades ecosystem. To address population structure and relatedness, next generation sequencing was used to rapidly produce species-specific microsatellite loci. The Roche 454 GS-FLX Titanium platform provided 6616 di-, tri- and tetra-nucleotide repeats in 117,516 sequences. Using stringent criteria, 24 of 26 selected tri- and tetra-nucleotide loci were polymerase chain reaction (PCR) amplified and 18 were polymorphic. An additional six cross-species loci were amplified, and the resulting 24 loci were incorporated into eight PCR multiplexes. Multi-locus genotypes yielded an average of 61% (39%–77%) heterozygosity and 3.7 (2–6) alleles per locus. Population-level studies using the developed microsatellites will track the invasion front and monitor population-suppression dynamics. Additionally, cross-species amplification was detected in the invasive Ball, P. regius, and Northern African python, P. sebae. These markers can be used to address the hybridization potential of Burmese pythons and the larger, more aggressive P. sebae.

  8. Sequencing the genome of the Burmese python (Python molurus bivittatus) as a model for studying extreme adaptations in snakes

    PubMed Central

    2011-01-01

    The Consortium for Snake Genomics is in the process of sequencing the genome and creating transcriptomic resources for the Burmese python. Here, we describe how this will be done, what analyses this work will include, and provide a timeline. PMID:21801464

  9. Betrayal: radio-tagged Burmese pythons reveal locations of conspecifics in Everglades National Park

    USGS Publications Warehouse

    Smith, Brian J.; Cherkiss, Michael S.; Hart, Kristen M.; Rochford, Michael R.; Selby, Thomas H.; Snow, Ray W; Mazzotti, Frank J.

    2016-01-01

    The “Judas” technique is based on the idea that a radio-tagged individual can be used to “betray” conspecifics during the course of its routine social behavior. The Burmese python (Python bivittatus) is an invasive constrictor in southern Florida, and few methods are available for its control. Pythons are normally solitary, but from December–April in southern Florida, they form breeding aggregations containing up to 8 individuals, providing an opportunity to apply the technique. We radio-tracked 25 individual adult pythons of both sexes during the breeding season from 2007–2012. Our goals were to (1) characterize python movements and determine habitat selection for betrayal events, (2) quantify betrayal rates of Judas pythons, and (3) compare the efficacy of this tool with current tools for capturing pythons, both in terms of cost per python removed (CPP) and catch per unit effort (CPUE). In a total of 33 python-seasons, we had 8 betrayal events (24 %) in which a Judas python led us to new pythons. Betrayal events occurred more frequently in lowland forest (including tree islands) than would be expected by chance alone. These 8 events resulted in the capture of 14 new individuals (1–4 new pythons per event). Our effort comparison shows that while the Judas technique is more costly than road cruising surveys per python removed, the Judas technique yields more large, reproductive females and is effective at a time of year that road cruising is not, making it a potential complement to the status quo removal effort.

  10. Characterization of carbonic anhydrase XIII in the erythrocytes of the Burmese python, Python molurus bivittatus.

    PubMed

    Esbaugh, A J; Secor, S M; Grosell, M

    2015-09-01

    Carbonic anhydrase (CA) is one of the most abundant proteins found in vertebrate erythrocytes with the majority of species expressing a low activity CA I and high activity CA II. However, several phylogenetic gaps remain in our understanding of the expansion of cytoplasmic CA in vertebrate erythrocytes. In particular, very little is known about isoforms from reptiles. The current study sought to characterize the erythrocyte isoforms from two squamate species, Python molurus and Nerodia rhombifer, which was combined with information from recent genome projects to address this important phylogenetic gap. Obtained sequences grouped closely with CA XIII in phylogenetic analyses. CA II mRNA transcripts were also found in erythrocytes, but found at less than half the levels of CA XIII. Structural analysis suggested similar biochemical activity as the respective mammalian isoforms, with CA XIII being a low activity isoform. Biochemical characterization verified that the majority of CA activity in the erythrocytes was due to a high activity CA II-like isoform; however, titration with copper supported the presence of two CA pools. The CA II-like pool accounted for 90 % of the total activity. To assess potential disparate roles of these isoforms a feeding stress was used to up-regulate CO2 excretion pathways. Significant up-regulation of CA II and the anion exchanger was observed; CA XIII was strongly down-regulated. While these results do not provide insight into the role of CA XIII in the erythrocytes, they do suggest that the presence of two isoforms is not simply a case of physiological redundancy. PMID:26005204

  11. Python Implementation for Local Correlation Tracking Analysis of Solar Data

    NASA Astrophysics Data System (ADS)

    Campos Rozo, J. I.; Vargas Domínguez, S.

    2015-12-01

    The Local Correlation Tracking (LCT) technique is a robust method that has been extensively applied to infer proper motions of structures in time series of images. In solar physics research, LCT is a useful tool to analyse the dynamics of plasma and the evolution of magnetic fields in the solar atmosphere at different spatial and temporal scales, among others (e.g granular and supergranular convective cells, meridional flows, etc) SunPy is a joint effort of, using the advantages of Python, developing tools to be applied for processing and analysis of solar data. In this work, a widget implemented in Python and Sunpy is developed, to generate a user-friendly graphical user interface (GUI) to control various parameters for the process of calculating flow maps of proper motions for a series of filtergrams.

  12. Expyriment: a Python library for cognitive and neuroscientific experiments.

    PubMed

    Krause, Florian; Lindemann, Oliver

    2014-06-01

    Expyriment is an open-source and platform-independent lightweight Python library for designing and conducting timing-critical behavioral and neuroimaging experiments. The major goal is to provide a well-structured Python library for script-based experiment development, with a high priority being the readability of the resulting program code. Expyriment has been tested extensively under Linux and Windows and is an all-in-one solution, as it handles stimulus presentation, the recording of input/output events, communication with other devices, and the collection and preprocessing of data. Furthermore, it offers a hierarchical design structure, which allows for an intuitive transition from the experimental design to a running program. It is therefore also suited for students, as well as for experimental psychologists and neuroscientists with little programming experience. PMID:24142834

  13. Developing PYTHON Codes for the Undergraduate ALFALFA Team

    NASA Astrophysics Data System (ADS)

    Troischt, Parker; Ryan, Nicholas; Alfalfa Team

    2016-03-01

    We describe here progress toward developing a number of new PYTHON routines to be used by members of the Undergraduate ALFALFA Team. The codes are designed to analyze HI spectra and assist in identifying and categorizing some of the intriguing sources found in the initial blind ALFALFA survey. Numerical integration is performed on extragalactic sources using 21cm line spectra produced with the L-Band Wide receiver at the National Astronomy and Ionosphere Center. Prior to the integration, polynomial fits are employed to obtain an appropriate baseline for each source. The codes developed here are part of a larger team effort to use new PYTHON routines in order to replace, upgrade, or supplement a wealth of existing IDL codes within the collaboration. This work has been supported by NSF Grant AST-1211005.

  14. From Start to Finish: Python for Space Missions

    NASA Astrophysics Data System (ADS)

    Barrett, P.; Berghea, C.; Dieck, C.; Evans, S.; Poole, C.; Veillette, D.

    2010-12-01

    The software development process for many space observatories is often disjoint and inefficient due to the use of multiple languages during the different phases of mission development. Code and algorithms that are often developed using an interactive, array language during the pathfinding efforts of Phase A are often rewritten in a non-interactive, compiled language for use in the production code for Phase C. This approach leads to inefficiency in both development time and cost and can introduce errors during the rewriting process. Python is one programming language that can be used as a high-level, array language and as an efficient, production language. This paper shows how Python will be used during the different phases of development of the Joint Milli-Arcsecond Pathfinder Survey (JMAPS) space mission with an emphasis on code and algorithm reuse from one phase to the next.

  15. Pyvolve: A Flexible Python Module for Simulating Sequences along Phylogenies

    PubMed Central

    Spielman, Stephanie J.; Wilke, Claus O.

    2015-01-01

    We introduce Pyvolve, a flexible Python module for simulating genetic data along a phylogeny using continuous-time Markov models of sequence evolution. Easily incorporated into Python bioinformatics pipelines, Pyvolve can simulate sequences according to most standard models of nucleotide, amino-acid, and codon sequence evolution. All model parameters are fully customizable. Users can additionally specify custom evolutionary models, with custom rate matrices and/or states to evolve. This flexibility makes Pyvolve a convenient framework not only for simulating sequences under a wide variety of conditions, but also for developing and testing new evolutionary models. Pyvolve is an open-source project under a FreeBSD license, and it is available for download, along with a detailed user-manual and example scripts, from http://github.com/sjspielman/pyvolve. PMID:26397960

  16. Pyvolve: A Flexible Python Module for Simulating Sequences along Phylogenies.

    PubMed

    Spielman, Stephanie J; Wilke, Claus O

    2015-01-01

    We introduce Pyvolve, a flexible Python module for simulating genetic data along a phylogeny using continuous-time Markov models of sequence evolution. Easily incorporated into Python bioinformatics pipelines, Pyvolve can simulate sequences according to most standard models of nucleotide, amino-acid, and codon sequence evolution. All model parameters are fully customizable. Users can additionally specify custom evolutionary models, with custom rate matrices and/or states to evolve. This flexibility makes Pyvolve a convenient framework not only for simulating sequences under a wide variety of conditions, but also for developing and testing new evolutionary models. Pyvolve is an open-source project under a FreeBSD license, and it is available for download, along with a detailed user-manual and example scripts, from http://github.com/sjspielman/pyvolve. PMID:26397960

  17. COOPR: A COmmon Optimization Python Repository v. 1.0

    Energy Science and Technology Software Center (ESTSC)

    2008-08-14

    Coopr integrates Python packages for defining optimizers, modeling optimization applications, and managing computational experiments. A major driver for Coopr development is the Pyomo package that can be used to define abstract problems, create concrete problem instances, and solve these instances with standard solvers. Other Coopr packages include EXACT, a framework for managing computational experiments, SUCASA, a tool for customizing integer programming solvers, and OPT, a generic optimization interface.

  18. PyRAT - python radiography analysis tool (u)

    SciTech Connect

    Temple, Brian A; Buescher, Kevin L; Armstrong, Jerawan C

    2011-01-14

    PyRAT is a radiography analysis tool used to reconstruction images of unknown 1-0 objects. The tool is written in Python and developed for use on LINUX and Windows platforms. The tool is capable of performing nonlinear inversions of the images with minimal manual interaction in the optimization process. The tool utilizes the NOMAD mixed variable optimization tool to perform the optimization.

  19. Psyplot: Visualizing rectangular and triangular Climate Model Data with Python

    NASA Astrophysics Data System (ADS)

    Sommer, Philipp

    2016-04-01

    The development and use of climate models often requires the visualization of geo-referenced data. Creating visualizations should be fast, attractive, flexible, easily applicable and easily reproducible. There is a wide range of software tools available for visualizing raster data, but they often are inaccessible to many users (e.g. because they are difficult to use in a script or have low flexibility). In order to facilitate easy visualization of geo-referenced data, we developed a new framework called "psyplot," which can aid earth system scientists with their daily work. It is purely written in the programming language Python and primarily built upon the python packages matplotlib, cartopy and xray. The package can visualize data stored on the hard disk (e.g. NetCDF, GeoTIFF, any other file format supported by the xray package), or directly from the memory or Climate Data Operators (CDOs). Furthermore, data can be visualized on a rectangular grid (following or not following the CF Conventions) and on a triangular grid (following the CF or UGRID Conventions). Psyplot visualizes 2D scalar and vector fields, enabling the user to easily manage and format multiple plots at the same time, and to export the plots into all common picture formats and movies covered by the matplotlib package. The package can currently be used in an interactive python session or in python scripts, and will soon be developed for use with a graphical user interface (GUI). Finally, the psyplot framework enables flexible configuration, allows easy integration into other scripts that uses matplotlib, and provides a flexible foundation for further development.

  20. Python as a federation tool for GENESIS 3.0.

    PubMed

    Cornelis, Hugo; Rodriguez, Armando L; Coop, Allan D; Bower, James M

    2012-01-01

    The GENESIS simulation platform was one of the first broad-scale modeling systems in computational biology to encourage modelers to develop and share model features and components. Supported by a large developer community, it participated in innovative simulator technologies such as benchmarking, parallelization, and declarative model specification and was the first neural simulator to define bindings for the Python scripting language. An important feature of the latest version of GENESIS is that it decomposes into self-contained software components complying with the Computational Biology Initiative federated software architecture. This architecture allows separate scripting bindings to be defined for different necessary components of the simulator, e.g., the mathematical solvers and graphical user interface. Python is a scripting language that provides rich sets of freely available open source libraries. With clean dynamic object-oriented designs, they produce highly readable code and are widely employed in specialized areas of software component integration. We employ a simplified wrapper and interface generator to examine an application programming interface and make it available to a given scripting language. This allows independent software components to be 'glued' together and connected to external libraries and applications from user-defined Python or Perl scripts. We illustrate our approach with three examples of Python scripting. (1) Generate and run a simple single-compartment model neuron connected to a stand-alone mathematical solver. (2) Interface a mathematical solver with GENESIS 3.0 to explore a neuron morphology from either an interactive command-line or graphical user interface. (3) Apply scripting bindings to connect the GENESIS 3.0 simulator to external graphical libraries and an open source three dimensional content creation suite that supports visualization of models based on electron microscopy and their conversion to computational models

  1. ObsPy: A Python Toolbox for Seismology

    NASA Astrophysics Data System (ADS)

    Wassermann, J. M.; Krischer, L.; Megies, T.; Barsch, R.; Beyreuther, M.

    2013-12-01

    Python combines the power of a full-blown programming language with the flexibility and accessibility of an interactive scripting language. Its extensive standard library and large variety of freely available high quality scientific modules cover most needs in developing scientific processing workflows. ObsPy is a community-driven, open-source project extending Python's capabilities to fit the specific needs that arise when working with seismological data. It a) comes with a continuously growing signal processing toolbox that covers most tasks common in seismological analysis, b) provides read and write support for many common waveform, station and event metadata formats and c) enables access to various data centers, webservices and databases to retrieve waveform data and station/event metadata. In combination with mature and free Python packages like NumPy, SciPy, Matplotlib, IPython, Pandas, lxml, and PyQt, ObsPy makes it possible to develop complete workflows in Python, ranging from reading locally stored data or requesting data from one or more different data centers via signal analysis and data processing to visualization in GUI and web applications, output of modified/derived data and the creation of publication-quality figures. All functionality is extensively documented and the ObsPy Tutorial and Gallery give a good impression of the wide range of possible use cases. ObsPy is tested and running on Linux, OS X and Windows and comes with installation routines for these systems. ObsPy is developed in a test-driven approach and is available under the LGPLv3 open source licence. Users are welcome to request help, report bugs, propose enhancements or contribute code via either the user mailing list or the project page on GitHub.

  2. Python as a Federation Tool for GENESIS 3.0

    PubMed Central

    Cornelis, Hugo; Rodriguez, Armando L.; Coop, Allan D.; Bower, James M.

    2012-01-01

    The GENESIS simulation platform was one of the first broad-scale modeling systems in computational biology to encourage modelers to develop and share model features and components. Supported by a large developer community, it participated in innovative simulator technologies such as benchmarking, parallelization, and declarative model specification and was the first neural simulator to define bindings for the Python scripting language. An important feature of the latest version of GENESIS is that it decomposes into self-contained software components complying with the Computational Biology Initiative federated software architecture. This architecture allows separate scripting bindings to be defined for different necessary components of the simulator, e.g., the mathematical solvers and graphical user interface. Python is a scripting language that provides rich sets of freely available open source libraries. With clean dynamic object-oriented designs, they produce highly readable code and are widely employed in specialized areas of software component integration. We employ a simplified wrapper and interface generator to examine an application programming interface and make it available to a given scripting language. This allows independent software components to be ‘glued’ together and connected to external libraries and applications from user-defined Python or Perl scripts. We illustrate our approach with three examples of Python scripting. (1) Generate and run a simple single-compartment model neuron connected to a stand-alone mathematical solver. (2) Interface a mathematical solver with GENESIS 3.0 to explore a neuron morphology from either an interactive command-line or graphical user interface. (3) Apply scripting bindings to connect the GENESIS 3.0 simulator to external graphical libraries and an open source three dimensional content creation suite that supports visualization of models based on electron microscopy and their conversion to computational

  3. A Community Python Library for Solar Physics (SunPy)

    NASA Astrophysics Data System (ADS)

    Christe, Steven; Shih, A. Y.; Ireland, J.; Perez-Suarez, D.; Mumford, S.; Hughitt, V. K.; Hewett, R.; Mayer, F.; SunPy Dev Team

    2013-07-01

    Python, a free, cross platform, general purpose, high-level programming language, has seen widespread adoption among the scientific community resulting in the availability of a large range of software, from numerical computation (NumPy, SciPy) and machine learning to spectral analysis and visualization (Matplotlib). SunPy is a data analysis toolkit specializing in providing the software necessary to analyze solar and heliospheric datasets in Python. It aims to provide a free and open-source alternative to the IDL-based SolarSoft (SSW) solar data analysis environment. We present the latest release of SunPy (0.3). This release includes a major refactor of the main SunPy code to improve ease of use for the user as well as a more consistent interface. SunPy provides downloading capability through integration with the Virtual Solar Observatory (VSO) and the the Heliophysics Event Knowledgebase (HEK). It can open image fits files from major solar missions (SDO/AIA, SOHO/EIT, SOHO/LASCO, STEREO) into WCS-aware maps. SunPy provides advanced time-series tools for data from mission such as GOES, SDO/EVE, and Proba2/LYRA as well as support for radio spectra (e.g. e-Callisto). We present examples of solar data analysis in SunPy, and show how Python-based solar data-analysis can leverage the many existing data analysis tools already available in Python. We discuss the future goals of the project and encourage interested users to become involved in the planning and development of SunPy.

  4. AstroAsciiData: ASCII table Python module

    NASA Astrophysics Data System (ADS)

    Kümmel, Martin; Haase, Jonas

    2013-11-01

    ASCII tables continue to be one of the most popular and widely used data exchange formats in astronomy. AstroAsciiData, written in Python, imports all reasonably well-formed ASCII tables. It retains formatting of data values, allows column-first access, supports SExtractor style headings, performs column sorting, and exports data to other formats, including FITS, Numpy/Numarray, and LaTeX table format. It also offers interchangeable comment character, column delimiter and null value.

  5. ELLIPT2D: A Flexible Finite Element Code Written Python

    SciTech Connect

    Pletzer, A.; Mollis, J.C.

    2001-03-22

    The use of the Python scripting language for scientific applications and in particular to solve partial differential equations is explored. It is shown that Python's rich data structure and object-oriented features can be exploited to write programs that are not only significantly more concise than their counter parts written in Fortran, C or C++, but are also numerically efficient. To illustrate this, a two-dimensional finite element code (ELLIPT2D) has been written. ELLIPT2D provides a flexible and easy-to-use framework for solving a large class of second-order elliptic problems. The program allows for structured or unstructured meshes. All functions defining the elliptic operator are user supplied and so are the boundary conditions, which can be of Dirichlet, Neumann or Robbins type. ELLIPT2D makes extensive use of dictionaries (hash tables) as a way to represent sparse matrices.Other key features of the Python language that have been widely used include: operator over loading, error handling, array slicing, and the Tkinter module for building graphical use interfaces. As an example of the utility of ELLIPT2D, a nonlinear solution of the Grad-Shafranov equation is computed using a Newton iterative scheme. A second application focuses on a solution of the toroidal Laplace equation coupled to a magnetohydrodynamic stability code, a problem arising in the context of magnetic fusion research.

  6. DREAMTools: a Python package for scoring collaborative challenges

    PubMed Central

    Cokelaer, Thomas; Bansal, Mukesh; Bare, Christopher; Bilal, Erhan; Bot, Brian M.; Chaibub Neto, Elias; Eduati, Federica; de la Fuente, Alberto; Gönen, Mehmet; Hill, Steven M.; Hoff, Bruce; Karr, Jonathan R.; Küffner, Robert; Menden, Michael P.; Meyer, Pablo; Norel, Raquel; Pratap, Abhishek; Prill, Robert J.; Weirauch, Matthew T.; Costello, James C.; Stolovitzky, Gustavo; Saez-Rodriguez, Julio

    2016-01-01

    DREAM challenges are community competitions designed to advance computational methods and address fundamental questions in system biology and translational medicine. Each challenge asks participants to develop and apply computational methods to either predict unobserved outcomes or to identify unknown model parameters given a set of training data. Computational methods are evaluated using an automated scoring metric, scores are posted to a public leaderboard, and methods are published to facilitate community discussions on how to build improved methods. By engaging participants from a wide range of science and engineering backgrounds, DREAM challenges can comparatively evaluate a wide range of statistical, machine learning, and biophysical methods. Here, we describe DREAMTools, a Python package for evaluating DREAM challenge scoring metrics. DREAMTools provides a command line interface that enables researchers to test new methods on past challenges, as well as a framework for scoring new challenges. As of March 2016, DREAMTools includes more than 80% of completed DREAM challenges. DREAMTools complements the data, metadata, and software tools available at the DREAM website http://dreamchallenges.org and on the Synapse platform at https://www.synapse.org. Availability:  DREAMTools is a Python package. Releases and documentation are available at http://pypi.python.org/pypi/dreamtools. The source code is available at http://github.com/dreamtools/dreamtools. PMID:27134723

  7. BioC implementations in Go, Perl, Python and Ruby.

    PubMed

    Liu, Wanli; Islamaj Doğan, Rezarta; Kwon, Dongseop; Marques, Hernani; Rinaldi, Fabio; Wilbur, W John; Comeau, Donald C

    2014-01-01

    As part of a communitywide effort for evaluating text mining and information extraction systems applied to the biomedical domain, BioC is focused on the goal of interoperability, currently a major barrier to wide-scale adoption of text mining tools. BioC is a simple XML format, specified by DTD, for exchanging data for biomedical natural language processing. With initial implementations in C++ and Java, BioC provides libraries of code for reading and writing BioC text documents and annotations. We extend BioC to Perl, Python, Go and Ruby. We used SWIG to extend the C++ implementation for Perl and one Python implementation. A second Python implementation and the Ruby implementation use native data structures and libraries. BioC is also implemented in the Google language Go. BioC modules are functional in all of these languages, which can facilitate text mining tasks. BioC implementations are freely available through the BioC site: http://bioc.sourceforge.net. Database URL: http://bioc.sourceforge.net/ PMID:24961236

  8. Parallel astronomical data processing with Python: Recipes for multicore machines

    NASA Astrophysics Data System (ADS)

    Singh, Navtej; Browne, Lisa-Marie; Butler, Ray

    2013-08-01

    High performance computing has been used in various fields of astrophysical research. But most of it is implemented on massively parallel systems (supercomputers) or graphical processing unit clusters. With the advent of multicore processors in the last decade, many serial software codes have been re-implemented in parallel mode to utilize the full potential of these processors. In this paper, we propose parallel processing recipes for multicore machines for astronomical data processing. The target audience is astronomers who use Python as their preferred scripting language and who may be using PyRAF/IRAF for data processing. Three problems of varied complexity were benchmarked on three different types of multicore processors to demonstrate the benefits, in terms of execution time, of parallelizing data processing tasks. The native multiprocessing module available in Python makes it a relatively trivial task to implement the parallel code. We have also compared the three multiprocessing approaches-Pool/Map, Process/Queue and Parallel Python. Our test codes are freely available and can be downloaded from our website.

  9. ACPYPE - AnteChamber PYthon Parser interfacE

    PubMed Central

    2012-01-01

    Background ACPYPE (or AnteChamber PYthon Parser interfacE) is a wrapper script around the ANTECHAMBER software that simplifies the generation of small molecule topologies and parameters for a variety of molecular dynamics programmes like GROMACS, CHARMM and CNS. It is written in the Python programming language and was developed as a tool for interfacing with other Python based applications such as the CCPN software suite (for NMR data analysis) and ARIA (for structure calculations from NMR data). ACPYPE is open source code, under GNU GPL v3, and is available as a stand-alone application at http://www.ccpn.ac.uk/acpype and as a web portal application at http://webapps.ccpn.ac.uk/acpype. Findings We verified the topologies generated by ACPYPE in three ways: by comparing with default AMBER topologies for standard amino acids; by generating and verifying topologies for a large set of ligands from the PDB; and by recalculating the structures for 5 protein–ligand complexes from the PDB. Conclusions ACPYPE is a tool that simplifies the automatic generation of topology and parameters in different formats for different molecular mechanics programmes, including calculation of partial charges, while being object oriented for integration with other applications. PMID:22824207

  10. GAiN: Distributed Array Computation with Python

    SciTech Connect

    Daily, Jeffrey A.

    2009-05-01

    Scientific computing makes use of very large, multidimensional numerical arrays - typically, gigabytes to terabytes in size - much larger than can fit on even the largest single compute node. Such arrays must be distributed across a "cluster" of nodes. Global Arrays is a cluster-based software system from Battelle Pacific Northwest National Laboratory that enables an efficient, portable, and parallel shared-memory programming interface to manipulate these arrays. Written in and for the C and FORTRAN programming languages, it takes advantage of high-performance cluster interconnections to allow any node in the cluster to access data on any other node very rapidly. The "numpy" module is the de facto standard for numerical calculation in the Python programming language, a language whose use is growing rapidly in the scientific and engineering communities. numpy provides a powerful N-dimensional array class as well as other scientific computing capabilities. However, like the majority of the core Python modules, numpy is inherently serial. Our system, GAiN (Global Arrays in NumPy), is a parallel extension to Python that accesses Global Arrays through numpy. This allows parallel processing and/or larger problem sizes to be harnessed almost transparently within new or existing numpy programs.

  11. BioC implementations in Go, Perl, Python and Ruby

    PubMed Central

    Liu, Wanli; Islamaj Doğan, Rezarta; Kwon, Dongseop; Marques, Hernani; Rinaldi, Fabio; Wilbur, W. John; Comeau, Donald C.

    2014-01-01

    As part of a communitywide effort for evaluating text mining and information extraction systems applied to the biomedical domain, BioC is focused on the goal of interoperability, currently a major barrier to wide-scale adoption of text mining tools. BioC is a simple XML format, specified by DTD, for exchanging data for biomedical natural language processing. With initial implementations in C++ and Java, BioC provides libraries of code for reading and writing BioC text documents and annotations. We extend BioC to Perl, Python, Go and Ruby. We used SWIG to extend the C++ implementation for Perl and one Python implementation. A second Python implementation and the Ruby implementation use native data structures and libraries. BioC is also implemented in the Google language Go. BioC modules are functional in all of these languages, which can facilitate text mining tasks. BioC implementations are freely available through the BioC site: http://bioc.sourceforge.net. Database URL: http://bioc.sourceforge.net/ PMID:24961236

  12. User-friendly parallelization of GAUDI applications with Python

    NASA Astrophysics Data System (ADS)

    Mato, Pere; Smith, Eoin

    2010-04-01

    GAUDI is a software framework in C++ used to build event data processing applications using a set of standard components with well-defined interfaces. Simulation, high-level trigger, reconstruction, and analysis programs used by several experiments are developed using GAUDI. These applications can be configured and driven by simple Python scripts. Given the fact that a considerable amount of existing software has been developed using serial methodology, and has existed in some cases for many years, implementation of parallelisation techniques at the framework level may offer a way of exploiting current multi-core technologies to maximize performance and reduce latencies without re-writing thousands/millions of lines of code. In the solution we have developed, the parallelization techniques are introduced to the high level Python scripts which configure and drive the applications, such that the core C++ application code requires no modification, and that end users need make only minimal changes to their scripts. The developed solution leverages from existing generic Python modules that support parallel processing. Naturally, the parallel version of a given program should produce results consistent with its serial execution. The evaluation of several prototypes incorporating various parallelization techniques are presented and discussed.

  13. Python for Information Theoretic Analysis of Neural Data

    PubMed Central

    Ince, Robin A. A.; Petersen, Rasmus S.; Swan, Daniel C.; Panzeri, Stefano

    2008-01-01

    Information theory, the mathematical theory of communication in the presence of noise, is playing an increasingly important role in modern quantitative neuroscience. It makes it possible to treat neural systems as stochastic communication channels and gain valuable, quantitative insights into their sensory coding function. These techniques provide results on how neurons encode stimuli in a way which is independent of any specific assumptions on which part of the neuronal response is signal and which is noise, and they can be usefully applied even to highly non-linear systems where traditional techniques fail. In this article, we describe our work and experiences using Python for information theoretic analysis. We outline some of the algorithmic, statistical and numerical challenges in the computation of information theoretic quantities from neural data. In particular, we consider the problems arising from limited sampling bias and from calculation of maximum entropy distributions in the presence of constraints representing the effects of different orders of interaction in the system. We explain how and why using Python has allowed us to significantly improve the speed and domain of applicability of the information theoretic algorithms, allowing analysis of data sets characterized by larger numbers of variables. We also discuss how our use of Python is facilitating integration with collaborative databases and centralised computational resources. PMID:19242557

  14. VPython: Python plus Animations in Stereo 3D

    NASA Astrophysics Data System (ADS)

    Sherwood, Bruce

    2004-03-01

    Python is a modern object-oriented programming language. VPython (http://vpython.org) is a combination of Python (http://python.org), the Numeric module from LLNL (http://www.pfdubois.com/numpy), and the Visual module created by David Scherer, all of which have been under continuous development as open source projects. VPython makes it easy to write programs that generate real-time, navigable 3D animations. The Visual module includes a set of 3D objects (sphere, cylinder, arrow, etc.), tools for creating other shapes, and support for vector algebra. The 3D renderer runs in a parallel thread, and animations are produced as a side effect of computations, freeing the programmer to concentrate on the physics. Applications include educational and research visualization. In the Fall of 2003 Hugh Fisher at the Australian National University, John Zelle at Wartburg College, and I contributed to a new stereo capability of VPython. By adding a single statement to an existing VPython program, animations can be viewed in true stereo 3D. One can choose several modes: active shutter glasses, passive polarized glasses, or colored glasses (e.g. red-cyan). The talk will demonstrate the new stereo capability and discuss the pros and cons of various schemes for display of stereo 3D for a large audience. Supported in part by NSF grant DUE-0237132.

  15. Marsh rabbit mortalities tie pythons to the precipitous decline of mammals in the Everglades

    PubMed Central

    McCleery, Robert A.; Sovie, Adia; Reed, Robert N.; Cunningham, Mark W.; Hunter, Margaret E.; Hart, Kristen M.

    2015-01-01

    To address the ongoing debate over the impact of invasive species on native terrestrial wildlife, we conducted a large-scale experiment to test the hypothesis that invasive Burmese pythons (Python molurus bivittatus) were a cause of the precipitous decline of mammals in Everglades National Park (ENP). Evidence linking pythons to mammal declines has been indirect and there are reasons to question whether pythons, or any predator, could have caused the precipitous declines seen across a range of mammalian functional groups. Experimentally manipulating marsh rabbits, we found that pythons accounted for 77% of rabbit mortalities within 11 months of their translocation to ENP and that python predation appeared to preclude the persistence of rabbit populations in ENP. On control sites, outside of the park, no rabbits were killed by pythons and 71% of attributable marsh rabbit mortalities were classified as mammal predations. Burmese pythons pose a serious threat to the faunal communities and ecological functioning of the Greater Everglades Ecosystem, which will probably spread as python populations expand their range. PMID:25788598

  16. Marsh rabbit mortalities tie pythons to the precipitous decline of mammals in the Everglades.

    PubMed

    McCleery, Robert A; Sovie, Adia; Reed, Robert N; Cunningham, Mark W; Hunter, Margaret E; Hart, Kristen M

    2015-04-22

    To address the ongoing debate over the impact of invasive species on native terrestrial wildlife, we conducted a large-scale experiment to test the hypothesis that invasive Burmese pythons (Python molurus bivittatus) were a cause of the precipitous decline of mammals in Everglades National Park (ENP). Evidence linking pythons to mammal declines has been indirect and there are reasons to question whether pythons, or any predator, could have caused the precipitous declines seen across a range of mammalian functional groups. Experimentally manipulating marsh rabbits, we found that pythons accounted for 77% of rabbit mortalities within 11 months of their translocation to ENP and that python predation appeared to preclude the persistence of rabbit populations in ENP. On control sites, outside of the park, no rabbits were killed by pythons and 71% of attributable marsh rabbit mortalities were classified as mammal predations. Burmese pythons pose a serious threat to the faunal communities and ecological functioning of the Greater Everglades Ecosystem, which will probably spread as python populations expand their range. PMID:25788598

  17. Marsh rabbit mortalities tie pythons to the precipitous decline of mammals in the Everglades

    USGS Publications Warehouse

    McCleery, Robert A.; Sovie, Adia; Reed, Robert N.; Cunningham, Mark W.; Hunter, Margaret E.; Hart, Kristen M.

    2015-01-01

    To address the ongoing debate over the impact of invasive species on native terrestrial wildlife, we conducted a large-scale experiment to test the hypothesis that invasive Burmese pythons (Python molurus bivittatus) were a cause of the precipitous decline of mammals in Everglades National Park (ENP). Evidence linking pythons to mammal declines has been indirect and there are reasons to question whether pythons, or any predator, could have caused the precipitous declines seen across a range of mammalian functional groups. Experimentally manipulating marsh rabbits, we found that pythons accounted for 77% of rabbit mortalities within 11 months of their translocation to ENP and that python predation appeared to preclude the persistence of rabbit populations in ENP. On control sites, outside of the park, no rabbits were killed by pythons and 71% of attributable marsh rabbit mortalities were classified as mammal predations. Burmese pythons pose a serious threat to the faunal communities and ecological functioning of the Greater Everglades Ecosystem, which will probably spread as python populations expand their range.

  18. Amateur Image Pipeline Processing using Python plus PyRAF

    NASA Astrophysics Data System (ADS)

    Green, Wayne

    2012-05-01

    A template pipeline spanning observing planning to publishing is offered as a basis for establishing a long term observing program. The data reduction pipeline encapsulates all policy and procedures, providing an accountable framework for data analysis and a teaching framework for IRAF. This paper introduces the technical details of a complete pipeline processing environment using Python, PyRAF and a few other languages. The pipeline encapsulates all processing decisions within an auditable framework. The framework quickly handles the heavy lifting of image processing. It also serves as an excellent teaching environment for astronomical data management and IRAF reduction decisions.

  19. An object oriented Python interface for atomistic simulations

    NASA Astrophysics Data System (ADS)

    Hynninen, T.; Himanen, L.; Parkkinen, V.; Musso, T.; Corander, J.; Foster, A. S.

    2016-01-01

    Programmable simulation environments allow one to monitor and control calculations efficiently and automatically before, during, and after runtime. Environments directly accessible in a programming environment can be interfaced with powerful external analysis tools and extensions to enhance the functionality of the core program, and by incorporating a flexible object based structure, the environments make building and analysing computational setups intuitive. In this work, we present a classical atomistic force field with an interface written in Python language. The program is an extension for an existing object based atomistic simulation environment.

  20. A Python-based IRAF Task Parameter Editor

    NASA Astrophysics Data System (ADS)

    de La Peña, M. D.

    As part of the development of a new Python-based CL for IRAF tasks by the Science Software Group at STScI, we have developed a GUI-based parameter editor for IRAF tasks using Tkinter. This new parameter editor is intended to provide the equivalent functionality of the IRAF EPAR task, but to make parameter editing easier by using appropriate user interface elements, such as menu choice lists, action buttons, and file browsers. This paper describes the design and functionality of the parameter editor as well as planned enhancements.

  1. The fast azimuthal integration Python library: pyFAI

    PubMed Central

    Ashiotis, Giannis; Deschildre, Aurore; Nawaz, Zubair; Wright, Jonathan P.; Karkoulis, Dimitrios; Picca, Frédéric Emmanuel; Kieffer, Jérôme

    2015-01-01

    pyFAI is an open-source software package designed to perform azimuthal integration and, correspondingly, two-dimensional regrouping on area-detector frames for small- and wide-angle X-ray scattering experiments. It is written in Python (with binary submodules for improved performance), a language widely accepted and used by the scientific community today, which enables users to easily incorporate the pyFAI library into their processing pipeline. This article focuses on recent work, especially the ease of calibration, its accuracy and the execution speed for integration.1 PMID:25844080

  2. LASER STRIPPING COMPUTING WITH THE PYTHON ORBIT CODE

    SciTech Connect

    Gorlov, Timofey V; Shishlo, Andrei P

    2009-01-01

    The laser assisted hydrogen stripping becomes a widely discussed alternative for the existing stripping foil approach. The simulation tool for this new approach is presented. The created application is implemented in form of extension module to the Python ORBIT parallel code that is under development at the SNS. The physical model of the application deals with quantum theory and allows calculating evolution and ionization of hydrogen atoms and ions affected by superposition of electromagnetic and laser fields. The algorithm, structure, benchmark cases, and results of simulations for several future and existing accelerators are discussed.

  3. Python GST Implementation v. 0.9 beta

    Energy Science and Technology Software Center (ESTSC)

    2015-12-18

    PyGSTi is an implementation of Gate Set Tomography in the python programming language. Gate Set Tomography (GST) is a theory and protocol for simultaneously estimating the state preparation, gate operations, and measurement effects of a physical system of one or many quantum bits (qubits). These estimates are based entirely on the statistics of experimental measurements, and their interpretation and analysis can provide a detailed understanding of the types of errors/imperfections in the physical system. Inmore » this way, GST provides not only a means of certifying the "goodness" of qubits but also a means of debugging (i.e. improving) them.« less

  4. Python GST Implementation v. 0.9 beta

    SciTech Connect

    Nielsen, Erik; Blume-Kohout, Robin; Rudinger, Kenneth; Gamble, John

    2015-12-18

    PyGSTi is an implementation of Gate Set Tomography in the python programming language. Gate Set Tomography (GST) is a theory and protocol for simultaneously estimating the state preparation, gate operations, and measurement effects of a physical system of one or many quantum bits (qubits). These estimates are based entirely on the statistics of experimental measurements, and their interpretation and analysis can provide a detailed understanding of the types of errors/imperfections in the physical system. In this way, GST provides not only a means of certifying the "goodness" of qubits but also a means of debugging (i.e. improving) them.

  5. PyVO: Python access to the Virtual Observatory

    NASA Astrophysics Data System (ADS)

    Graham, Matthew; Plante, Ray; Tody, Doug; Fitzpatrick, Mike

    2014-02-01

    PyVO provides access to remote data and services of the Virtual observatory (VO) using Python. It allows archive searches for data of a particular type or related to a particular topic and query submissions to obtain data to a particular archive to download selected data products. PyVO supports querying the VAO registry; simple data access services (DAL) to access images (SIA), source catalog records (Cone Search), spectra (SSA), and spectral line emission/absorption data (SLAP); and object name resolution (for converting names of objects in the sky into positions). PyVO requires both AstroPy and NumPy.

  6. Spherical Panorama Visualization of Astronomical Data with Blender and Python

    NASA Astrophysics Data System (ADS)

    Kent, Brian R.

    2016-06-01

    We describe methodology to generate 360 degree spherical panoramas of both 2D and 3D data. The techniques apply to a variety of astronomical data types - all sky maps, 2D and 3D catalogs as well as planetary surface maps. The results can be viewed in a desktop browser or interactively with a mobile phone or tablet. Static displays or panoramic video renderings of the data can be produced. We review the Python code and usage of the 3D Blender software for projecting maps onto 3D surfaces and the various tools for distributing visualizations.

  7. Adaptive responses to feeding in Burmese pythons: pay before pumping.

    PubMed

    Secor, S M; Diamond, J

    1995-06-01

    Burmese pythons normally consume large meals after long intervals. We measured gut contents, O2 consumption rates, small intestinal brush-border uptake rates of amino acids and glucose, organ masses and blood chemistry in pythons during the 30 days following ingestion of meals equivalent to 25% of their body mass. Within 1-3 days after ingestion, O2 consumption rates, intestinal nutrient uptake rates and uptake capacities peaked at 17, 6-26 and 11-24 times fasting levels, respectively. Small intestinal mass doubled, and other organs also increased in mass. Changes in blood chemistry included a 78% decline in PO2 and a large 'alkaline tide' associated with gastric acid section (i.e. a rise in blood pH and HCO3- concentrations and a fall in Cl- concentration). All of these values returned to fasting levels by the time of defecation at 8-14 days. The response of O2 consumption (referred to as specific dynamic action, SDA) is the largest, and the upregulation of intestinal nutrient transporters the second largest, response reported for any vertebrate upon feeding. The SDA is a large as the factorial rise in O2 consumption measured in mammalian sprinters and is sustained for much longer. The extra energy expended for digestion is equivalent to 32% of the meal's energy yield, with much of it being measured before the prey energy was absorbed. PMID:7782719

  8. New Python-based methods for data processing.

    PubMed

    Sauter, Nicholas K; Hattne, Johan; Grosse-Kunstleve, Ralf W; Echols, Nathaniel

    2013-07-01

    Current pixel-array detectors produce diffraction images at extreme data rates (of up to 2 TB h(-1)) that make severe demands on computational resources. New multiprocessing frameworks are required to achieve rapid data analysis, as it is important to be able to inspect the data quickly in order to guide the experiment in real time. By utilizing readily available web-serving tools that interact with the Python scripting language, it was possible to implement a high-throughput Bragg-spot analyzer (cctbx.spotfinder) that is presently in use at numerous synchrotron-radiation beamlines. Similarly, Python interoperability enabled the production of a new data-reduction package (cctbx.xfel) for serial femtosecond crystallography experiments at the Linac Coherent Light Source (LCLS). Future data-reduction efforts will need to focus on specialized problems such as the treatment of diffraction spots on interleaved lattices arising from multi-crystal specimens. In these challenging cases, accurate modeling of close-lying Bragg spots could benefit from the high-performance computing capabilities of graphics-processing units. PMID:23793153

  9. New Python-based methods for data processing

    PubMed Central

    Sauter, Nicholas K.; Hattne, Johan; Grosse-Kunstleve, Ralf W.; Echols, Nathaniel

    2013-01-01

    Current pixel-array detectors produce diffraction images at extreme data rates (of up to 2 TB h−1) that make severe demands on computational resources. New multiprocessing frameworks are required to achieve rapid data analysis, as it is important to be able to inspect the data quickly in order to guide the experiment in real time. By utilizing readily available web-serving tools that interact with the Python scripting language, it was possible to implement a high-throughput Bragg-spot analyzer (cctbx.spotfinder) that is presently in use at numerous synchrotron-radiation beamlines. Similarly, Python interoperability enabled the production of a new data-reduction package (cctbx.xfel) for serial femto­second crystallography experiments at the Linac Coherent Light Source (LCLS). Future data-reduction efforts will need to focus on specialized problems such as the treatment of diffraction spots on interleaved lattices arising from multi-crystal specimens. In these challenging cases, accurate modeling of close-lying Bragg spots could benefit from the high-performance computing capabilities of graphics-processing units. PMID:23793153

  10. A Distributed Python HPC Framework: ODIN, PyTrilinos, & Seamless

    SciTech Connect

    Grant, Robert

    2015-11-23

    Under this grant, three significant software packages were developed or improved, all with the goal of improving the ease-of-use of HPC libraries. The first component is a Python package, named DistArray (originally named Odin), that provides a high-level interface to distributed array computing. This interface is based on the popular and widely used NumPy package and is integrated with the IPython project for enhanced interactive parallel distributed computing. The second Python package is the Distributed Array Protocol (DAP) that enables separate distributed array libraries to share arrays efficiently without copying or sending messages. If a distributed array library supports the DAP, it is then automatically able to communicate with any other library that also supports the protocol. This protocol allows DistArray to communicate with the Trilinos library via PyTrilinos, which was also enhanced during this project. A third package, PyTrilinos, was extended to support distributed structured arrays (in addition to the unstructured arrays of its original design), allow more flexible distributed arrays (i.e., the restriction to double precision data was lifted), and implement the DAP. DAP support includes both exporting the protocol so that external packages can use distributed Trilinos data structures, and importing the protocol so that PyTrilinos can work with distributed data from external packages.

  11. Novel divergent nidovirus in a python with pneumonia.

    PubMed

    Bodewes, Rogier; Lempp, Charlotte; Schürch, Anita C; Habierski, Andre; Hahn, Kerstin; Lamers, Mart; von Dörnberg, Katja; Wohlsein, Peter; Drexler, Jan Felix; Haagmans, Bart L; Smits, Saskia L; Baumgärtner, Wolfgang; Osterhaus, Albert D M E

    2014-11-01

    The order Nidovirales contains large, enveloped viruses with a non-segmented positive-stranded RNA genome. Nidoviruses have been detected in man and various animal species, but, to date, there have been no reports of nidovirus in reptiles. In the present study, we describe the detection, characterization, phylogenetic analyses and disease association of a novel divergent nidovirus in the lung of an Indian python (Python molurus) with necrotizing pneumonia. Characterization of the partial genome (>33 000 nt) of this virus revealed several genetic features that are distinct from other nidoviruses, including a very large polyprotein 1a, a putative ribosomal frameshift signal that was identical to the frameshift signal of astroviruses and retroviruses and an accessory ORF that showed some similarity with the haemagglutinin-neuraminidase of paramyxoviruses. Analysis of genome organization and phylogenetic analysis of polyprotein 1ab suggests that this virus belongs to the subfamily Torovirinae. Results of this study provide novel insights into the genetic diversity within the order Nidovirales. PMID:25063552

  12. New Python-based methods for data processing

    SciTech Connect

    Sauter, Nicholas K. Hattne, Johan; Grosse-Kunstleve, Ralf W.; Echols, Nathaniel

    2013-07-01

    The Computational Crystallography Toolbox (cctbx) is a flexible software platform that has been used to develop high-throughput crystal-screening tools for both synchrotron sources and X-ray free-electron lasers. Plans for data-processing and visualization applications are discussed, and the benefits and limitations of using graphics-processing units are evaluated. Current pixel-array detectors produce diffraction images at extreme data rates (of up to 2 TB h{sup −1}) that make severe demands on computational resources. New multiprocessing frameworks are required to achieve rapid data analysis, as it is important to be able to inspect the data quickly in order to guide the experiment in real time. By utilizing readily available web-serving tools that interact with the Python scripting language, it was possible to implement a high-throughput Bragg-spot analyzer (cctbx.spotfinder) that is presently in use at numerous synchrotron-radiation beamlines. Similarly, Python interoperability enabled the production of a new data-reduction package (cctbx.xfel) for serial femtosecond crystallography experiments at the Linac Coherent Light Source (LCLS). Future data-reduction efforts will need to focus on specialized problems such as the treatment of diffraction spots on interleaved lattices arising from multi-crystal specimens. In these challenging cases, accurate modeling of close-lying Bragg spots could benefit from the high-performance computing capabilities of graphics-processing units.

  13. Conservative constraints on early cosmology with MONTE PYTHON

    SciTech Connect

    Audren, Benjamin; Lesgourgues, Julien; Benabed, Karim; Prunet, Simon E-mail: Julien.Lesgourgues@cern.ch E-mail: prunet@iap.fr

    2013-02-01

    Models for the latest stages of the cosmological evolution rely on a less solid theoretical and observational ground than the description of earlier stages like BBN and recombination. As suggested in a previous work by Vonlanthen et al., it is possible to tweak the analysis of CMB data in such way to avoid making assumptions on the late evolution, and obtain robust constraints on ''early cosmology parameters''. We extend this method in order to marginalise the results over CMB lensing contamination, and present updated results based on recent CMB data. Our constraints on the minimal early cosmology model are weaker than in a standard ΛCDM analysis, but do not conflict with this model. Besides, we obtain conservative bounds on the effective neutrino number and neutrino mass, showing no hints for extra relativistic degrees of freedom, and proving in a robust way that neutrinos experienced their non-relativistic transition after the time of photon decoupling. This analysis is also an occasion to describe the main features of the new parameter inference code MONTE PYTHON, that we release together with this paper. MONTE PYTHON is a user-friendly alternative to other public codes like COSMOMC, interfaced with the Boltzmann code CLASS.

  14. Programming biological models in Python using PySB

    PubMed Central

    Lopez, Carlos F; Muhlich, Jeremy L; Bachman, John A; Sorger, Peter K

    2013-01-01

    Mathematical equations are fundamental to modeling biological networks, but as networks get large and revisions frequent, it becomes difficult to manage equations directly or to combine previously developed models. Multiple simultaneous efforts to create graphical standards, rule-based languages, and integrated software workbenches aim to simplify biological modeling but none fully meets the need for transparent, extensible, and reusable models. In this paper we describe PySB, an approach in which models are not only created using programs, they are programs. PySB draws on programmatic modeling concepts from little b and ProMot, the rule-based languages BioNetGen and Kappa and the growing library of Python numerical tools. Central to PySB is a library of macros encoding familiar biochemical actions such as binding, catalysis, and polymerization, making it possible to use a high-level, action-oriented vocabulary to construct detailed models. As Python programs, PySB models leverage tools and practices from the open-source software community, substantially advancing our ability to distribute and manage the work of testing biochemical hypotheses. We illustrate these ideas using new and previously published models of apoptosis. PMID:23423320

  15. precession: Dynamics of spinning black-hole binaries with python

    NASA Astrophysics Data System (ADS)

    Gerosa, Davide; Kesden, Michael

    2016-06-01

    We present the numerical code precession, a new open-source python module to study the dynamics of precessing black-hole binaries in the post-Newtonian regime. The code provides a comprehensive toolbox to (i) study the evolution of the black-hole spins along their precession cycles, (ii) perform gravitational-wave-driven binary inspirals using both orbit-averaged and precession-averaged integrations, and (iii) predict the properties of the merger remnant through fitting formulas obtained from numerical-relativity simulations. precession is a ready-to-use tool to add the black-hole spin dynamics to larger-scale numerical studies such as gravitational-wave parameter estimation codes, population synthesis models to predict gravitational-wave event rates, galaxy merger trees and cosmological simulations of structure formation. precession provides fast and reliable integration methods to propagate statistical samples of black-hole binaries from/to large separations where they form to/from small separations where they become detectable, thus linking gravitational-wave observations of spinning black-hole binaries to their astrophysical formation history. The code is also a useful tool to compute initial parameters for numerical-relativity simulations targeting specific precessing systems. precession can be installed from the python Package Index, and it is freely distributed under version control on github, where further documentation is provided.

