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1

Zachary Justin Wartell May 2010, v2 Curriculum Vitae 1  

E-print Network

Zachary Justin Wartell May 2010, v2 Curriculum Vitae 1 Curriculum Vitae Zachary Justin Wartell Zachary Justin Wartell Department of Computer Science College of Computing and Informatics 9201 University-687-8442 Fax: 704-687-3516 #12;Zachary Justin Wartell May 2010, v2 Curriculum Vitae 2 1 Education Degree Year

Wartell, Zachary

2

Ball python  

NSDL National Science Digital Library

All reptiles are cold-blooded, meaning that they gather their warmth from the environment around them. Reptiles in captivity, like this ball python, need a sun lamp to stay warm. They could die if they get too cold.

Patrick Jean (muséum d'histoire naturelle de Nantes; )

2004-02-10

3

Python praktikum Alessandro Mammana  

E-print Network

Type "help", "copyright", "credits" or "license" for more information. >>> print("Hello Python!") Hello. >>> print("Hello Python!") Hello Python! >>> quit() ~> mkdir pyprakt ~> cd pyprakt ~/pyprakt> gedit code.py & print("hello world!") ~/pyprakt> python3 code.py hello world! #!/usr/bin/python3 print("hello world

Spang, Rainer

4

Tutorial on Nonparametric Chad Schafer and Larry Wasserman  

E-print Network

Tutorial on Nonparametric Inference With R Chad Schafer and Larry Wasserman cschafer@stat.cmu.edu larry@stat.cmu.edu Carnegie Mellon University Tutorial