  16. Hardware-accelerated interactive data visualization for neuroscience in Python

    PubMed Central

    Rossant, Cyrille; Harris, Kenneth D.

    2013-01-01

    Large datasets are becoming more and more common in science, particularly in neuroscience where experimental techniques are rapidly evolving. Obtaining interpretable results from raw data can sometimes be done automatically; however, there are numerous situations where there is a need, at all processing stages, to visualize the data in an interactive way. This enables the scientist to gain intuition, discover unexpected patterns, and find guidance about subsequent analysis steps. Existing visualization tools mostly focus on static publication-quality figures and do not support interactive visualization of large datasets. While working on Python software for visualization of neurophysiological data, we developed techniques to leverage the computational power of modern graphics cards for high-performance interactive data visualization. We were able to achieve very high performance despite the interpreted and dynamic nature of Python, by using state-of-the-art, fast libraries such as NumPy, PyOpenGL, and PyTables. We present applications of these methods to visualization of neurophysiological data. We believe our tools will be useful in a broad range of domains, in neuroscience and beyond, where there is an increasing need for scalable and fast interactive visualization. PMID:24391582

  17. Installing python software packages : the good, the bad and the ugly.

    SciTech Connect

    Hart, William Eugene

    2010-11-01

    These slides describe different strategies for installing Python software. Although I am a big fan of Python software development, robust strategies for software installation remains a challenge. This talk describes several different installation scenarios. The Good: the user has administrative privileges - Installing on Windows with an installer executable, Installing with Linux application utility, Installing a Python package from the PyPI repository, and Installing a Python package from source. The Bad: the user does not have administrative privileges - Using a virtual environment to isolate package installations, and Using an installer executable on Windows with a virtual environment. The Ugly: the user needs to install an extension package from source - Installing a Python extension package from source, and PyCoinInstall - Managing builds for Python extension packages. The last item referring to PyCoinInstall describes a utility being developed for the COIN-OR software, which is used within the operations research community. COIN-OR includes a variety of Python and C++ software packages, and this script uses a simple plug-in system to support the management of package builds and installation.

  18. An intuitive Python interface for Bioconductor libraries demonstrates the utility of language translators

    PubMed Central

    2010-01-01

    Background Computer languages can be domain-related, and in the case of multidisciplinary projects, knowledge of several languages will be needed in order to quickly implements ideas. Moreover, each computer language has relative strong points, making some languages better suited than others for a given task to be implemented. The Bioconductor project, based on the R language, has become a reference for the numerical processing and statistical analysis of data coming from high-throughput biological assays, providing a rich selection of methods and algorithms to the research community. At the same time, Python has matured as a rich and reliable language for the agile development of prototypes or final implementations, as well as for handling large data sets. Results The data structures and functions from Bioconductor can be exposed to Python as a regular library. This allows a fully transparent and native use of Bioconductor from Python, without one having to know the R language and with only a small community of translators required to know both. To demonstrate this, we have implemented such Python representations for key infrastructure packages in Bioconductor, letting a Python programmer handle annotation data, microarray data, and next-generation sequencing data. Conclusions Bioconductor is now not solely reserved to R users. Building a Python application using Bioconductor functionality can be done just like if Bioconductor was a Python package. Moreover, similar principles can be applied to other languages and libraries. Our Python package is available at: http://pypi.python.org/pypi/rpy2-bioconductor-extensions/ PMID:21210978

  19. Multiple papillomas in a diamond python, Morelia spilota spilota.

    PubMed

    Gull, Jessica M; Lange, Christian E; Favrot, Claude; Dorrestein, Gerry M; Hatt, Jean-Michel

    2012-12-01

    A 4-yr-old male diamond python (Morelia spilota spilota) was evaluated for multiple black papillated exophytic skin proliferations and signs of pneumonia. The histopathologic structure of the skin biopsy specimens led to the diagnosis of a benign papilloma-like neoplasia. In this case, papillomavirus DNA could be amplified from a biopsy sample with a broad range polymerase chain reaction. Nested pan-herpes polymerase chain reaction was negative, and herpesvirus inclusion bodies were not found. Because of the histologically benign nature of the papilloma, the skin proliferations were left untreated. Ten mo after the first presentation, the skin lesions had regressed almost completely; 34 mo later, only scars from the biopsies were left. PMID:23272369

  20. PyMidas--A Python Interface to ESO-MIDAS

    NASA Astrophysics Data System (ADS)

    Hook, R. N.; Maisala, S.; Oittinen, T.; Ullgren, M.; Vasko, K.; Savolainen, V.; Lindroos, J.; Anttila, M.; Solin, O.; Møller, P. M.; Banse, K.; Peron, M.

    2006-07-01

    Finland joined the European Southern Observatory in 2004, providing a contribution in kind of software expertise as part of its joining fee. This significant resource, called the Sampo project, will be devoted to exploring the options for the future of data reduction and analysis in an ESO context, to understanding user requirements and to performing a series of major pilot projects to investigate different technologies, approaches and architectures. The Sampo project {http://www.eso.org/sampo} will run for three years and aims to prepare the ESO community for the data analysis and reduction challenges of the next decades. The first major Sampo project is PyMidas, an interface from Python to the ESO-MIDAS data analysis and reduction system. This paper describes the motivation for this project, how it has been implemented and gives some examples of PyMidas in action.

  1. Python package for model STructure ANalysis (pySTAN)

    NASA Astrophysics Data System (ADS)

    Van Hoey, Stijn; van der Kwast, Johannes; Nopens, Ingmar; Seuntjens, Piet

    2013-04-01

    methods on a fair basis. We developed and present pySTAN (python framework for STructure Analysis), a python package containing a set of functions for model structure evaluation to provide the analysis of (hydrological) model structures. A selected set of algorithms for optimization, uncertainty and sensitivity analysis is currently available, together with a set of evaluation (objective) functions and input distributions to sample from. The methods are implemented model-independent and the python language provides the wrapper functions to apply administer external model codes. Different objective functions can be considered simultaneously with both statistical metrics and more hydrology specific metrics. By using so-called reStructuredText (sphinx documentation generator) and Python documentation strings (docstrings), the generation of manual pages is semi-automated and a specific environment is available to enhance both the readability and transparency of the code. It thereby enables a larger group of users to apply and compare these methods and to extend the functionalities.

  2. PYTRANSIT: fast and easy exoplanet transit modelling in PYTHON

    NASA Astrophysics Data System (ADS)

    Parviainen, Hannu

    2015-07-01

    We present a fast and user friendly exoplanet transit light-curve modelling package PYTRANSIT, implementing optimized versions of the Giménez and Mandel & Agol transit models. The package offers an object-oriented PYTHON interface to access the two models implemented natively in FORTRAN with OpenMP parallelization. A partial OpenCL version of the quadratic Mandel-Agol model is also included for GPU-accelerated computations. The aim of PYTRANSIT is to facilitate the analysis of photometric time series of exoplanet transits consisting of hundreds of thousands of data points, and of multipassband transit light curves from spectrophotometric observations, as a part of a researcher's programming toolkit for building complex, problem-specific analyses.

  3. PyGSM: Python interface to the Global Sky Model

    NASA Astrophysics Data System (ADS)

    Price, Danny C.

    2016-03-01

    PyGSM is a Python interface for the Global Sky Model (GSM, ascl:1011.010). The GSM is a model of diffuse galactic radio emission, constructed from a variety of all-sky surveys spanning the radio band (e.g. Haslam and WMAP). PyGSM uses the GSM to generate all-sky maps in Healpix format of diffuse Galactic radio emission from 10 MHz to 94 GHz. The PyGSM module provides visualization utilities, file output in FITS format, and the ability to generate observed skies for a given location and date. PyGSM requires Healpy, PyEphem (ascl:1112.014), and AstroPy (ascl:1304.002).

  4. pyIAST: Ideal adsorbed solution theory (IAST) Python package

    NASA Astrophysics Data System (ADS)

    Simon, Cory M.; Smit, Berend; Haranczyk, Maciej

    2016-03-01

    Ideal adsorbed solution theory (IAST) is a widely-used thermodynamic framework to readily predict mixed-gas adsorption isotherms from a set of pure-component adsorption isotherms. We present an open-source, user-friendly Python package, pyIAST, to perform IAST calculations for an arbitrary number of components. pyIAST supports several common analytical models to characterize the pure-component isotherms from experimental or simulated data. Alternatively, pyIAST can use numerical quadrature to compute the spreading pressure for IAST calculations by interpolating the pure-component isotherm data. pyIAST can also perform reverse IAST calculations, where one seeks the required gas phase composition to yield a desired adsorbed phase composition.

  5. PyRAT (python radiography analysis tool): overview

    SciTech Connect

    Armstrong, Jerawan C; Temple, Brian A; Buescher, Kevin L

    2011-01-14

    PyRAT was developed as a quantitative tool for robustly characterizing objects from radiographs to solve problems such as the hybrid nonlinear inverse problem. The optimization software library that was used is the nonsmooth optimization by MADS algorithm (NOMAD). Some of PyRAT's features are: (1) hybrid nonlinear inverse problem with calculated x-ray spectrum and detector response; (2) optimization based inversion approach with goal of identifying unknown object configurations - MVO problem; (3) using functionalities of Python libraries for radiographic image processing and analysis; (4) using the Tikhonov regularization method of linear inverse problem to recover partial information of object configurations; (5) using a priori knowledge of problem solutions to define feasible region and discrete neighbor for the MVO problem - initial data analysis + material library {yields} a priori knowledge; and (6) using the NOMAD (C++ version) software in the object.

  6. PyORBIT: A Python Shell For ORBIT

    SciTech Connect

    Jean-Francois Ostiguy; Jeffrey Holmes

    2003-07-01

    ORBIT is code developed at SNS to simulate beam dynamics in accumulation rings and synchrotrons. The code is structured as a collection of external C++ modules for SuperCode, a high level interpreter shell developed at LLNL in the early 1990s. SuperCode is no longer actively supported and there has for some time been interest in replacing it by a modern scripting language, while preserving the feel of the original ORBIT program. In this paper, we describe a new version of ORBIT where the role of SuperCode is assumed by Python, a free, well-documented and widely supported object-oriented scripting language. We also compare PyORBIT to ORBIT from the standpoint of features, performance and future expandability.

  7. Intraspecific scaling of arterial blood pressure in the Burmese python.

    PubMed

    Enok, Sanne; Slay, Christopher; Abe, Augusto S; Hicks, James W; Wang, Tobias

    2014-07-01

    Interspecific allometric analyses indicate that mean arterial blood pressure (MAP) increases with body mass of snakes and mammals. In snakes, MAP increases in proportion to the increased distance between the heart and the head, when the heart-head vertical distance is expressed as ρgh (where ρ is the density of blood, G: is acceleration due to gravity and h is the vertical distance above the heart), and the rise in MAP is associated with a larger heart to normalize wall stress in the ventricular wall. Based on measurements of MAP in Burmese pythons ranging from 0.9 to 3.7 m in length (0.20-27 kg), we demonstrate that although MAP increases with body mass, the rise in MAP is merely half of that predicted by heart-head distance. Scaling relationships within individual species, therefore, may not be accurately predicted by existing interspecific analyses. PMID:24737752

  8. A cross-validation package driving Netica with python

    USGS Publications Warehouse

    Fienen, Michael N.; Plant, Nathaniel G.

    2014-01-01

    Bayesian networks (BNs) are powerful tools for probabilistically simulating natural systems and emulating process models. Cross validation is a technique to avoid overfitting resulting from overly complex BNs. Overfitting reduces predictive skill. Cross-validation for BNs is known but rarely implemented due partly to a lack of software tools designed to work with available BN packages. CVNetica is open-source, written in Python, and extends the Netica software package to perform cross-validation and read, rebuild, and learn BNs from data. Insights gained from cross-validation and implications on prediction versus description are illustrated with: a data-driven oceanographic application; and a model-emulation application. These examples show that overfitting occurs when BNs become more complex than allowed by supporting data and overfitting incurs computational costs as well as causing a reduction in prediction skill. CVNetica evaluates overfitting using several complexity metrics (we used level of discretization) and its impact on performance metrics (we used skill).

  9. NIFTY: A versatile Python library for signal inference

    NASA Astrophysics Data System (ADS)

    Selig, Marco; Bell, Michael R.; Junklewitz, Henrik; Oppermann, Niels; Reinecke, Martin; Greiner, Maksim; Pachajoa, Carlos; Enßlin, Torsten A.

    2013-02-01

    NIFTY (Numerical Information Field TheorY) is a versatile library enables the development of signal inference algorithms that operate regardless of the underlying spatial grid and its resolution. Its object-oriented framework is written in Python, although it accesses libraries written in Cython, C++, and C for efficiency. NIFTY offers a toolkit that abstracts discretized representations of continuous spaces, fields in these spaces, and operators acting on fields into classes. Thereby, the correct normalization of operations on fields is taken care of automatically. This allows for an abstract formulation and programming of inference algorithms, including those derived within information field theory. Thus, NIFTY permits rapid prototyping of algorithms in 1D and then the application of the developed code in higher-dimensional settings of real world problems. NIFTY operates on point sets, n-dimensional regular grids, spherical spaces, their harmonic counterparts, and product spaces constructed as combinations of those.

  10. OPUS: A CORBA Pipeline for Java, Python, and Perl Applications

    NASA Astrophysics Data System (ADS)

    Miller, W. W., III; Sontag, C.; Rose, J. F.

    With the introduction of the OPUS CORBA mode, a limited subset of OPUS Applications Programming Interface (OAPI) functionality was cast into CORBA IDL so that both OPUS applications and the Java-based OPUS pipeline managers were able to use the same CORBA infrastructure to access information on blackboards. Exposing even more of the OAPI through CORBA interfaces benefits OPUS applications in similar ways. Those applications not developed in C++ could use CORBA to interact with OPUS facilities directly, providing that a CORBA binding exists for the programming language of choice. Other applications might benefit from running `outside' of the traditional file system-based OPUS environment like the Java managers and, in particular, on platforms not supported by OPUS. The enhancements to OPUS discussed in this paper have been exercised in both Java and Python, and the code for these examples are available on the web.

  11. Python interface generator for Fortran based codes (a code development aid)

    SciTech Connect

    Grote, D. P.

    2012-02-22

    Forthon generates links between Fortran and Python. Python is a high level, object oriented, interactive and scripting language that allows a flexible and versatile interface to computational tools. The Forthon package generates the necessary wrapping code which allows access to the Fortran database and to the Fortran subroutines and functions. This provides a development package where the computationally intensive parts of a code can be written in efficient Fortran, and the high level controlling code can be written in the much more versatile Python language.

  12. Python interface generator for Fortran based codes (a code development aid)

    Energy Science and Technology Software Center (ESTSC)

    2012-02-22

    Forthon generates links between Fortran and Python. Python is a high level, object oriented, interactive and scripting language that allows a flexible and versatile interface to computational tools. The Forthon package generates the necessary wrapping code which allows access to the Fortran database and to the Fortran subroutines and functions. This provides a development package where the computationally intensive parts of a code can be written in efficient Fortran, and the high level controlling codemore » can be written in the much more versatile Python language.« less

  13. Homing of invasive Burmese pythons in South Florida: evidence for map and compass senses in snakes

    USGS Publications Warehouse

    Pittman, Shannon E.; Hart, Kristen M.; Cherkiss, Michael S.; Snow, Ray W.; Fujisaki, Ikuko; Mazzotti, Frank J.; Dorcas, Michael E.

    2014-01-01

    Navigational ability is a critical component of an animal's spatial ecology and may influence the invasive potential of species. Burmese pythons (Python molurus bivittatus) are apex predators invasive to South Florida. We tracked the movements of 12 adult Burmese pythons in Everglades National Park, six of which were translocated 21–36 km from their capture locations. Translocated snakes oriented movement homeward relative to the capture location, and five of six snakes returned to within 5 km of the original capture location. Translocated snakes moved straighter and faster than control snakes and displayed movement path structure indicative of oriented movement. This study provides evidence that Burmese pythons have navigational map and compass senses and has implications for predictions of spatial spread and impacts as well as our understanding of reptile cognitive abilities.

  14. Homing of invasive Burmese pythons in South Florida: evidence for map and compass senses in snakes.

    PubMed

    Pittman, Shannon E; Hart, Kristen M; Cherkiss, Michael S; Snow, Ray W; Fujisaki, Ikuko; Smith, Brian J; Mazzotti, Frank J; Dorcas, Michael E

    2014-03-01

    Navigational ability is a critical component of an animal's spatial ecology and may influence the invasive potential of species. Burmese pythons (Python molurus bivittatus) are apex predators invasive to South Florida. We tracked the movements of 12 adult Burmese pythons in Everglades National Park, six of which were translocated 21-36 km from their capture locations. Translocated snakes oriented movement homeward relative to the capture location, and five of six snakes returned to within 5 km of the original capture location. Translocated snakes moved straighter and faster than control snakes and displayed movement path structure indicative of oriented movement. This study provides evidence that Burmese pythons have navigational map and compass senses and has implications for predictions of spatial spread and impacts as well as our understanding of reptile cognitive abilities. PMID:24647727

  15. Homing of invasive Burmese pythons in South Florida: evidence for map and compass senses in snakes

    PubMed Central

    Pittman, Shannon E.; Hart, Kristen M.; Cherkiss, Michael S.; Snow, Ray W.; Fujisaki, Ikuko; Smith, Brian J.; Mazzotti, Frank J.; Dorcas, Michael E.

    2014-01-01

    Navigational ability is a critical component of an animal's spatial ecology and may influence the invasive potential of species. Burmese pythons (Python molurus bivittatus) are apex predators invasive to South Florida. We tracked the movements of 12 adult Burmese pythons in Everglades National Park, six of which were translocated 21–36 km from their capture locations. Translocated snakes oriented movement homeward relative to the capture location, and five of six snakes returned to within 5 km of the original capture location. Translocated snakes moved straighter and faster than control snakes and displayed movement path structure indicative of oriented movement. This study provides evidence that Burmese pythons have navigational map and compass senses and has implications for predictions of spatial spread and impacts as well as our understanding of reptile cognitive abilities. PMID:24647727

  16. PyTrilinos: Recent Advances in the Python Interface to Trilinos

    SciTech Connect

    Spotz, William F.

    2012-01-01

    PyTrilinos is a set of Python interfaces to compiled Trilinos packages. This collection supports serial and parallel dense linear algebra, serial and parallel sparse linear algebra, direct and iterative linear solution techniques, algebraic and multilevel preconditioners, nonlinear solvers and continuation algorithms, eigensolvers and partitioning algorithms. Also included are a variety of related utility functions and classes, including distributed I/O, coloring algorithms and matrix generation. PyTrilinos vector objects are compatible with the popular NumPy Python package. As a Python front end to compiled libraries, PyTrilinos takes advantage of the flexibility and ease of use of Python, and the efficiency of the underlying C++, C and Fortran numerical kernels. This paper covers recent, previously unpublished advances in the PyTrilinos package.

  17. Environmental DNA (eDNA) sampling improves occurrence and detection estimates of invasive burmese pythons.

    PubMed

    Hunter, Margaret E; Oyler-McCance, Sara J; Dorazio, Robert M; Fike, Jennifer A; Smith, Brian J; Hunter, Charles T; Reed, Robert N; Hart, Kristen M

    2015-01-01

    Environmental DNA (eDNA) methods are used to detect DNA that is shed into the aquatic environment by cryptic or low density species. Applied in eDNA studies, occupancy models can be used to estimate occurrence and detection probabilities and thereby account for imperfect detection. However, occupancy terminology has been applied inconsistently in eDNA studies, and many have calculated occurrence probabilities while not considering the effects of imperfect detection. Low detection of invasive giant constrictors using visual surveys and traps has hampered the estimation of occupancy and detection estimates needed for population management in southern Florida, USA. Giant constrictor snakes pose a threat to native species and the ecological restoration of the Florida Everglades. To assist with detection, we developed species-specific eDNA assays using quantitative PCR (qPCR) for the Burmese python (Python molurus bivittatus), Northern African python (P. sebae), boa constrictor (Boa constrictor), and the green (Eunectes murinus) and yellow anaconda (E. notaeus). Burmese pythons, Northern African pythons, and boa constrictors are established and reproducing, while the green and yellow anaconda have the potential to become established. We validated the python and boa constrictor assays using laboratory trials and tested all species in 21 field locations distributed in eight southern Florida regions. Burmese python eDNA was detected in 37 of 63 field sampling events; however, the other species were not detected. Although eDNA was heterogeneously distributed in the environment, occupancy models were able to provide the first estimates of detection probabilities, which were greater than 91%. Burmese python eDNA was detected along the leading northern edge of the known population boundary. The development of informative detection tools and eDNA occupancy models can improve conservation efforts in southern Florida and support more extensive studies of invasive constrictors

  18. Sharma's Python Sign: A New Tubal Sign in Female Genital Tuberculosis

    PubMed Central

    Sharma, Jai Bhagwan

    2016-01-01

    Female genital tuberculosis (FGTB) is an important cause of infertility in developing countries. Various type of TB salpingitis can be endosalpingitis, exosalpingitis, interstitial TB salpingitis, and salpingitis isthmica nodosa. The fallopian tubes are thickened enlarged and tortuous. Unilateral or bilateral hydrosalpinx or pyosalpinx may be formed. A new sign python sign is presented in which fallopian tube looks like a blue python on dye testing in FGTB. PMID:27365923

  19. Obtaining and processing Daymet data using Python and ArcGIS

    USGS Publications Warehouse

    Bohms, Stefanie

    2013-01-01

    This set of scripts was developed to automate the process of downloading and mosaicking daily Daymet data to a user defined extent using ArcGIS and Python programming language. The three steps are downloading the needed Daymet tiles for the study area extent, converting the netcdf file to a tif raster format, and mosaicking those rasters to one file. The set of scripts is intended for all levels of experience with Python programming language and requires no scripting by the user.

  20. Sharma's Python Sign: A New Tubal Sign in Female Genital Tuberculosis.

    PubMed

    Sharma, Jai Bhagwan

    2016-01-01

    Female genital tuberculosis (FGTB) is an important cause of infertility in developing countries. Various type of TB salpingitis can be endosalpingitis, exosalpingitis, interstitial TB salpingitis, and salpingitis isthmica nodosa. The fallopian tubes are thickened enlarged and tortuous. Unilateral or bilateral hydrosalpinx or pyosalpinx may be formed. A new sign python sign is presented in which fallopian tube looks like a blue python on dye testing in FGTB. PMID:27365923

  1. Environmental DNA (eDNA) Sampling Improves Occurrence and Detection Estimates of Invasive Burmese Pythons

    PubMed Central

    Hunter, Margaret E.; Oyler-McCance, Sara J.; Dorazio, Robert M.; Fike, Jennifer A.; Smith, Brian J.; Hunter, Charles T.; Reed, Robert N.; Hart, Kristen M.

    2015-01-01

    Environmental DNA (eDNA) methods are used to detect DNA that is shed into the aquatic environment by cryptic or low density species. Applied in eDNA studies, occupancy models can be used to estimate occurrence and detection probabilities and thereby account for imperfect detection. However, occupancy terminology has been applied inconsistently in eDNA studies, and many have calculated occurrence probabilities while not considering the effects of imperfect detection. Low detection of invasive giant constrictors using visual surveys and traps has hampered the estimation of occupancy and detection estimates needed for population management in southern Florida, USA. Giant constrictor snakes pose a threat to native species and the ecological restoration of the Florida Everglades. To assist with detection, we developed species-specific eDNA assays using quantitative PCR (qPCR) for the Burmese python (Python molurus bivittatus), Northern African python (P. sebae), boa constrictor (Boa constrictor), and the green (Eunectes murinus) and yellow anaconda (E. notaeus). Burmese pythons, Northern African pythons, and boa constrictors are established and reproducing, while the green and yellow anaconda have the potential to become established. We validated the python and boa constrictor assays using laboratory trials and tested all species in 21 field locations distributed in eight southern Florida regions. Burmese python eDNA was detected in 37 of 63 field sampling events; however, the other species were not detected. Although eDNA was heterogeneously distributed in the environment, occupancy models were able to provide the first estimates of detection probabilities, which were greater than 91%. Burmese python eDNA was detected along the leading northern edge of the known population boundary. The development of informative detection tools and eDNA occupancy models can improve conservation efforts in southern Florida and support more extensive studies of invasive constrictors

  2. Environmental DNA (eDNA) sampling improves occurrence and detection estimates of invasive Burmese pythons

    USGS Publications Warehouse

    Hunter, Margaret E.; Oyler-McCance, Sara J.; Dorazio, Robert M.; Fike, Jennifer A.; Smith, Brian J.; Hunter, Charles T.; Reed, Robert N.; Hart, Kristen M.

    2015-01-01

    Environmental DNA (eDNA) methods are used to detect DNA that is shed into the aquatic environment by cryptic or low density species. Applied in eDNA studies, occupancy models can be used to estimate occurrence and detection probabilities and thereby account for imperfect detection. However, occupancy terminology has been applied inconsistently in eDNA studies, and many have calculated occurrence probabilities while not considering the effects of imperfect detection. Low detection of invasive giant constrictors using visual surveys and traps has hampered the estimation of occupancy and detection estimates needed for population management in southern Florida, USA. Giant constrictor snakes pose a threat to native species and the ecological restoration of the Florida Everglades. To assist with detection, we developed species-specific eDNA assays using quantitative PCR (qPCR) for the Burmese python (Python molurus bivittatus), Northern African python (P. sebae), boa constrictor (Boa constrictor), and the green (Eunectes murinus) and yellow anaconda (E. notaeus). Burmese pythons, Northern African pythons, and boa constrictors are established and reproducing, while the green and yellow anaconda have the potential to become established. We validated the python and boa constrictor assays using laboratory trials and tested all species in 21 field locations distributed in eight southern Florida regions. Burmese python eDNA was detected in 37 of 63 field sampling events; however, the other species were not detected. Although eDNA was heterogeneously distributed in the environment, occupancy models were able to provide the first estimates of detection probabilities, which were greater than 91%. Burmese python eDNA was detected along the leading northern edge of the known population boundary. The development of informative detection tools and eDNA occupancy models can improve conservation efforts in southern Florida and support more extensive studies of invasive constrictors

  3. Severe mammal declines coincide with proliferation of invasive Burmese pythons in Everglades National Park

    PubMed Central

    Dorcas, Michael E.; Willson, John D.; Reed, Robert N.; Snow, Ray W.; Rochford, Michael R.; Miller, Melissa A.; Meshaka, Walter E.; Andreadis, Paul T.; Mazzotti, Frank J.; Romagosa, Christina M.; Hart, Kristen M.

    2012-01-01

    Invasive species represent a significant threat to global biodiversity and a substantial economic burden. Burmese pythons, giant constricting snakes native to Asia, now are found throughout much of southern Florida, including all of Everglades National Park (ENP). Pythons have increased dramatically in both abundance and geographic range since 2000 and consume a wide variety of mammals and birds. Here we report severe apparent declines in mammal populations that coincide temporally and spatially with the proliferation of pythons in ENP. Before 2000, mammals were encountered frequently during nocturnal road surveys within ENP. In contrast, road surveys totaling 56,971 km from 2003–2011 documented a 99.3% decrease in the frequency of raccoon observations, decreases of 98.9% and 87.5% for opossum and bobcat observations, respectively, and failed to detect rabbits. Road surveys also revealed that these species are more common in areas where pythons have been discovered only recently and are most abundant outside the python's current introduced range. These findings suggest that predation by pythons has resulted in dramatic declines in mammals within ENP and that introduced apex predators, such as giant constrictors, can exert significant top-down pressure on prey populations. Severe declines in easily observed and/or common mammals, such as raccoons and bobcats, bode poorly for species of conservation concern, which often are more difficult to sample and occur at lower densities. PMID:22308381

  4. Severe mammal declines coincide with proliferation of invasive Burmese pythons in Everglades National Park

    USGS Publications Warehouse

    Dorcas, Michael E.; Wilson, John D.; Reed, Robert N.; Snow, Ray W.; Rochford, Michael R.; Miller, Melissa A.; Meshaka, Walter E., Jr.; Andreadis, Paul T.; Mazzotti, Frank J.; Romagosa, Christina M.; Hart, Kristen M.

    2012-01-01

    Invasive species represent a significant threat to global biodiversity and a substantial economic burden. Burmese pythons, giant constricting snakes native to Asia, now are found throughout much of southern Florida, including all of Everglades National Park (ENP). Pythons have increased dramatically in both abundance and geographic range since 2000 and consume a wide variety of mammals and birds. Here we report severe apparent declines in mammal populations that coincide temporally and spatially with the proliferation of pythons in ENP. Before 2000, mammals were encountered frequently during nocturnal road surveys within ENP. In contrast, road surveys totaling 56,971 km from 2003–2011 documented a 99.3% decrease in the frequency of raccoon observations, decreases of 98.9% and 87.5% for opossum and bobcat observations, respectively, and failed to detect rabbits. Road surveys also revealed that these species are more common in areas where pythons have been discovered only recently and are most abundant outside the python's current introduced range. These findings suggest that predation by pythons has resulted in dramatic declines in mammals within ENP and that introduced apex predators, such as giant constrictors, can exert significant top-down pressure on prey populations. Severe declines in easily observed and/or common mammals, such as raccoons and bobcats, bode poorly for species of conservation concern, which often are more difficult to sample and occur at lower densities.

  5. Using Python as a first programming environment for computational physics in developing countries

    NASA Astrophysics Data System (ADS)

    Akpojotor, Godfrey; Ehwerhemuepha, Louis; Echenim, Myron; Akpojotor, Famous

    2011-03-01

    Python unique features such its interpretative, multiplatform and object oriented nature as well as being a free and open source software creates the possibility that any user connected to the internet can download the entire package into any platform, install it and immediately begin to use it. Thus Python is gaining reputation as a preferred environment for introducing students and new beginners to programming. Therefore in Africa, the Python African Tour project has been launched and we are coordinating its use in computational science. We examine here the challenges and prospects of using Python for computational physics (CP) education in developing countries (DC). Then we present our project on using Python to simulate and aid the learning of laboratory experiments illustrated here by modeling of the simple pendulum and also to visualize phenomena in physics illustrated here by demonstrating the wave motion of a particle in a varying potential. This project which is to train both the teachers and our students on CP using Python can easily be adopted in other DC.

  6. Severe mammal declines coincide with proliferation of invasive Burmese pythons in Everglades National Park.

    PubMed

    Dorcas, Michael E; Willson, John D; Reed, Robert N; Snow, Ray W; Rochford, Michael R; Miller, Melissa A; Meshaka, Walter E; Andreadis, Paul T; Mazzotti, Frank J; Romagosa, Christina M; Hart, Kristen M

    2012-02-14

    Invasive species represent a significant threat to global biodiversity and a substantial economic burden. Burmese pythons, giant constricting snakes native to Asia, now are found throughout much of southern Florida, including all of Everglades National Park (ENP). Pythons have increased dramatically in both abundance and geographic range since 2000 and consume a wide variety of mammals and birds. Here we report severe apparent declines in mammal populations that coincide temporally and spatially with the proliferation of pythons in ENP. Before 2000, mammals were encountered frequently during nocturnal road surveys within ENP. In contrast, road surveys totaling 56,971 km from 2003-2011 documented a 99.3% decrease in the frequency of raccoon observations, decreases of 98.9% and 87.5% for opossum and bobcat observations, respectively, and failed to detect rabbits. Road surveys also revealed that these species are more common in areas where pythons have been discovered only recently and are most abundant outside the python's current introduced range. These findings suggest that predation by pythons has resulted in dramatic declines in mammals within ENP and that introduced apex predators, such as giant constrictors, can exert significant top-down pressure on prey populations. Severe declines in easily observed and/or common mammals, such as raccoons and bobcats, bode poorly for species of conservation concern, which often are more difficult to sample and occur at lower densities. PMID:22308381

  7. The Programming Language Python In Earth System Simulations

    NASA Astrophysics Data System (ADS)

    Gross, L.; Imranullah, A.; Mora, P.; Saez, E.; Smillie, J.; Wang, C.

    2004-12-01

    Mathematical models in earth sciences base on the solution of systems of coupled, non-linear, time-dependent partial differential equations (PDEs). The spatial and time-scale vary from a planetary scale and million years for convection problems to 100km and 10 years for fault systems simulations. Various techniques are in use to deal with the time dependency (e.g. Crank-Nicholson), with the non-linearity (e.g. Newton-Raphson) and weakly coupled equations (e.g. non-linear Gauss-Seidel). Besides these high-level solution algorithms discretization methods (e.g. finite element method (FEM), boundary element method (BEM)) are used to deal with spatial derivatives. Typically, large-scale, three dimensional meshes are required to resolve geometrical complexity (e.g. in the case of fault systems) or features in the solution (e.g. in mantel convection simulations). The modelling environment escript allows the rapid implementation of new physics as required for the development of simulation codes in earth sciences. Its main object is to provide a programming language, where the user can define new models and rapidly develop high-level solution algorithms. The current implementation is linked with the finite element package finley as a PDE solver. However, the design is open and other discretization technologies such as finite differences and boundary element methods could be included. escript is implemented as an extension of the interactive programming environment python (see www.python.org). Key concepts introduced are Data objects, which are holding values on nodes or elements of the finite element mesh, and linearPDE objects, which are defining linear partial differential equations to be solved by the underlying discretization technology. In this paper we will show the basic concepts of escript and will show how escript is used to implement a simulation code for interacting fault systems. We will show some results of large-scale, parallel simulations on an SGI Altix

  8. Hyperopt: a Python library for model selection and hyperparameter optimization

    NASA Astrophysics Data System (ADS)

    Bergstra, James; Komer, Brent; Eliasmith, Chris; Yamins, Dan; Cox, David D.

    2015-01-01

    Sequential model-based optimization (also known as Bayesian optimization) is one of the most efficient methods (per function evaluation) of function minimization. This efficiency makes it appropriate for optimizing the hyperparameters of machine learning algorithms that are slow to train. The Hyperopt library provides algorithms and parallelization infrastructure for performing hyperparameter optimization (model selection) in Python. This paper presents an introductory tutorial on the usage of the Hyperopt library, including the description of search spaces, minimization (in serial and parallel), and the analysis of the results collected in the course of minimization. This paper also gives an overview of Hyperopt-Sklearn, a software project that provides automatic algorithm configuration of the Scikit-learn machine learning library. Following Auto-Weka, we take the view that the choice of classifier and even the choice of preprocessing module can be taken together to represent a single large hyperparameter optimization problem. We use Hyperopt to define a search space that encompasses many standard components (e.g. SVM, RF, KNN, PCA, TFIDF) and common patterns of composing them together. We demonstrate, using search algorithms in Hyperopt and standard benchmarking data sets (MNIST, 20-newsgroups, convex shapes), that searching this space is practical and effective. In particular, we improve on best-known scores for the model space for both MNIST and convex shapes. The paper closes with some discussion of ongoing and future work.

  9. MCPB.py: A Python Based Metal Center Parameter Builder.

    PubMed

    Li, Pengfei; Merz, Kenneth M

    2016-04-25

    MCPB.py, a python based metal center parameter builder, has been developed to build force fields for the simulation of metal complexes employing the bonded model approach. It has an optimized code structure, with far fewer required steps than the previous developed MCPB program. It supports various AMBER force fields and more than 80 metal ions. A series of parametrization schemes to derive force constants and charge parameters are available within the program. We give two examples (one metalloprotein example and one organometallic compound example), indicating the program's ability to build reliable force fields for different metal ion containing complexes. The original version was released with AmberTools15. It is provided via the GNU General Public License v3.0 (GNU_GPL_v3) agreement and is free to download and distribute. MCPB.py provides a bridge between quantum mechanical calculations and molecular dynamics simulation software packages thereby enabling the modeling of metal ion centers. It offers an entry into simulating metal ions in a number of situations by providing an efficient way for researchers to handle the vagaries and difficulties associated with metal ion modeling. PMID:26913476

  10. SPOTting Model Parameters Using a Ready-Made Python Package

    PubMed Central

    Houska, Tobias; Kraft, Philipp; Chamorro-Chavez, Alejandro; Breuer, Lutz

    2015-01-01

    The choice for specific parameter estimation methods is often more dependent on its availability than its performance. We developed SPOTPY (Statistical Parameter Optimization Tool), an open source python package containing a comprehensive set of methods typically used to calibrate, analyze and optimize parameters for a wide range of ecological models. SPOTPY currently contains eight widely used algorithms, 11 objective functions, and can sample from eight parameter distributions. SPOTPY has a model-independent structure and can be run in parallel from the workstation to large computation clusters using the Message Passing Interface (MPI). We tested SPOTPY in five different case studies to parameterize the Rosenbrock, Griewank and Ackley functions, a one-dimensional physically based soil moisture routine, where we searched for parameters of the van Genuchten-Mualem function and a calibration of a biogeochemistry model with different objective functions. The case studies reveal that the implemented SPOTPY methods can be used for any model with just a minimal amount of code for maximal power of parameter optimization. They further show the benefit of having one package at hand that includes number of well performing parameter search methods, since not every case study can be solved sufficiently with every algorithm or every objective function. PMID:26680783

  11. SPOTting Model Parameters Using a Ready-Made Python Package.

    PubMed

    Houska, Tobias; Kraft, Philipp; Chamorro-Chavez, Alejandro; Breuer, Lutz

    2015-01-01

    The choice for specific parameter estimation methods is often more dependent on its availability than its performance. We developed SPOTPY (Statistical Parameter Optimization Tool), an open source python package containing a comprehensive set of methods typically used to calibrate, analyze and optimize parameters for a wide range of ecological models. SPOTPY currently contains eight widely used algorithms, 11 objective functions, and can sample from eight parameter distributions. SPOTPY has a model-independent structure and can be run in parallel from the workstation to large computation clusters using the Message Passing Interface (MPI). We tested SPOTPY in five different case studies to parameterize the Rosenbrock, Griewank and Ackley functions, a one-dimensional physically based soil moisture routine, where we searched for parameters of the van Genuchten-Mualem function and a calibration of a biogeochemistry model with different objective functions. The case studies reveal that the implemented SPOTPY methods can be used for any model with just a minimal amount of code for maximal power of parameter optimization. They further show the benefit of having one package at hand that includes number of well performing parameter search methods, since not every case study can be solved sufficiently with every algorithm or every objective function. PMID:26680783

  12. Screening_mgmt: a Python module for managing screening data.

    PubMed

    Helfenstein, Andreas; Tammela, Päivi

    2015-02-01

    High-throughput screening is an established technique in drug discovery and, as such, has also found its way into academia. High-throughput screening generates a considerable amount of data, which is why specific software is used for its analysis and management. The commercially available software packages are often beyond the financial limits of small-scale academic laboratories and, furthermore, lack the flexibility to fulfill certain user-specific requirements. We have developed a Python module, screening_mgmt, which is a lightweight tool for flexible data retrieval, analysis, and storage for different screening assays in one central database. The module reads custom-made analysis scripts and plotting instructions, and it offers a graphical user interface to import, modify, and display the data in a uniform manner. During the test phase, we used this module for the management of 10,000 data points of various origins. It has provided a practical, user-friendly tool for sharing and exchanging information between researchers. PMID:25381290

  13. SIMA: Python software for analysis of dynamic fluorescence imaging data

    PubMed Central

    Kaifosh, Patrick; Zaremba, Jeffrey D.; Danielson, Nathan B.; Losonczy, Attila

    2014-01-01

    Fluorescence imaging is a powerful method for monitoring dynamic signals in the nervous system. However, analysis of dynamic fluorescence imaging data remains burdensome, in part due to the shortage of available software tools. To address this need, we have developed SIMA, an open source Python package that facilitates common analysis tasks related to fluorescence imaging. Functionality of this package includes correction of motion artifacts occurring during in vivo imaging with laser-scanning microscopy, segmentation of imaged fields into regions of interest (ROIs), and extraction of signals from the segmented ROIs. We have also developed a graphical user interface (GUI) for manual editing of the automatically segmented ROIs and automated registration of ROIs across multiple imaging datasets. This software has been designed with flexibility in mind to allow for future extension with different analysis methods and potential integration with other packages. Software, documentation, and source code for the SIMA package and ROI Buddy GUI are freely available at http://www.losonczylab.org/sima/. PMID:25295002

  14. AstroML: Python-powered Machine Learning for Astronomy

    NASA Astrophysics Data System (ADS)

    Vander Plas, Jake; Connolly, A. J.; Ivezic, Z.

    2014-01-01

    As astronomical data sets grow in size and complexity, automated machine learning and data mining methods are becoming an increasingly fundamental component of research in the field. The astroML project (http://astroML.org) provides a common repository for practical examples of the data mining and machine learning tools used and developed by astronomical researchers, written in Python. The astroML module contains a host of general-purpose data analysis and machine learning routines, loaders for openly-available astronomical datasets, and fast implementations of specific computational methods often used in astronomy and astrophysics. The associated website features hundreds of examples of these routines being used for analysis of real astronomical datasets, while the associated textbook provides a curriculum resource for graduate-level courses focusing on practical statistics, machine learning, and data mining approaches within Astronomical research. This poster will highlight several of the more powerful and unique examples of analysis performed with astroML, all of which can be reproduced in their entirety on any computer with the proper packages installed.

  15. batman: BAsic Transit Model cAlculatioN in Python

    NASA Astrophysics Data System (ADS)

    Kreidberg, Laura

    2015-11-01

    I introduce batman, a Python package for modeling exoplanet transit and eclipse light curves. The batman package supports calculation of light curves for any radially symmetric stellar limb darkening law, using a new integration algorithm for models that cannot be quickly calculated analytically. The code uses C extension modules to speed up model calculation and is parallelized with OpenMP. For a typical light curve with 100 data points in transit, batman can calculate one million quadratic limb-darkened models in 30 s with a single 1.7 GHz Intel Core i5 processor. The same calculation takes seven minutes using the four-parameter nonlinear limb darkening model (computed to 1 ppm accuracy). Maximum truncation error for integrated models is an input parameter that can be set as low as 0.001 ppm, ensuring that the community is prepared for the precise transit light curves we anticipate measuring with upcoming facilities. The batman package is open source and publicly available at https://github.com/lkreidberg/batman.

  16. Oxygen transport is not compromised at high temperature in pythons.

    PubMed

    Fobian, Dannie; Overgaard, Johannes; Wang, Tobias

    2014-11-15

    To evaluate whether the 'oxygen and capacity limited thermal tolerance' model (OCLTT) applies to an air-breathing ectothermic vertebrate, we measured oxygen uptake (V̇(O₂)), cardiac performance and arterial blood gases during a progressive rise of temperature from 30 to 40°C in the snake Python regius. V̇(O₂) of fasting snakes increased exponentially with temperature whereas V̇(O₂) of digesting snakes at high temperatures plateaued at a level 3- to 4-fold above fasting. The high and sustained aerobic metabolism over the entire temperature range was supported by pronounced tachycardia at all temperatures, and both fasting and digesting snakes maintained a normal acid-base balance without any indication of anaerobic metabolism. All snakes also maintained high arterial PO2, even at temperatures close to the upper lethal temperature. Thus, there is no evidence of a reduced capacity for oxygen transport at high temperatures in either fasting or digesting snakes, suggesting that the upper thermal tolerance of this species is limited by other factors. PMID:25267848

  17. Review of the reticulated python (Python reticulatus Schneider, 1801) with the description of new subspecies from Indonesia

    NASA Astrophysics Data System (ADS)

    Auliya, M.; Mausfeld, P.; Schmitz, A.; Böhme, W.

    2002-04-01

    The geographically widespread Python reticulatus, the world's longest snake, has been largely neglected by taxonomists. Dwarfed individuals from Tanahjampea Island, Indonesia, differ strikingly in morphology. Phylogenetic relationships were analyzed using a 345-bp fragment of the cytochrome b gene for 12 specimens from different populations. Both genetic differences and morphological characters distinctly revealed two taxonomic subunits. The island populations of Tanahjampea and Selayar form two monophyletic lineages, supported by high bootstrap values, with distinct differences in color pattern and scalation. We consider these forms to represent two new subspecies. The Tanahjampea form is genetically related to populations of the Sunda Islands and mainland Southeast Asia, whereas the Selayar form is related to populations of Southwest Sulawesi. We conclude that, due to strong directional surface currents in this region, gene flow between Tanahjampea and Selayar is prevented. Sea-level changes during the Pleistocene probably contributed to the isolation of the two taxa described. Aspects of ecology and conservation status are briefly discussed. Electronic supplementary material to this paper can be obtained by using the Springer LINK server located at http://dx.doi.org/10.1007/s00114-002-0320-4.

  18. SunPy - Python for Solar Physics, Version 0.4

    NASA Astrophysics Data System (ADS)

    Christe, Steven; Mumford, Stuart; Perez-Suarez, David; Ireland, Jack; Shih, Albert Y.; Inglis, Andrew; Liedtke, Simon; Hewett, Russel

    2014-06-01

    We presents version 0.4 of SunPy, a community-developed Python package for solar physics. Python, a free, cross-platform, general-purpose, high-level programming language, has seen widespread adoption among the scientific community, resulting in the availability of a large number of software packages, from numerical computation NumPy, SciPy and machine learning (scikit-learn) to visualisation and plotting (matplotlib).SunPy is a data-analysis environment specialising in providing the software necessary to analyse solar and heliospheric datasets in Python. SunPy is open-source software (BSD licence) and has an open and transparent development workflow that anyone can contribute to. SunPy provides access to solar data through integration with the Virtual Solar Observatory (VSO), the Heliophysics Event Knowledgebase (HEK), and the HELiophysics Integrated Observatory (HELIO) webservices. It currently supports image data from major solar missions (e.g., SDO, SOHO, STEREO, and IRIS), time-series data from missions such as GOES, SDO/EVE, and PROBA2/LYRA, and radio spectra from e-Callisto and STEREO/SWAVES. We describe SunPy's functionality, provide examples of solar data analysis in SunPy, and show how Python-based solar data-analysis can leverage the many existing tools already available in Python. We discuss the future goals of the project and encourage interested users to become involved in the planning and development of SunPy.

  19. Parallel selective pressures drive convergent diversification of phenotypes in pythons and boas.

    PubMed

    Esquerré, Damien; Scott Keogh, J

    2016-07-01

    Pythons and boas are globally distributed and distantly related radiations with remarkable phenotypic and ecological diversity. We tested whether pythons, boas and their relatives have evolved convergent phenotypes when they display similar ecology. We collected geometric morphometric data on head shape for 1073 specimens representing over 80% of species. We show that these two groups display strong and widespread convergence when they occupy equivalent ecological niches and that the history of phenotypic evolution strongly matches the history of ecological diversification, suggesting that both processes are strongly coupled. These results are consistent with replicated adaptive radiation in both groups. We argue that strong selective pressures related to habitat-use have driven this convergence. Pythons and boas provide a new model system for the study of macro-evolutionary patterns of morphological and ecological evolution and they do so at a deeper level of divergence and global scale than any well-established adaptive radiation model systems. PMID:27264195

  20. The mechanical properties of the systemic and pulmonary arteries of Python regius correlate with blood pressures.

    PubMed

    van Soldt, Benjamin J; Danielsen, Carl Christian; Wang, Tobias

    2015-12-01

    Pythons are unique amongst snakes in having different pressures in the aortas and pulmonary arteries because of intraventricular pressure separation. In this study, we investigate whether this correlates with different blood vessel strength in the ball python Python regius. We excised segments from the left, right, and dorsal aortas, and from the two pulmonary arteries. These were subjected to tensile testing. We show that the aortic vessel wall is significantly stronger than the pulmonary artery wall in P. regius. Gross morphological characteristics (vessel wall thickness and correlated absolute amount of collagen content) are likely the most influential factors. Collagen fiber thickness and orientation are likely to have an effect, though the effect of collagen fiber type and cross-links between fibers will need further study. PMID:26780263

  1. Charming the Snake: Student Experiences with Python Programming as a Data Analysis Tool

    NASA Astrophysics Data System (ADS)

    Booker, Melissa; Ivers, C. B.; Piper, M.; Powers, L.; Ali, B.

    2014-01-01

    During the past year, twelve high school students and one undergraduate student participated in the NASA/IPAC Teacher Archive Research Program (NITARP) alongside three high school educators and one informal educator, gaining experience in using Python as a tool for analyzing the vast amount of photometry data available from the Herschel and Spitzer telescopes in the NGC 281 region. Use of Python appeared to produce two main positive gains: (1) a gain in student ability to successfully write and execute Python programs for the bulk analysis of data, and (2) a change in their perceptions of the utility of computer programming and of the students’ abilities to use programming to solve problems. We outline the trials, tribulations, successes, and failures of the teachers and students through this learning exercise and provide some recommendations for incorporating programming in scientific learning.

  2. Pteros 2.0: Evolution of the fast parallel molecular analysis library for C++ and python.

    PubMed

    Yesylevskyy, Semen O

    2015-07-15

    Pteros is the high-performance open-source library for molecular modeling and analysis of molecular dynamics trajectories. Starting from version 2.0 Pteros is available for C++ and Python programming languages with very similar interfaces. This makes it suitable for writing complex reusable programs in C++ and simple interactive scripts in Python alike. New version improves the facilities for asynchronous trajectory reading and parallel execution of analysis tasks by introducing analysis plugins which could be written in either C++ or Python in completely uniform way. The high level of abstraction provided by analysis plugins greatly simplifies prototyping and implementation of complex analysis algorithms. Pteros is available for free under Artistic License from http://sourceforge.net/projects/pteros/. PMID:25974373

  3. Analyzing rasters, vectors and time series using new Python interfaces in GRASS GIS 7

    NASA Astrophysics Data System (ADS)

    Petras, Vaclav; Petrasova, Anna; Chemin, Yann; Zambelli, Pietro; Landa, Martin; Gebbert, Sören; Neteler, Markus; Löwe, Peter

    2015-04-01

    GRASS GIS 7 is a free and open source GIS software developed and used by many scientists (Neteler et al., 2012). While some users of GRASS GIS prefer its graphical user interface, significant part of the scientific community takes advantage of various scripting and programing interfaces offered by GRASS GIS to develop new models and algorithms. Here we will present different interfaces added to GRASS GIS 7 and available in Python, a popular programming language and environment in geosciences. These Python interfaces are designed to satisfy the needs of scientists and programmers under various circumstances. PyGRASS (Zambelli et al., 2013) is a new object-oriented interface to GRASS GIS modules and libraries. The GRASS GIS libraries are implemented in C to ensure maximum performance and the PyGRASS interface provides an intuitive, pythonic access to their functionality. GRASS GIS Python scripting library is another way of accessing GRASS GIS modules. It combines the simplicity of Bash and the efficiency of the Python syntax. When full access to all low-level and advanced functions and structures from GRASS GIS library is required, Python programmers can use an interface based on the Python ctypes package. Ctypes interface provides complete, direct access to all functionality as it would be available to C programmers. GRASS GIS provides specialized Python library for managing and analyzing spatio-temporal data (Gebbert and Pebesma, 2014). The temporal library introduces space time datasets representing time series of raster, 3D raster or vector maps and allows users to combine various spatio-temporal operations including queries, aggregation, sampling or the analysis of spatio-temporal topology. We will also discuss the advantages of implementing scientific algorithm as a GRASS GIS module and we will show how to write such module in Python. To facilitate the development of the module, GRASS GIS provides a Python library for testing (Petras and Gebbert, 2014) which

  4. HOPE: A Python just-in-time compiler for astrophysical computations

    NASA Astrophysics Data System (ADS)

    Akeret, J.; Gamper, L.; Amara, A.; Refregier, A.

    2015-04-01

    The Python programming language is becoming increasingly popular for scientific applications due to its simplicity, versatility, and the broad range of its libraries. A drawback of this dynamic language, however, is its low runtime performance which limits its applicability for large simulations and for the analysis of large data sets, as is common in astrophysics and cosmology. While various frameworks have been developed to address this limitation, most focus on covering the complete language set, and either force the user to alter the code or are not able to reach the full speed of an optimised native compiled language. In order to combine the ease of Python and the speed of C++, we developed HOPE, a specialised Python just-in-time (JIT) compiler designed for numerical astrophysical applications. HOPE focuses on a subset of the language and is able to translate Python code into C++ while performing numerical optimisation on mathematical expressions at runtime. To enable the JIT compilation, the user only needs to add a decorator to the function definition. We assess the performance of HOPE by performing a series of benchmarks and compare its execution speed with that of plain Python, C++ and the other existing frameworks. We find that HOPE improves the performance compared to plain Python by a factor of 2 to 120, achieves speeds comparable to that of C++, and often exceeds the speed of the existing solutions. We discuss the differences between HOPE and the other frameworks, as well as future extensions of its capabilities. The fully documented HOPE package is available at http://hope.phys.ethz.ch and is published under the GPLv3 license on PyPI and GitHub.

  5. Python-based geometry preparation and simulation visualization toolkits for STEPS

    PubMed Central

    Chen, Weiliang; De Schutter, Erik

    2014-01-01

    STEPS is a stochastic reaction-diffusion simulation engine that implements a spatial extension of Gillespie's Stochastic Simulation Algorithm (SSA) in complex tetrahedral geometries. An extensive Python-based interface is provided to STEPS so that it can interact with the large number of scientific packages in Python. However, a gap existed between the interfaces of these packages and the STEPS user interface, where supporting toolkits could reduce the amount of scripting required for research projects. This paper introduces two new supporting toolkits that support geometry preparation and visualization for STEPS simulations. PMID:24782754

  6. ModFossa: A library for modeling ion channels using Python.

    PubMed

    Ferneyhough, Gareth B; Thibealut, Corey M; Dascalu, Sergiu M; Harris, Frederick C

    2016-06-01

    The creation and simulation of ion channel models using continuous-time Markov processes is a powerful and well-used tool in the field of electrophysiology and ion channel research. While several software packages exist for the purpose of ion channel modeling, most are GUI based, and none are available as a Python library. In an attempt to provide an easy-to-use, yet powerful Markov model-based ion channel simulator, we have developed ModFossa, a Python library supporting easy model creation and stimulus definition, complete with a fast numerical solver, and attractive vector graphics plotting. PMID:26932271

  7. Enhancements to Ginga: a Python Package for Building Astronomical Data Viewers

    NASA Astrophysics Data System (ADS)

    Jeschke, E.; Inagaki, T.; Kackley, R.

    2015-09-01

    Ginga is a toolkit for building astronomical image viewers. The package is available under a BSD license at github.com and has undergone continuous development since its introduction at ADASS 2012. The package may may be of interest to software developers who are looking for a solution for integrating FITS or numpy-based data visualization into their python programs and end users interested in FITS viewers (via the example reference viewer). We present the updates and enhanced capabilities of the package, including: support for additional GUI toolkits, WCS-based image mosaicing, image overlays, customizable user interface bindings, support for python3 and more.

  8. SPOTting model parameters using a ready-made Python package

    NASA Astrophysics Data System (ADS)

    Houska, Tobias; Kraft, Philipp; Breuer, Lutz

    2015-04-01

    The selection and parameterization of reliable process descriptions in ecological modelling is driven by several uncertainties. The procedure is highly dependent on various criteria, like the used algorithm, the likelihood function selected and the definition of the prior parameter distributions. A wide variety of tools have been developed in the past decades to optimize parameters. Some of the tools are closed source. Due to this, the choice for a specific parameter estimation method is sometimes more dependent on its availability than the performance. A toolbox with a large set of methods can support users in deciding about the most suitable method. Further, it enables to test and compare different methods. We developed the SPOT (Statistical Parameter Optimization Tool), an open source python package containing a comprehensive set of modules, to analyze and optimize parameters of (environmental) models. SPOT comes along with a selected set of algorithms for parameter optimization and uncertainty analyses (Monte Carlo, MC; Latin Hypercube Sampling, LHS; Maximum Likelihood, MLE; Markov Chain Monte Carlo, MCMC; Scuffled Complex Evolution, SCE-UA; Differential Evolution Markov Chain, DE-MCZ), together with several likelihood functions (Bias, (log-) Nash-Sutcliff model efficiency, Correlation Coefficient, Coefficient of Determination, Covariance, (Decomposed-, Relative-, Root-) Mean Squared Error, Mean Absolute Error, Agreement Index) and prior distributions (Binomial, Chi-Square, Dirichlet, Exponential, Laplace, (log-, multivariate-) Normal, Pareto, Poisson, Cauchy, Uniform, Weibull) to sample from. The model-independent structure makes it suitable to analyze a wide range of applications. We apply all algorithms of the SPOT package in three different case studies. Firstly, we investigate the response of the Rosenbrock function, where the MLE algorithm shows its strengths. Secondly, we study the Griewank function, which has a challenging response surface for

  9. Evaluation of the role of the cyclooxygenase signaling pathway during inflammation in skin and muscle tissues of ball pythons (Python regius).

    PubMed

    Sadler, Ryan A; Schumacher, Juergen P; Rathore, Kusum; Newkirk, Kim M; Cole, Grayson; Seibert, Rachel; Cekanova, Maria

    2016-05-01

    OBJECTIVE To determine degrees of production of cyclooxygenase (COX)-1 and -2 and other mediators of inflammation in noninflamed and inflamed skin and muscle tissues in ball pythons (Python regius). ANIMALS 6 healthy adult male ball pythons. PROCEDURES Biopsy specimens of noninflamed skin and muscle tissue were collected from anesthetized snakes on day 0. A 2-cm skin and muscle incision was then made 5 cm distal to the biopsy sites with a CO2 laser to induce inflammation. On day 7, biopsy specimens of skin and muscle tissues were collected from the incision sites. Inflamed and noninflamed tissue specimens were evaluated for production of COX-1, COX-2, phosphorylated protein kinase B (AKT), total AKT, nuclear factor κ-light-chain-enhancer of activated B cells, phosphorylated extracellular receptor kinases (ERKs) 1 and 2, and total ERK proteins by western blot analysis. Histologic evaluation was performed on H&E-stained tissue sections. RESULTS All biopsy specimens of inflamed skin and muscle tissues had higher histologic inflammation scores than did specimens of noninflamed tissue. Inflamed skin specimens had significantly greater production of COX-1 and phosphorylated ERK than did noninflamed skin specimens. Inflamed muscle specimens had significantly greater production of phosphorylated ERK and phosphorylated AKT, significantly lower production of COX-1, and no difference in production of COX-2, compared with production in noninflamed muscle specimens. CONCLUSIONS AND CLINICAL RELEVANCE Production of COX-1, but not COX-2, was significantly greater in inflamed versus noninflamed skin specimens from ball pythons. Additional research into the reptilian COX signaling pathway is warranted. PMID:27111016

  10. Tomopy: A Python toolbox to perform X-Ray data proessing and image reconstruction.

    Energy Science and Technology Software Center (ESTSC)

    2014-01-30

    Tomopy is a Python toolbox to perform x-ray data processing, image reconstruction and data exchange tasks at synchrotron facilities. The dependencies of the software are currently as follows: -Python related python standard library (http://docs.python.org/2/library/) numpy (http://www.numpy.org/) scipy (http://scipy.org/) matplotlib (http://matplotlip.org/) sphinx (http://sphinx-doc.org) pil (http://www.pythonware.com/products/pil/) pyhdf (http://pysclint.sourceforge.net/pyhdf/) h5py (http://www.h5py.org) pywt (http://www.pybytes.com/pywavelets/) file.py (https://pyspec.svn.sourceforge.net/svnroot/pyspec/trunk/pyspec/ccd/files.py) -C/C++ related: gridec (anonymous?? C-code written back in 1997 that uses standard C library) fftw (http://www.fftw.org/) tomoRecon (multi-threaded C++ verion of gridrec. Author:more » Mark Rivers from APS. http://cars9.uchicago.edu/software/epics/tomoRecon.html) epics (http://www.aps.anl.gov/epics/)« less

  11. Data management routines for reproducible research using the G-Node Python Client library.

    PubMed

    Sobolev, Andrey; Stoewer, Adrian; Pereira, Michael; Kellner, Christian J; Garbers, Christian; Rautenberg, Philipp L; Wachtler, Thomas

    2014-01-01

    Structured, efficient, and secure storage of experimental data and associated meta-information constitutes one of the most pressing technical challenges in modern neuroscience, and does so particularly in electrophysiology. The German INCF Node aims to provide open-source solutions for this domain that support the scientific data management and analysis workflow, and thus facilitate future data access and reproducible research. G-Node provides a data management system, accessible through an application interface, that is based on a combination of standardized data representation and flexible data annotation to account for the variety of experimental paradigms in electrophysiology. The G-Node Python Library exposes these services to the Python environment, enabling researchers to organize and access their experimental data using their familiar tools while gaining the advantages that a centralized storage entails. The library provides powerful query features, including data slicing and selection by metadata, as well as fine-grained permission control for collaboration and data sharing. Here we demonstrate key actions in working with experimental neuroscience data, such as building a metadata structure, organizing recorded data in datasets, annotating data, or selecting data regions of interest, that can be automated to large degree using the library. Compliant with existing de-facto standards, the G-Node Python Library is compatible with many Python tools in the field of neurophysiology and thus enables seamless integration of data organization into the scientific data workflow. PMID:24634654

  12. Tomopy: A Python toolbox to perform X-Ray data proessing and image reconstruction.

    SciTech Connect

    2014-01-30

    Tomopy is a Python toolbox to perform x-ray data processing, image reconstruction and data exchange tasks at synchrotron facilities. The dependencies of the software are currently as follows: -Python related python standard library (http://docs.python.org/2/library/) numpy (http://www.numpy.org/) scipy (http://scipy.org/) matplotlib (http://matplotlip.org/) sphinx (http://sphinx-doc.org) pil (http://www.pythonware.com/products/pil/) pyhdf (http://pysclint.sourceforge.net/pyhdf/) h5py (http://www.h5py.org) pywt (http://www.pybytes.com/pywavelets/) file.py (https://pyspec.svn.sourceforge.net/svnroot/pyspec/trunk/pyspec/ccd/files.py) -C/C++ related: gridec (anonymous?? C-code written back in 1997 that uses standard C library) fftw (http://www.fftw.org/) tomoRecon (multi-threaded C++ verion of gridrec. Author: Mark Rivers from APS. http://cars9.uchicago.edu/software/epics/tomoRecon.html) epics (http://www.aps.anl.gov/epics/)

  13. PCSIM: A Parallel Simulation Environment for Neural Circuits Fully Integrated with Python

    PubMed Central

    Pecevski, Dejan; Natschläger, Thomas; Schuch, Klaus

    2008-01-01

    The Parallel Circuit SIMulator (PCSIM) is a software package for simulation of neural circuits. It is primarily designed for distributed simulation of large scale networks of spiking point neurons. Although its computational core is written in C++, PCSIM's primary interface is implemented in the Python programming language, which is a powerful programming environment and allows the user to easily integrate the neural circuit simulator with data analysis and visualization tools to manage the full neural modeling life cycle. The main focus of this paper is to describe PCSIM's full integration into Python and the benefits thereof. In particular we will investigate how the automatically generated bidirectional interface and PCSIM's object-oriented modular framework enable the user to adopt a hybrid modeling approach: using and extending PCSIM's functionality either employing pure Python or C++ and thus combining the advantages of both worlds. Furthermore, we describe several supplementary PCSIM packages written in pure Python and tailored towards setting up and analyzing neural simulations. PMID:19543450

  14. GPAW - massively parallel electronic structure calculations with Python-based software.

    SciTech Connect

    Enkovaara, J.; Romero, N.; Shende, S.; Mortensen, J.

    2011-01-01

    Electronic structure calculations are a widely used tool in materials science and large consumer of supercomputing resources. Traditionally, the software packages for these kind of simulations have been implemented in compiled languages, where Fortran in its different versions has been the most popular choice. While dynamic, interpreted languages, such as Python, can increase the effciency of programmer, they cannot compete directly with the raw performance of compiled languages. However, by using an interpreted language together with a compiled language, it is possible to have most of the productivity enhancing features together with a good numerical performance. We have used this approach in implementing an electronic structure simulation software GPAW using the combination of Python and C programming languages. While the chosen approach works well in standard workstations and Unix environments, massively parallel supercomputing systems can present some challenges in porting, debugging and profiling the software. In this paper we describe some details of the implementation and discuss the advantages and challenges of the combined Python/C approach. We show that despite the challenges it is possible to obtain good numerical performance and good parallel scalability with Python based software.

  15. Data Flow of a Multiple Instrument On-Demand Processing Engine with Python and DPLKIT

    NASA Astrophysics Data System (ADS)

    Garcia, Joseph P.; Eloranta, Edwin; Garcia, Raymond K.

    2016-06-01

    The University of Wisconsin LIDAR Group's High Spectral Resolution LIDAR on-demand data website and processing codebase uses Python to explore coding techniques which facilitate a flexible codebase that is reusable for various outputs, cooperative multi-instrument products, and retains stability and maintainability without hindering dynamic experimentation.

  16. PyMercury: Interactive Python for the Mercury Monte Carlo Particle Transport Code

    SciTech Connect

    Iandola, F N; O'Brien, M J; Procassini, R J

    2010-11-29

    Monte Carlo particle transport applications are often written in low-level languages (C/C++) for optimal performance on clusters and supercomputers. However, this development approach often sacrifices straightforward usability and testing in the interest of fast application performance. To improve usability, some high-performance computing applications employ mixed-language programming with high-level and low-level languages. In this study, we consider the benefits of incorporating an interactive Python interface into a Monte Carlo application. With PyMercury, a new Python extension to the Mercury general-purpose Monte Carlo particle transport code, we improve application usability without diminishing performance. In two case studies, we illustrate how PyMercury improves usability and simplifies testing and validation in a Monte Carlo application. In short, PyMercury demonstrates the value of interactive Python for Monte Carlo particle transport applications. In the future, we expect interactive Python to play an increasingly significant role in Monte Carlo usage and testing.

  17. Automating Geographic Information Systems (GIS) through Python for the Hydrological Sciences

    NASA Astrophysics Data System (ADS)

    Madsen, K.

    2013-12-01

    Geographic Information Systems (GIS) have many applications in the hydrological sciences. However, GIS software is often expensive and difficult to automate. This paper will demonstrate how to automate GRASS GIS software using the Python programming language. Both GRASS GIS and Python are open source projects that are free for anyone to use. Automation of GIS processes is important when dealing with large-scale geographic studies, as large GIS maps are usually divided into discrete tiles. When conducting GIS transformations on such maps, the user must repeat the action for each tile, a process that is greatly expedited through automation. The paper will work through several examples of automated GIS processes and provide complete Python codes that demonstrate correct syntax for working with GRASS GIS applications. The provided examples will demonstrate automation of the following processes 1.) using raster math to calculate foliage thickness from LIDAR and DEM data; 2.) conducting raster interpolation from a set of vector points to develop a continuous hydraulic conductivity coverage; 3.) automating raster coloration to sync the coloration of a large number of raster tiles for website display, and 4.) constructing contoured vector lines from topography rasters. These examples programs will serve as the building blocks for readers, giving them the tools to automate any GIS process using Python and GRASS GIS.

  18. History Revenged: Monty Python Translates Chretien de Troyes's "Perceval, or the Story of the Grail" (Again).

    ERIC Educational Resources Information Center

    Murrell, Elizabeth

    1998-01-01

    Finds "Monty Python and the Holy Grail" functions as a "surprisingly accurate cultural translation" of de Troyes's "Perceval" text. Suggests that using such films helps students open a door upon film studies and discursive studies that will serve them well as they adapt to their own historical moment. (PA)

  19. Python and Roles of Variables in Introductory Programming: Experiences from Three Educational Institutions

    ERIC Educational Resources Information Center

    Nikula, Uolevi; Sajaniemi, Jorma; Tedre, Matti; Wray, Stuart

    2007-01-01

    Students often find that learning to program is hard. Introductory programming courses have high drop-out rates and students do not learn to program well. This paper presents experiences from three educational institutions where introductory programming courses were improved by adopting Python as the first programming language and roles of…

  20. CLIMLAB: a Python-based software toolkit for interactive, process-oriented climate modeling

    NASA Astrophysics Data System (ADS)

    Rose, B. E. J.

    2015-12-01

    Global climate is a complex emergent property of the rich interactions between simpler components of the climate system. We build scientific understanding of this system by breaking it down into component process models (e.g. radiation, large-scale dynamics, boundary layer turbulence), understanding each components, and putting them back together. Hands-on experience and freedom to tinker with climate models (whether simple or complex) is invaluable for building physical understanding. CLIMLAB is an open-ended software engine for interactive, process-oriented climate modeling. With CLIMLAB you can interactively mix and match model components, or combine simpler process models together into a more comprehensive model. It was created primarily to support classroom activities, using hands-on modeling to teach fundamentals of climate science at both undergraduate and graduate levels. CLIMLAB is written in Python and ties in with the rich ecosystem of open-source scientific Python tools for numerics and graphics. The IPython notebook format provides an elegant medium for distributing interactive example code. I will give an overview of the current capabilities of CLIMLAB, the curriculum we have developed thus far, and plans for the future. Using CLIMLAB requires some basic Python coding skills. We consider this an educational asset, as we are targeting upper-level undergraduates and Python is an increasingly important language in STEM fields. However CLIMLAB is well suited to be deployed as a computational back-end for a graphical gaming environment based on earth-system modeling.

  1. Assessing risks to humans from invasive Burmese pythons in Everglades National Park, Florida, USA

    USGS Publications Warehouse

    Reed, Robert N.; Snow, Ray W.

    2014-01-01

    Invasive Burmese pythons (Python molurus bivittatus) are now established across a large area of southern Florida, USA, including all of Everglades National Park (NP). The presence of these large-bodied snakes in the continental United States has attracted intense media attention, including regular reference to the possibility of these snakes preying on humans. Over the course of a decade (2003–2012), we solicited reports of apparently unprovoked strikes directed at humans in Everglades NP. We summarize the circumstances surrounding each of the 5 reported incidents, which occurred between 2006 and 2012. All strikes were directed toward biologists moving through flooded wetlands; 2 strikes resulted in minor injury and none resulted in constriction. We consider most of these strikes to be cases of “mistaken identity,” in which the python initiated a strike at a potential prey item but aborted its predatory behavior prior to constriction and ingestion. No strikes are known to have been directed at park visitors despite visitation rates averaging over one million per year during this period. We conclude that while risks to humans should not be completely discounted, the relative risk of a human being killed by a python in Everglades NP appears to be extremely low.

  2. Identification and comparison of marbofloxacin metabolites from the plasma of ball pythons (Python regius) and blue and gold macaws (Ara ararauna).

    PubMed

    Hunter, R P; Koch, D E; Coke, R L; Carpenter, J W; Isaza, R

    2007-06-01

    Marbofloxacin is a veterinary only, synthetic, broad spectrum fluoroquinolone antimicrobial agent. In mammals, approximately 40% of the oral dose of marbofloxacin is excreted unchanged in the urine; the remaining is excreted via the bile as unchanged drug in the feces. The Vd ranges from 1.1 (cattle) to 1.3 (dog, goat, swine) L/kg. Because of extra-label use of marbofloxacin in birds and reptiles, this study was designed to determine the profile of metabolites in plasma and compare the circulating metabolite profile between a reptile and an avian species. Six adult ball pythons (Python regius) and 10 blue and gold macaws (Ara ararauna) were used in this study. The macaws were dosed both i.v. and p.o. with a single 2.5 mg/kg administration where as the pythons received a single 10 mg/kg dose both i.v. and p.o. The metabolite profiles of marbofloxacin in the plasma of these species were determined using a high performance liquid chromatography system with a mass spectrometer for detection (LC/MS/MS). Mass spectra data generated from the snake and bird plasma samples were compared with previously reported LC/MS/MS mass spectral data. Evidence does not suggest differences due to route of administration (i.v. vs. p.o.) in either species. Four chromatographic peaks with resulting daughter spectrum were identified and represent 12 possible metabolite structures. All of the proposed metabolites, except for the N-oxide, appear to be unique to macaws. The potential metabolites identified in macaws appear to be very different than those reported for chickens. PMID:17472658

  3. Molecular cloning and characterization of satellite DNA sequences from constitutive heterochromatin of the habu snake (Protobothrops flavoviridis, Viperidae) and the Burmese python (Python bivittatus, Pythonidae).

    PubMed

    Matsubara, Kazumi; Uno, Yoshinobu; Srikulnath, Kornsorn; Seki, Risako; Nishida, Chizuko; Matsuda, Yoichi

    2015-12-01

    Highly repetitive DNA sequences of the centromeric heterochromatin provide valuable molecular cytogenetic markers for the investigation of genomic compartmentalization in the macrochromosomes and microchromosomes of sauropsids. Here, the relationship between centromeric heterochromatin and karyotype evolution was examined using cloned repetitive DNA sequences from two snake species, the habu snake (Protobothrops flavoviridis, Crotalinae, Viperidae) and Burmese python (Python bivittatus, Pythonidae). Three satellite DNA (stDNA) families were isolated from the heterochromatin of these snakes: 168-bp PFL-MspI from P. flavoviridis and 196-bp PBI-DdeI and 174-bp PBI-MspI from P. bivittatus. The PFL-MspI and PBI-DdeI sequences were localized to the centromeric regions of most chromosomes in the respective species, suggesting that the two sequences were the major components of the centromeric heterochromatin in these organisms. The PBI-MspI sequence was localized to the pericentromeric region of four chromosome pairs. The PFL-MspI and the PBI-DdeI sequences were conserved only in the genome of closely related species, Gloydius blomhoffii (Crotalinae) and Python molurus, respectively, although their locations on the chromosomes were slightly different. In contrast, the PBI-MspI sequence was also in the genomes of P. molurus and Boa constrictor (Boidae), and additionally localized to the centromeric regions of eight chromosome pairs in B. constrictor, suggesting that this sequence originated in the genome of a common ancestor of Pythonidae and Boidae, approximately 86 million years ago. The three stDNA sequences showed no genomic compartmentalization between the macrochromosomes and microchromosomes, suggesting that homogenization of the centromeric and/or pericentromeric stDNA sequences occurred in the macrochromosomes and microchromosomes of these snakes. PMID:26205503

  4. GMES: A Python package for solving Maxwell’s equations using the FDTD method

    NASA Astrophysics Data System (ADS)

    Chun, Kyungwon; Kim, Huioon; Kim, Hyounggyu; Jung, Kil Su; Chung, Youngjoo

    2013-04-01

    This paper describes GMES, a free Python package for solving Maxwell’s equations using the finite-difference time-domain (FDTD) method. The design of GMES follows the object-oriented programming (OOP) approach and adopts a unique design strategy where the voxels in the computational domain are grouped and then updated according to its material type. This piecewise updating scheme ensures that GMES can adopt OOP without losing its simple structure and time-stepping speed. The users can easily add various material types, sources, and boundary conditions into their code using the Python programming language. The key design features, along with the supported material types, excitation sources, boundary conditions and parallel calculations employed in GMES are also described in detail. Catalog identifier: AEOK_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEOK_v1_0.html Program obtainable from: CPC Program Library, Queen’s University, Belfast, N. Ireland Licensing provisions: GNU General Public License v3.0 No. of lines in distributed program, including test data, etc.: 17700 No. of bytes in distributed program, including test data, etc.: 89878 Distribution format: tar.gz Programming language: C++, Python. Computer: Any computer with a Unix-like system with a C++ compiler, and a Python interpreter; developed on 2.53 GHz Intel CoreTM i3. Operating system: Any Unix-like system; developed under Ubuntu 12.04 LTS 64 bit. Has the code been vectorized or parallelized?: Yes. Parallelized with MPI directives (optional). RAM: Problem dependent (a simulation with real valued electromagnetic field uses roughly 0.18 KB per Yee cell.) Classification: 10. External routines: SWIG [1], Cython [2], NumPy [3], SciPy [4], matplotlib [5], MPI for Python [6] Nature of problem: Classical electrodynamics Solution method: Finite-difference time-domain (FDTD) method Additional comments: This article describes version 0.9.5. The most recent version can be downloaded at the GMES

  5. Python-Based Scientific Analysis and Visualization of Precipitation Systems at NASA Marshall Space Flight Center

    NASA Technical Reports Server (NTRS)

    Lang, Timothy J.

    2015-01-01

    At NASA Marshall Space Flight Center (MSFC), Python is used several different ways to analyze and visualize precipitating weather systems. A number of different Python-based software packages have been developed, which are available to the larger scientific community. The approach in all these packages is to utilize pre-existing Python modules as well as to be object-oriented and scalable. The first package that will be described and demonstrated is the Python Advanced Microwave Precipitation Radiometer (AMPR) Data Toolkit, or PyAMPR for short. PyAMPR reads geolocated brightness temperature data from any flight of the AMPR airborne instrument over its 25-year history into a common data structure suitable for user-defined analyses. It features rapid, simplified (i.e., one line of code) production of quick-look imagery, including Google Earth overlays, swath plots of individual channels, and strip charts showing multiple channels at once. These plotting routines are also capable of significant customization for detailed, publication-ready figures. Deconvolution of the polarization-varying channels to static horizontally and vertically polarized scenes is also available. Examples will be given of PyAMPR's contribution toward real-time AMPR data display during the Integrated Precipitation and Hydrology Experiment (IPHEx), which took place in the Carolinas during May-June 2014. The second software package is the Marshall Multi-Radar/Multi-Sensor (MRMS) Mosaic Python Toolkit, or MMM-Py for short. MMM-Py was designed to read, analyze, and display three-dimensional national mosaicked reflectivity data produced by the NOAA National Severe Storms Laboratory (NSSL). MMM-Py can read MRMS mosaics from either their unique binary format or their converted NetCDF format. It can also read and properly interpret the current mosaic design (4 regional tiles) as well as mosaics produced prior to late July 2013 (8 tiles). MMM-Py can easily stitch multiple tiles together to provide a

  6. BioServices: a common Python package to access biological Web Services programmatically

    PubMed Central

    Cokelaer, Thomas; Pultz, Dennis; Harder, Lea M.; Serra-Musach, Jordi; Saez-Rodriguez, Julio

    2013-01-01

    Motivation: Web interfaces provide access to numerous biological databases. Many can be accessed to in a programmatic way thanks to Web Services. Building applications that combine several of them would benefit from a single framework. Results: BioServices is a comprehensive Python framework that provides programmatic access to major bioinformatics Web Services (e.g. KEGG, UniProt, BioModels, ChEMBLdb). Wrapping additional Web Services based either on Representational State Transfer or Simple Object Access Protocol/Web Services Description Language technologies is eased by the usage of object-oriented programming. Availability and implementation: BioServices releases and documentation are available at http://pypi.python.org/pypi/bioservices under a GPL-v3 license. Contact: cokelaer@ebi.ac.uk or bioservices@googlegroups.com Supplementary information: Supplementary data are available at Bioinformatics online. PMID:24064416

  7. Naval Observatory Vector Astrometry Software (NOVAS) Version 3.1:Fortran, C, and Python Editions

    NASA Astrophysics Data System (ADS)

    Kaplan, G. H.; Bangert, J. A.; Barron, E. G.; Bartlett, J. L.; Puatua, W.; Harris, W.; Barrett, P.

    2012-08-01

    The Naval Observatory Vector Astrometry Software (NOVAS) is a source - code library that provides common astrometric quantities and transformations to high precision. The library can supply, in one or two subroutine or function calls, the instantaneous celestial position of any star or planet in a variety of coordinate systems. NOVAS also provides access to all of the building blocks that go into such computations. NOVAS is used for a wide variety of applications, including the U.S. portions of The Astronomical Almanac and a number of telescope control systems. NOVAS uses IAU recommended models for Earth orientation, including the IAU 2006 precession theory, the IAU 2000A and 2000B nutation series, and diurnal rotation based on the celestial and terrestrial intermediate origins. Equinox - based quantities, such as sidereal time, are also supported. NOVAS Earth orientation calculations match those from SOFA at the sub - microarcsecond level for comparable transformations. NOVAS algorithms for aberration an d gravitational light deflection are equivalent, at the microarcsecond level, to those inherent in the current consensus VLBI delay algorithm. NOVAS can be easily connected to the JPL planetary/lunar ephemerides (e.g., DE405), and connections to IMCCE and IAA planetary ephemerides are planned. NOVAS Version 3.1 introduces a Python edition alongside the Fortran and C editions. The Python edition uses the computational code from the C edition and currently mimics the function calls of the C edition. Future versions will expand the functionality of the Python edition to exploit the object - oriented features of Python. In the Version 3.1 C edition, the ephemeris - access functions have been revised for use on 64 - bit systems and for improved performance in general. NOVAS source code, auxiliary files, and documentation are available from the USNO website (http://aa.usno.navy.mil/software/novas/novas_info.php).

  8. ObsPy: A Python Toolbox for Seismology and Seismological Observatories

    NASA Astrophysics Data System (ADS)

    Krischer, Lion; Megies, Tobias; Barsch, Robert; Beyreuther, Moritz; Wassermann, Joachim

    2013-04-01

    Python combines the power of a full-blown programming language with the flexibility and accessibility of an interactive scripting language. Its extensive standard library and large variety of freely available high quality scientific modules cover most needs in developing scientific processing workflows. ObsPy extends Python's capabilities to fit the specific needs that arise when working with seismological data. It a) comes with a continuously growing signal processing toolbox that covers most tasks common in seismological analysis, b) provides read and write support for many common waveform, station and event metadata formats and c) enables access to various data centers, webservices and databases to retrieve waveform data and station/event metadata. In combination with mature and free Python packages like NumPy, SciPy, Matplotlib, IPython and PyQt, ObsPy makes it possible to develop complete workflows in Python, ranging from reading locally stored data or requesting data from one or more different data centers via signal analysis and data processing to visualization in GUI and web applications, output of modified/derived data and the creation of publication-quality figures. All functionality is extensively documented and the ObsPy Tutorial and Gallery give a good impression of the wide range of possible use cases. ObsPy is tested and running on Linux, OS X and Windows and comes with installation routines for these systems. ObsPy is developed in a test-driven approach and is available under the GPL/LGPLv3 open source licences. Users are welcome to request help, report bugs, propose enhancements or contribute code via either the user mailing list or the project page on GitHub.

  9. PyXel: A Python Package for Astronomical X-ray Data Modeling

    NASA Astrophysics Data System (ADS)

    Ogrean, Georgiana

    2016-06-01

    PyXel is an new Python package for modeling astronomical X-ray imaging data. It is built on NumPy, SciPy, matplotlib, and Astropy, and distributed under an open-source license. The package aims to provide a common set of image analysis tools for astronomers working with extended X-ray sources. I will present an overview of its existing and planned features, and analysis examples based on public Chandra data.

  10. Calculations of lattice vibrational mode lifetimes using Jazz: a Python wrapper for LAMMPS

    NASA Astrophysics Data System (ADS)

    Gao, Y.; Wang, H.; Daw, M. S.

    2015-06-01

    Jazz is a new python wrapper for LAMMPS [1], implemented to calculate the lifetimes of vibrational normal modes based on forces as calculated for any interatomic potential available in that package. The anharmonic character of the normal modes is analyzed via the Monte Carlo-based moments approximation as is described in Gao and Daw [2]. It is distributed as open-source software and can be downloaded from the website http://jazz.sourceforge.net/.

  11. The Burmese python genome reveals the molecular basis for extreme adaptation in snakes.

    PubMed

    Castoe, Todd A; de Koning, A P Jason; Hall, Kathryn T; Card, Daren C; Schield, Drew R; Fujita, Matthew K; Ruggiero, Robert P; Degner, Jack F; Daza, Juan M; Gu, Wanjun; Reyes-Velasco, Jacobo; Shaney, Kyle J; Castoe, Jill M; Fox, Samuel E; Poole, Alex W; Polanco, Daniel; Dobry, Jason; Vandewege, Michael W; Li, Qing; Schott, Ryan K; Kapusta, Aurélie; Minx, Patrick; Feschotte, Cédric; Uetz, Peter; Ray, David A; Hoffmann, Federico G; Bogden, Robert; Smith, Eric N; Chang, Belinda S W; Vonk, Freek J; Casewell, Nicholas R; Henkel, Christiaan V; Richardson, Michael K; Mackessy, Stephen P; Bronikowski, Anne M; Bronikowsi, Anne M; Yandell, Mark; Warren, Wesley C; Secor, Stephen M; Pollock, David D

    2013-12-17

    Snakes possess many extreme morphological and physiological adaptations. Identification of the molecular basis of these traits can provide novel understanding for vertebrate biology and medicine. Here, we study snake biology using the genome sequence of the Burmese python (Python molurus bivittatus), a model of extreme physiological and metabolic adaptation. We compare the python and king cobra genomes along with genomic samples from other snakes and perform transcriptome analysis to gain insights into the extreme phenotypes of the python. We discovered rapid and massive transcriptional responses in multiple organ systems that occur on feeding and coordinate major changes in organ size and function. Intriguingly, the homologs of these genes in humans are associated with metabolism, development, and pathology. We also found that many snake metabolic genes have undergone positive selection, which together with the rapid evolution of mitochondrial proteins, provides evidence for extensive adaptive redesign of snake metabolic pathways. Additional evidence for molecular adaptation and gene family expansions and contractions is associated with major physiological and phenotypic adaptations in snakes; genes involved are related to cell cycle, development, lungs, eyes, heart, intestine, and skeletal structure, including GRB2-associated binding protein 1, SSH, WNT16, and bone morphogenetic protein 7. Finally, changes in repetitive DNA content, guanine-cytosine isochore structure, and nucleotide substitution rates indicate major shifts in the structure and evolution of snake genomes compared with other amniotes. Phenotypic and physiological novelty in snakes seems to be driven by system-wide coordination of protein adaptation, gene expression, and changes in the structure of the genome. PMID:24297902

  12. An Object-Oriented Python Implementation of an Intermediate-Level Atmospheric Model

    NASA Astrophysics Data System (ADS)

    Lin, J. W.

    2008-12-01

    The Neelin-Zeng Quasi-equilibrium Tropical Circulation Model (QTCM1) is a Fortran-based intermediate-level atmospheric model that includes simplified treatments of several physical processes, including a GCM-like convective scheme and a land-surface scheme with representations of different surface types, evaporation, and soil moisture. This model has been used in studies of the Madden-Julian oscillation, ENSO, and vegetation-atmosphere interaction effects on climate. Through the assumption of convective quasi-equilibrium in the troposphere, the QTCM1 is able to include full nonlinearity, resolve baroclinic disturbances, and generate a reasonable climatology, all at low computational cost. One year of simulation on a PC at 5.625 × 3.75 degree longitude-latitude resolution takes under three minutes of wall-clock time. The Python package qtcm implements the QTCM1 in a mixed-language environment that retains the speed of compiled Fortran while providing the benefits of Python's object-oriented framework and robust suite of utilities and datatypes. We describe key programming constructs used to create this modeling environment: the decomposition of model runs into Python objects, providing methods so visualization tools are attached to model runs, and the use of Python's mutable datatypes (lists and dictionaries) to implement the "run list" entity, which enables total runtime control of subroutine execution order and content. The result is an interactive modeling environment where the traditional sequence of "hypothesis → modeling → visualization and analysis" is opened up and made nonlinear and flexible. In this environment, science tasks such as parameter-space exploration and testing alternative parameterizations can be easily automated, without the need for multiple versions of the model code interacting with a bevy of makefiles and shell scripts. The environment also simplifies interfacing of the atmospheric model to other models (e.g., hydrologic models

  13. The Burmese python genome reveals the molecular basis for extreme adaptation in snakes

    PubMed Central

    Castoe, Todd A.; de Koning, A. P. Jason; Hall, Kathryn T.; Card, Daren C.; Schield, Drew R.; Fujita, Matthew K.; Ruggiero, Robert P.; Degner, Jack F.; Daza, Juan M.; Gu, Wanjun; Reyes-Velasco, Jacobo; Shaney, Kyle J.; Castoe, Jill M.; Fox, Samuel E.; Poole, Alex W.; Polanco, Daniel; Dobry, Jason; Vandewege, Michael W.; Li, Qing; Schott, Ryan K.; Kapusta, Aurélie; Minx, Patrick; Feschotte, Cédric; Uetz, Peter; Ray, David A.; Hoffmann, Federico G.; Bogden, Robert; Smith, Eric N.; Chang, Belinda S. W.; Vonk, Freek J.; Casewell, Nicholas R.; Henkel, Christiaan V.; Richardson, Michael K.; Mackessy, Stephen P.; Bronikowski, Anne M.; Yandell, Mark; Warren, Wesley C.; Secor, Stephen M.; Pollock, David D.

    2013-01-01

    Snakes possess many extreme morphological and physiological adaptations. Identification of the molecular basis of these traits can provide novel understanding for vertebrate biology and medicine. Here, we study snake biology using the genome sequence of the Burmese python (Python molurus bivittatus), a model of extreme physiological and metabolic adaptation. We compare the python and king cobra genomes along with genomic samples from other snakes and perform transcriptome analysis to gain insights into the extreme phenotypes of the python. We discovered rapid and massive transcriptional responses in multiple organ systems that occur on feeding and coordinate major changes in organ size and function. Intriguingly, the homologs of these genes in humans are associated with metabolism, development, and pathology. We also found that many snake metabolic genes have undergone positive selection, which together with the rapid evolution of mitochondrial proteins, provides evidence for extensive adaptive redesign of snake metabolic pathways. Additional evidence for molecular adaptation and gene family expansions and contractions is associated with major physiological and phenotypic adaptations in snakes; genes involved are related to cell cycle, development, lungs, eyes, heart, intestine, and skeletal structure, including GRB2-associated binding protein 1, SSH, WNT16, and bone morphogenetic protein 7. Finally, changes in repetitive DNA content, guanine-cytosine isochore structure, and nucleotide substitution rates indicate major shifts in the structure and evolution of snake genomes compared with other amniotes. Phenotypic and physiological novelty in snakes seems to be driven by system-wide coordination of protein adaptation, gene expression, and changes in the structure of the genome. PMID:24297902

  14. Computed tomography of the lung of healthy snakes of the species Python regius, Boa constrictor, Python reticulatus, Morelia viridis, Epicrates cenchria, and Morelia spilota.

    PubMed

    Pees, Michael; Kiefer, Ingmar; Thielebein, Jens; Oechtering, Gerhard; Krautwald-Junghanns, Maria-Elisabeth

    2009-01-01

    Thirty-nine healthy boid snakes representing six different species (Python regius, Boa constrictor, Python reticulatus, Morelia viridis, Epicrates cenchria, and Morelia spilota) were examined using computed tomography (CT) to characterize the normal appearance of the respiratory tissue. Assessment was done subjectively and densitometry was performed using a defined protocol. The length of the right lung was calculated to be 11.1% of the body length, without a significant difference between species. The length of the left lung in proportion to the right was dependent on the species examined. The most developed left lung was in P. regius (81.2%), whereas in B. constrictor, the left lung was vestigial or absent (24.7%). A median attenuation of -814.6 HU and a variability of 45.9 HU were calculated for all species with no significant difference between species. Within the species, a significantly higher attenuation was found for P. regius in the dorsal and cranial aspect of the lung compared with the ventral and caudal part. In B. constrictor, the reduced left lung was significantly hyperattenuating compared with the right lung. Results of this study emphasize the value of CT and provide basic reference data for assessment of the snake lung in these species. Veterinary Radiology & PMID:19788032

  15. pyNSMC: A Python Module for Null-Space Monte Carlo Uncertainty Analysis

    NASA Astrophysics Data System (ADS)

    White, J.; Brakefield, L. K.

    2015-12-01

    The null-space monte carlo technique is a non-linear uncertainty analyses technique that is well-suited to high-dimensional inverse problems. While the technique is powerful, the existing workflow for completing null-space monte carlo is cumbersome, requiring the use of multiple commandline utilities, several sets of intermediate files and even a text editor. pyNSMC is an open-source python module that automates the workflow of null-space monte carlo uncertainty analyses. The module is fully compatible with the PEST and PEST++ software suites and leverages existing functionality of pyEMU, a python framework for linear-based uncertainty analyses. pyNSMC greatly simplifies the existing workflow for null-space monte carlo by taking advantage of object oriented design facilities in python. The core of pyNSMC is the ensemble class, which draws and stores realized random vectors and also provides functionality for exporting and visualizing results. By relieving users of the tedium associated with file handling and command line utility execution, pyNSMC instead focuses the user on the important steps and assumptions of null-space monte carlo analysis. Furthermore, pyNSMC facilitates learning through flow charts and results visualization, which are available at many points in the algorithm. The ease-of-use of the pyNSMC workflow is compared to the existing workflow for null-space monte carlo for a synthetic groundwater model with hundreds of estimable parameters.

  16. Xray: N-dimensional, labeled arrays for analyzing physical datasets in Python

    NASA Astrophysics Data System (ADS)

    Hoyer, S.

    2015-12-01

    Efficient analysis of geophysical datasets requires tools that both preserve and utilize metadata, and that transparently scale to process large datas. Xray is such a tool, in the form of an open source Python library for analyzing the labeled, multi-dimensional array (tensor) datasets that are ubiquitous in the Earth sciences. Xray's approach pairs Python data structures based on the data model of the netCDF file format with the proven design and user interface of pandas, the popular Python data analysis library for labeled tabular data. On top of the NumPy array, xray adds labeled dimensions (e.g., "time") and coordinate values (e.g., "2015-04-10"), which it uses to enable a host of operations powered by these labels: selection, aggregation, alignment, broadcasting, split-apply-combine, interoperability with pandas and serialization to netCDF/HDF5. Many of these operations are enabled by xray's tight integration with pandas. Finally, to allow for easy parallelism and to enable its labeled data operations to scale to datasets that does not fit into memory, xray integrates with the parallel processing library dask.

  17. NEVESIM: event-driven neural simulation framework with a Python interface.

    PubMed

    Pecevski, Dejan; Kappel, David; Jonke, Zeno

    2014-01-01

    NEVESIM is a software package for event-driven simulation of networks of spiking neurons with a fast simulation core in C++, and a scripting user interface in the Python programming language. It supports simulation of heterogeneous networks with different types of neurons and synapses, and can be easily extended by the user with new neuron and synapse types. To enable heterogeneous networks and extensibility, NEVESIM is designed to decouple the simulation logic of communicating events (spikes) between the neurons at a network level from the implementation of the internal dynamics of individual neurons. In this paper we will present the simulation framework of NEVESIM, its concepts and features, as well as some aspects of the object-oriented design approaches and simulation strategies that were utilized to efficiently implement the concepts and functionalities of the framework. We will also give an overview of the Python user interface, its basic commands and constructs, and also discuss the benefits of integrating NEVESIM with Python. One of the valuable capabilities of the simulator is to simulate exactly and efficiently networks of stochastic spiking neurons from the recently developed theoretical framework of neural sampling. This functionality was implemented as an extension on top of the basic NEVESIM framework. Altogether, the intended purpose of the NEVESIM framework is to provide a basis for further extensions that support simulation of various neural network models incorporating different neuron and synapse types that can potentially also use different simulation strategies. PMID:25177291

  18. Ursgal, Universal Python Module Combining Common Bottom-Up Proteomics Tools for Large-Scale Analysis.

    PubMed

    Kremer, Lukas P M; Leufken, Johannes; Oyunchimeg, Purevdulam; Schulze, Stefan; Fufezan, Christian

    2016-03-01

    Proteomics data integration has become a broad field with a variety of programs offering innovative algorithms to analyze increasing amounts of data. Unfortunately, this software diversity leads to many problems as soon as the data is analyzed using more than one algorithm for the same task. Although it was shown that the combination of multiple peptide identification algorithms yields more robust results, it is only recently that unified approaches are emerging; however, workflows that, for example, aim to optimize search parameters or that employ cascaded style searches can only be made accessible if data analysis becomes not only unified but also and most importantly scriptable. Here we introduce Ursgal, a Python interface to many commonly used bottom-up proteomics tools and to additional auxiliary programs. Complex workflows can thus be composed using the Python scripting language using a few lines of code. Ursgal is easily extensible, and we have made several database search engines (X!Tandem, OMSSA, MS-GF+, Myrimatch, MS Amanda), statistical postprocessing algorithms (qvality, Percolator), and one algorithm that combines statistically postprocessed outputs from multiple search engines ("combined FDR") accessible as an interface in Python. Furthermore, we have implemented a new algorithm ("combined PEP") that combines multiple search engines employing elements of "combined FDR", PeptideShaker, and Bayes' theorem. PMID:26709623

  19. NEVESIM: event-driven neural simulation framework with a Python interface

    PubMed Central

    Pecevski, Dejan; Kappel, David; Jonke, Zeno

    2014-01-01

    NEVESIM is a software package for event-driven simulation of networks of spiking neurons with a fast simulation core in C++, and a scripting user interface in the Python programming language. It supports simulation of heterogeneous networks with different types of neurons and synapses, and can be easily extended by the user with new neuron and synapse types. To enable heterogeneous networks and extensibility, NEVESIM is designed to decouple the simulation logic of communicating events (spikes) between the neurons at a network level from the implementation of the internal dynamics of individual neurons. In this paper we will present the simulation framework of NEVESIM, its concepts and features, as well as some aspects of the object-oriented design approaches and simulation strategies that were utilized to efficiently implement the concepts and functionalities of the framework. We will also give an overview of the Python user interface, its basic commands and constructs, and also discuss the benefits of integrating NEVESIM with Python. One of the valuable capabilities of the simulator is to simulate exactly and efficiently networks of stochastic spiking neurons from the recently developed theoretical framework of neural sampling. This functionality was implemented as an extension on top of the basic NEVESIM framework. Altogether, the intended purpose of the NEVESIM framework is to provide a basis for further extensions that support simulation of various neural network models incorporating different neuron and synapse types that can potentially also use different simulation strategies. PMID:25177291

  20. A new open-source Python-based Space Weather data access, visualization, and analysis toolkit

    NASA Astrophysics Data System (ADS)

    de Larquier, S.; Ribeiro, A.; Frissell, N. A.; Spaleta, J.; Kunduri, B.; Thomas, E. G.; Ruohoniemi, J.; Baker, J. B.

    2013-12-01

    Space weather research relies heavily on combining and comparing data from multiple observational platforms. Current frameworks exist to aggregate some of the data sources, most based on file downloads via web or ftp interfaces. Empirical models are mostly fortran based and lack interfaces with more useful scripting languages. In an effort to improve data and model access, the SuperDARN community has been developing a Python-based Space Science Data Visualization Toolkit (DaViTpy). At the center of this development was a redesign of how our data (from 30 years of SuperDARN radars) was made available. Several access solutions are now wrapped into one convenient Python interface which probes local directories, a new remote NoSQL database, and an FTP server to retrieve the requested data based on availability. Motivated by the efficiency of this interface and the inherent need for data from multiple instruments, we implemented similar modules for other space science datasets (POES, OMNI, Kp, AE...), and also included fundamental empirical models with Python interfaces to enhance data analysis (IRI, HWM, MSIS...). All these modules and more are gathered in a single convenient toolkit, which is collaboratively developed and distributed using Github and continues to grow. While still in its early stages, we expect this toolkit will facilitate multi-instrument space weather research and improve scientific productivity.

  1. PyDPI: freely available python package for chemoinformatics, bioinformatics, and chemogenomics studies.

    PubMed

    Cao, Dong-Sheng; Liang, Yi-Zeng; Yan, Jun; Tan, Gui-Shan; Xu, Qing-Song; Liu, Shao

    2013-11-25

    The rapidly increasing amount of publicly available data in biology and chemistry enables researchers to revisit interaction problems by systematic integration and analysis of heterogeneous data. Herein, we developed a comprehensive python package to emphasize the integration of chemoinformatics and bioinformatics into a molecular informatics platform for drug discovery. PyDPI (drug-protein interaction with Python) is a powerful python toolkit for computing commonly used structural and physicochemical features of proteins and peptides from amino acid sequences, molecular descriptors of drug molecules from their topology, and protein-protein interaction and protein-ligand interaction descriptors. It computes 6 protein feature groups composed of 14 features that include 52 descriptor types and 9890 descriptors, 9 drug feature groups composed of 13 descriptor types that include 615 descriptors. In addition, it provides seven types of molecular fingerprint systems for drug molecules, including topological fingerprints, electro-topological state (E-state) fingerprints, MACCS keys, FP4 keys, atom pair fingerprints, topological torsion fingerprints, and Morgan/circular fingerprints. By combining different types of descriptors from drugs and proteins in different ways, interaction descriptors representing protein-protein or drug-protein interactions could be conveniently generated. These computed descriptors can be widely used in various fields relevant to chemoinformatics, bioinformatics, and chemogenomics. PyDPI is freely available via https://sourceforge.net/projects/pydpicao/. PMID:24047419

  2. Zachary D. Barker: Final DHS HS-STEM Report

    SciTech Connect

    Barker, Z D

    2008-08-14

    Working at Lawrence Livermore National Laboratory (LLNL) this summer has provided a very unique and special experience for me. I feel that the research opportunities given to me have allowed me to significantly benefit my research group, the laboratory, the Department of Homeland Security, and the Department of Energy. The researchers in the Single Particle Aerosol Mass Spectrometry (SPAMS) group were very welcoming and clearly wanted me to get the most out of my time in Livermore. I feel that my research partner, Veena Venkatachalam of MIT, and I have been extremely productive in meeting our research goals throughout this summer, and have learned much about working in research at a national laboratory such as Lawrence Livermore. I have learned much about the technical aspects of research while working at LLNL, however I have also gained important experience and insight into how research groups at national laboratories function. I believe that this internship has given me valuable knowledge and experience which will certainly help my transition to graduate study and a career in engineering. My work with Veena Venkatachalam in the SPAMS group this summer has focused on two major projects. Initially, we were tasked with an analysis of data collected by the group this past spring in a large public environment. The SPAMS instrument was deployed for over two months, collecting information on many of the ambient air particles circulating through the area. Our analysis of the particle data collected during this deployment concerned several aspects, including finding groups, or clusters, of particles that seemed to appear more during certain times of day, analyzing the mass spectral data of clusters and comparing them with mass spectral data of known substances, and comparing the real-time detection capability of the SPAMS instrument with that of a commercially available biological detection instrument. This analysis was performed in support of a group report to the Department of Homeland Security on the results of the deployment. The analysis of the deployment data revealed some interesting applications of the SPAMS instrument to homeland security situations. Using software developed in-house by SPAMS group member Dr. Paul Steele, Veena and I were able to cluster a subset of data over a certain timeframe (ranging from a single hour to an entire week). The software used makes clusters based on the mass spectral characteristics of the each particle in the data set, as well as other parameters. By looking more closely at the characteristics of individual clusters, including the mass spectra, conclusions could be made about what these particles are. This was achieved partially through examination and discussion of the mass spectral data with the members of the SPAMS group, as well as through comparison with known mass spectra collected from substances tested in the laboratory. In many cases, broad conclusions could be drawn about the identity of a cluster of particles.

  3. pyOpenMS: a Python-based interface to the OpenMS mass-spectrometry algorithm library.

    PubMed

    Röst, Hannes L; Schmitt, Uwe; Aebersold, Ruedi; Malmström, Lars

    2014-01-01

    pyOpenMS is an open-source, Python-based interface to the C++ OpenMS library, providing facile access to a feature-rich, open-source algorithm library for MS-based proteomics analysis. It contains Python bindings that allow raw access to the data structures and algorithms implemented in OpenMS, specifically those for file access (mzXML, mzML, TraML, mzIdentML among others), basic signal processing (smoothing, filtering, de-isotoping, and peak-picking) and complex data analysis (including label-free, SILAC, iTRAQ, and SWATH analysis tools). pyOpenMS thus allows fast prototyping and efficient workflow development in a fully interactive manner (using the interactive Python interpreter) and is also ideally suited for researchers not proficient in C++. In addition, our code to wrap a complex C++ library is completely open-source, allowing other projects to create similar bindings with ease. The pyOpenMS framework is freely available at https://pypi.python.org/pypi/pyopenms while the autowrap tool to create Cython code automatically is available at https://pypi.python.org/pypi/autowrap (both released under the 3-clause BSD licence). PMID:24420968

  4. Embedded Analytical Solutions Improve Accuracy in Convolution-Based Particle Tracking Models using Python

    NASA Astrophysics Data System (ADS)

    Starn, J. J.

    2013-12-01

    Particle tracking often is used to generate particle-age distributions that are used as impulse-response functions in convolution. A typical application is to produce groundwater solute breakthrough curves (BTC) at endpoint receptors such as pumping wells or streams. The commonly used semi-analytical particle-tracking algorithm based on the assumption of linear velocity gradients between opposing cell faces is computationally very fast when used in combination with finite-difference models. However, large gradients near pumping wells in regional-scale groundwater-flow models often are not well represented because of cell-size limitations. This leads to inaccurate velocity fields, especially at weak sinks. Accurate analytical solutions for velocity near a pumping well are available, and various boundary conditions can be imposed using image-well theory. Python can be used to embed these solutions into existing semi-analytical particle-tracking codes, thereby maintaining the integrity and quality-assurance of the existing code. Python (and associated scientific computational packages NumPy, SciPy, and Matplotlib) is an effective tool because of its wide ranging capability. Python text processing allows complex and database-like manipulation of model input and output files, including binary and HDF5 files. High-level functions in the language include ODE solvers to solve first-order particle-location ODEs, Gaussian kernel density estimation to compute smooth particle-age distributions, and convolution. The highly vectorized nature of NumPy arrays and functions minimizes the need for computationally expensive loops. A modular Python code base has been developed to compute BTCs using embedded analytical solutions at pumping wells based on an existing well-documented finite-difference groundwater-flow simulation code (MODFLOW) and a semi-analytical particle-tracking code (MODPATH). The Python code base is tested by comparing BTCs with highly discretized synthetic steady

  5. ObsPy: A Python Toolbox for Seismology - Recent Developments and Applications

    NASA Astrophysics Data System (ADS)

    Megies, T.; Krischer, L.; Barsch, R.; Sales de Andrade, E.; Beyreuther, M.

    2014-12-01

    ObsPy (http://www.obspy.org) is a community-driven, open-source project dedicated to building a bridge for seismology into the scientific Python ecosystem. It offersa) read and write support for essentially all commonly used waveform, station, and event metadata file formats with a unified interface,b) a comprehensive signal processing toolbox tuned to the needs of seismologists,c) integrated access to all large data centers, web services and databases, andd) convenient wrappers to legacy codes like libtau and evalresp.Python, currently the most popular language for teaching introductory computer science courses at top-ranked U.S. departments, is a full-blown programming language with the flexibility of an interactive scripting language. Its extensive standard library and large variety of freely available high quality scientific modules cover most needs in developing scientific processing workflows. Together with packages like NumPy, SciPy, Matplotlib, IPython, Pandas, lxml, and PyQt, ObsPy enables the construction of complete workflows in Python. These vary from reading locally stored data or requesting data from one or more different data centers through to signal analysis and data processing and on to visualizations in GUI and web applications, output of modified/derived data and the creation of publication-quality figures.ObsPy enjoys a large world-wide rate of adoption in the community. Applications successfully using it include time-dependent and rotational seismology, big data processing, event relocations, and synthetic studies about attenuation kernels and full-waveform inversions to name a few examples. All functionality is extensively documented and the ObsPy tutorial and gallery give a good impression of the wide range of possible use cases.We will present the basic features of ObsPy, new developments and applications, and a roadmap for the near future and discuss the sustainability of our open-source development model.

  6. SMMP v. 3.0—Simulating proteins and protein interactions in Python and Fortran

    NASA Astrophysics Data System (ADS)

    Meinke, Jan H.; Mohanty, Sandipan; Eisenmenger, Frank; Hansmann, Ulrich H. E.

    2008-03-01

    We describe a revised and updated version of the program package SMMP. SMMP is an open-source FORTRAN package for molecular simulation of proteins within the standard geometry model. It is designed as a simple and inexpensive tool for researchers and students to become familiar with protein simulation techniques. SMMP 3.0 sports a revised API increasing its flexibility, an implementation of the Lund force field, multi-molecule simulations, a parallel implementation of the energy function, Python bindings, and more. Program summaryTitle of program:SMMP Catalogue identifier:ADOJ_v3_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/ADOJ_v3_0.html Program obtainable from: CPC Program Library, Queen's University of Belfast, N. Ireland Licensing provisions:Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html Programming language used:FORTRAN, Python No. of lines in distributed program, including test data, etc.:52 105 No. of bytes in distributed program, including test data, etc.:599 150 Distribution format:tar.gz Computer:Platform independent Operating system:OS independent RAM:2 Mbytes Classification:3 Does the new version supersede the previous version?:Yes Nature of problem:Molecular mechanics computations and Monte Carlo simulation of proteins. Solution method:Utilizes ECEPP2/3, FLEX, and Lund potentials. Includes Monte Carlo simulation algorithms for canonical, as well as for generalized ensembles. Reasons for new version:API changes and increased functionality. Summary of revisions:Added Lund potential; parameters used in subroutines are now passed as arguments; multi-molecule simulations; parallelized energy calculation for ECEPP; Python bindings. Restrictions:The consumed CPU time increases with the size of protein molecule. Running time:Depends on the size of the simulated molecule.

  7. π Scope: python based scientific workbench with visualization tool for MDSplus data

    NASA Astrophysics Data System (ADS)

    Shiraiwa, S.

    2014-10-01

    π Scope is a python based scientific data analysis and visualization tool constructed on wxPython and Matplotlib. Although it is designed to be a generic tool, the primary motivation for developing the new software is 1) to provide an updated tool to browse MDSplus data, with functionalities beyond dwscope and jScope, and 2) to provide a universal foundation to construct interface tools to perform computer simulation and modeling for Alcator C-Mod. It provides many features to visualize MDSplus data during tokamak experiments including overplotting different signals and discharges, various plot types (line, contour, image, etc.), in-panel data analysis using python scripts, and publication quality graphics generation. Additionally, the logic to produce multi-panel plots is designed to be backward compatible with dwscope, enabling smooth migration for dwscope users. πScope uses multi-threading to reduce data transfer latency, and its object-oriented design makes it easy to modify and expand while the open source nature allows portability. A built-in tree data browser allows a user to approach the data structure both from a GUI and a script, enabling relatively complex data analysis workflow to be built quickly. As an example, an IDL-based interface to perform GENRAY/CQL3D simulations was ported on πScope, thus allowing LHCD simulation to be run between-shot using C-Mod experimental profiles. This workflow is being used to generate a large database to develop a LHCD actuator model for the plasma control system. Supported by USDoE Award DE-FC02-99ER54512.

  8. AIMBAT: A Python/Matplotlib Tool for Measuring Teleseismic Arrival Times

    NASA Astrophysics Data System (ADS)

    Lou, X.; van der Lee, S.; Lloyd, S.

    2013-12-01

    Python is an open-source, platform-independent, and object-oriented scripting language. It became more popular in the seismologist community since the appearance of ObsPy (Beyreuther et al. 2010, Megies et al. 2011), which provides a powerful framework for seismic data access and processing. This study introduces a new Python-based tool named AIMBAT (Automated and Interactive Measurement of Body-wave Arrival Times) for measuring teleseismic body-wave arrival times on large-scale seismic event data (Lou et al. 2013). Compared to ObsPy, AIMBAT is a lighter tool that is more focused on a particular aspect of seismic data processing. It originates from the widely used MCCC (Multi-Channel Cross-Correlation) method developed by VanDecar and Crosson (1990). On top of the original MCCC procedure, AIMBAT is automated in initial phase picking and is interactive in quality control. The core cross-correlation function is implemented in Fortran to boost up performance in addition to Python. The GUI (graphical user interface) of AIMBAT depends on Matplotlib's GUI-neutral widgets and event-handling API. A number of sorting and (de)selecting options are designed to facilitate the quality control of seismograms. By using AIMBAT, both relative and absolute teleseismic body-wave arrival times are measured. AIMBAT significantly improves efficiency and quality of the measurements. User interaction is needed only to pick the target phase arrival and to set a time window on the array stack. The package is easy to install and use, open-source, and is publicly available. Graphical user interface of AIMBAT.

  9. PyCOOL — A Cosmological Object-Oriented Lattice code written in Python

    SciTech Connect

    Sainio, J.

    2012-04-01

    There are a number of different phenomena in the early universe that have to be studied numerically with lattice simulations. This paper presents a graphics processing unit (GPU) accelerated Python program called PyCOOL that solves the evolution of scalar fields in a lattice with very precise symplectic integrators. The program has been written with the intention to hit a sweet spot of speed, accuracy and user friendliness. This has been achieved by using the Python language with the PyCUDA interface to make a program that is easy to adapt to different scalar field models. In this paper we derive the symplectic dynamics that govern the evolution of the system and then present the implementation of the program in Python and PyCUDA. The functionality of the program is tested in a chaotic inflation preheating model, a single field oscillon case and in a supersymmetric curvaton model which leads to Q-ball production. We have also compared the performance of a consumer graphics card to a professional Tesla compute card in these simulations. We find that the program is not only accurate but also very fast. To further increase the usefulness of the program we have equipped it with numerous post-processing functions that provide useful information about the cosmological model. These include various spectra and statistics of the fields. The program can be additionally used to calculate the generated curvature perturbation. The program is publicly available under GNU General Public License at https://github.com/jtksai/PyCOOL. Some additional information can be found from http://www.physics.utu.fi/tiedostot/theory/particlecosmology/pycool/.

  10. Powerlaw: a Python package for analysis of heavy-tailed distributions.

    PubMed

    Alstott, Jeff; Bullmore, Ed; Plenz, Dietmar

    2014-01-01

    Power laws are theoretically interesting probability distributions that are also frequently used to describe empirical data. In recent years, effective statistical methods for fitting power laws have been developed, but appropriate use of these techniques requires significant programming and statistical insight. In order to greatly decrease the barriers to using good statistical methods for fitting power law distributions, we developed the powerlaw Python package. This software package provides easy commands for basic fitting and statistical analysis of distributions. Notably, it also seeks to support a variety of user needs by being exhaustive in the options available to the user. The source code is publicly available and easily extensible. PMID:24489671

  11. And now for something completely different: Inattentional blindness during a Monty Python's Flying Circus sketch

    PubMed Central

    Wiseman, Richard; Watt, Caroline

    2015-01-01

    Perceptual science has frequently benefited from studying illusions created outside of academia. Here, we describe a striking, but little-known, example of inattentional blindness from the British comedy series “Monty Python's Flying Circus.” Viewers fail to attend to several highly incongruous characters in the sketch, despite these characters being clearly visible onscreen. The sketch has the potential to be a valuable research and teaching resource, as well as providing a vivid illustration of how people often fail to see something completely different. PMID:26034570

  12. ObsPy: A Python toolbox for Seismology, a Data Center Perspective

    NASA Astrophysics Data System (ADS)

    Beyreuther, Moritz; Barsch, Robert; Krischer, Lion; Megies, Tobias; Behr, Yannik; Wassermann, Joachim

    2010-05-01

    ObsPy: A Python toolbox for seismology (http://www.obspy.org) aims at filling the gap between interactive analysis and automatic data acquistion systems. Automatic batch analysis of continuous data streams or feeding a so far unknown formatted data stream into an acquistion system are two possible applications. Python provides a platform independent, free and open source interpreter language including a large collection of scientific open-source modules thus allowing rapid development of prototype code. ObsPy extends Python by providing the seismologist with basic seismological routines, e.g. MiniSEED, SAC, GSE2 read and write support, various pickers, filters, instrument correction... The data itself is stored in numpy.ndarrays allowing powerful numerical array-programming modules like NumPy (http://numpy.scipy.org) or SciPy (http://scipy.org) to be used. Also SeisComP3 has a Python API which makes use of the previous mentioned modules, thus making it easy to extend SeisComP3 with the help of the ObsPy library. Especially for data centers the ObsPy ArcLink and XSEED modules are of special interest. The ArcLink module makes it possible to easily automatically access the data via ArcLink or for testing the servers functionality. The XSEED module allows to convert data from dataless SEED to XML-SEED and back. The XML-SEED format is very verbose and easy extensible for internal purposes. For "public" distribution the resulting extended XML-SEED can always be converted back to the standard exchange format dataless SEED (loosing the additionally fields). An application of ObsPy is running on the Azores. Here, seismic data are continuous recorded with National Instruments digitizers which are writing data in an binary format every 10s. ObsPy is used to feed the data in EarthWorm and SeisComP3 by decoding the binary format every 30s and appending the new data to a MiniSEED file. The MiniSEED file is continuously scanned by the mseed_scan module of the seedlink server and

  13. GPUs and Python: A Recipe for Lightning-Fast Data Pipelines

    NASA Astrophysics Data System (ADS)

    Warner, C.; Packham, C.; Eikenberry, S. S.; Gonzalez, A.

    2012-09-01

    As arrays increase their pixel numbers and mosaics of arrays become more prevalent, the volume of data being produced per night is increasing rapidly. As we look forward to the LSST era, where 30TB of data per night will be produced, streamlined and rapid data reduction processes are becoming critical. Recent developments in the computer industry have led to the production of Graphics Processing Units (GPUs) which can contain hundreds of processing cores, each of which can process hundreds of threads concurrently. Nvidia's Compute Unified Device Architecture (CUDA) platform has allowed developers to take advantage of these modern GPUs and design massively parallel algorithms which can provide huge speed-ups of up to around a factor of 100 over CPU implementations. Data pipelines are perfectly suited to reap the benefits of massive parallelization because many of the algorithms in data processing are performed on a per-pixel basis on ever larger sets of images. In addition, the PyCUDA (http://mathema.tician.de/software/pycuda) module and python native C-API allow for CUDA code to be easily integrated into python code. Python has continued to gain momentum in the astronomical community, particularly as an attractive alternative to IDL or C code for data pipelines. Thus, the ability to link GPU-optimized CUDA code directly into python allows for existing data pipeline frameworks to be reused with new parallel algorithms. We present the initial results of parallelizing many of the more CPU-intensive algorithms in the Florida Analysis Tool Born Of Yearning for high quality scientific data (FATBOY) and discuss the implications for the future of data pipelines. We use an Nvidia 580 GTX GPU for our tests and find that the 580 GTX produces a speed-up of anywhere from a factor of around 10 up to a factor of 300 over CPU implementations for individual routines. We believe that it is possible to obtain an overall pipeline

  14. powerlaw: A Python Package for Analysis of Heavy-Tailed Distributions

    PubMed Central

    Alstott, Jeff; Bullmore, Ed; Plenz, Dietmar

    2014-01-01

    Power laws are theoretically interesting probability distributions that are also frequently used to describe empirical data. In recent years, effective statistical methods for fitting power laws have been developed, but appropriate use of these techniques requires significant programming and statistical insight. In order to greatly decrease the barriers to using good statistical methods for fitting power law distributions, we developed the powerlaw Python package. This software package provides easy commands for basic fitting and statistical analysis of distributions. Notably, it also seeks to support a variety of user needs by being exhaustive in the options available to the user. The source code is publicly available and easily extensible. PMID:24489671

  15. PREdator: a python based GUI for data analysis, evaluation and fitting

    PubMed Central

    2014-01-01

    The analysis of a series of experimental data is an essential procedure in virtually every field of research. The information contained in the data is extracted by fitting the experimental data to a mathematical model. The type of the mathematical model (linear, exponential, logarithmic, etc.) reflects the physical laws that underlie the experimental data. Here, we aim to provide a readily accessible, user-friendly python script for data analysis, evaluation and fitting. PREdator is presented at the example of NMR paramagnetic relaxation enhancement analysis.

  16. Cygrid: Cython-powered convolution-based gridding module for Python

    NASA Astrophysics Data System (ADS)

    Winkel, B.; Lenz, D.; Flöer, L.

    2016-06-01

    The Python module Cygrid grids (resamples) data to any collection of spherical target coordinates, although its typical application involves FITS maps or data cubes. The module supports the FITS world coordinate system (WCS) standard; its underlying algorithm is based on the convolution of the original samples with a 2D Gaussian kernel. A lookup table scheme allows parallelization of the code and is combined with the HEALPix tessellation of the sphere for fast neighbor searches. Cygrid's runtime scales between O(n) and O(nlog n), with n being the number of input samples.

  17. Lmfit: Non-Linear Least-Square Minimization and Curve-Fitting for Python

    NASA Astrophysics Data System (ADS)

    Newville, Matthew; Stensitzki, Till; Allen, Daniel B.; Rawlik, Michal; Ingargiola, Antonino; Nelson, Andrew

    2016-06-01

    Lmfit provides a high-level interface to non-linear optimization and curve fitting problems for Python. Lmfit builds on and extends many of the optimization algorithm of scipy.optimize, especially the Levenberg-Marquardt method from optimize.leastsq. Its enhancements to optimization and data fitting problems include using Parameter objects instead of plain floats as variables, the ability to easily change fitting algorithms, and improved estimation of confidence intervals and curve-fitting with the Model class. Lmfit includes many pre-built models for common lineshapes.

  18. i-PI: A Python interface for ab initio path integral molecular dynamics simulations

    NASA Astrophysics Data System (ADS)

    Ceriotti, Michele; More, Joshua; Manolopoulos, David E.

    2014-03-01

    Recent developments in path integral methodology have significantly reduced the computational expense of including quantum mechanical effects in the nuclear motion in ab initio molecular dynamics simulations. However, the implementation of these developments requires a considerable programming effort, which has hindered their adoption. Here we describe i-PI, an interface written in Python that has been designed to minimise the effort required to bring state-of-the-art path integral techniques to an electronic structure program. While it is best suited to first principles calculations and path integral molecular dynamics, i-PI can also be used to perform classical molecular dynamics simulations, and can just as easily be interfaced with an empirical forcefield code. To give just one example of the many potential applications of the interface, we use it in conjunction with the CP2K electronic structure package to showcase the importance of nuclear quantum effects in high-pressure water. Catalogue identifier: AERN_v1_0 Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AERN_v1_0.html Program obtainable from: CPC Program Library, Queen’s University, Belfast, N. Ireland Licensing provisions: GNU General Public License, version 3 No. of lines in distributed program, including test data, etc.: 138626 No. of bytes in distributed program, including test data, etc.: 3128618 Distribution format: tar.gz Programming language: Python. Computer: Multiple architectures. Operating system: Linux, Mac OSX, Windows. RAM: Less than 256 Mb Classification: 7.7. External routines: NumPy Nature of problem: Bringing the latest developments in the modelling of nuclear quantum effects with path integral molecular dynamics to ab initio electronic structure programs with minimal implementational effort. Solution method: State-of-the-art path integral molecular dynamics techniques are implemented in a Python interface. Any electronic structure code can be patched to receive the atomic

  19. QuTiP 2: A Python framework for the dynamics of open quantum systems

    NASA Astrophysics Data System (ADS)

    Johansson, J. R.; Nation, P. D.; Nori, Franco

    2013-04-01

    We present version 2 of QuTiP, the Quantum Toolbox in Python. Compared to the preceding version [J.R. Johansson, P.D. Nation, F. Nori, Comput. Phys. Commun. 183 (2012) 1760.], we have introduced numerous new features, enhanced performance, and made changes in the Application Programming Interface (API) for improved functionality and consistency within the package, as well as increased compatibility with existing conventions used in other scientific software packages for Python. The most significant new features include efficient solvers for arbitrary time-dependent Hamiltonians and collapse operators, support for the Floquet formalism, and new solvers for Bloch-Redfield and Floquet-Markov master equations. Here we introduce these new features, demonstrate their use, and give a summary of the important backward-incompatible API changes introduced in this version. Catalog identifier: AEMB_v2_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEMB_v2_0.html Program obtainable from: CPC Program Library, Queen’s University, Belfast, N. Ireland Licensing provisions: GNU General Public License, version 3 No. of lines in distributed program, including test data, etc.: 33625 No. of bytes in distributed program, including test data, etc.: 410064 Distribution format: tar.gz Programming language: Python. Computer: i386, x86-64. Operating system: Linux, Mac OSX. RAM: 2+ Gigabytes Classification: 7. External routines: NumPy, SciPy, Matplotlib, Cython Catalog identifier of previous version: AEMB_v1_0 Journal reference of previous version: Comput. Phys. Comm. 183 (2012) 1760 Does the new version supercede the previous version?: Yes Nature of problem: Dynamics of open quantum systems Solution method: Numerical solutions to Lindblad, Floquet-Markov, and Bloch-Redfield master equations, as well as the Monte Carlo wave function method. Reasons for new version: Compared to the preceding version we have introduced numerous new features, enhanced performance, and made changes in

  20. eblur/dust: a modular python approach for dust extinction and scattering

    NASA Astrophysics Data System (ADS)

    Corrales, Lia

    2016-03-01

    I will present a library of python codes -- github.com/eblur/dust -- which calculate dust scattering and extinction properties from the IR to the X-ray. The modular interface allows for custom defined dust grain size distributions, optical constants, and scattering physics. These codes are currently undergoing a major overhaul to include multiple scattering effects, parallel processing, parameterized grain size distributions beyond power law, and optical constants for different grain compositions. I use eblur/dust primarily to study dust scattering images in the X-ray, but they may be extended to applications at other wavelengths.

  1. Digesting pythons quickly oxidize the proteins in their meals and save the lipids for later.

    PubMed

    McCue, Marshall D; Guzman, R Marena; Passement, Celeste A

    2015-07-01

    Pythons digesting rodent meals exhibit up to 10-fold increases in their resting metabolic rate (RMR); this increase in RMR is termed specific dynamic action (SDA). Studies have shown that SDA is partially fueled by oxidizing dietary nutrients, yet it remains unclear whether the proteins and the lipids in their meals contribute equally to this energy demand. We raised two populations of mice on diets labeled with either [(13)C]leucine or [(13)C]palmitic acid to intrinsically enrich the proteins and lipids in their bodies, respectively. Ball pythons (Python regius) were fed whole mice (and pureed mice 3 weeks later), after which we measured their metabolic rates and the δ(13)C in the breath. The δ(13)C values in the whole bodies of the protein- and lipid-labeled mice were generally similar (i.e. 5.7±4.7‰ and 2.8±5.4‰, respectively) but the oxidative kinetics of these two macronutrient pools were quite different. We found that the snakes oxidized 5% of the protein and only 0.24% of the lipids in their meals within 14 days. Oxidation of the dietary proteins peaked 24 h after ingestion, at which point these proteins provided ∼90% of the metabolic requirement of the snakes, and by 14 days the oxidation of these proteins decreased to nearly zero. The oxidation of the dietary lipids peaked 1 day later, at which point these lipids supplied ∼25% of the energy demand. Fourteen days after ingestion, these lipids were still being oxidized and continued to account for ∼25% of the metabolic rate. Pureeing the mice reduced the cost of gastric digestion and decreased SDA by 24%. Pureeing also reduced the oxidation of dietary proteins by 43%, but it had no effect on the rates of dietary lipid oxidation. Collectively, these results demonstrate that pythons are able to effectively partition the two primary metabolic fuels in their meals. This approach of uniquely labeling the different components of the diet will allow researchers to examine new questions about

  2. Py4CAtS - Python tools for line-by-line modelling of infrared atmospheric radiative transfer

    NASA Astrophysics Data System (ADS)

    Schreier, Franz; García, Sebastián Gimeno

    2013-05-01

    Py4CAtS — Python scripts for Computational ATmospheric Spectroscopy is a Python re-implementation of the Fortran infrared radiative transfer code GARLIC, where compute-intensive code sections utilize the Numeric/Scientific Python modules for highly optimized array-processing. The individual steps of an infrared or microwave radiative transfer computation are implemented in separate scripts to extract lines of relevant molecules in the spectral range of interest, to compute line-by-line cross sections for given pressure(s) and temperature(s), to combine cross sections to absorption coefficients and optical depths, and to integrate along the line-of-sight to transmission and radiance/intensity. The basic design of the package, numerical and computational aspects relevant for optimization, and a sketch of the typical workflow are presented.

  3. cloudPEST - A python module for cloud-computing deployment of PEST, a program for parameter estimation

    USGS Publications Warehouse

    Fienen, Michael N.; Kunicki, Thomas C.; Kester, Daniel E.

    2011-01-01

    This report documents cloudPEST-a Python module with functions to facilitate deployment of the model-independent parameter estimation code PEST on a cloud-computing environment. cloudPEST makes use of low-level, freely available command-line tools that interface with the Amazon Elastic Compute Cloud (EC2(TradeMark)) that are unlikely to change dramatically. This report describes the preliminary setup for both Python and EC2 tools and subsequently describes the functions themselves. The code and guidelines have been tested primarily on the Windows(Registered) operating system but are extensible to Linux(Registered).

  4. Species identification of protected carpet pythons suitable for degraded forensic samples.

    PubMed

    Ciavaglia, Sherryn; Donnellan, Stephen; Henry, Julianne; Linacre, Adrian

    2014-09-01

    In this paper we report on the identification of a section of mitochondrial DNA that can be used to identify the species of protected and illegally traded pythons of the genus Morelia. Successful enforcement of wildlife laws requires forensic tests that can identify the species nominated in the relevant legislation. The potentially degraded state of evidentiary samples requires that forensic investigation using molecular genetic species identification is optimized to interrogate small fragments of DNA. DNA was isolated from 35 samples of Morelia spilota from which the complete cytochrome b was sequenced. The ND6 gene was also sequenced in 32 of these samples. Additional DNA sequences were generated from 9 additional species of Morelia. The sequences were aligned by Geneious and imported into MEGA to create phylogenetic trees based on the entire complex of approximately 1,706 base pairs (bp). To mimic degraded DNA, which is usually found in forensic cases, short sub-sections of the full alignment were used to generate phylogenetic trees. The sub-sections that had the greatest DNA sequence information were in parts of the cytochrome b gene. Our results highlight that legislation is presently informed by inadequate taxonomy. We demonstrated that a 278 bp region of the cytochrome b gene recovered the topology of the phylogenetic tree found with the entire gene sequence and correctly identified species of Morelia with a high degree of confidence. The locus described in this report will assist in the successful prosecution of alleged illegal trade in python species. PMID:24915762

  5. Streamlining an IRAF data reduction process Pythonically with Astropy and NDMapper

    NASA Astrophysics Data System (ADS)

    Turner, James

    2016-03-01

    In the course of re-writing my typical top-level GMOS-IFU data reduction sequence in Python for a research project, I have developed a small module that helps express the scientific process in a relatively intuitive way as a Pythonic series of operations on NDData collections, mapped to files, with existing IRAF steps integrated almost seamlessly (pending their eventual replacement). For scientific end-user purposes, this experiment aims to obviate a need for pipeline machinery, favouring simple control flow in the main script and retaining a smooth transition from high-level process description to lower-level libraries by encapsulating necessary bookeeping within the data representation and simple wrappers. The I/O abstraction should make support for file formats other than FITS (eg. ASDF) straightforward to add. This work-in-progress can be found at https://github.com/jehturner/ndmapper and I intend to split its functionality involving IRAF or instrument processing into a separate "ndprocess" module as the prototype nears completion, leaving a core "ndmapper" package, without any special dependencies, as a general add-on for nddata.

  6. PylotDB - A Database Management, Graphing, and Analysis Tool Written in Python

    Energy Science and Technology Software Center (ESTSC)

    2012-01-04

    PylotDB, written completely in Python, provides a user interface (UI) with which to interact with, analyze, graph data from, and manage open source databases such as MySQL. The UI mitigates the user having to know in-depth knowledge of the database application programming interface (API). PylotDB allows the user to generate various kinds of plots from user-selected data; generate statistical information on text as well as numerical fields; backup and restore databases; compare database tables acrossmore » different databases as well as across different servers; extract information from any field to create new fields; generate, edit, and delete databases, tables, and fields; generate or read into a table CSV data; and similar operations. Since much of the database information is brought under control of the Python computer language, PylotDB is not intended for huge databases for which MySQL and Oracle, for example, are better suited. PylotDB is better suited for smaller databases that might be typically needed in a small research group situation. PylotDB can also be used as a learning tool for database applications in general.« less

  7. PyMVPA: A Python toolbox for multivariate pattern analysis of fMRI data

    PubMed Central

    Hanke, Michael; Halchenko, Yaroslav O.; Sederberg, Per B.; Hanson, Stephen José; Haxby, James V.; Pollmann, Stefan

    2009-01-01

    Decoding patterns of neural activity onto cognitive states is one of the central goals of functional brain imaging. Standard univariate fMRI analysis methods, which correlate cognitive and perceptual function with the blood oxygenation-level dependent (BOLD) signal, have proven successful in identifying anatomical regions based on signal increases during cognitive and perceptual tasks. Recently, researchers have begun to explore new multivariate techniques that have proven to be more flexible, more reliable, and more sensitive than standard univariate analysis. Drawing on the field of statistical learning theory, these new classifier-based analysis techniques possess explanatory power that could provide new insights into the functional properties of the brain. However, unlike the wealth of software packages for univariate analyses, there are few packages that facilitate multivariate pattern classification analyses of fMRI data. Here we introduce a Python-based, cross-platform, and open-source software toolbox, called PyMVPA, for the application of classifier-based analysis techniques to fMRI datasets. PyMVPA makes use of Python's ability to access libraries written in a large variety of programming languages and computing environments to interface with the wealth of existing machine-learning packages. We present the framework in this paper and provide illustrative examples on its usage, features, and programmability. PMID:19184561

  8. Low cost of gastric acid secretion during digestion in ball pythons.

    PubMed

    Nørgaard, Simon; Andreassen, Kim; Malte, Christian Lind; Enok, Sanne; Wang, Tobias

    2016-04-01

    Due to their large metabolic responses to digestion (specific dynamic action, SDA), snakes represent an interesting animal group to identify the underlying mechanisms for the postprandial rise in metabolism. The SDA response results from the energetic costs of many different processes ranging over prey handling, secretions by the digestive system, synthesis of enzymes, plasticity of most visceral organs, as well as protein synthesis and nitrogen excretion. The contribution of the individual mechanisms, however, remains elusive. Gastric acid secretion has been proposed to account for more than half of the SDA response, while other studies report much lower contributions of the gastric processes. To investigate the energetic cost of gastric acid secretion, ball pythons (Python regius) were fed meals with added amounts of bone meal (up to 25 g bone meal kg(-1) snake) to achieve a five-fold rise in the buffer capacity of the meals. Direct measurements within the stomach lumen showed similar reduction in gastric pH when buffer capacity was increased, but we found no effects on the rise in oxygen consumption over the first three days of digestion. There was, however, a slower return of oxygen consumption to resting baseline. We conclude that gastric acid secretion only contributes modestly to the SDA response and propose that post-absorptive processes, such as increased protein synthesis, are likely to underlie the SDA response. PMID:26802791

  9. PylotDB - A Database Management, Graphing, and Analysis Tool Written in Python

    SciTech Connect

    Barnette, Daniel W.

    2012-01-04

    PylotDB, written completely in Python, provides a user interface (UI) with which to interact with, analyze, graph data from, and manage open source databases such as MySQL. The UI mitigates the user having to know in-depth knowledge of the database application programming interface (API). PylotDB allows the user to generate various kinds of plots from user-selected data; generate statistical information on text as well as numerical fields; backup and restore databases; compare database tables across different databases as well as across different servers; extract information from any field to create new fields; generate, edit, and delete databases, tables, and fields; generate or read into a table CSV data; and similar operations. Since much of the database information is brought under control of the Python computer language, PylotDB is not intended for huge databases for which MySQL and Oracle, for example, are better suited. PylotDB is better suited for smaller databases that might be typically needed in a small research group situation. PylotDB can also be used as a learning tool for database applications in general.

  10. Using Python to generate AHPS-based precipitation simulations over CONUS using Amazon distributed computing

    NASA Astrophysics Data System (ADS)

    Machalek, P.; Kim, S. M.; Berry, R. D.; Liang, A.; Small, T.; Brevdo, E.; Kuznetsova, A.

    2012-12-01

    We describe how the Climate Corporation uses Python and Clojure, a language impleneted on top of Java, to generate climatological forecasts for precipitation based on the Advanced Hydrologic Prediction Service (AHPS) radar based daily precipitation measurements. A 2-year-long forecasts is generated on each of the ~650,000 CONUS land based 4-km AHPS grids by constructing 10,000 ensembles sampled from a 30-year reconstructed AHPS history for each grid. The spatial and temporal correlations between neighboring AHPS grids and the sampling of the analogues are handled by Python. The parallelization for all the 650,000 CONUS stations is further achieved by utilizing the MAP-REDUCE framework (http://code.google.com/edu/parallel/mapreduce-tutorial.html). Each full scale computational run requires hundreds of nodes with up to 8 processors each on the Amazon Elastic MapReduce (http://aws.amazon.com/elasticmapreduce/) distributed computing service resulting in 3 terabyte datasets. We further describe how we have productionalized a monthly run of the simulations process at full scale of the 4km AHPS grids and how the resultant terabyte sized datasets are handled.

  11. Pyff – A Pythonic Framework for Feedback Applications and Stimulus Presentation in Neuroscience

    PubMed Central

    Venthur, Bastian; Scholler, Simon; Williamson, John; Dähne, Sven; Treder, Matthias S.; Kramarek, Maria T.; Müller, Klaus-Robert; Blankertz, Benjamin

    2010-01-01

    This paper introduces Pyff, the Pythonic feedback framework for feedback applications and stimulus presentation. Pyff provides a platform-independent framework that allows users to develop and run neuroscientific experiments in the programming language Python. Existing solutions have mostly been implemented in C++, which makes for a rather tedious programming task for non-computer-scientists, or in Matlab, which is not well suited for more advanced visual or auditory applications. Pyff was designed to make experimental paradigms (i.e., feedback and stimulus applications) easily programmable. It includes base classes for various types of common feedbacks and stimuli as well as useful libraries for external hardware such as eyetrackers. Pyff is also equipped with a steadily growing set of ready-to-use feedbacks and stimuli. It can be used as a standalone application, for instance providing stimulus presentation in psychophysics experiments, or within a closed loop such as in biofeedback or brain–computer interfacing experiments. Pyff communicates with other systems via a standardized communication protocol and is therefore suitable to be used with any system that may be adapted to send its data in the specified format. Having such a general, open-source framework will help foster a fruitful exchange of experimental paradigms between research groups. In particular, it will decrease the need of reprogramming standard paradigms, ease the reproducibility of published results, and naturally entail some standardization of stimulus presentation. PMID:21160550

  12. Trichinella papuae and Trichinella zimbabwensis induce infection in experimentally infected varans, caimans, pythons and turtles.

    PubMed

    Pozio, E; Marucci, G; Casulli, A; Sacchi, L; Mukaratirwa, S; Foggin, C M; La Rosa, G

    2004-03-01

    The discovery of Trichinella zimbabwensis in farm crocodiles of Zimbabwe has opened up a new frontier in the epidemiology of the Trichinella genus. The objective of the present study was to investigate the infectivity of encapsulated species (T. spiralis, T. nativa, T. britovi, T. murrelli and T. nelsoni) and non-encapsulated species (T. pseudospiralis, T. papuae and T. zimbabwensis) in caimans (Caiman crocodilus), varans (Varanus exanthematicus), pythons (Python molurus bivittatus) and turtles (Pelomedusa subrufa) raised at their natural temperature range (26-32 degrees C). Mice and chickens were used as controls. At 6 days post-infection (p.i.), adult worms were detected in the small intestine of reptiles infected with T. papuae and T. zimbabwensis, of chickens infected with T. pseudospiralis and of mice infected with all encapsulated and non-encapsulated species. At 60 days p.i., T. papuae and T. zimbabwensis adult worms were collected from the intestine of varans and caimans and larvae from muscles of the four reptile species, T. pseudospiralis larvae from muscles of chickens, and larvae of all Trichinella species from mouse muscles. The highest reproductive capacity index of both T. papuae and T. zimbabwensis was observed in varans. The results show that T. papuae and T. zimbabwensis are able to complete their entire life-cycle in both poikilothermic and homoiothermic animals. PMID:15074882

  13. A parallelized Python based Multi-Point Thomson Scattering analysis in NSTX-U

    NASA Astrophysics Data System (ADS)

    Miller, Jared; Diallo, Ahmed; Leblanc, Benoit

    2014-10-01

    Multi-Point Thomson Scattering (MPTS) is a reliable and accurate method of finding the temperature, density, and pressure of a magnetically confined plasma. Nd:YAG (1064 nm) lasers are fired into the plasma with a frequency of 60 Hz, and the light is Doppler shifted by Thomson scattering. Polychromators on the midplane of the tokamak pick up the light at various radii/scattering angles, and the avalanche photodiode's voltages are added to an MDSplus tree for later analysis. This project ports and optimizes the prior serial IDL MPTS code into a well-documented Python package that runs in parallel. Since there are 30 polychromators in the current NSTX setup (12 more will be added when NSTX-U is completed), using parallelism offers vast savings in performance. NumPy and SciPy further accelerate numerical calculations and matrix operations, Matplotlib and PyQt make an intuitive GUI with plots of the output, and Multiprocessing parallelizes the computationally intensive calculations. The Python package was designed with portability and flexibility in mind so it can be adapted for use in any polychromator-based MPTS system.

  14. Nmrglue: An Open Source Python Package for the Analysis of Multidimensional NMR Data

    PubMed Central

    Helmus, Jonathan J.; Jaroniec, Christopher P.

    2013-01-01

    Nmrglue, an open source Python package for working with multidimensional NMR data, is described. When used in combination with other Python scientific libraries, nmrglue provides a highly flexible and robust environment for spectral processing, analysis and visualization and includes a number of common utilities such as linear prediction, peak picking and lineshape fitting. The package also enables existing NMR software programs to be readily tied together, currently facilitating the reading, writing and conversion of data stored in Bruker, Agilent/Varian, NMRPipe, Sparky, SIMPSON, and Rowland NMR Toolkit file formats. In addition to standard applications, the versatility offered by nmrglue makes the package particularly suitable for tasks that include manipulating raw spectrometer data files, automated quantitative analysis of multidimensional NMR spectra with irregular lineshapes such as those frequently encountered in the context of biomacromolecular solid-state NMR, and rapid implementation and development of unconventional data processing methods such as covariance NMR and other non-Fourier approaches. Detailed documentation, install files and source code for nmrglue are freely available at http://nmrglue.com. The source code can be redistributed and modified under the New BSD license. PMID:23456039

  15. Automatic Parallelization of Numerical Python Applications using the Global Arrays Toolkit

    SciTech Connect

    Daily, Jeffrey A.; Lewis, Robert R.

    2011-11-30

    Global Arrays is a software system from Pacific Northwest National Laboratory that enables an efficient, portable, and parallel shared-memory programming interface to manipulate distributed dense arrays. The NumPy module is the de facto standard for numerical calculation in the Python programming language, a language whose use is growing rapidly in the scientific and engineering communities. NumPy provides a powerful N-dimensional array class as well as other scientific computing capabilities. However, like the majority of the core Python modules, NumPy is inherently serial. Using a combination of Global Arrays and NumPy, we have reimplemented NumPy as a distributed drop-in replacement called Global Arrays in NumPy (GAiN). Serial NumPy applications can become parallel, scalable GAiN applications with only minor source code changes. Scalability studies of several different GAiN applications will be presented showing the utility of developing serial NumPy codes which can later run on more capable clusters or supercomputers.

  16. Python for Development of OpenMP and CUDA Kernels for Multidimensional Data

    SciTech Connect

    Bell, Zane W; Davidson, Gregory G; D'Azevedo, Ed F; Evans, Thomas M; Joubert, Wayne; Munro Jr, John K; Patlolla, Dilip Reddy; Vacaliuc, Bogdan

    2011-01-01

    Design of data structures for high performance computing (HPC) is one of the principal challenges facing researchers looking to utilize heterogeneous computing machinery. Heterogeneous systems derive cost, power, and speed efficiency by being composed of the appropriate hardware for the task. Yet, each type of processor requires a specific organization of the application state in order to achieve peak performance. Discovering this and refactoring the code can be a challenging and time-consuming task for the researcher, as the data structures and the computational model must be co-designed. We present a methodology that uses Python as the environment for which to explore tradeoffs in both the data structure design as well as the code executing on the computation accelerator. Our method enables multi-dimensional arrays to be used effectively in any target environment. We have chosen to focus on OpenMP and CUDA environments, thus exploring the development of optimized kernels for the two most common classes of computing hardware available today: multi-core CPU and GPU. Python s large palette of file and network access routines, its associative indexing syntax and support for common HPC environments makes it relevant for diverse hardware ranging from laptops through computing clusters to the highest performance supercomputers. Our work enables researchers to accelerate the development of their codes on the computing hardware of their choice.

  17. Jungle Giants: Assessing Sustainable Harvesting in a Difficult-to-Survey Species (Python reticulatus).

    PubMed

    Natusch, Daniel J D; Lyons, Jessica A; Mumpuni; Riyanto, Awal; Shine, Richard

    2016-01-01

    Sustainability of wildlife harvests is critical but difficult to assess. Evaluations of sustainability typically combine modelling with the measurement of underlying abundances. For many taxa harvested in developing countries, however, abundances are near-impossible to survey and a lack of detailed ecological information impedes the reliability of models. In such cases, repeated surveys of the attributes of harvested individuals may provide more robust information on sustainability. If the numbers, sizes and other demographic attributes of animals taken for the commercial trade do not change over biologically significant time intervals (decades), there is a prima facie case that the harvest is indeed sustainable. Here, we report the results of examinations of > 4,200 reticulated pythons (Python reticulatus) taken for the commercial leather industry in northern and southern Sumatra, Indonesia. The numbers, mean body sizes, clutch sizes, sizes at maturity and proportion of giant specimens have not decreased between our first surveys (1995) and repeat surveys (2015). Thus, despite assumptions to the contrary, the harvest appears to be sustainable. We use our data to inform the design of future monitoring programs for this species. Our study underpins the need for robust science to inform wildlife trade policy and decision-making, and urges wildlife managers to assess sustainability of difficult-to-survey terrestrial wildlife by drawing inferences directly from the harvest itself. PMID:27391138

  18. Cygrid: A fast Cython-powered convolution-based gridding module for Python

    NASA Astrophysics Data System (ADS)

    Winkel, B.; Lenz, D.; Flöer, L.

    2016-06-01

    Context. Data gridding is a common task in astronomy and many other science disciplines. It refers to the resampling of irregularly sampled data to a regular grid. Aims: We present cygrid, a library module for the general purpose programming language Python. Cygrid can be used to resample data to any collection of target coordinates, although its typical application involves FITS maps or data cubes. The FITS world coordinate system standard is supported. Methods: The regridding algorithm is based on the convolution of the original samples with a kernel of arbitrary shape. We introduce a lookup table scheme that allows us to parallelize the gridding and combine it with the HEALPix tessellation of the sphere for fast neighbor searches. Results: We show that for n input data points, cygrids runtime scales between O(n) and O(nlog n) and analyze the performance gain that is achieved using multiple CPU cores. We also compare the gridding speed with other techniques, such as nearest-neighbor, and linear and cubic spline interpolation. Conclusions: Cygrid is a very fast and versatile gridding library that significantly outperforms other third-party Python modules, such as the linear and cubic spline interpolation provided by SciPy. http://https://github.com/bwinkel/cygrid

  19. Jungle Giants: Assessing Sustainable Harvesting in a Difficult-to-Survey Species (Python reticulatus)

    PubMed Central

    Natusch, Daniel J. D.; Lyons, Jessica A.; Mumpuni; Riyanto, Awal; Shine, Richard

    2016-01-01

    Sustainability of wildlife harvests is critical but difficult to assess. Evaluations of sustainability typically combine modelling with the measurement of underlying abundances. For many taxa harvested in developing countries, however, abundances are near-impossible to survey and a lack of detailed ecological information impedes the reliability of models. In such cases, repeated surveys of the attributes of harvested individuals may provide more robust information on sustainability. If the numbers, sizes and other demographic attributes of animals taken for the commercial trade do not change over biologically significant time intervals (decades), there is a prima facie case that the harvest is indeed sustainable. Here, we report the results of examinations of > 4,200 reticulated pythons (Python reticulatus) taken for the commercial leather industry in northern and southern Sumatra, Indonesia. The numbers, mean body sizes, clutch sizes, sizes at maturity and proportion of giant specimens have not decreased between our first surveys (1995) and repeat surveys (2015). Thus, despite assumptions to the contrary, the harvest appears to be sustainable. We use our data to inform the design of future monitoring programs for this species. Our study underpins the need for robust science to inform wildlife trade policy and decision-making, and urges wildlife managers to assess sustainability of difficult-to-survey terrestrial wildlife by drawing inferences directly from the harvest itself. PMID:27391138

  20. Pyff - a pythonic framework for feedback applications and stimulus presentation in neuroscience.

    PubMed

    Venthur, Bastian; Scholler, Simon; Williamson, John; Dähne, Sven; Treder, Matthias S; Kramarek, Maria T; Müller, Klaus-Robert; Blankertz, Benjamin

    2010-01-01

    This paper introduces Pyff, the Pythonic feedback framework for feedback applications and stimulus presentation. Pyff provides a platform-independent framework that allows users to develop and run neuroscientific experiments in the programming language Python. Existing solutions have mostly been implemented in C++, which makes for a rather tedious programming task for non-computer-scientists, or in Matlab, which is not well suited for more advanced visual or auditory applications. Pyff was designed to make experimental paradigms (i.e., feedback and stimulus applications) easily programmable. It includes base classes for various types of common feedbacks and stimuli as well as useful libraries for external hardware such as eyetrackers. Pyff is also equipped with a steadily growing set of ready-to-use feedbacks and stimuli. It can be used as a standalone application, for instance providing stimulus presentation in psychophysics experiments, or within a closed loop such as in biofeedback or brain-computer interfacing experiments. Pyff communicates with other systems via a standardized communication protocol and is therefore suitable to be used with any system that may be adapted to send its data in the specified format. Having such a general, open-source framework will help foster a fruitful exchange of experimental paradigms between research groups. In particular, it will decrease the need of reprogramming standard paradigms, ease the reproducibility of published results, and naturally entail some standardization of stimulus presentation. PMID:21160550

  1. An ecological risk assessment of nonnative boas and pythons as potentially invasive species in the United States.

    PubMed

    Reed, Robert N

    2005-06-01

    The growing international trade in live wildlife has the potential to result in continuing establishment of nonnative animal populations in the United States. Snakes may pose particularly high risks as potentially invasive species, as exemplified by the decimation of Guam's vertebrate fauna by the accidentally introduced brown tree snake. Herein, ecological and commercial predictors of the likelihood of establishment of invasive populations were used to model risk associated with legal commercial imports of 23 species of boas, pythons, and relatives into the United States during the period 1989-2000. Data on ecological variables were collected from multiple sources, while data on commercial variables were collated from import records maintained by the U.S. Fish and Wildlife Service. Results of the risk-assessment models indicate that species including boa constrictors (Boa constrictor), ball pythons (Python regius), and reticulated pythons (P. reticulatus) may pose particularly high risks as potentially invasive species. Recommendations for reducing risk of establishment of invasive populations of snakes and/or pathogens include temporary quarantine of imports to increase detection rates of nonnative pathogens, increasing research attention to reptile pathogens, reducing the risk that nonnative snakes will reach certain areas with high numbers of federally listed species (such as the Florida Keys), and attempting to better educate individuals purchasing reptiles. PMID:16022706

  2. Rapid Microsatellite Marker Development Using Next Generation Pyrosequencing to Inform Invasive Burmese Python—Python molurus bivittatus—Management

    PubMed Central

    Hunter, Margaret E.; Hart, Kristen M.

    2013-01-01

    Invasive species represent an increasing threat to native ecosystems, harming indigenous taxa through predation, habitat modification, cross-species hybridization and alteration of ecosystem processes. Additionally, high economic costs are associated with environmental damage, restoration and control measures. The Burmese python, Python molurus bivittatus, is one of the most notable invasive species in the US, due to the threat it poses to imperiled species and the Greater Everglades ecosystem. To address population structure and relatedness, next generation sequencing was used to rapidly produce species-specific microsatellite loci. The Roche 454 GS-FLX Titanium platform provided 6616 di-, tri- and tetra-nucleotide repeats in 117,516 sequences. Using stringent criteria, 24 of 26 selected tri- and tetra-nucleotide loci were polymerase chain reaction (PCR) amplified and 18 were polymorphic. An additional six cross-species loci were amplified, and the resulting 24 loci were incorporated into eight PCR multiplexes. Multi-locus genotypes yielded an average of 61% (39%–77%) heterozygosity and 3.7 (2–6) alleles per locus. Population-level studies using the developed microsatellites will track the invasion front and monitor population-suppression dynamics. Additionally, cross-species amplification was detected in the invasive Ball, P. regius, and Northern African python, P. sebae. These markers can be used to address the hybridization potential of Burmese pythons and the larger, more aggressive P. sebae. PMID:23449030

  3. A Pure-Python Robust Frequency Band Automatic Phase Picker for Seismic Monitoring

    NASA Astrophysics Data System (ADS)

    Chen, C.; Holland, A. A.

    2013-12-01

    We modify the FPPICK algorithm of Lomax et al. (2012) and implement an automatic phase picking algorithm implemented in Python. The algorithm takes advantage of existing seismological Python libraries, Obspy. The algorithm is designed to work on a variety of instrumentation and automatically adapts to different sampling rates. The time series signals are band-pass filtered for each band, octave, considered within the picker algorithm. The energy of the signal is calculated over an averaging window and multiplied by the instantaneous energy of the signal. This energy time-series is the statistic we can then examine for each frequency band considered. The summary statistic, which allows the identification of a trigger, is simply the maximum value of any frequency bands energy statistic at each sample time. A trigger is identified by using a control chart type statistic to identify when our statistics summary is changing rapidly and exceeds a specified number of standard deviations from the mean of the summary energy statistic. This has the advantage that the picker parameters don't necessarily need to be modified when processing data from a wide variety of instrumentation with different response characteristics. The algorithm also contains a method to determine the first motion direction associated with a pick as well as an uncertainty for the pick. As with any automatic phase identification system false picks can and do occur. A few simple algorithms are implemented to avoid false-picks, the picker can be configured not to include these checks. These algorithms remove picks that occur very close in time, and picks for which a phase has a smaller RMS than the previous time interval. The algorithm uses many techniques within Numpy to improve computation times. The algorithm effectively picks both P- and S-phase from local and regional earthquakes with only small amounts of picker parameter modifications. The picker can pick both P and S phases on local and regional

  4. Escript: Open Source Environment For Solving Large-Scale Geophysical Joint Inversion Problems in Python

    NASA Astrophysics Data System (ADS)

    Gross, Lutz; Altinay, Cihan; Fenwick, Joel; Smith, Troy

    2014-05-01

    The program package escript has been designed for solving mathematical modeling problems using python, see Gross et al. (2013). Its development and maintenance has been funded by the Australian Commonwealth to provide open source software infrastructure for the Australian Earth Science community (recent funding by the Australian Geophysical Observing System EIF (AGOS) and the AuScope Collaborative Research Infrastructure Scheme (CRIS)). The key concepts of escript are based on the terminology of spatial functions and partial differential equations (PDEs) - an approach providing abstraction from the underlying spatial discretization method (i.e. the finite element method (FEM)). This feature presents a programming environment to the user which is easy to use even for complex models. Due to the fact that implementations are independent from data structures simulations are easily portable across desktop computers and scalable compute clusters without modifications to the program code. escript has been successfully applied in a variety of applications including modeling mantel convection, melting processes, volcanic flow, earthquakes, faulting, multi-phase flow, block caving and mineralization (see Poulet et al. 2013). The recent escript release (see Gross et al. (2013)) provides an open framework for solving joint inversion problems for geophysical data sets (potential field, seismic and electro-magnetic). The strategy bases on the idea to formulate the inversion problem as an optimization problem with PDE constraints where the cost function is defined by the data defect and the regularization term for the rock properties, see Gross & Kemp (2013). This approach of first-optimize-then-discretize avoids the assemblage of the - in general- dense sensitivity matrix as used in conventional approaches where discrete programming techniques are applied to the discretized problem (first-discretize-then-optimize). In this paper we will discuss the mathematical framework for

  5. SpacePy - a Python-based library of tools for the space sciences

    SciTech Connect

    Morley, Steven K; Welling, Daniel T; Koller, Josef; Larsen, Brian A; Henderson, Michael G

    2010-01-01

    Space science deals with the bodies within the solar system and the interplanetary medium; the primary focus is on atmospheres and above - at Earth the short timescale variation in the the geomagnetic field, the Van Allen radiation belts and the deposition of energy into the upper atmosphere are key areas of investigation. SpacePy is a package for Python, targeted at the space sciences, that aims to make basic data analysis, modeling and visualization easier. It builds on the capabilities of the well-known NumPy and MatPlotLib packages. Publication quality output direct from analyses is emphasized. The SpacePy project seeks to promote accurate and open research standards by providing an open environment for code development. In the space physics community there has long been a significant reliance on proprietary languages that restrict free transfer of data and reproducibility of results. By providing a comprehensive, open-source library of widely used analysis and visualization tools in a free, modern and intuitive language, we hope that this reliance will be diminished. SpacePy includes implementations of widely used empirical models, statistical techniques used frequently in space science (e.g. superposed epoch analysis), and interfaces to advanced tools such as electron drift shell calculations for radiation belt studies. SpacePy also provides analysis and visualization tools for components of the Space Weather Modeling Framework - currently this only includes the BATS-R-US 3-D magnetohydrodynamic model and the RAM ring current model - including streamline tracing in vector fields. Further development is currently underway. External libraries, which include well-known magnetic field models, high-precision time conversions and coordinate transformations are wrapped for access from Python using SWIG and f2py. The rest of the tools have been implemented directly in Python. The provision of open-source tools to perform common tasks will provide openness in the

  6. Data Provenance as a Tool for Debugging Hydrological Models based on Python

    NASA Astrophysics Data System (ADS)

    Wombacher, A.; Huq, M.; Wada, Y.; Van Beek, R.

    2012-12-01

    There is an increase in data volume used in hydrological modeling. The increasing data volume requires additional efforts in debugging models since a single output value is influenced by a multitude of input values. Thus, it is difficult to keep an overview among the data dependencies. Further, knowing these dependencies, it is a tedious job to infer all the relevant data values. The aforementioned data dependencies are also known as data provenance, i.e. the determination of how a particular value has been created and processed. The proposed tool infers the data provenance automatically from a python script and visualizes the dependencies as a graph without executing the script. To debug the model the user specifies the value of interest in space and time. The tool infers all related data values and displays them in the graph. The tool has been evaluated by hydrologists developing a model for estimating the global water demand [1]. The model uses multiple different data sources. The script we analysed has 120 lines of codes and used more than 3000 individual files, each of them representing a raster map of 360*720 cells. After importing the data of the files into a SQLite database, the data consumes around 40 GB of memory. Using the proposed tool a modeler is able to select individual values and infer which values have been used to calculate the value. Especially in cases of outliers or missing values it is a beneficial tool to provide the modeler with efficient information to investigate the unexpected behavior of the model. The proposed tool can be applied to many python scripts and has been tested with other scripts in different contexts. In case a python code contains an unknown function or class the tool requests additional information about the used function or class to enable the inference. This information has to be entered only once and can be shared with colleagues or in the community. Reference [1] Y. Wada, L. P. H. van Beek, D. Viviroli, H. H. Drr, R

  7. libNeuroML and PyLEMS: using Python to combine procedural and declarative modeling approaches in computational neuroscience

    PubMed Central

    Vella, Michael; Cannon, Robert C.; Crook, Sharon; Davison, Andrew P.; Ganapathy, Gautham; Robinson, Hugh P. C.; Silver, R. Angus; Gleeson, Padraig

    2014-01-01

    NeuroML is an XML-based model description language, which provides a powerful common data format for defining and exchanging models of neurons and neuronal networks. In the latest version of NeuroML, the structure and behavior of ion channel, synapse, cell, and network model descriptions are based on underlying definitions provided in LEMS, a domain-independent language for expressing hierarchical mathematical models of physical entities. While declarative approaches for describing models have led to greater exchange of model elements among software tools in computational neuroscience, a frequent criticism of XML-based languages is that they are difficult to work with directly. Here we describe two Application Programming Interfaces (APIs) written in Python (http://www.python.org), which simplify the process of developing and modifying models expressed in NeuroML and LEMS. The libNeuroML API provides a Python object model with a direct mapping to all NeuroML concepts defined by the NeuroML Schema, which facilitates reading and writing the XML equivalents. In addition, it offers a memory-efficient, array-based internal representation, which is useful for handling large-scale connectomics data. The libNeuroML API also includes support for performing common operations that are required when working with NeuroML documents. Access to the LEMS data model is provided by the PyLEMS API, which provides a Python implementation of the LEMS language, including the ability to simulate most models expressed in LEMS. Together, libNeuroML and PyLEMS provide a comprehensive solution for interacting with NeuroML models in a Python environment. PMID:24795618

  8. libNeuroML and PyLEMS: using Python to combine procedural and declarative modeling approaches in computational neuroscience.

    PubMed

    Vella, Michael; Cannon, Robert C; Crook, Sharon; Davison, Andrew P; Ganapathy, Gautham; Robinson, Hugh P C; Silver, R Angus; Gleeson, Padraig

    2014-01-01

    NeuroML is an XML-based model description language, which provides a powerful common data format for defining and exchanging models of neurons and neuronal networks. In the latest version of NeuroML, the structure and behavior of ion channel, synapse, cell, and network model descriptions are based on underlying definitions provided in LEMS, a domain-independent language for expressing hierarchical mathematical models of physical entities. While declarative approaches for describing models have led to greater exchange of model elements among software tools in computational neuroscience, a frequent criticism of XML-based languages is that they are difficult to work with directly. Here we describe two Application Programming Interfaces (APIs) written in Python (http://www.python.org), which simplify the process of developing and modifying models expressed in NeuroML and LEMS. The libNeuroML API provides a Python object model with a direct mapping to all NeuroML concepts defined by the NeuroML Schema, which facilitates reading and writing the XML equivalents. In addition, it offers a memory-efficient, array-based internal representation, which is useful for handling large-scale connectomics data. The libNeuroML API also includes support for performing common operations that are required when working with NeuroML documents. Access to the LEMS data model is provided by the PyLEMS API, which provides a Python implementation of the LEMS language, including the ability to simulate most models expressed in LEMS. Together, libNeuroML and PyLEMS provide a comprehensive solution for interacting with NeuroML models in a Python environment. PMID:24795618

  9. PySLHA: a Pythonic interface to SUSY Les Houches Accord data

    NASA Astrophysics Data System (ADS)

    Buckley, Andy

    2015-10-01

    This paper describes the PySLHA package, a Python language module and program collection for reading, writing and visualising SUSY model data in the SLHA format. PySLHA can read and write SLHA data in a very general way, including the official SLHA2 extension and user customisations, and with arbitrarily deep indexing of data block entries and a dedicated, intuitive interface for particle data and decay information. The draft SLHA3 XSECTION feature is also fully supported. PySLHA can additionally read and write the legacy ISAWIG model format, and provides format conversion scripts. A publication-quality mass spectrum and decay chain plotting tool, slhaplot, is included in the package.

  10. TEMPy: a Python library for assessment of three-dimensional electron microscopy density fits

    PubMed Central

    Farabella, Irene; Vasishtan, Daven; Joseph, Agnel Praveen; Pandurangan, Arun Prasad; Sahota, Harpal; Topf, Maya

    2015-01-01

    Three-dimensional electron microscopy is currently one of the most promising techniques used to study macromolecular assemblies. Rigid and flexible fitting of atomic models into density maps is often essential to gain further insights into the assemblies they represent. Currently, tools that facilitate the assessment of fitted atomic models and maps are needed. TEMPy (template and electron microscopy comparison using Python) is a toolkit designed for this purpose. The library includes a set of methods to assess density fits in intermediate-to-low resolution maps, both globally and locally. It also provides procedures for single-fit assessment, ensemble generation of fits, clustering, and multiple and consensus scoring, as well as plots and output files for visualization purposes to help the user in analysing rigid and flexible fits. The modular nature of TEMPy helps the integration of scoring and assessment of fits into large pipelines, making it a tool suitable for both novice and expert structural biologists. PMID:26306092

  11. uPy: a ubiquitous computer graphics Python API with Biological Modeling Applications

    PubMed Central

    Autin, L.; Johnson, G.; Hake, J.; Olson, A.; Sanner, M.

    2015-01-01

    In this paper we describe uPy, an extension module for the Python programming language that provides a uniform abstraction of the APIs of several 3D computer graphics programs called hosts, including: Blender, Maya, Cinema4D, and DejaVu. A plugin written with uPy is a unique piece of code that will run in all uPy-supported hosts. We demonstrate the creation of complex plug-ins for molecular/cellular modeling and visualization and discuss how uPy can more generally simplify programming for many types of projects (not solely science applications) intended for multi-host distribution. uPy is available at http://upy.scripps.edu PMID:24806987

  12. uPy: a ubiquitous CG Python API with biological-modeling applications.

    PubMed

    Autin, Ludovic; Johnson, Graham; Hake, Johan; Olson, Arthur; Sanner, Michel

    2012-01-01

    The uPy Python extension module provides a uniform abstraction of the APIs of several 3D computer graphics programs (called hosts), including Blender, Maya, Cinema 4D, and DejaVu. A plug-in written with uPy can run in all uPy-supported hosts. Using uPy, researchers have created complex plug-ins for molecular and cellular modeling and visualization. uPy can simplify programming for many types of projects (not solely science applications) intended for multihost distribution. It's available at http://upy.scripps.edu. The first featured Web extra is a video that shows interactive analysis of a calcium dynamics simulation. YouTube URL: http://youtu.be/wvs-nWE6ypo. The second featured Web extra is a video that shows rotation of the HIV virus. YouTube URL: http://youtu.be/vEOybMaRoKc. PMID:24806987

  13. Python based integration of GEM detector electronics with JET data acquisition system

    NASA Astrophysics Data System (ADS)

    Zabołotny, Wojciech M.; Byszuk, Adrian; Chernyshova, Maryna; Cieszewski, Radosław; Czarski, Tomasz; Dalley, Simon; Hogben, Colin; Jakubowska, Katarzyna L.; Kasprowicz, Grzegorz; Poźniak, Krzysztof; Rzadkiewicz, Jacek; Scholz, Marek; Shumack, Amy

    2014-11-01

    This paper presents the system integrating the dedicated measurement and control electronic systems for Gas Electron Multiplier (GEM) detectors with the Control and Data Acquisition system (CODAS) in the JET facility in Culham, England. The presented system performs the high level procedures necessary to calibrate the GEM detector and to protect it against possible malfunctions or dangerous changes in operating conditions. The system also allows control of the GEM detectors from CODAS, setting of their parameters, checking their state, starting the plasma measurement and to reading the results. The system has been implemented using the Python language, using the advanced libraries for implementation of network communication protocols, for object based hardware management and for data processing.

  14. MSNoise: a Python Package for Monitoring Seismic Velocity Changes using Ambient Seismic Noise

    NASA Astrophysics Data System (ADS)

    Lecocq, Thomas; Caudron, Corentin; Brenguier, Florent

    2014-05-01

    We present MSNoise, a complete software suite to compute relative seismic velocity changes under a seismic network, using ambient seismic noise. The whole is written in Python, from the monitoring of data archives, to the production of high quality figures. All steps have been optimized to only compute the necessary steps and to use 'job'-based processing. All steps can be changed by matching the in/outs. MSNoise exposes an API for communication with the data archive and the database. We present a validation of the software on a dataset acquired during the UnderVolc project on the Piton de la Fournaise Volcano, La Réunion Island, France, for which precursory relative changes of seismic velocity are visible for three eruptions betwee 2009 and 2011. MSNoise is available on http://www.msnoise.org

  15. The adaptive significance of ontogenetic colour change in a tropical python.

    PubMed

    Wilson, David; Heinsohn, Robert; Endler, John A

    2007-02-22

    Ontogenetic colour change is typically associated with changes in size, vulnerability or habitat, but assessment of its functional significance requires quantification of the colour signals from the receivers' perspective. The tropical python, Morelia viridis, is an ideal species to establish the functional significance of ontogenetic colour change. Neonates hatch either yellow or red and both the morphs change to green with age. Here, we show that colour change from red or yellow to green provides camouflage from visually oriented avian predators in the different habitats used by juveniles and adults. This reflects changes in foraging behaviour and vulnerability as individuals mature and provides a rare demonstration of the adaptive value of ontogenetic colour change. PMID:17443961

  16. Radio Astronomy Tools in Python: Spectral-cube, pvextractor, and more

    NASA Astrophysics Data System (ADS)

    Ginsburg, A.; Robitaille, T.; Beaumont, C.; Rosolowsky, E.; Leroy, A.; Brogan, C.; Hunter, T.; Teuben, P.; Brisbin, D.

    2015-12-01

    The radio-astro-tools organization has been established to facilitate development of radio and millimeter analysis tools by the scientific community. The first packages developed under its umbrella are: • The spectral-cube package, for reading, writing, and analyzing spectral data cubes • The pvextractor package for extracting position-velocity slices from position-position-velocity cubes along aribitrary paths • The radio-beam package to handle gaussian beams in the context of the astropy quantity and unit framework • casa-python to enable installation of these packages - and any other - into users' CASA environments without conflicting with the underlying CASA package. Community input in the form of code contributions, suggestions, questions and commments is welcome on all of these tools. They can all be found at http://radio-astro-tools.github.io.

  17. Heavy Analysis and Light Virtualization of Water Use Data with Python

    NASA Astrophysics Data System (ADS)

    Kim, H.; Bijoor, N.; Famiglietti, J. S.

    2014-12-01

    Water utilities possess a large amount of water data that could be used to inform urban ecohydrology, management decisions, and conservation policies, but such data are rarely analyzed owing to difficulty in analyzation, visualization, and interpretion. We have developed a high performance computing resource for this purpose. We partnered with 6 water agencies in Orange County who provided 10 years of parcel-level monthly water use billing data for a pilot study. The first challenge that we overcame was to refine all human errors and unify the many different formats of data over all agencies. Second, we tested and applied experimental approaches to the data, including complex calculations, with high efficiency. Third, we developed a method to refine the data so it can be browsed along a time series index and/or geo-spatial queries with high efficiency, no matter how large the data. Python scientific libraries were the best match to handle arbitrary data sets in our environment. Further milestones include agency entry, sets of formulae, and maintaining 15M rows X 70 columns of data with high performance of cpu-bound processes. To deal with billions of rows, we performed an analysis virtualization stack by leveraging iPython parallel computing. With this architecture, one agency could be considered one computing node or virtual machine that maintains its own data sets respectively. For example, a big agency could use a large node, and a small agency could use a micro node. Under the minimum required raw data specs, more agencies could be analyzed. The program developed in this study simplifies data analysis, visualization, and interpretation of large water datasets, and can be used to analyze large data volumes from water agencies nationally or worldwide.

  18. An Introduction to Programming for Bioscientists: A Python-Based Primer.

    PubMed

    Ekmekci, Berk; McAnany, Charles E; Mura, Cameron

    2016-06-01

    Computing has revolutionized the biological sciences over the past several decades, such that virtually all contemporary research in molecular biology, biochemistry, and other biosciences utilizes computer programs. The computational advances have come on many fronts, spurred by fundamental developments in hardware, software, and algorithms. These advances have influenced, and even engendered, a phenomenal array of bioscience fields, including molecular evolution and bioinformatics; genome-, proteome-, transcriptome- and metabolome-wide experimental studies; structural genomics; and atomistic simulations of cellular-scale molecular assemblies as large as ribosomes and intact viruses. In short, much of post-genomic biology is increasingly becoming a form of computational biology. The ability to design and write computer programs is among the most indispensable skills that a modern researcher can cultivate. Python has become a popular programming language in the biosciences, largely because (i) its straightforward semantics and clean syntax make it a readily accessible first language; (ii) it is expressive and well-suited to object-oriented programming, as well as other modern paradigms; and (iii) the many available libraries and third-party toolkits extend the functionality of the core language into virtually every biological domain (sequence and structure analyses, phylogenomics, workflow management systems, etc.). This primer offers a basic introduction to coding, via Python, and it includes concrete examples and exercises to illustrate the language's usage and capabilities; the main text culminates with a final project in structural bioinformatics. A suite of Supplemental Chapters is also provided. Starting with basic concepts, such as that of a "variable," the Chapters methodically advance the reader to the point of writing a graphical user interface to compute the Hamming distance between two DNA sequences. PMID:27271528

  19. An Introduction to Programming for Bioscientists: A Python-Based Primer

    PubMed Central

    Mura, Cameron

    2016-01-01

    Computing has revolutionized the biological sciences over the past several decades, such that virtually all contemporary research in molecular biology, biochemistry, and other biosciences utilizes computer programs. The computational advances have come on many fronts, spurred by fundamental developments in hardware, software, and algorithms. These advances have influenced, and even engendered, a phenomenal array of bioscience fields, including molecular evolution and bioinformatics; genome-, proteome-, transcriptome- and metabolome-wide experimental studies; structural genomics; and atomistic simulations of cellular-scale molecular assemblies as large as ribosomes and intact viruses. In short, much of post-genomic biology is increasingly becoming a form of computational biology. The ability to design and write computer programs is among the most indispensable skills that a modern researcher can cultivate. Python has become a popular programming language in the biosciences, largely because (i) its straightforward semantics and clean syntax make it a readily accessible first language; (ii) it is expressive and well-suited to object-oriented programming, as well as other modern paradigms; and (iii) the many available libraries and third-party toolkits extend the functionality of the core language into virtually every biological domain (sequence and structure analyses, phylogenomics, workflow management systems, etc.). This primer offers a basic introduction to coding, via Python, and it includes concrete examples and exercises to illustrate the language’s usage and capabilities; the main text culminates with a final project in structural bioinformatics. A suite of Supplemental Chapters is also provided. Starting with basic concepts, such as that of a “variable,” the Chapters methodically advance the reader to the point of writing a graphical user interface to compute the Hamming distance between two DNA sequences. PMID:27271528

  20. PySP : modeling and solving stochastic mixed-integer programs in Python.

    SciTech Connect

    Woodruff, David L.; Watson, Jean-Paul

    2010-08-01

    Although stochastic programming is a powerful tool for modeling decision-making under uncertainty, various impediments have historically prevented its widespread use. One key factor involves the ability of non-specialists to easily express stochastic programming problems as extensions of deterministic models, which are often formulated first. A second key factor relates to the difficulty of solving stochastic programming models, particularly the general mixed-integer, multi-stage case. Intricate, configurable, and parallel decomposition strategies are frequently required to achieve tractable run-times. We simultaneously address both of these factors in our PySP software package, which is part of the COIN-OR Coopr open-source Python project for optimization. To formulate a stochastic program in PySP, the user specifies both the deterministic base model and the scenario tree with associated uncertain parameters in the Pyomo open-source algebraic modeling language. Given these two models, PySP provides two paths for solution of the corresponding stochastic program. The first alternative involves writing the extensive form and invoking a standard deterministic (mixed-integer) solver. For more complex stochastic programs, we provide an implementation of Rockafellar and Wets Progressive Hedging algorithm. Our particular focus is on the use of Progressive Hedging as an effective heuristic for approximating general multi-stage, mixed-integer stochastic programs. By leveraging the combination of a high-level programming language (Python) and the embedding of the base deterministic model in that language (Pyomo), we are able to provide completely generic and highly configurable solver implementations. PySP has been used by a number of research groups, including our own, to rapidly prototype and solve difficult stochastic programming problems.

  1. morphforge: a toolbox for simulating small networks of biologically detailed neurons in Python

    PubMed Central

    Hull, Michael J.; Willshaw, David J.

    2014-01-01

    The broad structure of a modeling study can often be explained over a cup of coffee, but converting this high-level conceptual idea into graphs of the final simulation results may require many weeks of sitting at a computer. Although models themselves can be complex, often many mental resources are wasted working around complexities of the software ecosystem such as fighting to manage files, interfacing between tools and data formats, finding mistakes in code or working out the units of variables. morphforge is a high-level, Python toolbox for building and managing simulations of small populations of multicompartmental biophysical model neurons. An entire in silico experiment, including the definition of neuronal morphologies, channel descriptions, stimuli, visualization and analysis of results can be written within a single short Python script using high-level objects. Multiple independent simulations can be created and run from a single script, allowing parameter spaces to be investigated. Consideration has been given to the reuse of both algorithmic and parameterizable components to allow both specific and stochastic parameter variations. Some other features of the toolbox include: the automatic generation of human-readable documentation (e.g., PDF files) about a simulation; the transparent handling of different biophysical units; a novel mechanism for plotting simulation results based on a system of tags; and an architecture that supports both the use of established formats for defining channels and synapses (e.g., MODL files), and the possibility to support other libraries and standards easily. We hope that this toolbox will allow scientists to quickly build simulations of multicompartmental model neurons for research and serve as a platform for further tool development. PMID:24478690

  2. ObsPy: A Python toolbox for seismology - Current state, applications, and ecosystem around it

    NASA Astrophysics Data System (ADS)

    Krischer, L.; Megies, T.; Sales de Andrade, E.; Barsch, R.; Beyreuther, M.

    2015-12-01

    ObsPy (http://www.obspy.org) is a community-driven, open-source project offering a bridge for seismology into the scientific Python ecosystem. It provides read and write support for essentially all commonly used waveform, station, and event metadata formats with a unified interface, a comprehensive signal processing toolbox tuned to the needs of seismologists, integrated access to all large data centers, web services and databases, and convenient wrappers to third party codes like libmseed and evalresp. Python, in contrast to many other languages and tools, is simple enough to enable an exploratory and interactive coding style desired by many scientists. At the same time it is a full-fledged programming language usable by software engineers to build complex and large programs. This combination makes it very suitable for use in seismology where research code often has to be translated to stable and production ready environments. It furthermore offers many freely available high quality scientific modules covering most needs in developing scientific software.ObsPy has been in constant development for more than 5 years and nowadays enjoys a large rate of adoption in the community with thousands of users. Successful applications include time-dependent and rotational seismology, big data processing, event relocations, and synthetic studies about attenuation kernels and full-waveform inversions to name a few examples. Additionally it sparked the development of several more specialized packages slowly building a modern seismological ecosystem around it.This contribution will give a short introduction and overview of ObsPy and highlight a number of us cases and software built around it. We will furthermore discuss the issue of sustainability of scientific software.

  3. ObsPy: A Python toolbox for seismology - Current state, applications, and ecosystem around it

    NASA Astrophysics Data System (ADS)

    Lecocq, Thomas; Megies, Tobias; Krischer, Lion; Sales de Andrade, Elliott; Barsch, Robert; Beyreuther, Moritz

    2016-04-01

    ObsPy (http://www.obspy.org) is a community-driven, open-source project offering a bridge for seismology into the scientific Python ecosystem. It provides * read and write support for essentially all commonly used waveform, station, and event metadata formats with a unified interface, * a comprehensive signal processing toolbox tuned to the needs of seismologists, * integrated access to all large data centers, web services and databases, and * convenient wrappers to third party codes like libmseed and evalresp. Python, in contrast to many other languages and tools, is simple enough to enable an exploratory and interactive coding style desired by many scientists. At the same time it is a full-fledged programming language usable by software engineers to build complex and large programs. This combination makes it very suitable for use in seismology where research code often has to be translated to stable and production ready environments. It furthermore offers many freely available high quality scientific modules covering most needs in developing scientific software. ObsPy has been in constant development for more than 5 years and nowadays enjoys a large rate of adoption in the community with thousands of users. Successful applications include time-dependent and rotational seismology, big data processing, event relocations, and synthetic studies about attenuation kernels and full-waveform inversions to name a few examples. Additionally it sparked the development of several more specialized packages slowly building a modern seismological ecosystem around it. This contribution will give a short introduction and overview of ObsPy and highlight a number of use cases and software built around it. We will furthermore discuss the issue of sustainability of scientific software.

  4. morphforge: a toolbox for simulating small networks of biologically detailed neurons in Python.

    PubMed

    Hull, Michael J; Willshaw, David J

    2013-01-01

    The broad structure of a modeling study can often be explained over a cup of coffee, but converting this high-level conceptual idea into graphs of the final simulation results may require many weeks of sitting at a computer. Although models themselves can be complex, often many mental resources are wasted working around complexities of the software ecosystem such as fighting to manage files, interfacing between tools and data formats, finding mistakes in code or working out the units of variables. morphforge is a high-level, Python toolbox for building and managing simulations of small populations of multicompartmental biophysical model neurons. An entire in silico experiment, including the definition of neuronal morphologies, channel descriptions, stimuli, visualization and analysis of results can be written within a single short Python script using high-level objects. Multiple independent simulations can be created and run from a single script, allowing parameter spaces to be investigated. Consideration has been given to the reuse of both algorithmic and parameterizable components to allow both specific and stochastic parameter variations. Some other features of the toolbox include: the automatic generation of human-readable documentation (e.g., PDF files) about a simulation; the transparent handling of different biophysical units; a novel mechanism for plotting simulation results based on a system of tags; and an architecture that supports both the use of established formats for defining channels and synapses (e.g., MODL files), and the possibility to support other libraries and standards easily. We hope that this toolbox will allow scientists to quickly build simulations of multicompartmental model neurons for research and serve as a platform for further tool development. PMID:24478690

  5. Optimizing python-based ROOT I/O with PyPy's tracing just-in-time compiler

    NASA Astrophysics Data System (ADS)

    Tlp Lavrijsen, Wim

    2012-12-01

    The Python programming language allows objects and classes to respond dynamically to the execution environment. Most of this, however, is made possible through language hooks which by definition can not be optimized and thus tend to be slow. The PyPy implementation of Python includes a tracing just in time compiler (JIT), which allows similar dynamic responses but at the interpreter-, rather than the application-level. Therefore, it is possible to fully remove the hooks, leaving only the dynamic response, in the optimization stage for hot loops, if the types of interest are opened up to the JIT. A general opening up of types to the JIT, based on reflection information, has already been developed (cppyy). The work described in this paper takes it one step further by customizing access to ROOT I/O to the JIT, allowing for fully automatic optimizations.

  6. A new species of Hepatozoon (Apicomplexa: Adeleorina) from Python regius (Serpentes: Pythonidae) and its experimental transmission by a mosquito vector.

    PubMed

    Sloboda, Michal; Kamler, Martin; Bulantová, Jana; Votýpka, Jan; Modrý, David

    2007-10-01

    Hepatozoon ayorgbor n. sp. is described from specimens of Python regius imported from Ghana. Gametocytes were found in the peripheral blood of 43 of 55 snakes examined. Localization of gametocytes was mainly inside the erythrocytes; free gametocytes were found in 15 (34.9%) positive specimens. Infections of laboratory-reared Culex quinquefasciatus feeding on infected snakes, as well as experimental infection of juvenile Python regius by ingestion of infected mosquitoes, were performed to complete the life cycle. Similarly, transmission to different snake species (Boa constrictor and Lamprophis fuliginosus) and lizards (Lepidodactylus lugubris) was performed to assess the host specificity. Isolates were compared with Hepatozoon species from sub-Saharan reptiles and described as a new species based on the morphology, phylogenetic analysis, and a complete life cycle. PMID:18163356

  7. Python Processing and Version Control using VisTrails for the Netherlands Hydrological Instrument (Invited)

    NASA Astrophysics Data System (ADS)

    Verkaik, J.

    2013-12-01

    The Netherlands Hydrological Instrument (NHI) model predicts water demands in periods of drought, supporting the Dutch decision makers in taking operational as well as long-term decisions with respect to the water supply. Other applications of NHI are predicting fresh-salt interaction, nutrient loadings, and agriculture change. The NHI model consists of several coupled models: a saturated groundwater model (MODFLOW), an unsaturated groundwater model (MetaSWAP), a sub-catchment surface water model (MOZART), and a distribution network of surface waters model (DM/SOBEK). Each of these models requires specific, usually large, input data that may be the result of sophisticated schematization workflows. Input data can also be dependent on each other, for example, the precipitation data is input for the unsaturated zone model (cells) as well as for the surface water models (polygons). For efficient data management, we developed several Python tools such that the modeler or stakeholder can use the model in a user-friendly manner, and data is managed in a consistent, transparent and reproducible way. Two open source Python tools are presented here: the data version control module for the workflow manager VisTrails called FileSync, and the NHI model control script that uses FileSync. VisTrails is an open-source scientific workflow and provenance management system that provides support for simulations, data exploration and visualization. Since VisTrails does not directly support version control we developed a version control module called FileSync. With this generic module, the user can synchronize data from and to his workflow through a dialog window. The FileSync dialog calls the FileSync script that is command-line based and performs the actual data synchronization. This script allows the user to easily create a model repository, upload and download data, create releases and define scenarios. The data synchronization approach applied here differs from systems as Subversion

  8. VarPy: A python library for volcanology and rock physics data analysis

    NASA Astrophysics Data System (ADS)

    Filgueira, Rosa; Atkinson, Malcom; Bell, Andrew; Snelling, Brawen; Main, Ian

    2014-05-01

    The increasing prevalence of digital instrumentation in volcanology and rock physics is leading to a wealth of data, which in turn is increasing the need for computational analyses and models. Today, these are largely developed by each individual or researcher. The introduction of a shared library that can be used for this purpose has several benefits: 1. when an existing function in the library meets a need recognised by a researcher it is usually much less effort than developing ones own code; 2. once functions are established and multiply used they become better tested, more reliable and eventually trusted by the community; 3. use of the same functions by different researchers makes it easier to compare results and to compare the skill of rival analysis and modelling methods; and 4. in the longer term the cost of maintaining these functions is shared over a wide community and they therefore have greater duration. Python is a high-level interpreted programming language, with capabilities for object-oriented programming. Often scientists choose this language to program their programs because of the increased productivity it provides. Although, there are many software tools available for interactive data analysis and development, there are not libraries designed specifically for volcanology and rock physics data. Therefore, we propose a new Python open-source toolbox called "VarPy" to facilitate rapid application development for rock physicists and volcanologists, which allow users to define their own workflows to develop models, analyses and visualisations. This proposal is triggered by our work on data assimilation in the NERC EFFORT (Earthquake and Failure Forecasting in Real Time) project, using data provided by the NERC CREEP 2 experimental project and volcanic experiments from INVG observatory Etna and IGN observatory Hierro as a test cases. In EFFORT project we are developing a scientist gateway which offers services for collecting and sharing volcanology

  9. What parts of the US mainland are climatically suitable for invasive alien pythons spreading from Everglades National Park?

    USGS Publications Warehouse

    Rodda, G.H.; Jarnevich, C.S.; Reed, R.N.

    2009-01-01

    The Burmese Python (Python molurus bivittatus) is now well established in southern Florida and spreading northward. The factors likely to limit this spread are unknown, but presumably include climate or are correlated with climate. We compiled monthly rainfall and temperature statistics from 149 stations located near the edge of the python's native range in Asia (Pakistan east to China and south to Indonesia). The southern and eastern native range limits extend to saltwater, leaving unresolved the species' climatic tolerances in those areas. The northern and western limits are associated with cold and aridity respectively. We plotted mean monthly rainfall against mean monthly temperature for the 149 native range weather stations to identify the climate conditions inhabited by pythons in their native range, and mapped areas of the coterminous United States with the same climate today and projected for the year 2100. We accounted for both dry-season aestivation and winter hibernation (under two scenarios of hibernation duration). The potential distribution was relatively insensitive to choice of scenario for hibernation duration. US areas climatically matched at present ranged up the coasts and across the south from Delaware to Oregon, and included most of California, Texas, Oklahoma, Arkansas, Louisiana, Mississippi, Alabama, Florida, Georgia, and South and North Carolina. By the year 2100, projected areas of potential suitable climate extend northward beyond the current limit to include parts of the states of Washington, Colorado, Illinois, Indiana, Ohio, West Virginia, Pennsylvania, New Jersey, and New York. Thus a substantial portion of the mainland US is potentially vulnerable to this ostensibly tropical invader. ?? 2008 Springer Science+Business Media B.V.

  10. Fast prototyping of wavelet spatio-temporal RS fusion with Raingauge time series with GDAL and Python-DWT

    NASA Astrophysics Data System (ADS)

    Chemin, Yann

    2013-04-01

    Availability of rainfall time-series is limited in many parts of the World, and the continuity of such records is variable. This research endeavors to extend actual daily rainfall observations to ungauged areas using vegetation response as witnessed by remote sensing data and taking into account rainfall event histograms as well as cumulative total daily rainfall, over a period of 11 years. Open Source code development permitted to gain on several aspects. The first one pertains to space, Python and its numerical part (NumPy) are scientifically concise, as a bonus to be expressive. The second is the availability of the Discrete Wavelet Transform (DWT) in Python already, which permitted to reduce the Wavelet Transform to a small set of instructions, clarifying and simplifying the understanding of the code once it reaches the Public Domain. GDAL interface permitted to load satellite imagery and write fused rainfall time-series in spatio-temporal dimensions. Other scientific tool from Numerical Python were also used in the process of developing the algorithm (scipy.stats.stats and scipy.interpolate.griddata). Due to the large amount of days (4019) and the kilometer based resolution of the vegetation RS data, it takes about a week for the code to resolve the fusion problem. An attempt at using an multicore interpolation implementation in Python (hpgl) which unfortunately was not an active project anymore, though certainly deserving interest. Results show that rainfall events histograms can be reconstructed, and that total cumulative rainfall is estimated with 85% accuracy, using a surrounding network of rain gauges at 30-50 km of distance from the point of study. This research can strengthen various types of research and applications such as ungauged basins research, regional climate modeling, agricultural insurance systems, etc. Further development aims at porting the code to distributed computing.

  11. Large-scale automated image analysis for computational profiling of brain tissue surrounding implanted neuroprosthetic devices using Python.

    PubMed

    Rey-Villamizar, Nicolas; Somasundar, Vinay; Megjhani, Murad; Xu, Yan; Lu, Yanbin; Padmanabhan, Raghav; Trett, Kristen; Shain, William; Roysam, Badri

    2014-01-01

    In this article, we describe the use of Python for large-scale automated server-based bio-image analysis in FARSIGHT, a free and open-source toolkit of image analysis methods for quantitative studies of complex and dynamic tissue microenvironments imaged by modern optical microscopes, including confocal, multi-spectral, multi-photon, and time-lapse systems. The core FARSIGHT modules for image segmentation, feature extraction, tracking, and machine learning are written in C++, leveraging widely used libraries including ITK, VTK, Boost, and Qt. For solving complex image analysis tasks, these modules must be combined into scripts using Python. As a concrete example, we consider the problem of analyzing 3-D multi-spectral images of brain tissue surrounding implanted neuroprosthetic devices, acquired using high-throughput multi-spectral spinning disk step-and-repeat confocal microscopy. The resulting images typically contain 5 fluorescent channels. Each channel consists of 6000 × 10,000 × 500 voxels with 16 bits/voxel, implying image sizes exceeding 250 GB. These images must be mosaicked, pre-processed to overcome imaging artifacts, and segmented to enable cellular-scale feature extraction. The features are used to identify cell types, and perform large-scale analysis for identifying spatial distributions of specific cell types relative to the device. Python was used to build a server-based script (Dell 910 PowerEdge servers with 4 sockets/server with 10 cores each, 2 threads per core and 1TB of RAM running on Red Hat Enterprise Linux linked to a RAID 5 SAN) capable of routinely handling image datasets at this scale and performing all these processing steps in a collaborative multi-user multi-platform environment. Our Python script enables efficient data storage and movement between computers and storage servers, logs all the processing steps, and performs full multi-threaded execution of all codes, including open and closed-source third party libraries. PMID:24808857

  12. Raman spectroscopic studies of the skins of the Sahara sand viper, the carpet python and the American black rat snake

    NASA Astrophysics Data System (ADS)

    Edwards, H. G. M.; Farwell, D. W.; Williams, A. C.; Barry, B. W.

    1993-07-01

    Vibrational Raman spectra of the skins of the snakes Cerastes vipera (Sahara sand viper) and Morelia argus (carpet python) have been recorded for the first time using visible and IR laser excitation. Full vibrational assignments are proposed and comparisons made with vibrational Raman spectra of the snake Elaphe obsoleta (American black rat snake); such studies may be important in correlating the permeabilities of human and snake skins to drugs and contaminants.

  13. FMC: a one-liner Python program to manage, classify and plot focal mechanisms

    NASA Astrophysics Data System (ADS)

    Álvarez-Gómez, José A.

    2014-05-01

    The analysis of earthquake focal mechanisms (or Seismic Moment Tensor, SMT) is a key tool on seismotectonics research. Each focal mechanism is characterized by several location parameters of the earthquake hypocenter, the earthquake size (magnitude and scalar moment tensor) and some geometrical characteristics of the rupture (nodal planes orientations, SMT components and/or SMT main axes orientations). The aim of FMC is to provide a simple but powerful tool to manage focal mechanism data. The data should be input to the program formatted as one of two of the focal mechanisms formatting options of the GMT (Generic Mapping Tools) package (Wessel and Smith, 1998): the Harvard CMT convention and the single nodal plane Aki and Richards (1980) convention. The former is a SMT format that can be downloaded directly from the Global CMT site (http://www.globalcmt.org/), while the later is the simplest way to describe earthquake rupture data. FMC is programmed in Python language, which is distributed as Open Source GPL-compatible, and therefore can be used to develop Free Software. Python runs on almost any machine, and has a wide support and presence in any operative system. The program has been conceived with the modularity and versatility of the classical UNIX-like tools. Is called from the command line and can be easily integrated into shell scripts (*NIX systems) or batch files (DOS/Windows systems). The program input and outputs can be done by means of ASCII files or using standard input (or redirection "<"), standard output (screen or redirection ">") and pipes ("|"). By default FMC will read the input and write the output as a Harvard CMT (psmeca formatted) ASCII file, although other formats can be used. Optionally FMC will produce a classification diagram representing the rupture type of the focal mechanisms processed. In order to count with a detailed classification of the focal mechanisms I decided to classify the focal mechanism in a series of fields that include

  14. Zephyr: Open-source Parallel Seismic Waveform Inversion in an Integrated Python-based Framework

    NASA Astrophysics Data System (ADS)

    Smithyman, B. R.; Pratt, R. G.; Hadden, S. M.

    2015-12-01

    Seismic Full-Waveform Inversion (FWI) is an advanced method to reconstruct wave properties of materials in the Earth from a series of seismic measurements. These methods have been developed by researchers since the late 1980s, and now see significant interest from the seismic exploration industry. As researchers move towards implementing advanced numerical modelling (e.g., 3D, multi-component, anisotropic and visco-elastic physics), it is desirable to make use of a modular approach, minimizing the effort developing a new set of tools for each new numerical problem. SimPEG (http://simpeg.xyz) is an open source project aimed at constructing a general framework to enable geophysical inversion in various domains. In this abstract we describe Zephyr (https://github.com/bsmithyman/zephyr), which is a coupled research project focused on parallel FWI in the seismic context. The software is built on top of Python, Numpy and IPython, which enables very flexible testing and implementation of new features. Zephyr is an open source project, and is released freely to enable reproducible research. We currently implement a parallel, distributed seismic forward modelling approach that solves the 2.5D (two-and-one-half dimensional) viscoacoustic Helmholtz equation at a range modelling frequencies, generating forward solutions for a given source behaviour, and gradient solutions for a given set of observed data. Solutions are computed in a distributed manner on a set of heterogeneous workers. The researcher's frontend computer may be separated from the worker cluster by a network link to enable full support for computation on remote clusters from individual workstations or laptops. The present codebase introduces a numerical discretization equivalent to that used by FULLWV, a well-known seismic FWI research codebase. This makes it straightforward to compare results from Zephyr directly with FULLWV. The flexibility introduced by the use of a Python programming environment makes

  15. The InSAR Scientific Computing Environment (ISCE): A Python Framework for Earth Science

    NASA Astrophysics Data System (ADS)

    Rosen, P. A.; Gurrola, E. M.; Agram, P. S.; Sacco, G. F.; Lavalle, M.

    2015-12-01

    The InSAR Scientific Computing Environment (ISCE, funded by NASA ESTO) provides a modern computing framework for geodetic image processing of InSAR data from a diverse array of radar satellites and aircraft. ISCE is both a modular, flexible, and extensible framework for building software components and applications as well as a toolbox of applications for processing raw or focused InSAR and Polarimetric InSAR data. The ISCE framework contains object-oriented Python components layered to construct Python InSAR components that manage legacy Fortran/C InSAR programs. Components are independently configurable in a layered manner to provide maximum control. Polymorphism is used to define a workflow in terms of abstract facilities for each processing step that are realized by specific components at run-time. This enables a single workflow to work on either raw or focused data from all sensors. ISCE can serve as the core of a production center to process Level-0 radar data to Level-3 products, but is amenable to interactive processing approaches that allow scientists to experiment with data to explore new ways of doing science with InSAR data. The NASA-ISRO SAR (NISAR) Mission will deliver data of unprecedented quantity and quality, making possible global-scale studies in climate research, natural hazards, and Earth's ecosystems. ISCE is planned as the foundational element in processing NISAR data, enabling a new class of analyses that take greater advantage of the long time and large spatial scales of these new data. NISAR will be but one mission in a constellation of radar satellites in the future delivering such data. ISCE currently supports all publicly available strip map mode space-borne SAR data since ERS and is expected to include support for upcoming missions. ISCE has been incorporated into two prototype cloud-based systems that have demonstrated its elasticity in addressing larger data processing problems in a "production" context and its ability to be

  16. Python tools for rapid development, calibration, and analysis of generalized groundwater-flow models

    NASA Astrophysics Data System (ADS)

    Starn, J. J.; Belitz, K.

    2014-12-01

    National-scale water-quality data sets for the United States have been available for several decades; however, groundwater models to interpret these data are available for only a small percentage of the country. Generalized models may be adequate to explain and project groundwater-quality trends at the national scale by using regional scale models (defined as watersheds at or between the HUC-6 and HUC-8 levels). Coast-to-coast data such as the National Hydrologic Dataset Plus (NHD+) make it possible to extract the basic building blocks for a model anywhere in the country. IPython notebooks have been developed to automate the creation of generalized groundwater-flow models from the NHD+. The notebook format allows rapid testing of methods for model creation, calibration, and analysis. Capabilities within the Python ecosystem greatly speed up the development and testing of algorithms. GeoPandas is used for very efficient geospatial processing. Raster processing includes the Geospatial Data Abstraction Library and image processing tools. Model creation is made possible through Flopy, a versatile input and output writer for several MODFLOW-based flow and transport model codes. Interpolation, integration, and map plotting included in the standard Python tool stack also are used, making the notebook a comprehensive platform within on to build and evaluate general models. Models with alternative boundary conditions, number of layers, and cell spacing can be tested against one another and evaluated by using water-quality data. Novel calibration criteria were developed by comparing modeled heads to land-surface and surface-water elevations. Information, such as predicted age distributions, can be extracted from general models and tested for its ability to explain water-quality trends. Groundwater ages then can be correlated with horizontal and vertical hydrologic position, a relation that can be used for statistical assessment of likely groundwater-quality conditions

  17. Uncertainty quantification of surface-water/groundwater exchange estimates in large wetland systems using Python

    NASA Astrophysics Data System (ADS)

    Hughes, J. D.; Metz, P. A.

    2014-12-01

    Most watershed studies include observation-based water budget analyses to develop first-order estimates of significant flow terms. Surface-water/groundwater (SWGW) exchange is typically assumed to be equal to the residual of the sum of inflows and outflows in a watershed. These estimates of SWGW exchange, however, are highly uncertain as a result of the propagation of uncertainty inherent in the calculation or processing of the other terms of the water budget, such as stage-area-volume relations, and uncertainties associated with land-cover based evapotranspiration (ET) rate estimates. Furthermore, the uncertainty of estimated SWGW exchanges can be magnified in large wetland systems that transition from dry to wet during wet periods. Although it is well understood that observation-based estimates of SWGW exchange are uncertain it is uncommon for the uncertainty of these estimates to be directly quantified. High-level programming languages like Python can greatly reduce the effort required to (1) quantify the uncertainty of estimated SWGW exchange in large wetland systems and (2) evaluate how different approaches for partitioning land-cover data in a watershed may affect the water-budget uncertainty. We have used Python with the Numpy, Scipy.stats, and pyDOE packages to implement an unconstrained Monte Carlo approach with Latin Hypercube sampling to quantify the uncertainty of monthly estimates of SWGW exchange in the Floral City watershed of the Tsala Apopka wetland system in west-central Florida, USA. Possible sources of uncertainty in the water budget analysis include rainfall, ET, canal discharge, and land/bathymetric surface elevations. Each of these input variables was assigned a probability distribution based on observation error or spanning the range of probable values. The Monte Carlo integration process exposes the uncertainties in land-cover based ET rate estimates as the dominant contributor to the uncertainty in SWGW exchange estimates. We will discuss

  18. Geospatial Data Stream Processing in Python Using FOSS4G Components

    NASA Astrophysics Data System (ADS)

    McFerren, G.; van Zyl, T.

    2016-06-01

    One viewpoint of current and future IT systems holds that there is an increase in the scale and velocity at which data are acquired and analysed from heterogeneous, dynamic sources. In the earth observation and geoinformatics domains, this process is driven by the increase in number and types of devices that report location and the proliferation of assorted sensors, from satellite constellations to oceanic buoy arrays. Much of these data will be encountered as self-contained messages on data streams - continuous, infinite flows of data. Spatial analytics over data streams concerns the search for spatial and spatio-temporal relationships within and amongst data "on the move". In spatial databases, queries can assess a store of data to unpack spatial relationships; this is not the case on streams, where spatial relationships need to be established with the incomplete data available. Methods for spatially-based indexing, filtering, joining and transforming of streaming data need to be established and implemented in software components. This article describes the usage patterns and performance metrics of a number of well known FOSS4G Python software libraries within the data stream processing paradigm. In particular, we consider the RTree library for spatial indexing, the Shapely library for geometric processing and transformation and the PyProj library for projection and geodesic calculations over streams of geospatial data. We introduce a message oriented Python-based geospatial data streaming framework called Swordfish, which provides data stream processing primitives, functions, transports and a common data model for describing messages, based on the Open Geospatial Consortium Observations and Measurements (O&M) and Unidata Common Data Model (CDM) standards. We illustrate how the geospatial software components are integrated with the Swordfish framework. Furthermore, we describe the tight temporal constraints under which geospatial functionality can be invoked when

  19. Using the Python language and the CMOR2 library to create PMIPn-CMIPn compliant model output

    NASA Astrophysics Data System (ADS)

    Peterschmitt, Jean-Yves; Doutriaux, Charles

    2015-04-01

    The Paleoclimate Modelling Intercomparison Project (PMIP) is a long standing initiative that has provided an efficient mechanism for coordinating paleoclimate modelling activities that provide valuable information on the mechanisms of climate change, the identification of key feedbacks operating in the climate system and, through model evaluation, the capability of climate models to reproduce climates different from today. The third phase of PMIP (aka PMIP 3) started in 2009 (the fourth phase is about to start) and followed the requirements specified by CMIP5 (Coupled Model Intercomparison Project). Generating data files following strict Model Intercomparison Projects (MIPs) standards (NetCDF format, file and variable names, file structure, metadata information, directory hierarchy, etc…) has been a key to the success of many recent Model Intercomparison Projects. It is unfortunately not always easy to convert proprietary model output format to the required standards, and this has prevented some smaller modelling groups from sharing their data. We will present how the Python version of the CMOR2 (Climate Model Output Rewriter) library bundled with the UV-CDAT Python distribution (Ultrascale Visualization Climate Data Analysis Tools) can be used to easily convert raw model output to the appropriate MIP shareable format. References: http://pmip3.lsce.ipsl.fr/ http://cmip-pcmdi.llnl.gov/cmip5/output_req.html http://www2-pcmdi.llnl.gov/cmor http://uvcdat.llnl.gov/ https://www.python.org/

  20. Nengo: a Python tool for building large-scale functional brain models

    PubMed Central

    Bekolay, Trevor; Bergstra, James; Hunsberger, Eric; DeWolf, Travis; Stewart, Terrence C.; Rasmussen, Daniel; Choo, Xuan; Voelker, Aaron Russell; Eliasmith, Chris

    2014-01-01

    Neuroscience currently lacks a comprehensive theory of how cognitive processes can be implemented in a biological substrate. The Neural Engineering Framework (NEF) proposes one such theory, but has not yet gathered significant empirical support, partly due to the technical challenge of building and simulating large-scale models with the NEF. Nengo is a software tool that can be used to build and simulate large-scale models based on the NEF; currently, it is the primary resource for both teaching how the NEF is used, and for doing research that generates specific NEF models to explain experimental data. Nengo 1.4, which was implemented in Java, was used to create Spaun, the world's largest functional brain model (Eliasmith et al., 2012). Simulating Spaun highlighted limitations in Nengo 1.4's ability to support model construction with simple syntax, to simulate large models quickly, and to collect large amounts of data for subsequent analysis. This paper describes Nengo 2.0, which is implemented in Python and overcomes these limitations. It uses simple and extendable syntax, simulates a benchmark model on the scale of Spaun 50 times faster than Nengo 1.4, and has a flexible mechanism for collecting simulation results. PMID:24431999

  1. A new algorithm to reduce noise in microscopy images implemented with a simple program in python.

    PubMed

    Papini, Alessio

    2012-03-01

    All microscopical images contain noise, increasing when (e.g., transmission electron microscope or light microscope) approaching the resolution limit. Many methods are available to reduce noise. One of the most commonly used is image averaging. We propose here to use the mode of pixel values. Simple Python programs process a given number of images, recorded consecutively from the same subject. The programs calculate the mode of the pixel values in a given position (a, b). The result is a new image containing in (a, b) the mode of the values. Therefore, the final pixel value corresponds to that read in at least two of the pixels in position (a, b). The application of the program on a set of images obtained by applying salt and pepper noise and GIMP hurl noise with 10-90% standard deviation showed that the mode performs better than averaging with three-eight images. The data suggest that the mode would be more efficient (in the sense of a lower number of recorded images to process to reduce noise below a given limit) for lower number of total noisy pixels and high standard deviation (as impulse noise and salt and pepper noise), while averaging would be more efficient when the number of varying pixels is high, and the standard deviation is low, as in many cases of Gaussian noise affected images. The two methods may be used serially. PMID:21898664

  2. The Integrated Plasma Simulator: A Flexible Python Framework for Coupled Multiphysics Simulation

    SciTech Connect

    Foley, Samantha S; Elwasif, Wael R; Bernholdt, David E

    2011-11-01

    High-fidelity coupled multiphysics simulations are an increasingly important aspect of computational science. In many domains, however, there has been very limited experience with simulations of this sort, therefore research in coupled multiphysics often requires computational frameworks with significant flexibility to respond to the changing directions of the physics and mathematics. This paper presents the Integrated Plasma Simulator (IPS), a framework designed for loosely coupled simulations of fusion plasmas. The IPS provides users with a simple component architecture into which a wide range of existing plasma physics codes can be inserted as components. Simulations can take advantage of multiple levels of parallelism supported in the IPS, and can be controlled by a high-level ``driver'' component, or by other coordination mechanisms, such as an asynchronous event service. We describe the requirements and design of the framework, and how they were implemented in the Python language. We also illustrate the flexibility of the framework by providing examples of different types of simulations that utilize various features of the IPS.

  3. Analysis and Visualization of Multi-Scale Astrophysical Simulations using Python and NumPy

    SciTech Connect

    Turk, M.; /KIPAC, Menlo Park

    2008-09-30

    The study the origins of cosmic structure requires large-scale computer simulations beginning with well-constrained, observationally-determined, initial conditions. We use Adaptive Mesh Refinement to conduct multi-resolution simulations spanning twelve orders of magnitude in spatial dimensions and over twenty orders of magnitude in density. These simulations must be analyzed and visualized in a manner that is fast, accurate, and reproducible. I present 'yt,' a cross-platform analysis toolkit written in Python. 'yt' consists of a data-management layer for transporting and tracking simulation outputs, a plotting layer, a parallel analysis layer for handling mesh-based and particle-based data, as well as several interfaces. I demonstrate how the origins of cosmic structure--from the scale of clusters of galaxies down to the formation of individual stars--can be analyzed and visualized using a NumPy-based toolkit. Additionally, I discuss efforts to port this analysis code to other adaptive mesh refinement data formats, enabling direct comparison of data between research groups using different methods to simulate the same objects.

  4. pyAudioAnalysis: An Open-Source Python Library for Audio Signal Analysis

    PubMed Central

    Giannakopoulos, Theodoros

    2015-01-01

    Audio information plays a rather important role in the increasing digital content that is available today, resulting in a need for methodologies that automatically analyze such content: audio event recognition for home automations and surveillance systems, speech recognition, music information retrieval, multimodal analysis (e.g. audio-visual analysis of online videos for content-based recommendation), etc. This paper presents pyAudioAnalysis, an open-source Python library that provides a wide range of audio analysis procedures including: feature extraction, classification of audio signals, supervised and unsupervised segmentation and content visualization. pyAudioAnalysis is licensed under the Apache License and is available at GitHub (https://github.com/tyiannak/pyAudioAnalysis/). Here we present the theoretical background behind the wide range of the implemented methodologies, along with evaluation metrics for some of the methods. pyAudioAnalysis has been already used in several audio analysis research applications: smart-home functionalities through audio event detection, speech emotion recognition, depression classification based on audio-visual features, music segmentation, multimodal content-based movie recommendation and health applications (e.g. monitoring eating habits). The feedback provided from all these particular audio applications has led to practical enhancement of the library. PMID:26656189

  5. ORBKIT: A modular python toolbox for cross-platform postprocessing of quantum chemical wavefunction data.

    PubMed

    Hermann, Gunter; Pohl, Vincent; Tremblay, Jean Christophe; Paulus, Beate; Hege, Hans-Christian; Schild, Axel

    2016-06-15

    ORBKIT is a toolbox for postprocessing electronic structure calculations based on a highly modular and portable Python architecture. The program allows computing a multitude of electronic properties of molecular systems on arbitrary spatial grids from the basis set representation of its electronic wavefunction, as well as several grid-independent properties. The required data can be extracted directly from the standard output of a large number of quantum chemistry programs. ORBKIT can be used as a standalone program to determine standard quantities, for example, the electron density, molecular orbitals, and derivatives thereof. The cornerstone of ORBKIT is its modular structure. The existing basic functions can be arranged in an individual way and can be easily extended by user-written modules to determine any other derived quantity. ORBKIT offers multiple output formats that can be processed by common visualization tools (VMD, Molden, etc.). Additionally, ORBKIT possesses routines to order molecular orbitals computed at different nuclear configurations according to their electronic character and to interpolate the wavefunction between these configurations. The program is open-source under GNU-LGPLv3 license and freely available at https://github.com/orbkit/orbkit/. This article provides an overview of ORBKIT with particular focus on its capabilities and applicability, and includes several example calculations. © 2016 Wiley Periodicals, Inc. PMID:27043934

  6. Using Python Scripting and Web Frameworks to Access Spatial and Temporal Data via KML

    NASA Astrophysics Data System (ADS)

    Erickson, T. A.; Koziol, B. W.

    2010-12-01

    Ever increasing volumes of spatial and temporal data about our world are being made available for download by various organizations. However, the data formats used are generally well-suited for storing large volumes of data but are not directly usable without specialized software. Also in recent years, there has been a wide adoption of easy-to-use virtual globe browsers (such as Google Earth) for viewing spatial and temporal datasets, but these applications cannot directly work with many large datasets due to the formats and/or the size of the datasets. This work presents several examples of Python-based data systems for accessing, filtering, and transforming large and complex spatial and temporal datasets into KML, an Open Geospatial Consortium (OGC) standard used for visualization and annotation of two-dimensional and three-dimensional data. Precipitable water forecasted by the NCEP Global Forecast System (GFS) model. Soil moisture content for December 2024 predicted by an IPCC model (GFDL R30).

  7. pyAudioAnalysis: An Open-Source Python Library for Audio Signal Analysis.

    PubMed

    Giannakopoulos, Theodoros

    2015-01-01

    Audio information plays a rather important role in the increasing digital content that is available today, resulting in a need for methodologies that automatically analyze such content: audio event recognition for home automations and surveillance systems, speech recognition, music information retrieval, multimodal analysis (e.g. audio-visual analysis of online videos for content-based recommendation), etc. This paper presents pyAudioAnalysis, an open-source Python library that provides a wide range of audio analysis procedures including: feature extraction, classification of audio signals, supervised and unsupervised segmentation and content visualization. pyAudioAnalysis is licensed under the Apache License and is available at GitHub (https://github.com/tyiannak/pyAudioAnalysis/). Here we present the theoretical background behind the wide range of the implemented methodologies, along with evaluation metrics for some of the methods. pyAudioAnalysis has been already used in several audio analysis research applications: smart-home functionalities through audio event detection, speech emotion recognition, depression classification based on audio-visual features, music segmentation, multimodal content-based movie recommendation and health applications (e.g. monitoring eating habits). The feedback provided from all these particular audio applications has led to practical enhancement of the library. PMID:26656189

  8. ProtPOS: a python package for the prediction of protein preferred orientation on a surface

    PubMed Central

    Ngai, Jimmy C. F.; Mak, Pui-In; Siu, Shirley W. I.

    2016-01-01

    Summary: Atomistic molecular dynamics simulation is a promising technique to investigate the energetics and dynamics in the protein–surface adsorption process which is of high relevance to modern biotechnological applications. To increase the chance of success in simulating the adsorption process, favorable orientations of the protein at the surface must be determined. Here, we present ProtPOS which is a lightweight and easy-to-use python package that can predict low-energy protein orientations on a surface of interest. It combines a fast conformational sampling algorithm with the energy calculation of GROMACS. The advantage of ProtPOS is it allows users to select any force fields suitable for the system at hand and provide structural output readily available for further simulation studies. Availability and Implementation: ProtPOS is freely available for academic and non-profit uses at http://cbbio.cis.umac.mo/software/protpos Supplementary information: Supplementary data are available at Bioinformatics online. Contact: shirleysiu@umac.mo PMID:27153619

  9. Magnetic resonance imaging volumetry for noninvasive measures of phenotypic flexibility during digestion in Burmese pythons.

    PubMed

    Hansen, Kasper; Pedersen, Pil Birkefeldt Møller; Pedersen, Michael; Wang, Tobias

    2013-01-01

    Pythons are renowned for the profound phenotypical flexibility of their visceral organs in response to ingestion of large meals following prolonged fasting. Traditionally, the phenotypic changes are studied by determining organ mass of snakes killed at different times during digestion. Here we evaluate the use of magnetic resonance imaging (MRI) for in vivo measurements of the visceral organs in fasting and digesting snakes. Twelve snakes were MRI scanned immediately before the organs were removed and weighed to provide direct comparison of the two methods. Both methods provided similar estimates for the mass of liver, gallbladder, and pancreas, whereas MRI overestimated the size of the heart and small intestine, probably because blood and digesta contributed to the volume determined by MRI. The correlations were used to derive wet organ mass from MRI-based volumes to evaluate the mass development through repeated MRI scans of five digesting snakes. MRI was performed at fasting and 24, 48, 72, 132, and 500 h after eating a meal corresponding to 25% of body mass. This observation period revealed a reversible volume upregulation of the visceral organs, supporting the view that successive MRI facilitates in vivo investigations of structural changes accompanied by digestion. PMID:23303329

  10. MSNoise: a Python Package for Monitoring Seismic Velocity Changes using Ambient Seismic Noise

    NASA Astrophysics Data System (ADS)

    Lecocq, T.; Caudron, C.; Brenguier, F.

    2013-12-01

    Earthquakes occur every day all around the world and are recorded by thousands of seismic stations. In between earthquakes, stations are recording "noise". In the last 10 years, the understanding of this noise and its potential usage have been increasing rapidly. The method, called "seismic interferometry", uses the principle that seismic waves travel between two recorders and are multiple-scattered in the medium. By cross-correlating the two records, one gets an information on the medium below/between the stations. The cross-correlation function (CCF) is a proxy to the Green Function of the medium. Recent developments of the technique have shown those CCF can be used to image the earth at depth (3D seismic tomography) or study the medium changes with time. We present MSNoise, a complete software suite to compute relative seismic velocity changes under a seismic network, using ambient seismic noise. The whole is written in Python, from the monitoring of data archives, to the production of high quality figures. All steps have been optimized to only compute the necessary steps and to use 'job'-based processing. We present a validation of the software on a dataset acquired during the UnderVolc[1] project on the Piton de la Fournaise Volcano, La Réunion Island, France, for which precursory relative changes of seismic velocity are visible for three eruptions betwee 2009 and 2011.

  11. NIFTY - Numerical Information Field Theory. A versatile PYTHON library for signal inference

    NASA Astrophysics Data System (ADS)

    Selig, M.; Bell, M. R.; Junklewitz, H.; Oppermann, N.; Reinecke, M.; Greiner, M.; Pachajoa, C.; Enßlin, T. A.

    2013-06-01

    NIFTy (Numerical Information Field Theory) is a software package designed to enable the development of signal inference algorithms that operate regardless of the underlying spatial grid and its resolution. Its object-oriented framework is written in Python, although it accesses libraries written in Cython, C++, and C for efficiency. NIFTy offers a toolkit that abstracts discretized representations of continuous spaces, fields in these spaces, and operators acting on fields into classes. Thereby, the correct normalization of operations on fields is taken care of automatically without concerning the user. This allows for an abstract formulation and programming of inference algorithms, including those derived within information field theory. Thus, NIFTy permits its user to rapidly prototype algorithms in 1D, and then apply the developed code in higher-dimensional settings of real world problems. The set of spaces on which NIFTy operates comprises point sets, n-dimensional regular grids, spherical spaces, their harmonic counterparts, and product spaces constructed as combinations of those. The functionality and diversity of the package is demonstrated by a Wiener filter code example that successfully runs without modification regardless of the space on which the inference problem is defined. NIFTy homepage http://www.mpa-garching.mpg.de/ift/nifty/; Excerpts of this paper are part of the NIFTy source code and documentation.

  12. Phylogeographic analysis of the green python, Morelia viridis, reveals cryptic diversity.

    PubMed

    Rawlings, Lesley H; Donnellan, Stephen C

    2003-04-01

    Green pythons, which are regionally variable in colour patterns, are found throughout the lowland rainforest of New Guinea and adjacent far northeastern Australia. The species is popular in commercial trade and management of this trade and its impacts on natural populations could be assisted by molecular identification tools. We used mitochondrial nucleotide sequences and a limited allozyme data to test whether significantly differentiated populations occur within the species range. Phylogenetic analysis of mtDNA sequences revealed hierarchal phylogeographic structure both within New Guinea and between New Guinea and Australia. Strongly supported reciprocally monophyletic mitochondrial lineages, northern and southern, were found either side of the central mountain range that runs nearly the length of New Guinea. Limited allozyme data suggest that population differentiation is reflected in the nuclear as well as the mitochondrial genome. A previous morphological analysis did not find any phenotypic concordance with the pattern of differentiation observed in the molecular data. The southern mitochondrial lineage includes all of the Australian haplotypes, which form a single lineage, nested among the southern New Guinean haplotypes. PMID:12679069

  13. pyLIDEM: A Python-Based Tool to Delineate Coastal Watersheds Using LIDAR Data

    NASA Astrophysics Data System (ADS)

    O'Banion, R.; Alameddine, I.; Gronewold, A.; Reckhow, K.

    2008-12-01

    Accurately identifying the boundary of a watershed is one of the most fundamental and important steps in any hydrological assessment. Representative applications include defining a study area, predicting overland flow, estimating groundwater infiltration, modeling pollutant accumulation and wash-off rates, and evaluating effectiveness of pollutant mitigation measures. The United States Environmental Protection Agency (USEPA) Total Maximum Daily Load (TMDL) program, the most comprehensive water quality management program in the United States (US), is just one example of an application in which accurate and efficient watershed delineation tools play a critical role. For example, many impaired water bodies currently being addressed through the TMDL program drain small coastal watersheds with relatively flat terrain, making watershed delineation particularly challenging. Most of these TMDL studies use 30-meter digital elevation models (DEMs) that rarely capture all of the small elevation changes in coastal watersheds, leading to errors not only in watershed boundary delineation, but in subsequent model predictions (such as watershed runoff flow and pollutant deposition rate predictions) for which watershed attributes are key inputs. Manually delineating these low-relief coastal watersheds through the use of expert knowledge of local water flow patterns, often produces relatively accurate (and often more accurate) watershed boundaries as compared to the boundaries generated by the 30-meter DEMs. Yet, manual delineation is a costly and time consuming procedure that is often not opted for. There is a growing need, therefore, particularly to address the ongoing needs of the TMDL program (and similar environmental management programs), for software tools which can utilize high resolution topography data to more accurately delineate coastal watersheds. Here, we address this need by developing pyLIDEM (python LIdar DEM), a python-based tool which processes bare earth high

  14. Dispel4py: An Open-Source Python library for Data-Intensive Seismology

    NASA Astrophysics Data System (ADS)

    Filgueira, Rosa; Krause, Amrey; Spinuso, Alessandro; Klampanos, Iraklis; Danecek, Peter; Atkinson, Malcolm

    2015-04-01

    Scientific workflows are a necessary tool for many scientific communities as they enable easy composition and execution of applications on computing resources while scientists can focus on their research without being distracted by the computation management. Nowadays, scientific communities (e.g. Seismology) have access to a large variety of computing resources and their computational problems are best addressed using parallel computing technology. However, successful use of these technologies requires a lot of additional machinery whose use is not straightforward for non-experts: different parallel frameworks (MPI, Storm, multiprocessing, etc.) must be used depending on the computing resources (local machines, grids, clouds, clusters) where applications are run. This implies that for achieving the best applications' performance, users usually have to change their codes depending on the features of the platform selected for running them. This work presents dispel4py, a new open-source Python library for describing abstract stream-based workflows for distributed data-intensive applications. Special care has been taken to provide dispel4py with the ability to map abstract workflows to different platforms dynamically at run-time. Currently dispel4py has four mappings: Apache Storm, MPI, multi-threading and sequential. The main goal of dispel4py is to provide an easy-to-use tool to develop and test workflows in local resources by using the sequential mode with a small dataset. Later, once a workflow is ready for long runs, it can be automatically executed on different parallel resources. dispel4py takes care of the underlying mappings by performing an efficient parallelisation. Processing Elements (PE) represent the basic computational activities of any dispel4Py workflow, which can be a seismologic algorithm, or a data transformation process. For creating a dispel4py workflow, users only have to write very few lines of code to describe their PEs and how they are

  15. Nipype: A Flexible, Lightweight and Extensible Neuroimaging Data Processing Framework in Python

    PubMed Central

    Gorgolewski, Krzysztof; Burns, Christopher D.; Madison, Cindee; Clark, Dav; Halchenko, Yaroslav O.; Waskom, Michael L.; Ghosh, Satrajit S.

    2011-01-01

    Current neuroimaging software offer users an incredible opportunity to analyze their data in different ways, with different underlying assumptions. Several sophisticated software packages (e.g., AFNI, BrainVoyager, FSL, FreeSurfer, Nipy, R, SPM) are used to process and analyze large and often diverse (highly multi-dimensional) data. However, this heterogeneous collection of specialized applications creates several issues that hinder replicable, efficient, and optimal use of neuroimaging analysis approaches: (1) No uniform access to neuroimaging analysis software and usage information; (2) No framework for comparative algorithm development and dissemination; (3) Personnel turnover in laboratories often limits methodological continuity and training new personnel takes time; (4) Neuroimaging software packages do not address computational efficiency; and (5) Methods sections in journal articles are inadequate for reproducing results. To address these issues, we present Nipype (Neuroimaging in Python: Pipelines and Interfaces; http://nipy.org/nipype), an open-source, community-developed, software package, and scriptable library. Nipype solves the issues by providing Interfaces to existing neuroimaging software with uniform usage semantics and by facilitating interaction between these packages using Workflows. Nipype provides an environment that encourages interactive exploration of algorithms, eases the design of Workflows within and between packages, allows rapid comparative development of algorithms and reduces the learning curve necessary to use different packages. Nipype supports both local and remote execution on multi-core machines and clusters, without additional scripting. Nipype is Berkeley Software Distribution licensed, allowing anyone unrestricted usage. An open, community-driven development philosophy allows the software to quickly adapt and address the varied needs of the evolving neuroimaging community, especially in the context of increasing demand for

  16. pyGrav, a Python-based program for handling and processing relative gravity data

    NASA Astrophysics Data System (ADS)

    Hector, Basile; Hinderer, Jacques

    2016-06-01

    pyGrav is a Python-based open-source software dedicated to the complete processing of relative-gravity data. It is particularly suited for time-lapse gravity surveys where high precision is sought. Its purpose is to bind together single-task processing codes in a user-friendly interface for handy and fast treatment of raw gravity data from many stations of a network. The intuitive object-based implementation allows to easily integrate additional functions (reading/writing routines, processing schemes, data plots) related to the appropriate object (a station, a loop, or a survey). This makes pyGrav an evolving tool. Raw data can be corrected for tides and air pressure effects. The data selection step features a double table-plot graphical window with either manual or automatic selection according to specific thresholds on data channels (tilts, gravity values, gravity standard deviation, duration of measurements, etc.). Instrumental drifts and gravity residuals are obtained by least square analysis of the dataset. This first step leads to the gravity simple differences between a reference point and any point of the network. When different repetitions of the network are done, the software computes then the gravity double differences and associated errors. The program has been tested on two specific case studies: a large dataset acquired for the study of water storage changes on a small catchment in West Africa, and a dataset operated and processed by several different users for geothermal studies in northern Alsace, France. In both cases, pyGrav proved to be an efficient and easy-to-use solution for the effective processing of relative-gravity data.

  17. Preliminary Third Year Results from the Python Microwave Background Anisotropy Experiment

    NASA Astrophysics Data System (ADS)

    Platt, S. R.; Dragovan, M.; Ruhl, J. E.; Kovak, J.

    1995-05-01

    We report preliminary results from the third year of observations from Amundsen-Scott South Pole Station with the Python microwave background anisotropy experiment. The instrument is a five channel bolometer array, with detectors operating at 50 mK, cooled by a hybrid (3) He-Adiabatic demagnetization refrigerator. Single-mode waveguide optics center the passband at 90 GHz, and couple the detector system to the 0.75m off-axis parabolic primary mirror of the telescope. The measured beamwidths are 0.75(deg) . We combine a fast 5 Hz 3-beam chop of a vertical flat plate with slow .025 Hz telescope beamswitches to produce a gradient-free 4-beam pattern on the sky. The chop amplitude is 2.75(deg) . In each of the first two years of observations with this system we scanned the same 22 fields, centered at alpha = 23.32, delta = -49.5. In both years we detected statistically significant fluctuations with an amplitude of Delta T / T ~ 3.3 times 10(-5) for an anisotropy model with a Gaussian autocorrelation function with a coherence angle of theta_c = 1(deg) . Our new results incorporate observations made in December 1994 that intersperse these fields and sample more fully this region of sky. The entire three year data set will be used to make a difference map of the structure of anisotropy at intermediate angular scales across a 6(deg) times 22(deg) region of sky. This work was supported by the National Science Foundation under a cooperative agreement with the Center for Astrophysical Research in Antarctica (CARA), grant number NSF OPP 89-20223, M.D.'s PYI grant NSF AST 90-57089, and the James S. McDonnell Foundation. CARA is a National Science Foundation Science and Technology Center.

  18. Multi-basin, Multi-sector Drought Economic Impact Model in Python: Development and Applications

    NASA Astrophysics Data System (ADS)

    Gutenson, J. L.; Zhu, L.; Ernest, A. N. S.; Oubeidillah, A.; Bearden, B.; Johnson, T. G.

    2015-12-01

    Drought is one of the most economically disastrous natural hazards, one whose impacts are exacerbated by the lack of abrupt onset and offset that define tornados and hurricanes. In the United States, about 30 billion dollars losses is caused by drought in 2012, resulting in widespread economic impacts for societies, industries, agriculture, and recreation. And in California, the drought cost statewide economic losses about 2.2 billion, with a total loss of 17,100 seasonal and part-time jobs. Driven by a variety of factors including climate change, population growth, increased water demands, alteration to land cover, drought occurs widely all over the world. Drought economic consequence assessment tool are greatly needed to allow decision makers and stakeholders to anticipate and manage effectively. In this study, current drought economic impact modeling methods were reviewed. Most of these models only deal with the impact in the agricultural sector with a focus on a single basin; few of these models analyze long term impact. However, drought impacts are rarely restricted to basin boundaries, and cascading economic impacts are likely to be significant. A holistic approach to multi-basin, multi-sector drought economic impact assessment is needed.In this work, we developed a new model for drought economic impact assessment, Drought Economic Impact Model in Python (PyDEM). This model classified all business establishments into thirteen categories based on NAICS, and using a continuous dynamic social accounting matrix approach, coupled with calculation of the indirect consequences for the local and regional economies and the various resilience. In addition, Environmental Policy Integrated Climate model was combined for analyzing drought caused soil erosion together with agriculture production, and then the long term impacts of drought were achieved. A visible output of this model was presented in GIS. In this presentation, Choctawhatchee-Pea-Yellow River Basins, Alabama

  19. Annotated checklist of the recent and extinct pythons (Serpentes, Pythonidae), with notes on nomenclature, taxonomy, and distribution

    PubMed Central

    Schleip, Wulf D.; O’Shea, Mark

    2010-01-01

    Abstract McDiarmid et al. (1999) published the first part of their planned taxonomic catalog of the snakes of the world. Since then, several new python taxa have been described in both the scientific literature and non-peer-reviewed publications. This checklist evaluates the nomenclatural status of the names and discusses the taxonomic status of the new taxa, and aims to continue the work of McDiarmid et al. (1999) for the family Pythonidae, covering the period 1999 to 2010. Numerous new taxa are listed, and where appropriate recent synonymies are included and annotations are made. A checklist and a taxonomic identification key of valid taxa are provided. PMID:21594030

  20. Combustion-chamber Performance Characteristics of a Python Turbine-propeller Engine Investigated in Altitude Wind Tunnel

    NASA Technical Reports Server (NTRS)

    Campbell, Carl E

    1951-01-01

    Combustion-chamber performance characteristics of a Python turbine-propeller engine were determined from investigation of a complete engine over a range of engine speeds and shaft horsepowers at simulated altitudes. Results indicated the effect of engine operating conditions and altitude on combustion efficiency and combustion-chamber total pressure losses. Performance of this vaporizing type combustion chamber was also compared with several atomizing type combustion chambers. Over the range of test conditions investigated, combustion efficiency varied from approximately 0.95 to 0.99.

  1. Weighing empirical and hypothetical evidence for assessing potential invasive species range limits: a review of the case of Burmese pythons in the USA.

    PubMed

    Engeman, Richard; Avery, Michael L; Jacobson, Elliott

    2014-10-01

    Range expansion potential is an important consideration for prioritizing management actions against an invasive species. Understanding the potential for range expansion by invasive reptiles such as the Burmese python can be challenging, because the lack of knowledge on fundamental physiological and behavioral constraints initially forces reliance on modeling to predict hypothetical invasive range potential. Hypothetical predictions for Burmese python range limits in the USA have been highly divergent, from only extreme South Florida and the extreme southern Gulf edge of Texas to a broad swath over the southern third of the continental USA. Empirical observations on python thermal tolerances and behavioral abilities to cope with more temperate temperatures became evident during a cold spell in December 2009-January 2010. We review and highlight important considerations for improving invasive range estimation methodology, deciding between competing range predictions, and the importance of having, and applying, empirical data to aid in decision making. PMID:24943887

  2. Giant Constrictors: Biological and Management Profiles and an Establishment Risk Assessment for Nine Large Species of Pythons, Anacondas, and the Boa Constrictor

    USGS Publications Warehouse

    Reed, Robert N.; Rodda, Gordon H.

    2009-01-01

    Giant Constrictors: Biological and Management Profiles and an Establishment Risk Assessment for Nine Large Species of Pythons, Anacondas, and the Boa Constrictor, estimates the ecological risks associated with colonization of the United States by nine large constrictors. The nine include the world's four largest snake species (Green Anaconda, Eunectes murinus; Indian or Burmese Python, Python molurus; Northern African Python, Python sebae; and Reticulated Python, Broghammerus reticulatus), the Boa Constrictor (Boa constrictor), and four species that are ecologically or visually similar to one of the above (Southern African Python, Python natalensis; Yellow Anaconda, Eunectes notaeus; DeSchauensee's Anaconda, Eunectes deschauenseei; and Beni Anaconda, Eunectes beniensis). At present, the only probable pathway by which these species would become established in the United States is the pet trade. Although importation for the pet trade involves some risk that these animals could become established as exotic or invasive species, it does not guarantee such establishment. Federal regulators have the task of appraising the importation risks and balancing those risks against economic, social, and ecological benefits associated with the importation. The risk assessment quantifies only the ecological risks, recognizing that ecosystem processes are complex and only poorly understood. The risk assessment enumerates the types of economic impacts that may be experienced, but leaves quantification of economic costs to subsequent studies. Primary factors considered in judging the risk of establishment were: (1) history of establishment in other countries, (2) number of each species in commerce, (3) suitability of U.S. climates for each species, and (4) natural history traits, such as reproductive rate and dispersal ability, that influence the probability of establishment, spread, and impact. In addition, the risk assessment reviews all management tools for control of invasive giant

  3. A Python Plug-in Based Computational Framework for Spatially Distributed Environmental and Earth Sciences Modelling

    NASA Astrophysics Data System (ADS)

    Willgoose, G. R.

    2009-12-01

    One of the pioneering landform evolution models, SIBERIA, while developed in the 1980’s is still widely used in the science community and is a key component of engineering software used to assess the long-term stability of man-made landforms such as rehabilitated mine sites and nuclear waste repositories. While SIBERIA is very reliable, computationally fast and well tested (both its underlying science and the computer code) the range of emerging applications have challenged the ability of the author to maintain and extend the underlying computer code. Moreover, the architecture of the SIBERIA code is not well suited to collaborative extension of its capabilities without often triggering forking of the code base. This paper describes a new modelling framework designed to supersede SIBERIA (as well as other earth sciences codes by the author) called TelluSim. The design is such that it is potentially more than simply a new landform evolution model, but TelluSim is a more general dynamical system modelling framework using time evolving GIS data as its spatial discretisation. TelluSim is designed as an open modular framework facilitating open-sourcing of the code, while addressing compromises made in the original design of SIBERIA in the 1980’s. An important aspect of the design of TelluSim was to minimise the overhead in interfacing the modules with TelluSim, and minimise any requirement for recoding of existing software, so eliminating a major disadvantage of more complex frameworks. The presentation will discuss in more detail the reasoning behind the design of TelluSim, and experiences of the advantages and disadvantages of using Python relative to other approaches (e.g. Matlab, R). The paper will discuss examples of how TelluSim has facilitated the incorporation and testing of new algorithms, and environmental processes, and the support for novel science and data testing methodologies. It will also discuss plans to link TelluSim with other open source

  4. Novel phospholipase A2 inhibitors from python serum are potent peptide antibiotics.

    PubMed

    Samy, Ramar Perumal; Thwin, Maung Maung; Stiles, Brad G; Satyanarayana-Jois, Seetharama; Chinnathambi, Arunachalam; Zayed, M E; Alharbi, Sulaiman Ali; Siveen, Kodappully Sivaraman; Sikka, Sakshi; Kumar, Alan Prem; Sethi, Gautam; Lim, Lina Hsiu Kim

    2015-04-01

    Antimicrobial peptides (AMPs) play a vital role in defense against resistant bacteria. In this study, eight different AMPs synthesized from Python reticulatus serum protein were tested for bactericidal activity against various Gram-positive and Gram-negative bacteria (Staphylococcus aureus, Burkholderia pseudomallei (KHW and TES strains), and Proteus vulgaris) using a disc-diffusion method (20 μg/disc). Among the tested peptides, phospholipase A2 inhibitory peptide (PIP)-18[59-76], β-Asp65-PIP[59-67], D-Ala66-PNT.II, and D60,65E-PIP[59-67] displayed the most potent bactericidal activity against all tested pathogens in a dose-dependent manner (100-6.8 μg/ml), with a remarkable activity noted against S. aureus at 6.8 μg/ml dose within 6 h of incubation. Determination of minimum inhibitory concentrations (MICs) by a micro-broth dilution method at 100-3.125 μg/ml revealed that PIP-18[59-76], β-Asp65-PIP[59-67] and D-Ala66-PNT.II peptides exerted a potent inhibitory effect against S. aureus and B. pseudomallei (KHW) (MICs 3.125 μg/ml), while a much less inhibitory potency (MICs 12.5 μg/ml) was noted for β-Asp65-PIP[59-67] and D-Ala66-PNT.II peptides against B. pseudomallei (TES). Higher doses of peptides had no effect on the other two strains (i.e., Klebsiella pneumoniae and Streptococcus pneumoniae). Overall, PIP-18[59-76] possessed higher antimicrobial activity than that of chloramphenicol (CHL), ceftazidime (CF) and streptomycin (ST) (30 μg/disc). When the two most active peptides, PIP-18[59-76] and β-Asp65-PIP[59-67], were applied topically at a 150 mg/kg dose for testing wound healing activity in a mouse model of S. aureus infection, the former accelerates faster wound healing than the latter peptide at 14 days post-treatment. The western blot data suggest that the topical application of peptides (PIP-18[59-67] and β-Asp65-PIP[59-67]) modulates NF-kB mediated wound repair in mice with relatively little haemolytic (100-1.56 μg/ml) and cytotoxic (1000

  5. Continued Development of Python-Based Thomson Data Analysis and Associated Visualization Tool for NSTX-U

    NASA Astrophysics Data System (ADS)

    Wallace, William; Miller, Jared; Diallo, Ahmed

    2015-11-01

    MultiPoint Thomson Scattering (MPTS) is an established, accurate method of finding the temperature, density, and pressure of a magnetically confined plasma. Two Nd:YAG (1064 nm) lasers are fired into the plasma with a effective frequency of 60 Hz, and the light is Doppler shifted by Thomson scattering. Polychromators on the NSTX-U midplane collect the scattered photons at various radii/scattering angles, and the avalanche photodiode voltages are saved to an MDSplus tree for later analysis. IDL code is then used to determine plasma temperature, pressure, and density from the captured polychromator measurements via Selden formulas. [1] Previous work [2] converted the single-processor IDL code into Python code, and prepared a new architecture for multiprocessing MPTS in parallel. However, that work was not completed to the generation of output data and curve fits that match with the previous IDL. This project refactored the Python code into a object-oriented architecture, and created a software test suite for the new architecture which allowed identification of the code which generated the difference in output. Another effort currently underway is to display the Thomson data in an intuitive, interactive format. This work was supported in part by the U.S. Department of Energy, Office of Science, Office of Workforce Development for Teachers and Scientists (WDTS) under the Community College Internship (CCI) program.

  6. Graph-based active learning of agglomeration (GALA): a Python library to segment 2D and 3D neuroimages

    PubMed Central

    Nunez-Iglesias, Juan; Kennedy, Ryan; Plaza, Stephen M.; Chakraborty, Anirban; Katz, William T.

    2014-01-01

    The aim in high-resolution connectomics is to reconstruct complete neuronal connectivity in a tissue. Currently, the only technology capable of resolving the smallest neuronal processes is electron microscopy (EM). Thus, a common approach to network reconstruction is to perform (error-prone) automatic segmentation of EM images, followed by manual proofreading by experts to fix errors. We have developed an algorithm and software library to not only improve the accuracy of the initial automatic segmentation, but also point out the image coordinates where it is likely to have made errors. Our software, called gala (graph-based active learning of agglomeration), improves the state of the art in agglomerative image segmentation. It is implemented in Python and makes extensive use of the scientific Python stack (numpy, scipy, networkx, scikit-learn, scikit-image, and others). We present here the software architecture of the gala library, and discuss several designs that we consider would be generally useful for other segmentation packages. We also discuss the current limitations of the gala library and how we intend to address them. PMID:24772079

  7. A Python Implementation of an Intermediate-Level Tropical Circulation Model and Implications for How Modeling Science is Done

    NASA Astrophysics Data System (ADS)

    Lin, J. W. B.

    2015-12-01

    Historically, climate models have been developed incrementally and in compiled languages like Fortran. While the use of legacy compiledlanguages results in fast, time-tested code, the resulting model is limited in its modularity and cannot take advantage of functionalityavailable with modern computer languages. Here we describe an effort at using the open-source, object-oriented language Pythonto create more flexible climate models: the package qtcm, a Python implementation of the intermediate-level Neelin-Zeng Quasi-Equilibrium Tropical Circulation model (QTCM1) of the atmosphere. The qtcm package retains the core numerics of QTCM1, written in Fortran, to optimize model performance but uses Python structures and utilities to wrap the QTCM1 Fortran routines and manage model execution. The resulting "mixed language" modeling package allows order and choice of subroutine execution to be altered at run time, and model analysis and visualization to be integrated in interactively with model execution at run time. This flexibility facilitates more complex scientific analysis using less complex code than would be possible using traditional languages alone and provides tools to transform the traditional "formulate hypothesis → write and test code → run model → analyze results" sequence into a feedback loop that can be executed automatically by the computer.

  8. CellLab-CTS 2015: a Python library for continuous-time stochastic cellular automaton modeling using Landlab

    NASA Astrophysics Data System (ADS)

    Tucker, G. E.; Hobley, D. E. J.; Hutton, E.; Gasparini, N. M.; Istanbulluoglu, E.; Adams, J. M.; Nudurupati, S. S.

    2015-11-01

    CellLab-CTS 2015 is a Python-language software library for creating two-dimensional, continuous-time stochastic (CTS) cellular automaton models. The model domain consists of a set of grid nodes, with each node assigned an integer state-code that represents its condition or composition. Adjacent pairs of nodes may undergo transitions to different states, according to a user-defined average transition rate. A model is created by writing a Python code that defines the possible states, the transitions, and the rates of those transitions. The code instantiates, initializes, and runs one of four object classes that represent different types of CTS model. CellLab-CTS provides the option of using either square or hexagonal grid cells. The software provides the ability to treat particular grid-node states as moving particles, and to track their position over time. Grid nodes may also be assigned user-defined properties, which the user can update after each transition through the use of a callback function. As a component of the Landlab modeling framework, CellLab-CTS models take advantage of a suite of Landlab's tools and capabilities, such as support for standardized input and output.

  9. The Social Tunnel Versus the Python: A New Way to Understand the Impact of Baby Booms and Baby Busts on a Society.

    ERIC Educational Resources Information Center

    McFalls, Joseph A.; And Others

    1986-01-01

    Maintains that the "python analogy," often used to help students understand the negative societal impact of unusually small or large age cohorts, is better replaced by the social tunnel analogy, which is diagramed and illustrated with reference to the educational problems experienced in the United States as a result of the World War II baby boom.…

  10. Development of a 3D Potential Field Forward Modelling System in Python

    NASA Astrophysics Data System (ADS)

    Cole, P.

    2012-12-01

    The collection of potential field data has long been a standard part of geophysical exploration. Specifically, airborne magnetic data is collected routinely in any brown-fields area, because of the low cost and fast acquisition rate compared to other geophysical techniques. However, the interpretation of such data can be a daunting task, especially when 3D models are becoming more necessary. The current trend in modelling software is to follow either the modelling of individual profiles, which are then "joined" up into 3D sections, or to model in a full 3D using polygonal based models (Singh and Guptasarma, 2001). Unfortunately, both techniques have disadvantages. When modelling in 2.5D the impact of other profiles is not truly available on your current profile being modelled, and vice versa. The problem is not present in 3D, but 3D polygonal models, while being easy to construct the initial model, are not as easy to make fast changes to. In some cases, the entire model must be recreated from scratch. The ability to easily change a model is the very basis of forward modelling. With this is mind, the objective of the project was to: 1) Develop software which was truly modelling in 3D 2) Create a system which would allow the rapid changing of the 3D model, without the need to recreate the model. The solution was to adopt a voxel based approach, rather than a polygonal approach. The solution for a cube (Blakely 1996) was used to calculate potential field for each voxel. The voxels are then summed over the entire volume. The language used was python, because of its huge capacity for scientific development. It enables full 3D visualisation as well as complex mathematical routines. Some properties worth noting are: 1) Although 200 rows by 200 columns by 200 layers would imply 8 million calculations, in reality, since the calculation for adjacent voxels produces the same result, only 200 calculations are necessary. 2) Changes to susceptibility and density do not affect

  11. Using Python for scientific computing: Efficient and flexible evaluation of the statistical characteristics of functions with multivariate random inputs

    NASA Astrophysics Data System (ADS)

    Chudoba, R.; Sadílek, V.; Rypl, R.; Vořechovský, M.

    2013-02-01

    This paper examines the feasibility of high-level Python based utilities for numerically intensive applications via an example of a multidimensional integration for the evaluation of the statistical characteristics of a random variable. We discuss the approaches to the implementation of mathematically formulated incremental expressions using high-level scripting code and low-level compiled code. Due to the dynamic typing of the Python language, components of the algorithm can be easily coded in a generic way as algorithmic templates. Using the Enthought Development Suite they can be effectively assembled into a flexible computational framework that can be configured to execute the code for arbitrary combinations of integration schemes and versions of instantiated code. The paper describes the development cycle using a simple running example involving averaging of a random two-parametric function that includes discontinuity. This example is also used to compare the performance of the available algorithmic and executional features. The implemented package including further examples and the results of performance studies have been made available via the free repository [1] and CPCP library. Program summaryProgram title: spirrid Catalogue identifier: AENL_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AENL_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: Special licence provided by the author No. of lines in distributed program, including test data, etc.: 10722 No. of bytes in distributed program, including test data, etc.: 157099 Distribution format: tar.gz Programming language: Python and C. Computer: PC. Operating system: LINUX, UNIX, Windows. Classification: 4.13, 6.2. External routines: NumPy (http://numpy.scipy.org/), SciPy (http://www.scipy.com) Nature of problem: Evaluation of the statistical moments of a function of random variables. Solution method: Direct multidimensional

  12. ESMPy and OpenClimateGIS: Python Interfaces for High Performance Grid Remapping and Geospatial Dataset Manipulation

    NASA Astrophysics Data System (ADS)

    O'Kuinghttons, Ryan; Koziol, Benjamin; Oehmke, Robert; DeLuca, Cecelia; Theurich, Gerhard; Li, Peggy; Jacob, Joseph

    2016-04-01

    The Earth System Modeling Framework (ESMF) Python interface (ESMPy) supports analysis and visualization in Earth system modeling codes by providing access to a variety of tools for data manipulation. ESMPy started as a Python interface to the ESMF grid remapping package, which provides mature and robust high-performance and scalable grid remapping between 2D and 3D logically rectangular and unstructured grids and sets of unconnected data. ESMPy now also interfaces with OpenClimateGIS (OCGIS), a package that performs subsetting, reformatting, and computational operations on climate datasets. ESMPy exposes a subset of ESMF grid remapping utilities. This includes bilinear, finite element patch recovery, first-order conservative, and nearest neighbor grid remapping methods. There are also options to ignore unmapped destination points, mask points on source and destination grids, and provide grid structure in the polar regions. Grid remapping on the sphere takes place in 3D Cartesian space, so the pole problem is not an issue as it can be with other grid remapping software. Remapping can be done between any combination of 2D and 3D logically rectangular and unstructured grids with overlapping domains. Grid pairs where one side of the regridding is represented by an appropriate set of unconnected data points, as is commonly found with observational data streams, is also supported. There is a developing interoperability layer between ESMPy and OpenClimateGIS (OCGIS). OCGIS is a pure Python, open source package designed for geospatial manipulation, subsetting, and computation on climate datasets stored in local NetCDF files or accessible remotely via the OPeNDAP protocol. Interfacing with OCGIS has brought GIS-like functionality to ESMPy (i.e. subsetting, coordinate transformations) as well as additional file output formats (i.e. CSV, ESRI Shapefile). ESMPy is distinguished by its strong emphasis on open source, community governance, and distributed development. The user

  13. LANL12-RS-107J PYTHON Radiography Analysis Tool (PyRAT). Mid-Year Deliverable Report for FY15

    SciTech Connect

    Temple, Brian Allen; Armstrong, Jerawan Chudoung

    2015-04-14

    This document is a mid-year report on a deliverable for the PYTHON Radiography Analysis Tool (PyRAT) for project LANL12-RS-107J in FY15. The deliverable is deliverable number 2 in the work package and is titled “Add the ability to read in more types of image file formats in PyRAT”. Right now PyRAT can only read in uncompressed TIF files (tiff files). It is planned to expand the file formats that can be read by PyRAT, making it easier to use in more situations. A summary of the file formats added include jpeg, jpg, png and formatted ASCII files.

  14. Wilber 3: A Python-Django Web Application For Acquiring Large-scale Event-oriented Seismic Data

    NASA Astrophysics Data System (ADS)

    Newman, R. L.; Clark, A.; Trabant, C. M.; Karstens, R.; Hutko, A. R.; Casey, R. E.; Ahern, T. K.

    2013-12-01

    Since 2001, the IRIS Data Management Center (DMC) WILBER II system has provided a convenient web-based interface for locating seismic data related to a particular event, and requesting a subset of that data for download. Since its launch, both the scale of available data and the technology of web-based applications have developed significantly. Wilber 3 is a ground-up redesign that leverages a number of public and open-source projects to provide an event-oriented data request interface with a high level of interactivity and scalability for multiple data types. Wilber 3 uses the IRIS/Federation of Digital Seismic Networks (FDSN) web services for event data, metadata, and time-series data. Combining a carefully optimized Google Map with the highly scalable SlickGrid data API, the Wilber 3 client-side interface can load tens of thousands of events or networks/stations in a single request, and provide instantly responsive browsing, sorting, and filtering of event and meta data in the web browser, without further reliance on the data service. The server-side of Wilber 3 is a Python-Django application, one of over a dozen developed in the last year at IRIS, whose common framework, components, and administrative overhead represent a massive savings in developer resources. Requests for assembled datasets, which may include thousands of data channels and gigabytes of data, are queued and executed using the Celery distributed Python task scheduler, giving Wilber 3 the ability to operate in parallel across a large number of nodes.

  15. Darwin and evolutionary tales in leukemia. The Ham-Wasserman Lecture.

    PubMed

    Greaves, Mel

    2009-01-01

    All cancers evolve by a process of genetic diversification and "natural selection" akin to the process first described by Charles Darwin for species evolution. The evolutionary, natural history of childhood acute lymphoblastic leukemia (ALL) is almost entirely covert, clinically silent and well advanced by the point of diagnosis. It has, however, been possible to backtrack this process by molecular scrutiny of appropriate clinical samples: (i) leukemic clones in monozygotic twins that are either concordant or discordant for ALL; (ii) archived neonatal blood spots or Guthrie cards from individuals who later developed leukemia; and (iii) stored, viable cord blood cells. These studies indicate prenatal initiation of leukemia by chromosome translocation and gene fusion (or hyperdiploidy) and the post-natal acquisition of multiple, gene copy number alterations (CNAs), mostly deletions. The prenatal or first "hit" occurs very commonly, exceeding the clinical rate of ALL by some 100x and indicating a low rate of penetrance or evolutionary progression. The acquisition of the critical, secondary CNAs requires some Darwinian selective advantage to expand numbers of cells at risk, and the cytokine TGF beta is able to exercise this function. The clonal architecture of ALL has been investigated by single cell analysis with multicolor probes to mutant genes. The data reveal not a linear sequence of mutation acquisition with clonal succession but rather considerable complexity with a tree-like or branching structure of genetically distinct subclones very reminiscent of Darwin's original 1837 evolutionary divergence diagram. This evolutionary pattern has important implications for stem cells in ALL, for the origins of relapse and for therapeutic targeting. PMID:20008176

  16. Processing of terabytes of data for seismic noise analysis with the Python codes of the Whisper Suite. (Invited)

    NASA Astrophysics Data System (ADS)

    Briand, X.; Campillo, M.; Brenguier, F.; Boue, P.; Poli, P.; Roux, P.; Takeda, T.

    2013-12-01

    The Whisper Suite, as part of the ERC project Whisper (whisper.obs.ujf-grenoble.fr), is developed with the high-level programming language Python and uses intensively the scientific libraries Scipy and Obspy, which is dedicated to the seismological community (www.obspy.org). The Whisper Suite consists of several tools. It provides a flexible way to specify a pipeline of seismogram processing. The user can define his own sequence of treatments, can use the Python libraries he needs and eventually, can add his processing procedure to the Whisper Suite. Another package is dedicated to the computation of correlations. When dealing with large data set, computational time becomes a major difficulty and we devoted a lot of efforts to make possible the fast processing of the large data sets produced by the present day dense seismic networks. With the Whisper Suite, we manage currently more than 150TB of data for ambient noise analysis. For the computations of 68 millions correlations (daily, 5Hz, correlation window 3600s) on a 50 core cluster, with a dedicated disk array, the required time is 4 days. With a distributed storage (Irods) and a grid of clusters (mode best effort), both provided by the University of Grenoble, we compute currently one year of 4-hours correlations for 550 3C stations of the Hi-Net Japanese Network in one day (about 350 millions individual correlations) . Note that the quadratic space complexity can be critical. We developed also codes for the analysis of the correlations. The Whisper Suite is used to make challenging observations using cross-correlation techniques at various scales in the Earth. We present some examples of applications. Using a global data set of available broadband stations, we discuss the emergence of the complete teleseismic body wave wave field, including the deep phases used for imaging of the mantle and the core. The giant 2011 Tohoku-oki earthquake and the records of the dense Hi-Net array offer an opportunity to analyze

  17. Molecular identification of Sarcocystis spp. helped to define the origin of green pythons (Morelia viridis) confiscated in Germany.

    PubMed

    Moré, Gastón; Pantchev, Nikola; Herrmann, Daland C; Vrhovec, Majda Globokar; Öfner, Sabine; Conraths, Franz J; Schares, Gereon

    2014-04-01

    Sarcocystis spp. represent apicomplexan parasites. They usually have a heteroxenous life cycle. Around 200 species have been described, affecting a wide range of animals worldwide, including reptiles. In recent years, large numbers of reptiles have been imported into Europe as pets and, as a consequence, animal welfare and species protection issues emerged. A sample of pooled feces from four confiscated green pythons (Morelia viridis) containing Sarcocystis spp. sporocysts was investigated. These snakes were imported for the pet trade and declared as being captive-bred. Full length 18S rRNA genes were amplified, cloned into plasmids and sequenced. Two different Sarcocystis spp. sequences were identified and registered as Sarcocystis sp. from M. viridis in GenBank. Both showed a 95-97% sequence identity with the 18S rRNA gene of Sarcocystis singaporensis. Phylogenetic analysis positioned these sequences together with other Sarcocystis spp. from snakes and rodents as definitive and intermediate hosts (IH), respectively. Sequence data and also the results of clinical and parasitological examinations suggest that the snakes were definitive hosts for Sarcocystis spp. that circulate in wild IH. Thus, it seems unlikely that the infected snakes had been legally bred. Our research shows that information on the infection of snakes with Sarcocystis spp. may be used to assess compliance with regulations on the trade with wildlife species. PMID:24476633

  18. NMRbot: Python scripts enable high-throughput data collection on current Bruker BioSpin NMR spectrometers.

    PubMed

    Clos, Lawrence J; Jofre, M Fransisca; Ellinger, James J; Westler, William M; Markley, John L

    2013-06-01

    To facilitate the high-throughput acquisition of nuclear magnetic resonance (NMR) experimental data on large sets of samples, we have developed a simple and straightforward automated methodology that capitalizes on recent advances in Bruker BioSpin NMR spectrometer hardware and software. Given the daunting challenge for non-NMR experts to collect quality spectra, our goal was to increase user accessibility, provide customized functionality, and improve the consistency and reliability of resultant data. This methodology, NMRbot, is encoded in a set of scripts written in the Python programming language accessible within the Bruker BioSpin TopSpin™ software. NMRbot improves automated data acquisition and offers novel tools for use in optimizing experimental parameters on the fly. This automated procedure has been successfully implemented for investigations in metabolomics, small-molecule library profiling, and protein-ligand titrations on four Bruker BioSpin NMR spectrometers at the National Magnetic Resonance Facility at Madison. The investigators reported benefits from ease of setup, improved spectral quality, convenient customizations, and overall time savings. PMID:23678341

  19. Tribological analysis of the ventral scale structure in a Python regius in relation to laser textured surfaces

    NASA Astrophysics Data System (ADS)

    Abdel-Aal, H. A.; El Mansori, M.

    2013-09-01

    Laser texturing is one of the leading technologies applied to modify surface topography. To date, however, a standardized procedure to generate deterministic textures is virtually non-existent. In nature, especially in squamata, there are many examples of deterministic structured textures that allow species to control friction and condition their tribological response for efficient function. In this work, we draw a comparison between industrial surfaces and reptilian surfaces. We chose the Python regius species as a bio-analogue with a deterministic surface. We first study the structural make up of the ventral scales of the snake (both construction and metrology). We further compare the metrological features of the ventral scales to experimentally recommended performance indicators of industrial surfaces extracted from open literature. The results indicate the feasibility of engineering a laser textured surface based on the reptilian ornamentation constructs. It is shown that the metrological features, key to efficient function of a rubbing deterministic surface, are already optimized in the reptile. We further show that optimization in reptilian surfaces is based on synchronizing surface form, textures and aspects to condition the frictional response. Mimicking reptilian surfaces, we argue, may form a design methodology potentially capable of generating advanced deterministic surface constructs capable of efficient tribological function.

  20. Application of python-based Abaqus preprocess and postprocess technique in analysis of gearbox vibration and noise reduction

    NASA Astrophysics Data System (ADS)

    Yi, Guilian; Sui, Yunkang; Du, Jiazheng

    2011-06-01

    To reduce vibration and noise, a damping layer and constraint layer are usually pasted on the inner surface of a gearbox thin shell, and their thicknesses are the main parameters in the vibration and noise reduction design. The normal acceleration of the point on the gearbox surface is the main index that can reflect the vibration and noise of that point, and the normal accelerations of different points can reflect the degree of the vibration and noise of the whole structure. The K-S function is adopted to process many points' normal accelerations as the comprehensive index of the vibration characteristics of the whole structure, and the vibration acceleration level is adopted to measure the degree of the vibration and noise. Secondary development of the Abaqus preprocess and postprocess on the basis of the Python scripting programming automatically modifies the model parameters, submits the job, and restarts the analysis totally, which avoids the tedious work of returning to the Abaqus/CAE for modifying and resubmitting and improves the speed of the preprocess and postprocess and the computational efficiency.

  1. C++QEDv2 Milestone 10: A C++/Python application-programming framework for simulating open quantum dynamics

    NASA Astrophysics Data System (ADS)

    Sandner, Raimar; Vukics, András

    2014-09-01

    The v2 Milestone 10 release of C++QED is primarily a feature release, which also corrects some problems of the previous release, especially as regards the build system. The adoption of C++11 features has led to many simplifications in the codebase. A full doxygen-based API manual [1] is now provided together with updated user guides. A largely automated, versatile new testsuite directed both towards computational and physics features allows for quickly spotting arising errors. The states of trajectories are now savable and recoverable with full binary precision, allowing for trajectory continuation regardless of evolution method (single/ensemble Monte Carlo wave-function or Master equation trajectory). As the main new feature, the framework now presents Python bindings to the highest-level programming interface, so that actual simulations for given composite quantum systems can now be performed from Python. Catalogue identifier: AELU_v2_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AELU_v2_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: yes No. of lines in distributed program, including test data, etc.: 492422 No. of bytes in distributed program, including test data, etc.: 8070987 Distribution format: tar.gz Programming language: C++/Python. Computer: i386-i686, x86 64. Operating system: In principle cross-platform, as yet tested only on UNIX-like systems (including Mac OS X). RAM: The framework itself takes about 60MB, which is fully shared. The additional memory taken by the program which defines the actual physical system (script) is typically less than 1MB. The memory storing the actual data scales with the system dimension for state-vector manipulations, and the square of the dimension for density-operator manipulations. This might easily be GBs, and often the memory of the machine limits the size of the simulated system. Classification: 4.3, 4.13, 6.2. External routines: Boost C

  2. Forecast of spatially distributed runoff dynamics in the Finger Lakes region using an interactive web tool and Python

    NASA Astrophysics Data System (ADS)

    Dahlke, H. E.; Easton, Z. M.; Fuka, D. R.; Rao, N. S.; Steenhuis, T. S.

    2008-12-01

    To optimize NPS pollution reduction efficiency of Best Management Practices (BMPs) in NY State, various models have been developed that can effectively delineate runoff and contaminant source areas in the landscape. In the Finger Lakes region with shallow, permeable soils, underlain by a restricting layer, saturation excess runoff is the dominant mechanism of nutrient transport. In watersheds characterized by these conditions, runoff originates from areas in the landscape that expand and contract seasonally and are therefore often termed as variable source areas (VSAs). Hence, consideration should be given to the spatial distribution of VSA in the watershed during the planning process of BMPs. However, in practice the applied hydrological models often require extensive expertise and effort to be used on a routine basis for BMP planning. In order to simplify the BMP planning process, we developed an interactive web-based tool for Salmon Creek watershed, NY that locates VSA and calculates their probability of saturation. The interactive web tool incorporates hydrologic, geographic and land management information in an ESRI ArcIMS framework and presents the resulting VSA maps online. For the web tool we developed a Python-based application that calculates the surface runoff potential of the 230 km2 Salmon Creek watershed on the basis of a water balance model and free precipitation and temperature data from the National Climatic Data Center. Areas of high surface runoff potential are distributed via a soil topographic index to capture VSA dynamics. Further, the application is used to calculate a one to two day prediction of the spatial extent of VSA using free web- provided weather forecasts. The web tool is designed to interactively assist planners and especially farmers in the BMP planning process on a simplified expertise level. It can be used on a daily basis to locate fields with low runoff risk that could, potentially receive more liberal nutrient applications

  3. A biomolecular electrostatics solver using Python, GPUs and boundary elements that can handle solvent-filled cavities and Stern layers

    NASA Astrophysics Data System (ADS)

    Cooper, Christopher D.; Bardhan, Jaydeep P.; Barba, L. A.

    2014-03-01

    The continuum theory applied to biomolecular electrostatics leads to an implicit-solvent model governed by the Poisson-Boltzmann equation. Solvers relying on a boundary integral representation typically do not consider features like solvent-filled cavities or ion-exclusion (Stern) layers, due to the added difficulty of treating multiple boundary surfaces. This has hindered meaningful comparisons with volume-based methods, and the effects on accuracy of including these features has remained unknown. This work presents a solver called PyGBe that uses a boundary-element formulation and can handle multiple interacting surfaces. It was used to study the effects of solvent-filled cavities and Stern layers on the accuracy of calculating solvation energy and binding energy of proteins, using the well-known APBS finite-difference code for comparison. The results suggest that if required accuracy for an application allows errors larger than about 2% in solvation energy, then the simpler, single-surface model can be used. When calculating binding energies, the need for a multi-surface model is problem-dependent, becoming more critical when ligand and receptor are of comparable size. Comparing with the APBS solver, the boundary-element solver is faster when the accuracy requirements are higher. The cross-over point for the PyGBe code is on the order of 1-2% error, when running on one GPU card (NVIDIA Tesla C2075), compared with APBS running on six Intel Xeon CPU cores. PyGBe achieves algorithmic acceleration of the boundary element method using a treecode, and hardware acceleration using GPUs via PyCuda from a user-visible code that is all Python. The code is open-source under MIT license.

  4. A biomolecular electrostatics solver using Python, GPUs and boundary elements that can handle solvent-filled cavities and Stern layers.

    PubMed

    Cooper, Christopher D; Bardhan, Jaydeep P; Barba, L A

    2014-03-01

    The continuum theory applied to biomolecular electrostatics leads to an implicit-solvent model governed by the Poisson-Boltzmann equation. Solvers relying on a boundary integral representation typically do not consider features like solvent-filled cavities or ion-exclusion (Stern) layers, due to the added difficulty of treating multiple boundary surfaces. This has hindered meaningful comparisons with volume-based methods, and the effects on accuracy of including these features has remained unknown. This work presents a solver called PyGBe that uses a boundary-element formulation and can handle multiple interacting surfaces. It was used to study the effects of solvent-filled cavities and Stern layers on the accuracy of calculating solvation energy and binding energy of proteins, using the well-known apbs finite-difference code for comparison. The results suggest that if required accuracy for an application allows errors larger than about 2% in solvation energy, then the simpler, single-surface model can be used. When calculating binding energies, the need for a multi-surface model is problem-dependent, becoming more critical when ligand and receptor are of comparable size. Comparing with the apbs solver, the boundary-element solver is faster when the accuracy requirements are higher. The cross-over point for the PyGBe code is in the order of 1-2% error, when running on one gpu card (nvidia Tesla C2075), compared with apbs running on six Intel Xeon cpu cores. PyGBe achieves algorithmic acceleration of the boundary element method using a treecode, and hardware acceleration using gpus via PyCuda from a user-visible code that is all Python. The code is open-source under MIT license. PMID:25284826

  5. ST-HASSET for volcanic hazard assessment: A Python tool for evaluating the evolution of unrest indicators

    NASA Astrophysics Data System (ADS)

    Bartolini, Stefania; Sobradelo, Rosa; Martí, Joan

    2016-08-01

    Short-term hazard assessment is an important part of the volcanic management cycle, above all at the onset of an episode of volcanic agitation (unrest). For this reason, one of the main tasks of modern volcanology is to use monitoring data to identify and analyse precursory signals and so determine where and when an eruption might occur. This work follows from Sobradelo and Martí [Short-term volcanic hazard assessment through Bayesian inference: retrospective application to the Pinatubo 1991 volcanic crisis. Journal of Volcanology and Geothermal Research 290, 111, 2015] who defined the principle for a new methodology for conducting short-term hazard assessment in unrest volcanoes. Using the same case study, the eruption on Pinatubo (15 June 1991), this work introduces a new free Python tool, ST-HASSET, for implementing Sobradelo and Martí (2015) methodology in the time evolution of unrest indicators in the volcanic short-term hazard assessment. Moreover, this tool is designed for complementing long-term hazard assessment with continuous monitoring data when the volcano goes into unrest. It is based on Bayesian inference and transforms different pre-eruptive monitoring parameters into a common probabilistic scale for comparison among unrest episodes from the same volcano or from similar ones. This allows identifying common pre-eruptive behaviours and patterns. ST-HASSET is especially designed to assist experts and decision makers as a crisis unfolds, and allows detecting sudden changes in the activity of a volcano. Therefore, it makes an important contribution to the analysis and interpretation of relevant data for understanding the evolution of volcanic unrest.

  6. New ArcGIS tools developed for stream network extraction and basin delineations using Python and java script

    NASA Astrophysics Data System (ADS)

    Omran, Adel; Dietrich, Schröder; Abouelmagd, Abdou; Michael, Märker

    2016-09-01

    Damages caused by flash floods hazards are an increasing phenomenon, especially in arid and semi-arid areas. Thus, the need to evaluate these areas based on their flash flood risk using maps and hydrological models is also becoming more important. For ungauged watersheds a tentative analysis can be carried out based on the geomorphometric characteristics of the terrain. To process regions with larger watersheds, where perhaps hundreds of watersheds have to be delineated, processed and classified, the overall process need to be automated. GIS packages such as ESRI's ArcGIS offer a number of sophisticated tools that help regarding such analysis. Yet there are still gaps and pitfalls that need to be considered if the tools are combined into a geoprocessing model to automate the complete assessment workflow. These gaps include issues such as i) assigning stream order according to Strahler theory, ii) calculating the threshold value for the stream network extraction, and iii) determining the pour points for each of the nodes of the Strahler ordered stream network. In this study a complete automated workflow based on ArcGIS Model Builder using standard tools will be introduced and discussed. Some additional tools have been implemented to complete the overall workflow. These tools have been programmed using Python and Java in the context of ArcObjects. The workflow has been applied to digital data from the southwestern Sinai Peninsula, Egypt. An optimum threshold value has been selected to optimize drainage configuration by statistically comparing all of the extracted stream configuration results from DEM with the available reference data from topographic maps. The code has succeeded in estimating the correct ranking of specific stream orders in an automatic manner without additional manual steps. As a result, the code has proven to save time and efforts; hence it's considered a very useful tool for processing large catchment basins.

  7. Python Executable Script for Estimating Two Effective Parameters to Individualize Brain-Computer Interfaces: Individual Alpha Frequency and Neurophysiological Predictor.

    PubMed

    Alonso-Valerdi, Luz María

    2016-01-01

    A brain-computer interface (BCI) aims to establish communication between the human brain and a computing system so as to enable the interaction between an individual and his environment without using the brain output pathways. Individuals control a BCI system by modulating their brain signals through mental tasks (e.g., motor imagery or mental calculation) or sensory stimulation (e.g., auditory, visual, or tactile). As users modulate their brain signals at different frequencies and at different levels, the appropriate characterization of those signals is necessary. The modulation of brain signals through mental tasks is furthermore a skill that requires training. Unfortunately, not all the users acquire such skill. A practical solution to this problem is to assess the user probability of controlling a BCI system. Another possible solution is to set the bandwidth of the brain oscillations, which is highly sensitive to the users' age, sex and anatomy. With this in mind, NeuroIndex, a Python executable script, estimates a neurophysiological prediction index and the individual alpha frequency (IAF) of the user in question. These two parameters are useful to characterize the user EEG signals, and decide how to go through the complex process of adapting the human brain and the computing system on the basis of previously proposed methods. NeuroIndeX is not only the implementation of those methods, but it also complements the methods each other and provides an alternative way to obtain the prediction parameter. However, an important limitation of this application is its dependency on the IAF value, and some results should be interpreted with caution. The script along with some electroencephalographic datasets are available on a GitHub repository in order to corroborate the functionality and usability of this application. PMID:27445783

  8. A biomolecular electrostatics solver using Python, GPUs and boundary elements that can handle solvent-filled cavities and Stern layers

    PubMed Central

    Cooper, Christopher D.; Bardhan, Jaydeep P.; Barba, L. A.

    2014-01-01

    The continuum theory applied to biomolecular electrostatics leads to an implicit-solvent model governed by the Poisson-Boltzmann equation. Solvers relying on a boundary integral representation typically do not consider features like solvent-filled cavities or ion-exclusion (Stern) layers, due to the added difficulty of treating multiple boundary surfaces. This has hindered meaningful comparisons with volume-based methods, and the effects on accuracy of including these features has remained unknown. This work presents a solver called PyGBe that uses a boundary-element formulation and can handle multiple interacting surfaces. It was used to study the effects of solvent-filled cavities and Stern layers on the accuracy of calculating solvation energy and binding energy of proteins, using the well-known apbs finite-difference code for comparison. The results suggest that if required accuracy for an application allows errors larger than about 2% in solvation energy, then the simpler, single-surface model can be used. When calculating binding energies, the need for a multi-surface model is problem-dependent, becoming more critical when ligand and receptor are of comparable size. Comparing with the apbs solver, the boundary-element solver is faster when the accuracy requirements are higher. The cross-over point for the PyGBe code is in the order of 1–2% error, when running on one gpu card (nvidia Tesla C2075), compared with apbs running on six Intel Xeon cpu cores. PyGBe achieves algorithmic acceleration of the boundary element method using a treecode, and hardware acceleration using gpus via PyCuda from a user-visible code that is all Python. The code is open-source under MIT license. PMID:25284826

  9. Python Executable Script for Estimating Two Effective Parameters to Individualize Brain-Computer Interfaces: Individual Alpha Frequency and Neurophysiological Predictor

    PubMed Central

    Alonso-Valerdi, Luz María

    2016-01-01

    A brain-computer interface (BCI) aims to establish communication between the human brain and a computing system so as to enable the interaction between an individual and his environment without using the brain output pathways. Individuals control a BCI system by modulating their brain signals through mental tasks (e.g., motor imagery or mental calculation) or sensory stimulation (e.g., auditory, visual, or tactile). As users modulate their brain signals at different frequencies and at different levels, the appropriate characterization of those signals is necessary. The modulation of brain signals through mental tasks is furthermore a skill that requires training. Unfortunately, not all the users acquire such skill. A practical solution to this problem is to assess the user probability of controlling a BCI system. Another possible solution is to set the bandwidth of the brain oscillations, which is highly sensitive to the users' age, sex and anatomy. With this in mind, NeuroIndex, a Python executable script, estimates a neurophysiological prediction index and the individual alpha frequency (IAF) of the user in question. These two parameters are useful to characterize the user EEG signals, and decide how to go through the complex process of adapting the human brain and the computing system on the basis of previously proposed methods. NeuroIndeX is not only the implementation of those methods, but it also complements the methods each other and provides an alternative way to obtain the prediction parameter. However, an important limitation of this application is its dependency on the IAF value, and some results should be interpreted with caution. The script along with some electroencephalographic datasets are available on a GitHub repository in order to corroborate the functionality and usability of this application. PMID:27445783

  10. On the tradeoffs of programming language choice for numerical modelling in geoscience. A case study comparing modern Fortran, C++/Blitz++ and Python/NumPy.

    NASA Astrophysics Data System (ADS)

    Jarecka, D.; Arabas, S.; Fijalkowski, M.; Gaynor, A.

    2012-04-01

    The language of choice for numerical modelling in geoscience has long been Fortran. A choice of a particular language and coding paradigm comes with different set of tradeoffs such as that between performance, ease of use (and ease of abuse), code clarity, maintainability and reusability, availability of open source compilers, debugging tools, adequate external libraries and parallelisation mechanisms. The availability of trained personnel and the scale and activeness of the developer community is of importance as well. We present a short comparison study aimed at identification and quantification of these tradeoffs for a particular example of an object oriented implementation of a parallel 2D-advection-equation solver in Python/NumPy, C++/Blitz++ and modern Fortran. The main angles of comparison will be complexity of implementation, performance of various compilers or interpreters and characterisation of the "added value" gained by a particular choice of the language. The choice of the numerical problem is dictated by the aim to make the comparison useful and meaningful to geoscientists. Python is chosen as a language that traditionally is associated with ease of use, elegant syntax but limited performance. C++ is chosen for its traditional association with high performance but even higher complexity and syntax obscurity. Fortran is included in the comparison for its widespread use in geoscience often attributed to its performance. We confront the validity of these traditional views. We point out how the usability of a particular language in geoscience depends on the characteristics of the language itself and the availability of pre-existing software libraries (e.g. NumPy, SciPy, PyNGL, PyNIO, MPI4Py for Python and Blitz++, Boost.Units, Boost.MPI for C++). Having in mind the limited complexity of the considered numerical problem, we present a tentative comparison of performance of the three implementations with different open source compilers including CPython and

  11. metaseq: a Python package for integrative genome-wide analysis reveals relationships between chromatin insulators and associated nuclear mRNA

    PubMed Central

    Dale, Ryan K.; Matzat, Leah H.; Lei, Elissa P.

    2014-01-01

    Here we introduce metaseq, a software library written in Python, which enables loading multiple genomic data formats into standard Python data structures and allows flexible, customized manipulation and visualization of data from high-throughput sequencing studies. We demonstrate its practical use by analyzing multiple datasets related to chromatin insulators, which are DNA–protein complexes proposed to organize the genome into distinct transcriptional domains. Recent studies in Drosophila and mammals have implicated RNA in the regulation of chromatin insulator activities. Moreover, the Drosophila RNA-binding protein Shep has been shown to antagonize gypsy insulator activity in a tissue-specific manner, but the precise role of RNA in this process remains unclear. Better understanding of chromatin insulator regulation requires integration of multiple datasets, including those from chromatin-binding, RNA-binding, and gene expression experiments. We use metaseq to integrate RIP- and ChIP-seq data for Shep and the core gypsy insulator protein Su(Hw) in two different cell types, along with publicly available ChIP-chip and RNA-seq data. Based on the metaseq-enabled analysis presented here, we propose a model where Shep associates with chromatin cotranscriptionally, then is recruited to insulator complexes in trans where it plays a negative role in insulator activity. PMID:25063299

  12. Pancreatitis, very early compared with normal start of enteral feeding (PYTHON trial): design and rationale of a randomised controlled multicenter trial

    PubMed Central

    2011-01-01

    Background In predicted severe acute pancreatitis, infections have a negative effect on clinical outcome. A start of enteral nutrition (EN) within 24 hours of onset may reduce the number of infections as compared to the current practice of starting an oral diet and EN if necessary at 3-4 days after admission. Methods/Design The PYTHON trial is a randomised controlled, parallel-group, superiority multicenter trial. Patients with predicted severe acute pancreatitis (Imrie-score ≥ 3 or APACHE-II score ≥ 8 or CRP > 150 mg/L) will be randomised to EN within 24 hours or an oral diet and EN if necessary, after 72 hours after hospital admission. During a 3-year period, 208 patients will be enrolled from 20 hospitals of the Dutch Pancreatitis Study Group. The primary endpoint is a composite of mortality or infections (bacteraemia, infected pancreatic or peripancreatic necrosis, pneumonia) during hospital stay or within 6 months following randomisation. Secondary endpoints include other major morbidity (e.g. new onset organ failure, need for intervention), intolerance of enteral feeding and total costs from a societal perspective. Discussion The PYTHON trial is designed to show that a very early (< 24 h) start of EN reduces the combined endpoint of mortality or infections as compared to the current practice of an oral diet and EN if necessary at around 72 hours after admission for predicted severe acute pancreatitis. Trial Registration ISRCTN: ISRCTN18170985 PMID:21392395

  13. 3-D Numerical Simulation and Analysis of Complex Fiber Geometry RaFC Materials with High Volume Fraction and High Aspect Ratio based on ABAQUS PYTHON

    NASA Astrophysics Data System (ADS)

    Jin, BoCheng

    2011-12-01

    Organic and inorganic fiber reinforced composites with innumerable fiber orientation distributions and fiber geometries are abundantly available in several natural and synthetic structures. Inorganic glass fiber composites have been introduced to numerous applications due to their economical fabrication and tailored structural properties. Numerical characterization of such composite material systems is necessitated due to their intrinsic statistical nature, which renders extensive experimentation prohibitively time consuming and costly. To predict various mechanical behavior and characterizations of Uni-Directional Fiber Composites (UDFC) and Random Fiber Composites (RaFC), we numerically developed Representative Volume Elements (RVE) with high accuracy and efficiency and with complex fiber geometric representations encountered in uni-directional and random fiber networks. In this thesis, the numerical simulations of unidirectional RaFC fiber strand RVE models (VF>70%) are first presented by programming in ABAQUS PYTHON. Secondly, when the cross sectional aspect ratios (AR) of the second phase fiber inclusions are not necessarily one, various types of RVE models with different cross sectional shape fibers are simulated and discussed. A modified random sequential absorption algorithm is applied to enhance the volume fraction number (VF) of the RVE, which the mechanical properties represents the composite material. Thirdly, based on a Spatial Segment Shortest Distance (SSSD) algorithm, a 3-Dimentional RaFC material RVE model is simulated in ABAQUS PYTHON with randomly oriented and distributed straight fibers of high fiber aspect ratio (AR=100:1) and volume fraction (VF=31.8%). Fourthly, the piecewise multi-segments fiber geometry is obtained in MATLAB environment by a modified SSSD algorithm. Finally, numerical methods including the polynomial curve fitting and piecewise quadratic and cubic B-spline interpolation are applied to optimize the RaFC fiber geometries

  14. Pool-hmm: a Python program for estimating the allele frequency spectrum and detecting selective sweeps from next generation sequencing of pooled samples

    PubMed Central

    Boitard, Simon; Kofler, Robert; Françoise, Pierre; Robelin, David; Schlötterer, Christian; Futschik, Andreas

    2013-01-01

    Due to its cost effectiveness, next generation sequencing of pools of individuals (Pool-Seq) is becoming a popular strategy for genome-wide estimation of allele frequencies in population samples. As the allele frequency spectrum provides information about past episodes of selection, Pool-seq is also a promising design for genomic scans for selection. However, no software tool has yet been developed for selection scans based on Pool-Seq data. We introduce Pool-hmm, a Python program for the estimation of allele frequencies and the detection of selective sweeps in a Pool-Seq sample. Pool-hmm includes several options that allow a flexible analysis of Pool-Seq data, and can be run in parallel on several processors. Source code and documentation for Pool-hmm is freely available at https://qgsp.jouy.inra.fr/. PMID:23311589

  15. Performance and Operational Characteristics of a Python Turbine-propeller Engine at Simulated Altitude Conditions / Carl L. Meyer and Lavern A. Johnson

    NASA Technical Reports Server (NTRS)

    Meyer, Carl L; Johnson, Lavern A

    1952-01-01

    The performance and operational characteristics of a Python turbine-propeller engine were investigated at simulated altitude conditions in the NACA Lewis altitude wind tunnel. In the performance phase, data were obtained over a range of engine speeds and exhaust nozzle areas at altitudes from 10,000 to 40,000 feet at a single cowl-inlet ram pressure ratio; independent control of engine speed and fuel flow was used to obtain a range of powers at each engine speed. Engine performance data obtained at a given altitude could not be used to predict performance accurately at other altitudes by use of the standard air pressure and temperature generalizing factors. At a given engine speed and turbine-inlet total temperature, a greater portion of the total available energy was converted to propulsive power as the altitude increased.

  16. Pipe dream? Envisioning a grassroots Python ecosystem of open, common software tools and data access in support of river and coastal biogeochemical research (Invited)

    NASA Astrophysics Data System (ADS)

    Mayorga, E.

    2013-12-01

    Practical, problem oriented software developed by scientists and graduate students in domains lacking a strong software development tradition is often balkanized into the scripting environments provided by dominant, typically proprietary tools. In environmental fields, these tools include ArcGIS, Matlab, SAS, Excel and others, and are often constrained to specific operating systems. While this situation is the outcome of rational choices, it limits the dissemination of useful tools and their integration into loosely coupled frameworks that can meet wider needs and be developed organically by groups addressing their own needs. Open-source dynamic languages offer the advantages of an accessible programming syntax, a wealth of pre-existing libraries, multi-platform access, linkage to community libraries developed in lower level languages such as C or FORTRAN, and access to web service infrastructure. Python in particular has seen a large and increasing uptake in scientific communities, as evidenced by the continued growth of the annual SciPy conference. Ecosystems with distinctive physical structures and organization, and mechanistic processes that are well characterized, are both factors that have often led to the grass-roots development of useful code meeting the needs of a range of communities. In aquatic applications, examples include river and watershed analysis tools (River Tools, Taudem, etc), and geochemical modules such as CO2SYS, PHREEQ and LOADEST. I will review the state of affairs and explore the potential offered by a Python tool ecosystem in supporting aquatic biogeochemistry and water quality research. This potential is multi-faceted and broadly involves accessibility to lone grad students, access to a wide community of programmers and problem solvers via online resources such as StackExchange, and opportunities to leverage broader cyberinfrastructure efforts and tools, including those from widely different domains. Collaborative development of such

  17. pySeismicFMM: Python based travel time calculation in regular 2D and 3D grids in Cartesian and geographic coordinates using Fast Marching Method

    NASA Astrophysics Data System (ADS)

    Polkowski, Marcin

    2016-04-01

    Seismic wave travel time calculation is the most common numerical operation in seismology. The most efficient is travel time calculation in 1D velocity model - for given source, receiver depths and angular distance time is calculated within fraction of a second. Unfortunately, in most cases 1D is not enough to encounter differentiating local and regional structures. Whenever possible travel time through 3D velocity model has to be calculated. It can be achieved using ray calculation or time propagation in space. While single ray path calculation is quick it is complicated to find the ray path that connects source with the receiver. Time propagation in space using Fast Marching Method seems more efficient in most cases, especially when there are multiple receivers. In this presentation a Python module pySeismicFMM is presented - simple and very efficient tool for calculating travel time from sources to receivers. Calculation requires regular 2D or 3D velocity grid either in Cartesian or geographic coordinates. On desktop class computer calculation speed is 200k grid cells per second. Calculation has to be performed once for every source location and provides travel time to all receivers. pySeismicFMM is free and open source. Development of this tool is a part of authors PhD thesis. National Science Centre Poland provided financial support for this work via NCN grant DEC-2011/02/A/ST10/00284.

  18. Documentation and Instructions for Running Two Python Scripts that Aid in Setting up 3D Measurements using the Polytec 3D Scanning Laser Doppler Vibrometer.

    SciTech Connect

    Rohe, Daniel Peter

    2015-08-24

    Sandia National Laboratories has recently purchased a Polytec 3D Scanning Laser Doppler Vibrometer for vibration measurement. This device has proven to be a very nice tool for making vibration measurements, and has a number of advantages over traditional sensors such as accelerometers. The non-contact nature of the laser vibrometer means there is no mass loading due to measuring the response. Additionally, the laser scanning heads can position the laser spot much more quickly and accurately than placing an accelerometer or performing a roving hammer impact. The disadvantage of the system is that a significant amount of time must be invested to align the lasers with each other and the part so that the laser spots can be accurately positioned. The Polytec software includes a number of nice tools to aid in this procedure; however, certain portions are still tedious. Luckily, the Polytec software is readily extensible by programming macros for the system, so tedious portions of the procedure can be made easier by automating the process. The Polytec Software includes a WinWrap (similar to Visual Basic) editor and interface to run macros written in that programming language. The author, however, is much more proficient in Python, and the latter also has a much larger set of libraries that can be used to create very complex macros, while taking advantage of Python’s inherent readability and maintainability.

  19. PyDII: A python framework for computing equilibrium intrinsic point defect concentrations and extrinsic solute site preferences in intermetallic compounds

    NASA Astrophysics Data System (ADS)

    Ding, Hong; Medasani, Bharat; Chen, Wei; Persson, Kristin A.; Haranczyk, Maciej; Asta, Mark

    2015-08-01

    Point defects play an important role in determining the structural stability and mechanical behavior of intermetallic compounds. To help quantitatively understand the point defect properties in these compounds, we developed PyDII, a Python program that performs thermodynamic calculations of equilibrium intrinsic point defect concentrations and extrinsic solute site preferences in intermetallics. The algorithm implemented in PyDII is built upon a dilute-solution thermodynamic formalism with a set of defect excitation energies calculated from first-principles density-functional theory methods. The analysis module in PyDII enables automated calculations of equilibrium intrinsic antisite and vacancy concentrations as a function of composition and temperature (over ranges where the dilute solution formalism is accurate) and the point defect concentration changes arising from addition of an extrinsic substitutional solute species. To demonstrate the applications of PyDII, we provide examples for intrinsic point defect concentrations in NiAl and Al3 V and site preferences for Ti, Mo and Fe solutes in NiAl.

  20. Data for giant constrictors - Biological management profiles and an establishment risk assessment for nine large species of pythons, anacondas, and the boa constrictor

    USGS Publications Warehouse

    Jarnevich, C.S.; Rodda, G.H.; Reed, R.N.

    2011-01-01

    Giant Constrictors' Climate Space The giant constrictors' climate space data set represents the information needed to recreate the climate space and climate matching analyses in Reed and Rodda (2009). A detailed methodology and results are included in that report. The data include locations for nine species of large constrictors including Python molurus, Broghammerus reticulatus, P. sebae, P. natalensis, Boa constrictor, Eunectes notaeus, E. deschauenseei, E. beniensis, and E. murinus. The locations are from published sources. Climate data are included for monthly precipitation and average monthly temperature along with the species locations. The individual spreadsheets of location data match the figures in the Reed and Rodda (2009) report, illustrating areas of the mainland United States that match the climate envelope of the native range. The precipitation and temperature data at each location were used to determine the climate space for each species. Graphs of climate space formed the basis for the algorithms in the data set, and more details can be found in Reed and Rodda (2009). These algorithms were used in ArcGIS to generate maps of areas in the United States that matched the climate space of locations of the snakes in their native range. We discovered a rounding error in ArcGIS in the implementation of the algorithms, which has been corrected here. Therefore the shapefiles are slightly different than those appearing in the risk assessment figures illustrating areas of the United States that match the climate envelope of the species in their native ranges. However, the suitable localities are not different at the scale of intended use for these maps, although there are more noticeable differences between areas classified as 'too cold' and 'too hot'.

  1. PyBetVH: A Python tool for probabilistic volcanic hazard assessment and for generation of Bayesian hazard curves and maps

    NASA Astrophysics Data System (ADS)

    Tonini, Roberto; Sandri, Laura; Anne Thompson, Mary

    2015-06-01

    PyBetVH is a completely new, free, open-source and cross-platform software implementation of the Bayesian Event Tree for Volcanic Hazard (BET_VH), a tool for estimating the probability of any magmatic hazardous phenomenon occurring in a selected time frame, accounting for all the uncertainties. New capabilities of this implementation include the ability to calculate hazard curves which describe the distribution of the exceedance probability as a function of intensity (e.g., tephra load) on a grid of points covering the target area. The computed hazard curves are (i) absolute (accounting for the probability of eruption in a given time frame, and for all the possible vent locations and eruptive sizes) and (ii) Bayesian (computed at different percentiles, in order to quantify the epistemic uncertainty). Such curves allow representation of the full information contained in the probabilistic volcanic hazard assessment (PVHA) and are well suited to become a main input to quantitative risk analyses. PyBetVH allows for interactive visualization of both the computed hazard curves, and the corresponding Bayesian hazard/probability maps. PyBetVH is designed to minimize the efforts of end users, making PVHA results accessible to people who may be less experienced in probabilistic methodologies, e.g. decision makers. The broad compatibility of Python language has also allowed PyBetVH to be installed on the VHub cyber-infrastructure, where it can be run online or downloaded at no cost. PyBetVH can be used to assess any type of magmatic hazard from any volcano. Here we illustrate how to perform a PVHA through PyBetVH using the example of analyzing tephra fallout from the Okataina Volcanic Centre (OVC), New Zealand, and highlight the range of outputs that the tool can generate.

  2. dispel4py : An Open Source Python Framework for Encoding, Mapping and Reusing Seismic Continuous Data Streams: Intensive Analysis and Data Mining.

    NASA Astrophysics Data System (ADS)

    Filgueira, R.; Krause, A.; Atkinson, M.; Spinuso, A.; Klampanos, I.; Magnoni, F.; Casarotti, E.; Vilotte, J. P.

    2015-12-01

    Scientific workflows are needed by many scientific communities, such as seismology, as they enable easy composition and execution of applications, enabling scientists to focus on their research without being distracted by arranging computation and data management. However, there are challenges to be addressed. In many systems users have to adapt their codes and data movement as they change from one HPC-architecture to another. They still need to be aware of the computing architectures available for achieving the best application performance. We present dispel4py, an open-source framework presented as a Python library for encoding and automating data-intensive scientific methods as a graph of operations coupled together by data-streams. It enables scientists to develop and experiment with their own data-intensive applications using their familiar work environment. These are then automatically mapped to a variety of HPC-architectures, i.e., MPI, multiprocessing, Storm and Spark frameworks, increasing the chances to reuse their applications in different computing resources. dispel4py comes with data provenance, as shown in the screenshot, and with an information registry that can be accessed transparently from within workflows. dispel4py has been enhanced with a new run-time adaptive compression strategy to reduce the data stream volume and a diagnostic tool which monitors workflow performance and computes the most efficient parallelisation to use. dispel4py has been used by seismologists in the project VERCE for seismic ambient noise cross-correlation applications and for orchestrated HPC wave simulation and data misfit analysis workflows; two data-intensive problems that are common in today's research practice. Both have been tested in several local computing resources and later submitted to a variety of European PRACE HPC-architectures (e.g. SuperMUC & CINECA) for longer runs without change. Results show that dispel4py is an easy tool for developing, sharing and

  3. Estimating Dbh of Trees Employing Multiple Linear Regression of the best Lidar-Derived Parameter Combination Automated in Python in a Natural Broadleaf Forest in the Philippines

    NASA Astrophysics Data System (ADS)

    Ibanez, C. A. G.; Carcellar, B. G., III; Paringit, E. C.; Argamosa, R. J. L.; Faelga, R. A. G.; Posilero, M. A. V.; Zaragosa, G. P.; Dimayacyac, N. A.

    2016-06-01

    Diameter-at-Breast-Height Estimation is a prerequisite in various allometric equations estimating important forestry indices like stem volume, basal area, biomass and carbon stock. LiDAR Technology has a means of directly obtaining different forest parameters, except DBH, from the behavior and characteristics of point cloud unique in different forest classes. Extensive tree inventory was done on a two-hectare established sample plot in Mt. Makiling, Laguna for a natural growth forest. Coordinates, height, and canopy cover were measured and types of species were identified to compare to LiDAR derivatives. Multiple linear regression was used to get LiDAR-derived DBH by integrating field-derived DBH and 27 LiDAR-derived parameters at 20m, 10m, and 5m grid resolutions. To know the best combination of parameters in DBH Estimation, all possible combinations of parameters were generated and automated using python scripts and additional regression related libraries such as Numpy, Scipy, and Scikit learn were used. The combination that yields the highest r-squared or coefficient of determination and lowest AIC (Akaike's Information Criterion) and BIC (Bayesian Information Criterion) was determined to be the best equation. The equation is at its best using 11 parameters at 10mgrid size and at of 0.604 r-squared, 154.04 AIC and 175.08 BIC. Combination of parameters may differ among forest classes for further studies. Additional statistical tests can be supplemented to help determine the correlation among parameters such as Kaiser- Meyer-Olkin (KMO) Coefficient and the Barlett's Test for Spherecity (BTS).

  4. Creative Computing with Landlab: Open-Source Python Software for Building and Exploring 2D Models of Earth-Surface Dynamics

    NASA Astrophysics Data System (ADS)

    Tucker, G. E.; Hobley, D. E.; Gasparini, N. M.; Hutton, E.; Istanbulluoglu, E.; Nudurupati, S.; Adams, J. M.

    2013-12-01

    Computer models help us explore the consequences of scientific hypotheses at a level of precision and quantification that is impossible for our unaided minds. The process of writing and debugging the necessary code is often time-consuming, however, and this cost can inhibit progress. The code-development barrier can be especially problematic when a field is rapidly unearthing new data and new ideas, as is presently the case in surface dynamics. To help meet the need for rapid, flexible model development, we have written a prototype software framework for two-dimensional numerical modeling of planetary surface processes. The Landlab software can be used to develop new models from scratch, to create models from existing components, or a combination of the two. Landlab provides a gridding module that allows you to create and configure a model grid in just a few lines of code. Grids can be regular or unstructured, and can readily be used to implement staggered-grid numerical solutions to equations for various types of geophysical flow. The gridding module provides built-in functions for common numerical operations, such as calculating gradients and integrating fluxes around the perimeter of cells. Landlab is written in Python, a high-level language that enables rapid code development and takes advantage of a wealth of libraries for scientific computing and graphical output. Landlab also provides a framework for assembling new models from combinations of pre-built components. This capability is illustrated with several examples, including flood inundation, long-term landscape evolution, impact cratering, post-wildfire erosion, and ecohydrology. Interoperability with the Community Surface Dynamics Modeling System (CSDMS) Model-Coupling Framework allows models created in Landlab to be combined with other CSDMS models, which helps to bring frontier problems in landscape and seascape dynamics within closer theoretical reach.

  5. PyMS: a Python toolkit for processing of gas chromatography-mass spectrometry (GC-MS) data. Application and comparative study of selected tools

    PubMed Central

    2012-01-01

    Background Gas chromatography–mass spectrometry (GC-MS) is a technique frequently used in targeted and non-targeted measurements of metabolites. Most existing software tools for processing of raw instrument GC-MS data tightly integrate data processing methods with graphical user interface facilitating interactive data processing. While interactive processing remains critically important in GC-MS applications, high-throughput studies increasingly dictate the need for command line tools, suitable for scripting of high-throughput, customized processing pipelines. Results PyMS comprises a library of functions for processing of instrument GC-MS data developed in Python. PyMS currently provides a complete set of GC-MS processing functions, including reading of standard data formats (ANDI- MS/NetCDF and JCAMP-DX), noise smoothing, baseline correction, peak detection, peak deconvolution, peak integration, and peak alignment by dynamic programming. A novel common ion single quantitation algorithm allows automated, accurate quantitation of GC-MS electron impact (EI) fragmentation spectra when a large number of experiments are being analyzed. PyMS implements parallel processing for by-row and by-column data processing tasks based on Message Passing Interface (MPI), allowing processing to scale on multiple CPUs in distributed computing environments. A set of specifically designed experiments was performed in-house and used to comparatively evaluate the performance of PyMS and three widely used software packages for GC-MS data processing (AMDIS, AnalyzerPro, and XCMS). Conclusions PyMS is a novel software package for the processing of raw GC-MS data, particularly suitable for scripting of customized processing pipelines and for data processing in batch mode. PyMS provides limited graphical capabilities and can be used both for routine data processing and interactive/exploratory data analysis. In real-life GC-MS data processing scenarios PyMS performs as well or better than

  6. New implementation of OGC Web Processing Service in Python programming language. PyWPS-4 and issues we are facing with processing of large raster data using OGC WPS

    NASA Astrophysics Data System (ADS)

    Čepický, Jáchym; Moreira de Sousa, Luís

    2016-06-01

    The OGC® Web Processing Service (WPS) Interface Standard provides rules for standardizing inputs and outputs (requests and responses) for geospatial processing services, such as polygon overlay. The standard also defines how a client can request the execution of a process, and how the output from the process is handled. It defines an interface that facilitates publishing of geospatial processes and client discovery of processes and and binding to those processes into workflows. Data required by a WPS can be delivered across a network or they can be available at a server. PyWPS was one of the first implementations of OGC WPS on the server side. It is written in the Python programming language and it tries to connect to all existing tools for geospatial data analysis, available on the Python platform. During the last two years, the PyWPS development team has written a new version (called PyWPS-4) completely from scratch. The analysis of large raster datasets poses several technical issues in implementing the WPS standard. The data format has to be defined and validated on the server side and binary data have to be encoded using some numeric representation. Pulling raster data from remote servers introduces security risks, in addition, running several processes in parallel has to be possible, so that system resources are used efficiently while preserving security. Here we discuss these topics and illustrate some of the solutions adopted within the PyWPS implementation.

  7. Sunshine virus in Australian pythons.

    PubMed

    Hyndman, Timothy H; Shilton, Cathy M; Doneley, Robert J T; Nicholls, Philip K

    2012-12-28

    Sunshine virus is a recently discovered novel paramyxovirus that is associated with illness in snakes. It does not phylogenetically cluster within either of the two currently accepted paramyxoviral subfamilies. It is therefore only distantly related to the only other known genus of reptilian paramyxoviruses, Ferlavirus, which clusters within the Paramyxovirinae subfamily. Clinical and diagnostic aspects associated with Sunshine virus are as yet undescribed. The objective of this paper was to report the clinical presentation, virus isolation, PCR testing and pathology associated with Sunshine virus infection. Clinical records and samples from naturally occurring cases were obtained from two captive snake collections and the archives of a veterinary diagnostic laboratory. The clinical signs that are associated with Sunshine virus infection are localised to the neurorespiratory systems or are non-specific (e.g. lethargy, inappetence). Out of 15 snakes that were infected with Sunshine virus (detected in any organ by either virus isolation or PCR), the virus was isolated from four out of ten (4/10) sampled brains, 3/10 sampled lungs and 2/7 pooled samples of kidney and liver. In these same 15 snakes, PCR was able to successfully detect Sunshine virus in fresh-frozen brain (11/11), kidney (7/8), lung (8/11) and liver (5/8); and various formalin-fixed paraffin-embedded tissues (7/8). During a natural outbreak of Sunshine virus in a collection of 32 snakes, the virus could be detected in five out of 39 combined oral-cloacal swabs that were collected from 23 of these snakes over a 105 day period. All snakes that were infected with Sunshine virus were negative for reovirus and ferlavirus by PCR. Snakes infected with Sunshine virus reliably exhibited hindbrain white matter spongiosis and gliosis with extension to the surrounding grey matter and neuronal necrosis evident in severe cases. Five out of eight infected snakes also exhibited mild bronchointerstitial pneumonia. Infection with Sunshine virus should be considered by veterinarians investigating disease outbreaks in snakes, particularly those that are associated with neurorespiratory disease. PMID:22883310

  8. Using Python for Pedigree Analysis

    Technology Transfer Automated Retrieval System (TEKTRAN)

    A pedigree is a way of describing a population of people or animals in terms of genetic relationships among individuals. Pedigrees are of interest to many people, including scientists, animal and plant breeders, and genealogists. They are used to assess the diversity of populations, in combination ...

  9. ArcNEMO, a spatially distributed nutrient emission model developed in Python to quantify losses of nitrogen and phosphorous from agriculture to surface waters

    NASA Astrophysics Data System (ADS)

    Van Opstal, Mattias; Tits, Mia; Beckers, Veronique; Batelaan, Okke; Van Orshoven, Jos; Elsen, Annemie; Diels, Jan; D'heygere, Tom; Van Hoof, Kor

    2014-05-01

    Pollution of surface water bodies with nitrogen (N) and phosphorous (P) from agricultural sources is a major problem in areas with intensive agriculture in Europe. The Flemish Environment Agency requires information on how spatially explicit policy measures on manure and fertilizer use, and changes in land use and soil management affect the N and P concentration in the surface waters in the region of Flanders, Belgium. To assist in this, a new spatially distributed, mechanistic nutrient emission model was developed in the open-source language Python. The model is called ArcNEMO (Nutrient Emission MOdel). The model is fully integrated in ArcGIS, but could be easily adapted to work with open-source GIS software. In Flanders, detailed information is available each year on the delineation of each agricultural parcel and the crops grown on them. Parcels are linked to farms, and for each farm yearly manure and fertilizer use is available. To take full advantage of this information and to be able to simulate nutrient losses to the high-density surface water network, the model makes use of grid cells of 50 by 50m. A fertilizer allocation model was developed to calculate from the yearly parcel and farm data the fertilizer and manure input per grid cell for further use in the ArcNEMO-model. The model architecture was chosen such that the model can be used to simulate spatially explicit monthly discharge and losses of N and P to the surface water for the whole of Flanders (13,500 km²) over periods of 10-20 years. The extended time period is necessary because residence times in groundwater and the rates of organic matter turnover imply that water quality reacts slowly to changes of land use and fertilization practices. Vertical water flow and nutrient transport in the unsaturated zone are described per grid cell using a cascading bucket-type model with daily time steps. Groundwater flow is described by solving the 2D-groundwater flow equation using an explicit numerical

  10. Zachary-Fort Lauderdale pipeline construction and conversion project: final supplement to final environmental impact statement. Docket No. CP74-192

    SciTech Connect

    1980-05-01

    This Final Supplement to the Final Environmental Impact Statement (Final Supplement) evaluates the economic, engineering, and environmental aspects of newly developed alternatives to an abandonment/conversion project proposed by Florida Gas Transmission Company (Florida Gas). It also updates the staff's previous FEIS and studies revisions to the original proposal. Wherever possible, the staff has adopted portions of its previous FEIS in lieu of reprinting portions of that analysis which require no change. 60 references, 8 figures, 35 tables.

  11. To designate the facility of the United States Postal Service located at 16105 Swingley Ridge Road in Chesterfield, Missouri, as the "Sgt. Zachary M. Fisher Post Office".

    THOMAS, 113th Congress

    Rep. Wagner, Ann [R-MO-2

    2014-12-04

    12/09/2014 Received in the Senate and Read twice and referred to the Committee on Homeland Security and Governmental Affairs. (All Actions) Tracker: This bill has the status Passed HouseHere are the steps for Status of Legislation:

  12. To designate the facility of the United States Postal Service located at 23 Genesee Street in Hornell, New York, as the "Zachary Smith Post Office Building".

    THOMAS, 111th Congress

    Rep. Massa, Eric J. J. [D-NY-29

    2010-02-23

    02/23/2010 Referred to the House Committee on Oversight and Government Reform. (All Actions) Notes: For further action, see H.R.5051, which became Public Law 111-218 on 8/3/2010. Tracker: This bill has the status IntroducedHere are the steps for Status of Legislation:

  13. A bill to designate the facility of the United States Postal Service located at 23 Genesee Street in Hornell, New York, as the "Zachary Smith Post Office Building".

    THOMAS, 111th Congress

    Sen. Gillibrand, Kirsten E. [D-NY

    2010-04-14

    05/25/2010 Held at the desk. (All Actions) Notes: For further action, see H.R.5051, which became Public Law 111-218 on 8/3/2010. Tracker: This bill has the status Passed SenateHere are the steps for Status of Legislation:

  14. Introduction of the Python script STRinNGS for analysis of STR regions in FASTQ or BAM files and expansion of the Danish STR sequence database to 11 STRs.

    PubMed

    Friis, Susanne L; Buchard, Anders; Rockenbauer, Eszter; Børsting, Claus; Morling, Niels

    2016-03-01

    This work introduces the in-house developed Python application STRinNGS for analysis of STR sequence elements in BAM or FASTQ files. STRinNGS identifies sequence reads with STR loci by their flanking sequences, it analyses the STR sequence and the flanking regions, and generates a report with the assigned SNP-STR alleles. The main output file from STRinNGS contains all sequences with read counts above 1% of the total number of reads per locus. STR sequences are automatically named according to the nomenclature used previously and according to the repeat unit definitions in STRBase (http://www.cstl.nist.gov/strbase/). The sequences are named with (1) the locus name, (2) the length of the repeat region divided by the length of the repeat unit, (3) the sequence(s) of the repeat unit(s) followed by the number of repeats and (4) variations in the flanking regions. Lower case letters in the main output file are used to flag sequences with previously unknown variations in the STRs. SNPs in the flanking regions are named by their "rs" numbers and the nucleotides in the SNP position. Data from 207 Danes sequenced with the Ion Torrent™ HID STR 10-plex that amplified nine STRs (CSF1PO, D3S1358, D5S818, D7S820, D8S1179, D16S539, TH01, TPOX, vWA), and Amelogenin was analysed with STRinNGS. Sequencing uncovered five common SNPs near four STRs and revealed 20 new alleles in the 207 Danes. Three short homopolymers in the D8S1179 flanking regions caused frequent sequencing errors. In 29 of 3726 allele calls (0.8%), sequences with homopolymer errors were falsely assigned as true alleles. An in-house developed script in R compensated for these errors by compiling sequence reads that had identical STR sequences and identical nucleotides in the five common SNPs. In the output file from the R script, all SNP-STR haplotype calls were correct. The 207 samples and six additional samples were sequenced for D3S1358, D12S391, and D21S11 using the 454 GS Junior platform in this and a

  15. PyCS : Python Curve Shifting

    NASA Astrophysics Data System (ADS)

    Tewes, Malte

    2015-09-01

    PyCS is a software toolbox to estimate time delays between multiple images of strongly lensed quasars, from resolved light curves such as obtained by the COSMOGRAIL monitoring program. The pycs package defines a collection of classes and high level functions, that you can script in a flexible way. PyCS makes it easy to compare different point estimators (including your own) without much code integration. The package heavily depends on numpy, scipy, and matplotlib.

  16. MTpy: A Python toolbox for magnetotellurics

    USGS Publications Warehouse

    Krieger, Lars; Peacock, Jared R.

    2014-01-01

    In this paper, we introduce the structure and concept of MTpy  . Additionally, we show some examples from an everyday work-flow of MT data processing: the generation of standard EDI data files from raw electric (E-) and magnetic flux density (B-) field time series as input, the conversion into MiniSEED data format, as well as the generation of a graphical data representation in the form of a Phase Tensor pseudosection.

  17. Python Engine Installed in Altitude Wind Tunnel

    NASA Technical Reports Server (NTRS)

    1949-01-01

    An engine mechanic checks instrumentation prior to an investigation of engine operating characteristics and thrust control of a large turboprop engine with counter-rotating propellers under high-altitude flight conditions in the 20-foot-dianieter test section of the Altitude Wind Tunnel at the Lewis Flight Propulsion Laboratory of the National Advisory Committee for Aeronautics, Cleveland, Ohio, now known as the John H. Glenn Research Center at Lewis Field.

  18. Medieval Romances: "Perceval" to "Monty Python."

    ERIC Educational Resources Information Center

    Jehle, Dorothy M.

    A selection of romances from medieval literature can be used successfully in undergraduate literature classes to trace the appearance and relevance of medieval themes, motifs, and characters in works of modern poetry, fiction, and film. New scholarly editions, historiographies, translations, and modernizations give both teachers and students more…

  19. 78 FR 39307 - National Environmental Policy Act: Implementing Procedures; Addition to Categorical Exclusions...

    Federal Register 2010, 2011, 2012, 2013, 2014

    2013-07-01

    ... snakes (Burmese python (Python molurus), Northern African python (Python sebae), Southern African python (Python natalensis), and yellow anaconda (Eunectes notaeus), 2012). The issues addressed in the EAs...

  20. The Funeral of Froggy the Frog: The Child as Dramatist, Designer, and Realist

    ERIC Educational Resources Information Center

    Cummins, Lauren

    2004-01-01

    One sunny afternoon, six-year-old Zachary and his friend John Michael, four and a half, discovered a dead frog in a bag of clay in the garage. Zachary proposed, "Let's have a funeral for the frog." This is how the funeral drama of Froggy the Frog began. This article describes the play experiences of Zachary and John Michael as designers,…

  1. Gully measurement strategies in a pixel using python

    NASA Astrophysics Data System (ADS)

    Wells, Robert; Momm, Henrique; Bennett, Sean; Dabney, Seth

    2015-04-01

    Gullies are often the single largest sediment sources within the landscape; however, measurement and process description of these channels presents challenges that have limited complete understanding. A strategy currently being employed in the field and laboratory to measure topography of gullies utilizes inexpensive, off-the-shelf cameras and software. Photogrammetry may be entering an enlightened period, as users have numerous choices (cameras, lenses, and software) and many are utilizing the technology to define their surroundings; however, the key for those seeking answers will be what happens once topography is represented as a three-dimensional digital surface model. Perhaps the model can be compared with another model to visualize change, either in topography or in vegetation cover, or both. With these models of our landscape, prediction technology should be rejuvenated and/or reinvented. Over the past several decades, researchers have endeavored to capture the erosion process and transfer these observations through oral and written word. Several have hypothesized a fundamental system for gully expression in the landscape; however, this understanding has not transferred well into our prediction technology. Unlike many materials, soils often times do not behave in a predictable fashion. Which soil physical properties lend themselves to erosion process description? In most cases, several disciplines are required to visualize the erosion process and its impact on our landscape. With a small camera, the landscape becomes more accessible and this accessibility will lead to a deeper understanding and development of uncompromised erosion theory. Why? Conservation of our soil resources is inherently linked to a complete understanding of soil wasting.

  2. DiffPy-CMI-Python libraries for Complex Modeling Initiative

    Energy Science and Technology Software Center (ESTSC)

    2014-02-01

    Software to manipulate and describe crystal and molecular structures and set up structural refinements from multiple experimental inputs. Calculation and simulation of structure derived physical quantities. Library for creating customized refinements of atomic structures from available experimental and theoretical inputs.

  3. Pablo Python Looks at Animals. [Multimedia Educational Kit].

    ERIC Educational Resources Information Center

    Sullivan, Rick; Green, David

    Teachers and students can view the world of animals together through an exploration of how-and-why questions about animals in this curriculum unit. The problem-solving and critical thinking skills of students are improved through interactive activities involving oral and written communication, mathematics, creative arts, music, dance, and creative…

  4. Crates and Transform: Python Interfaces for Data Analysis

    NASA Astrophysics Data System (ADS)

    Lyn, J.; Cresitello-Dittmar, M.; Evans, I.; Evans, J. D.

    2014-05-01

    With its flexible design and ease-of-use, Crates and Transform have been incorporated into the Chandra X-Ray Center's (CXC) data visualization and fitting tools and data processing scripts to facilitate a wide variety of tasks. Chandra's fitting and modeling application, called Sherpa, uses Crates as an underlying data access module, taking advantage of its ability to interpret standard Flexible Image Transport System (FITS) files, such as Redistribution Matrix Files (RMF), Auxiliary Response Files(ARF), and both types of Pulse Height Analysis (PHA) files. The Chandra Imaging and Plotting System (ChIPS) tool utilizes the associated Transform module for visualizing data in different World Coordinate Systems (WCS). By using the CXC DataModel (DM) as a backend, Crates can perform advanced filtering and binning techniques on data. This capability, combined with its simple Application Programming Interface, make it ideal for incorporation into our data analysis scripts, aiding with operations from simple keyword manipulation to creating and writing multiple Header Definition Unit (HDU) files. Crates and Transform are available respectively as the pycrates and pytransform modules within the Chandra Interactive Analysis of Observations (CIAO) environment to assist users with their own analysis threads. In this paper, we will illustrate the capabilities of the Crates and Transform modules and how they are being used within the CXC for analysis.

  5. Peristaltic pumping in an elastic tube: feeding the hungry python

    NASA Astrophysics Data System (ADS)

    Takagi, Daisuke; Balmforth, Neil

    2010-11-01

    Biological ducts convey contents like food in the digestive system by peristaltic action, propagating waves of muscular contraction and relaxation. The motion is investigated theoretically by considering a radial force of sinusoidal or Gaussian form moving steadily down a fluid-filled axisymmetric tube. Effects of the prescribed force on the resultant fluid flow and elastic deformation of the tube wall are presented. The flow can induce a rigid object suspended in the fluid to propel in different ways, as demonstrated in numerous examples.

  6. Establishing a Novel Modeling Tool: A Python-Based Interface for a Neuromorphic Hardware System

    PubMed Central

    Brüderle, Daniel; Müller, Eric; Davison, Andrew; Muller, Eilif; Schemmel, Johannes; Meier, Karlheinz

    2008-01-01

    Neuromorphic hardware systems provide new possibilities for the neuroscience modeling community. Due to the intrinsic parallelism of the micro-electronic emulation of neural computation, such models are highly scalable without a loss of speed. However, the communities of software simulator users and neuromorphic engineering in neuroscience are rather disjoint. We present a software concept that provides the possibility to establish such hardware devices as valuable modeling tools. It is based on the integration of the hardware interface into a simulator-independent language which allows for unified experiment descriptions that can be run on various simulation platforms without modification, implying experiment portability and a huge simplification of the quantitative comparison of hardware and simulator results. We introduce an accelerated neuromorphic hardware device and describe the implementation of the proposed concept for this system. An example setup and results acquired by utilizing both the hardware system and a software simulator are demonstrated. PMID:19562085

  7. Teaching Introductory GIS Programming to Geographers Using an Open Source Python Approach

    ERIC Educational Resources Information Center

    Etherington, Thomas R.

    2016-01-01

    Computer programming is not commonly taught to geographers as a part of geographic information system (GIS) courses, but the advent of NeoGeography, big data and open GIS means that programming skills are becoming more important. To encourage the teaching of programming to geographers, this paper outlines a course based around a series of…

  8. The python project: a unique model for extending research opportunities to undergraduate students.

    PubMed

    Harvey, Pamela A; Wall, Christopher; Luckey, Stephen W; Langer, Stephen; Leinwand, Leslie A

    2014-01-01

    Undergraduate science education curricula are traditionally composed of didactic instruction with a small number of laboratory courses that provide introductory training in research techniques. Research on learning methodologies suggests this model is relatively ineffective, whereas participation in independent research projects promotes enhanced knowledge acquisition and improves retention of students in science. However, availability of faculty mentors and limited departmental budgets prevent the majority of students from participating in research. A need therefore exists for this important component in undergraduate education in both small and large university settings. A course was designed to provide students with the opportunity to engage in a research project in a classroom setting. Importantly, the course collaborates with a sponsor's laboratory, producing a symbiotic relationship between the classroom and the laboratory and an evolving course curriculum. Students conduct a novel gene expression study, with their collective data being relevant to the ongoing research project in the sponsor's lab. The success of this course was assessed based on the quality of the data produced by the students, student perception data, student learning gains, and on whether the course promoted interest in and preparation for careers in science. In this paper, we describe the strategies and outcomes of this course, which represents a model for efficiently providing research opportunities to undergraduates. PMID:25452492

  9. 75 FR 11808 - Injurious Wildlife Species; Listing the Boa Constrictor, Four Python Species, and Four Anaconda...

    Federal Register 2010, 2011, 2012, 2013, 2014

    2010-03-12

    ... South Florida. The Service published a notice of inquiry in the Federal Register (73 FR 5784; January 31... prey items include alligators, antelopes, dogs, deer, jackals, goats, porcupines, wild boars, pangolins..., including such large prey as deer and crocodilians (alligators are a type of crocodilian). The...

  10. VAVUQ, Python and Matlab freeware for Verification and Validation, Uncertainty Quantification

    NASA Astrophysics Data System (ADS)

    Courtney, J. E.; Zamani, K.; Bombardelli, F. A.; Fleenor, W. E.

    2015-12-01

    A package of scripts is presented for automated Verification and Validation (V&V) and Uncertainty Quantification (UQ) for engineering codes that approximate Partial Differential Equations (PDFs). The code post-processes model results to produce V&V and UQ information. This information can be used to assess model performance. Automated information on code performance can allow for a systematic methodology to assess the quality of model approximations. The software implements common and accepted code verification schemes. The software uses the Method of Manufactured Solutions (MMS), the Method of Exact Solution (MES), Cross-Code Verification, and Richardson Extrapolation (RE) for solution (calculation) verification. It also includes common statistical measures that can be used for model skill assessment. Complete RE can be conducted for complex geometries by implementing high-order non-oscillating numerical interpolation schemes within the software. Model approximation uncertainty is quantified by calculating lower and upper bounds of numerical error from the RE results. The software is also able to calculate the Grid Convergence Index (GCI), and to handle adaptive meshes and models that implement mixed order schemes. Four examples are provided to demonstrate the use of the software for code and solution verification, model validation and uncertainty quantification. The software is used for code verification of a mixed-order compact difference heat transport solver; the solution verification of a 2D shallow-water-wave solver for tidal flow modeling in estuaries; the model validation of a two-phase flow computation in a hydraulic jump compared to experimental data; and numerical uncertainty quantification for 3D CFD modeling of the flow patterns in a Gust erosion chamber.

  11. SkData: data sets and algorithm evaluation protocols in Python

    NASA Astrophysics Data System (ADS)

    Bergstra, James; Pinto, Nicolas; Cox, David D.

    2015-01-01

    Machine learning benchmark data sets come in all shapes and sizes, whereas classification algorithms assume sanitized input, such as (x, y) pairs with vector-valued input x and integer class label y. Researchers and practitioners know all too well how tedious it can be to get from the URL of a new data set to a NumPy ndarray suitable for e.g. pandas or sklearn. The SkData library handles that work for a growing number of benchmark data sets (small and large) so that one-off in-house scripts for downloading and parsing data sets can be replaced with library code that is reliable, community-tested, and documented. The SkData library also introduces an open-ended formalization of training and testing protocols that facilitates direct comparison with published research. This paper describes the usage and architecture of the SkData library.

  12. Phenix - a comprehensive python-based system for macromolecular structure solution

    SciTech Connect

    Terwilliger, Thomas C; Hung, Li - Wei; Adams, Paul D; Afonine, Pavel V; Bunkoczi, Gabor; Chen, Vincent B; Davis, Ian; Echols, Nathaniel; Headd, Jeffrey J; Grosse Kunstleve, Ralf W; Mccoy, Airlie J; Moriarty, Nigel W; Oeffner, Robert; Read, Randy J; Richardson, David C; Richardson, Jane S; Zwarta, Peter H

    2009-01-01

    Macromolecular X-ray crystallography is routinely applied to understand biological processes at a molecular level. However, significant time and effort are still required to solve and complete many of these structures because of the need for manual interpretation of complex numerical data using many software packages, and the repeated use of interactive three-dimensional graphics. Phenix has been developed to provide a comprehensive system for crystallographic structure solution with an emphasis on automation of all procedures. This has relied on the development of algorithms that minimize or eliminate subjective input, the development of algorithms that automate procedures that are traditionally performed by hand, and finally the development of a framework that allows a tight integration between the algorithms.

  13. Kudi: A free open-source python library for the analysis of properties along reaction paths.

    PubMed

    Vogt-Geisse, Stefan

    2016-05-01

    With increasing computational capabilities, an ever growing amount of data is generated in computational chemistry that contains a vast amount of chemically relevant information. It is therefore imperative to create new computational tools in order to process and extract this data in a sensible way. Kudi is an open source library that aids in the extraction of chemical properties from reaction paths. The straightforward structure of Kudi makes it easy to use for users and allows for effortless implementation of new capabilities, and extension to any quantum chemistry package. A use case for Kudi is shown for the tautomerization reaction of formic acid. Kudi is available free of charge at www.github.com/stvogt/kudi. PMID:27107577

  14. The Python Project: A Unique Model for Extending Research Opportunities to Undergraduate Students

    PubMed Central

    Harvey, Pamela A.; Wall, Christopher; Luckey, Stephen W.; Langer, Stephen

    2014-01-01

    Undergraduate science education curricula are traditionally composed of didactic instruction with a small number of laboratory courses that provide introductory training in research techniques. Research on learning methodologies suggests this model is relatively ineffective, whereas participation in independent research projects promotes enhanced knowledge acquisition and improves retention of students in science. However, availability of faculty mentors and limited departmental budgets prevent the majority of students from participating in research. A need therefore exists for this important component in undergraduate education in both small and large university settings. A course was designed to provide students with the opportunity to engage in a research project in a classroom setting. Importantly, the course collaborates with a sponsor's laboratory, producing a symbiotic relationship between the classroom and the laboratory and an evolving course curriculum. Students conduct a novel gene expression study, with their collective data being relevant to the ongoing research project in the sponsor's lab. The success of this course was assessed based on the quality of the data produced by the students, student perception data, student learning gains, and on whether the course promoted interest in and preparation for careers in science. In this paper, we describe the strategies and outcomes of this course, which represents a model for efficiently providing research opportunities to undergraduates. PMID:25452492

  15. pySPACE—a signal processing and classification environment in Python

    PubMed Central

    Krell, Mario M.; Straube, Sirko; Seeland, Anett; Wöhrle, Hendrik; Teiwes, Johannes; Metzen, Jan H.; Kirchner, Elsa A.; Kirchner, Frank

    2013-01-01

    In neuroscience large amounts of data are recorded to provide insights into cerebral information processing and function. The successful extraction of the relevant signals becomes more and more challenging due to increasing complexities in acquisition techniques and questions addressed. Here, automated signal processing and machine learning tools can help to process the data, e.g., to separate signal and noise. With the presented software pySPACE (http://pyspace.github.io/pyspace), signal processing algorithms can be compared and applied automatically on time series data, either with the aim of finding a suitable preprocessing, or of training supervised algorithms to classify the data. pySPACE originally has been built to process multi-sensor windowed time series data, like event-related potentials from the electroencephalogram (EEG). The software provides automated data handling, distributed processing, modular build-up of signal processing chains and tools for visualization and performance evaluation. Included in the software are various algorithms like temporal and spatial filters, feature generation and selection, classification algorithms, and evaluation schemes. Further, interfaces to other signal processing tools are provided and, since pySPACE is a modular framework, it can be extended with new algorithms according to individual needs. In the presented work, the structural hierarchies are described. It is illustrated how users and developers can interface the software and execute offline and online modes. Configuration of pySPACE is realized with the YAML format, so that programming skills are not mandatory for usage. The concept of pySPACE is to have one comprehensive tool that can be used to perform complete signal processing and classification tasks. It further allows to define own algorithms, or to integrate and use already existing libraries. PMID:24399965

  16. appaloosa: Python-based flare finding code for Kepler light curves

    NASA Astrophysics Data System (ADS)

    Davenport, James R. A.

    2016-08-01

    The appaloosa suite automates flare-finding in every Kepler light curves. It builds quiescent light curve models that include long- and short-cadence data through iterative de-trending and includes completeness estimates via artificial flare injection and recovery tests.

  17. pySPACE-a signal processing and classification environment in Python.

    PubMed

    Krell, Mario M; Straube, Sirko; Seeland, Anett; Wöhrle, Hendrik; Teiwes, Johannes; Metzen, Jan H; Kirchner, Elsa A; Kirchner, Frank

    2013-01-01

    In neuroscience large amounts of data are recorded to provide insights into cerebral information processing and function. The successful extraction of the relevant signals becomes more and more challenging due to increasing complexities in acquisition techniques and questions addressed. Here, automated signal processing and machine learning tools can help to process the data, e.g., to separate signal and noise. With the presented software pySPACE (http://pyspace.github.io/pyspace), signal processing algorithms can be compared and applied automatically on time series data, either with the aim of finding a suitable preprocessing, or of training supervised algorithms to classify the data. pySPACE originally has been built to process multi-sensor windowed time series data, like event-related potentials from the electroencephalogram (EEG). The software provides automated data handling, distributed processing, modular build-up of signal processing chains and tools for visualization and performance evaluation. Included in the software are various algorithms like temporal and spatial filters, feature generation and selection, classification algorithms, and evaluation schemes. Further, interfaces to other signal processing tools are provided and, since pySPACE is a modular framework, it can be extended with new algorithms according to individual needs. In the presented work, the structural hierarchies are described. It is illustrated how users and developers can interface the software and execute offline and online modes. Configuration of pySPACE is realized with the YAML format, so that programming skills are not mandatory for usage. The concept of pySPACE is to have one comprehensive tool that can be used to perform complete signal processing and classification tasks. It further allows to define own algorithms, or to integrate and use already existing libraries. PMID:24399965

  18. A Python tool to set up relative free energy calculations in GROMACS.

    PubMed

    Klimovich, Pavel V; Mobley, David L

    2015-11-01

    Free energy calculations based on molecular dynamics (MD) simulations have seen a tremendous growth in the last decade. However, it is still difficult and tedious to set them up in an automated manner, as the majority of the present-day MD simulation packages lack that functionality. Relative free energy calculations are a particular challenge for several reasons, including the problem of finding a common substructure and mapping the transformation to be applied. Here we present a tool, alchemical-setup.py, that automatically generates all the input files needed to perform relative solvation and binding free energy calculations with the MD package GROMACS. When combined with Lead Optimization Mapper (LOMAP; Liu et al. in J Comput Aided Mol Des 27(9):755-770, 2013), recently developed in our group, alchemical-setup.py allows fully automated setup of relative free energy calculations in GROMACS. Taking a graph of the planned calculations and a mapping, both computed by LOMAP, our tool generates the topology and coordinate files needed to perform relative free energy calculations for a given set of molecules, and provides a set of simulation input parameters. The tool was validated by performing relative hydration free energy calculations for a handful of molecules from the SAMPL4 challenge (Mobley et al. in J Comput Aided Mol Des 28(4):135-150, 2014). Good agreement with previously published results and the straightforward way in which free energy calculations can be conducted make alchemical-setup.py a promising tool for automated setup of relative solvation and binding free energy calculations. PMID:26487189

  19. Tabizi Pythons and Clendro Hawks: Using Imaginary Animals to Achieve Real Knowledge about Ecosystems

    ERIC Educational Resources Information Center

    Rockow, Michael

    2007-01-01

    The author describes how he used to teach a unit on food webs and ecosystems using actual food webs as models. However, the models used by the author tend to be either too simplistic or too complicated for his students. A few years ago, he solved these problems by making up his own food web, complete with invented plants and animals. The model has…

  20. The Python Project: A Unique Model for Extending Research Opportunities to Undergraduate Students

    ERIC Educational Resources Information Center

    Harvey, Pamela A.; Wall, Christopher; Luckey, Stephen W.; Langer, Stephen; Leinwand, Leslie A.

    2014-01-01

    Undergraduate science education curricula are traditionally composed of didactic instruction with a small number of laboratory courses that provide introductory training in research techniques. Research on learning methodologies suggests this model is relatively ineffective, whereas participation in independent research projects promotes enhanced…

  1. From Monty Python to Total Recall: A Feature Film Activity for the Cognitive Psychology Course.

    ERIC Educational Resources Information Center

    Conner, David B.

    1996-01-01

    Describes a college psychology course activity designed to help students define the parameters of cognitive psychology. Students selected a feature film and a journal article that represented some aspect of cognitive psychology. They then wrote a paper discussing the theoretical and empirical connections between the sources and the topic. (MJP)

  2. Pyro: A Python-Based Versatile Programming Environment for Teaching Robotics

    ERIC Educational Resources Information Center

    Blank, Douglas; Kumar, Deepak; Meeden, Lisa; Yanco, Holly

    2004-01-01

    In this article we describe a programming framework called Pyro, which provides a set of abstractions that allows students to write platform-independent robot programs. This project is unique because of its focus on the pedagogical implications of teaching mobile robotics via a top-down approach. We describe the background of the project, its…

  3. MEANS: python package for Moment Expansion Approximation, iNference and Simulation

    PubMed Central

    Fan, Sisi; Geissmann, Quentin; Lakatos, Eszter; Lukauskas, Saulius; Ale, Angelique; Babtie, Ann C.; Kirk, Paul D. W.; Stumpf, Michael P. H.

    2016-01-01

    Motivation: Many biochemical systems require stochastic descriptions. Unfortunately these can only be solved for the simplest cases and their direct simulation can become prohibitively expensive, precluding thorough analysis. As an alternative, moment closure approximation methods generate equations for the time-evolution of the system’s moments and apply a closure ansatz to obtain a closed set of differential equations; that can become the basis for the deterministic analysis of the moments of the outputs of stochastic systems. Results: We present a free, user-friendly tool implementing an efficient moment expansion approximation with parametric closures that integrates well with the IPython interactive environment. Our package enables the analysis of complex stochastic systems without any constraints on the number of species and moments studied and the type of rate laws in the system. In addition to the approximation method our package provides numerous tools to help non-expert users in stochastic analysis. Availability and implementation: https://github.com/theosysbio/means Contacts: m.stumpf@imperial.ac.uk or e.lakatos13@imperial.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27153663

  4. Landlab: a new, open-source, modular, Python-based tool for modelling Earth surface dynamics

    NASA Astrophysics Data System (ADS)

    Hobley, Daniel; Adams, Jordan; Gasparini, Nicole; Hutton, Eric; Istanbulluoglu, Erkan; Nudurupati, Sai Siddhartha; Tucker, Gregory

    2016-04-01

    The ability to model surface processes and to couple them to both subsurface and atmospheric regimes has proven an invaluable tool in the Earth and planetary sciences. However, creation of a new model typically demands a very large investment of time, and modification of an existing model to address a new problem typically means the new work is constrained to its detriment by model adaptations for a different problem. Landlab is a new software framework explicitly designed to accelerate the development of new process models by providing: 1. a set of tools and existing grid structures to make development of new process components faster and easier; 2. a suite of stable, modular, and interoperable process components onto which new components can be added; and 3. a set of example models built with these components. Landlab's structure makes it ideal not only for fully developed modelling applications, but also for model prototyping and classroom use. Here we illustrate some of Landlab's capabilities, emphasizing its breadth of application and ease of use.

  5. 38 CFR 60.2 - Definitions.

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... facility for the purpose of evaluating claims by veterans. Temporary lodging means: (1) Lodging at a Fisher... donated to VA by the Zachary and Elizabeth M. Fisher Armed Services Foundation or Fisher House...

  6. Weatherizing America

    ScienceCinema

    Stewart, Zachary; Bergeron, T.J.; Barth, Dale; Qualis, Xavier; Sewall, Travis; Fransen, Richard; Gill, Tony;

    2013-05-29

    As Recovery Act money arrives to expand home weatherization programs across the country, Zachary Stewart of Phoenix, Ariz., and others have found an exciting opportunity not only to start working again, but also to find a calling.

  7. High School Football Players Suffer More Symptoms After Concussion

    MedlinePlus

    ... researcher Zachary Kerr, from the Datalys Center for Sports Injury Research and Prevention, in Indianapolis. With mounting evidence ... Ph.D., M.P.H., Datalys Center for Sports Injury Research and Prevention, Indianapolis; John Kuluz, M.D., ...

  8. Weatherizing America

    SciTech Connect

    Stewart, Zachary; Bergeron, T.J.; Barth, Dale; Qualis, Xavier; Sewall, Travis; Fransen, Richard; Gill, Tony

    2009-01-01

    As Recovery Act money arrives to expand home weatherization programs across the country, Zachary Stewart of Phoenix, Ariz., and others have found an exciting opportunity not only to start working again, but also to find a calling.

  9. Exploratory trial to determine the efficacy of the PYthon and the PYthon Magnum slow-release insecticide ear tags for the control of midges (Culicoides spp.), attacking sheep and cattle and flies attacking cattle.

    PubMed

    Goosen, H; de Vries, P J T; Fletcher, M G

    2012-08-01

    This study investigated the prophylactic action of the chemical combination zeta-cypermethrin and piperonyl butoxide, administered by means of slow-release insecticide-impregnated ear tags, against biting midges (Culicoides spp) attacking sheep and against midges, horn flies (Haematobia irritant), stable flies (Stomoxys calcitrans), and houseflies (Musca domestica) attacking cattle. Treated sheep and cattle were protected 100 percent against blood-feeding midges for two months and there was a clear reduction in the number of midges collected from treated animals. Three days after the ear tags were attached to cattle, the number of horn flies on the cattle was reduced to practically zero and remained at a low level until the end of the trial (day 85). There was also a strong reduction in the numbers of stable flies and houseflies counted. PMID:22930983

  10. fibmeasure: Python/Cython module to find the center of back-illuminated optical fibers in metrology images

    NASA Astrophysics Data System (ADS)

    Gilbert, James

    2016-03-01

    fibmeasure finds the precise locations of the centers of back-illuminated optical fibers in images. It was developed for astronomical fiber positioning feedback via machine vision cameras and is optimized for high-magnification images where fibers appear as resolvable circles. It was originally written during the design of the WEAVE pick-and-place fiber positioner for the William Herschel Telescope.

  11. The Unlock Project: a Python-based framework for practical brain-computer interface communication "app" development.

    PubMed

    Brumberg, Jonathan S; Lorenz, Sean D; Galbraith, Byron V; Guenther, Frank H

    2012-01-01

    In this paper we present a framework for reducing the development time needed for creating applications for use in non-invasive brain-computer interfaces (BCI). Our framework is primarily focused on facilitating rapid software "app" development akin to current efforts in consumer portable computing (e.g. smart phones and tablets). This is accomplished by handling intermodule communication without direct user or developer implementation, instead relying on a core subsystem for communication of standard, internal data formats. We also provide a library of hardware interfaces for common mobile EEG platforms for immediate use in BCI applications. A use-case example is described in which a user with amyotrophic lateral sclerosis participated in an electroencephalography-based BCI protocol developed using the proposed framework. We show that our software environment is capable of running in real-time with updates occurring 50-60 times per second with limited computational overhead (5 ms system lag) while providing accurate data acquisition and signal analysis. PMID:23366434

  12. Efficient multi-objective calibration of a computationally intensive hydrologic model with parallel computing software in Python

    Technology Transfer Automated Retrieval System (TEKTRAN)

    With enhanced data availability, distributed watershed models for large areas with high spatial and temporal resolution are increasingly used to understand water budgets and examine effects of human activities and climate change/variability on water resources. Developing parallel computing software...

  13. Modeling Multiphase Coastal and Hydraulic Processes in an Interactive Python Environment with the Open Source Proteus Toolkit

    NASA Astrophysics Data System (ADS)

    Kees, C. E.; Farthing, M. W.; Ahmadia, A. J.; Bakhtyar, R.; Miller, C. T.

    2014-12-01

    Hydrology is dominated by multiphase flow processes, due to the importance of capturing water's interaction with soil and air phases. Unfortunately, many different mathematical model formulations are required to model particular processes and scales of interest, and each formulation often requires specialized numerical methods. The Proteus toolkit is a software package for research on models for coastal and hydraulic processes and improvements in numerics, particularly 3D multiphase processes and parallel numerics. The models considered include multiphase flow, shallow water flow, turbulent free surface flow, and various flow-driven processes. We will discuss the objectives of Proteus and recent evolution of the toolkit's design as well as present examples of how it has been used used to construct computational models of multiphase flows for the US Army Corps of Engineers. Proteus is also an open source toolkit authored primarily within the US Army Corps of Engineers, and used, developed, and maintained by a small community of researchers in both theoretical modeling and computational methods research. We will discuss how open source and community development practices have played a role in the creation of Proteus.

  14. SAE2.py : a python script to automate parameter studies using SCREAMER with application to magnetic switching on Z.

    SciTech Connect

    Orndorff-Plunkett, Franklin

    2011-05-01

    The SCREAMER simulation code is widely used at Sandia National Laboratories for designing and simulating pulsed power accelerator experiments on super power accelerators. A preliminary parameter study of Z with a magnetic switching retrofit illustrates the utility of the automating script for optimizing pulsed power designs. SCREAMER is a circuit based code commonly used in pulsed-power design and requires numerous iterations to find optimal configurations. System optimization using simulations like SCREAMER is by nature inefficient and incomplete when done manually. This is especially the case when the system has many interactive elements whose emergent effects may be unforeseeable and complicated. For increased completeness, efficiency and robustness, investigators should probe a suitably confined parameter space using deterministic, genetic, cultural, ant-colony algorithms or other computational intelligence methods. I have developed SAE2 - a user-friendly, deterministic script that automates the search for optima of pulsed-power designs with SCREAMER. This manual demonstrates how to make input decks for SAE2 and optimize any pulsed-power design that can be modeled using SCREAMER. Application of SAE2 to magnetic switching on model of a potential Z refurbishment illustrates the power of SAE2. With respect to the manual optimization, the automated optimization resulted in 5% greater peak current (10% greater energy) and a 25% increase in safety factor for the most highly stressed element.

  15. The Unlock Project: A Python-based framework for practical brain-computer interface communication “app” development

    PubMed Central

    Brumberg, Jonathan S.; Lorenz, Sean D.; Galbraith, Byron V.; Guenther, Frank H.

    2013-01-01

    In this paper we present a framework for reducing the development time needed for creating applications for use in non-invasive brain-computer interfaces (BCI). Our framework is primarily focused on facilitating rapid software “app” development akin to current efforts in consumer portable computing (e.g. smart phones and tablets). This is accomplished by handling intermodule communication without direct user or developer implementation, instead relying on a core subsystem for communication of standard, internal data formats. We also provide a library of hardware interfaces for common mobile EEG platforms for immediate use in BCI applications. A use-case example is described in which a user with amyotrophic lateral sclerosis participated in an electroencephalography-based BCI protocol developed using the proposed framework. We show that our software environment is capable of running in real-time with updates occurring 50–60 times per second with limited computational overhead (5 ms system lag) while providing accurate data acquisition and signal analysis. PMID:23366434

  16. 77 FR 61627 - Endangered Species; Marine Mammals; Receipt of Applications for Permit

    Federal Register 2010, 2011, 2012, 2013, 2014

    2012-10-10

    ... Heads of Executive Departments and Agencies of January 21, 2009--Transparency and Open Government (74 FR...) Indian python (Python molurus molurus) Panamanian golden frog (Atelopus zeteki) Applicant: Duke...

  17. 77 FR 51819 - Endangered Species; Receipt of Applications for Permit

    Federal Register 2010, 2011, 2012, 2013, 2014

    2012-08-27

    ... Heads of Executive Departments and Agencies of January 21, 2009--Transparency and Open Government (74 FR... tortoise (Chelonoidis nigra) Radiated tortoise (Astrochelys radiata) Indian python (Python molurus...

  18. 78 FR 76171 - Endangered Species; Receipt of Applications for Permit

    Federal Register 2010, 2011, 2012, 2013, 2014

    2013-12-16

    ... Heads of Executive Departments and Agencies of January 21, 2009--Transparency and Open Government (74 FR... terrestris) Golden parakeet (Guarouba guarouba) Indian python (Python molurus molurus) Nile...

  19. 78 FR 34118 - Endangered Species; Marine Mammals; Receipt of Applications for Permit

    Federal Register 2010, 2011, 2012, 2013, 2014

    2013-06-06

    ... of January 21, 2009--Transparency and Open Government (74 FR 4685; January 26, 2009), which call on... (Elephas maximus) Koala (Phascolarctos cinereus) Indian python (Python molurus molurus) Applicant:...

  20. "Creating Unity from Diversity: Finding Our Commonalities, Respecting Our Differences." Presenter Abstracts of the Annual National Conference of the National Multicultural Institute (9th, Washington, D.C., May 19-22, 1994).

    ERIC Educational Resources Information Center

    National Multicultural Inst., Washington, DC.

    This is primarily a collection of abstracts for training workshops for professionals in the field of multicultural education. The abstracts are: (1) "An Exploration of the Unspoken: A Group Relations Approach to Multicultural Dialogue" (Zachary G. Green); (2) "Exploring Our Cultural Assumptions" (Daniel Rivera); (3) "Challenging Homophobia:…

  1. Meeting the Needs of Students Who Plan Careers in Ornamental Horticulture Occupations

    ERIC Educational Resources Information Center

    Simmons, J. C.

    1977-01-01

    Due to increasing urbanization of the area, focus of the vocational agriculture department at Zachary High School, near Baton Rouge, Louisiana, has changed from production agriculture to ornamental horticulture. The area supervisor describes the facilities for student instruction and work experience in the school's greenhouses and grounds. (MF)

  2. Opening the Black Box of Social Cognitive Mapping

    ERIC Educational Resources Information Center

    Neal, Zachary P.; Neal, Jennifer Watling

    2013-01-01

    This article provides Zachary P. Neal and Jennifer Watling Neal's response to Thomas W. Farmer and Hongling Xie's commentary on Neal and Neal's "Multiple Meanings of Peer Groups in Social Cognitive Mapping." Neal and Neal assert that many of Farmer and Xie's comments highlight the motivation behind their original…

  3. 38 CFR 60.2 - Definitions.

    Code of Federal Regulations, 2012 CFR

    2012-07-01

    ... 60.2 Pensions, Bonuses, and Veterans' Relief DEPARTMENT OF VETERANS AFFAIRS (CONTINUED) FISHER HOUSES... House which is a housing facility that is located at or near a VA health care facility, that is... donated to VA by the Zachary and Elizabeth M. Fisher Armed Services Foundation or Fisher House...

  4. 38 CFR 60.2 - Definitions.

    Code of Federal Regulations, 2011 CFR

    2011-07-01

    ... 60.2 Pensions, Bonuses, and Veterans' Relief DEPARTMENT OF VETERANS AFFAIRS (CONTINUED) FISHER HOUSES... House which is a housing facility that is located at or near a VA health care facility, that is... donated to VA by the Zachary and Elizabeth M. Fisher Armed Services Foundation or Fisher House...

  5. Making Colonial Subjects: Education in the Age of Empire

    ERIC Educational Resources Information Center

    Hall, Catherine

    2008-01-01

    This article explores two attempts to envisage a new global world, one created by the West, and to create new colonial subjects. One of these attempts was in Sierra Leone in the 1790s, the other in India in the 1830s. The two case studies are seen through the lens of a father and son, Zachary and Thomas Babington Macaulay, each a representative…

  6. Proceedings of the Annual Meeting of the Association for Education in Journalism and Mass Communication (85th, Miami, Florida, August 5-8, 2002). Commission on the Status of Women Division.

    ERIC Educational Resources Information Center

    2002

    The Commission on the Status of Women Division of the proceedings contains the following 6 papers: "Relationship Content in Four Men's and Women's Magazines" (Alexis Zachary and Bryan Denham); "Mind the Gender Gap: Gender Differences in Motivation to Contribute Online Content" (Cindy Royal); "Peering through the Glass Ceiling of the Boys' Club:…

  7. Arapahos on the Great Plains. Student Workbook.

    ERIC Educational Resources Information Center

    Spoonhunter, Bob; Woodenlegs, Martha

    The student workbook is derived from "An Ethnological Report on Cheyenne and Arapaho: Aboriginal Occupation," by Zachary Gussow and "Northern Snows to Southern Summers--An Arapaho Odyssey," by Bob Spoonhunter. The first section discusses the Arapaho origins by recounting many different legends that explain how they arrived on the Great Plains. The…

  8. Keats: A Collection of Critical Essays. Twentieth Century Views Series.

    ERIC Educational Resources Information Center

    Bate, Walter Jackson, Ed.

    One of a series of works aimed at presenting contemporary critical opinion on major authors, this collection includes essays by Walter Jackson Bate, T. S. Eliot, Douglas Bush, Richard H. Fogle, Jack Stillinger, Harold Bloom, David Perkins, Earl Wasserman, and D. G. James--all dealing with the biography and literary work of John Keats. Designed for…

  9. Effects of Stimulus Duration and Choice Delay on Visual Categorization in Pigeons

    ERIC Educational Resources Information Center

    Lazareva, Olga F.; Wasserman, Edward A.

    2009-01-01

    We [Lazareva, O. F., Freiburger, K. L., & Wasserman, E. A. (2004). "Pigeons concurrently categorize photographs at both basic and superordinate levels." "Psychonomic Bulletin and Review," 11, 1111-1117] previously trained four pigeons to classify color photographs into their basic-level categories (cars, chairs, flowers, or people) or into their…

  10. 78 FR 54255 - HRSA's Bureau of Health Professions Advanced Education Nursing Traineeship Program

    Federal Register 2010, 2011, 2012, 2013, 2014

    2013-09-03

    ... Education Nursing Traineeship Program AGENCY: Health Resources and Services Administration (HRSA), HHS... Education Nursing Traineeship (AENT) program. Effective fiscal year (FY) 2014, AENT support for part-time... practitioners and nurse midwives. FOR FURTHER INFORMATION CONTACT: Joan Wasserman, DrPH, RN, Advanced...

  11. 76 FR 8990 - Hours of Service of Drivers; Availability of Supplemental Documents and Corrections to Notice of...

    Federal Register 2010, 2011, 2012, 2013, 2014

    2011-02-16

    ..., please see the preamble to the December 2010 HOS NPRM (75 FR 82170; December 29, 2010). The docket for... December 29, 2010 (75 FR 82170): ] 1. Page 82177. Correct footnote 17 to read as follows: \\17\\ Balkin, T..., J.A.M. & Wasserman, J.F., ``Improving Heavy-Duty Diesel Truck Ergonomics to Reduce Fatigue...

  12. Pupil Dilation and Object Permanence in Infants

    ERIC Educational Resources Information Center

    Sirois, Sylvain; Jackson, Iain R.

    2012-01-01

    This paper examines the relative merits of looking time and pupil diameter measures in the study of early cognitive abilities of infants. Ten-month-old infants took part in a modified version of the classic drawbridge experiment used to study object permanence (Baillargeon, Spelke, & Wasserman, 1985). The study involved a factorial design where…

  13. Photometric Measurements of 343 Ostara and Other Asteroids at Hobbs Observatory

    NASA Astrophysics Data System (ADS)

    Ford, Lyle; Stecher, George; Lorenzen, Kayla; Cook, Cole

    2009-07-01

    We observed 343 Ostara on 2008 October 4 and obtained R and V standard magnitudes. The period was found to be significantly greater than the previously reported value of 6.42 hours. Measurements of 2660 Wasserman and (17010) 199 CQ72 made on 2008 March 25 are also reported.

  14. Factors Mediating Alcohol Craving and Relapse: Stress, Compulsivity, and Genetics

    PubMed Central

    Rodd, Zachary A.; Anstrom, Kristin K; Knapp, Darin J.; Racz, Ildiko; Zimmer, Andreas; Serra, Salvatore; Bell, Richard L.; Woodward, Donald J.; Breese, George R.; Colombo, Giancarlo

    2010-01-01

    This article represents the proceedings of a symposium at the 2004 annual meeting of the International Society for Biomedical Research on Alcoholism in Heidelberg, Germany. The symposium was organized by Zachary A. Rodd and Giancarlo Colombo. The presentations were (1) Pharmacological reversal of cycled withdrawal-sensitized or stress-sensitized withdrawal anxiety and enhanced ethanol drinking, by Darin J. Knapp and George R. Breese, (2) Alcohol craving and relapse in rats genetically selected for high alcohol preference, by Zachary A. Rodd and Richard L. Bell, (3) Exposure to stress increases dopaminergic burst firing in awake rats, by Kristin Anstrom and Donald J. Woodward, (4) Involvement of cannabinoid CB1 and GABAB receptors in the control of relapse-like drinking in alcohol-preferring Sardinian alcohol-preferring rats by Giancarlo Colombo and Salvatore Serra, and (5) Stress-induced ethanol drinking in CB1−/−, POMC, and PENK knockout mice, by Idiko Racz and Andreas Zimmer. PMID:16088996

  15. Orbital stability in combined uniform axial and three-dimensional wiggler magnetic fields for free-electron lasers

    NASA Technical Reports Server (NTRS)

    Johnston, S.

    1984-01-01

    Zachary Phys. Rev. A 29 (6), 3224 (1984) recently analyzed the instability of relativistic-electron helical trajectories in combined uniform axial and helical wiggler magnetic fields when the radial variation of the wiggler field is taken into account. It is shown here that the type 2 instability comprised of secular terms growing linearly in time, identified by Zachary and earlier by Diament Phys. Rev. A 23 (5), 2537 (1981), is an artifact of simple perturbation theory. A multiple-time-scale perturbation analysis reveals a nonsecular evolution on a slower time scale which accommodates an arbitrary initial perturbation. It is shown that, in the absence of exponential instability, the electron seeks a modified helical orbit more appropriate to its perturbed state and oscillates stably about it. Thus, the perturbed motion is oscillatory but nonsecular, and hence the helical orbits are stable.

  16. Combinatorial Laplacian and entropy of simplicial complexes associated with complex networks

    NASA Astrophysics Data System (ADS)

    Maletić, S.; Rajković, M.

    2012-09-01

    Simplicial complexes represent useful and accurate models of complex networks and complex systems in general. We explore the properties of spectra of combinatorial Laplacian operator of simplicial complexes and show its relationship with connectivity properties of the Q-vector and with connectivities of cliques in the simplicial clique complex. We demonstrate the need for higher order analysis in complex networks and compare the results with ordinary graph spectra. Methods and results are obtained using social network of the Zachary karate club.

  17. Stata Hybrids: Updates and Ideas

    NASA Technical Reports Server (NTRS)

    Fieldler, James

    2014-01-01

    At last year's Stata conference I presented two projects for using Python with Stata: a plugin that embeds the Python programming language within Stata and code for using Stata data sets in Python. In this talk I will describe some small improvements being made to these projects, and I will present other ideas for combining tools with Stata. Some of these ideas use Python, some use JavaScript and a web browser.

  18. PyUtilib: A Pythos Utility Library v. 1.0

    Energy Science and Technology Software Center (ESTSC)

    2010-01-07

    PyUtilib is a collection of Python utilities that are used by Python packages developed at Sandia National Laboratories, including the Coopr and FAST Python packages. PyUtilib includes facilities for managing factories, subprocess management, interfacing with Excel, and applying numerical techniques.

  19. MISR Toolkit

    Atmospheric Science Data Center

    2014-05-07

    ... x64). Its core interface is C. There are also bindings for Python and IDL. It is available as source and Windows binaries (zip or installer). The python and IDL binary modules require Python 2.7 and IDL 8.2 respectively. ...

  20. Heider balance in human networks

    NASA Astrophysics Data System (ADS)

    Gawroński, P.; Kułakowski, K.

    2005-07-01

    Recently, a continuous dynamics was proposed to simulate dynamics of interpersonal relations in a society represented by a fully connected graph. The final state of such a society was found to be identical with the so-called Heider balance (HB), where the society is divided into two mutually hostile groups. In the continuous model, a polarization of opinions was found in HB. Here we demonstrate that the polarization occurs also in Barabási-Albert networks, where the Heider balance is not necessarily present. In the second part of this work we demonstrate the results of our formalism, when applied to reference examples: the Southern women and the Zachary club.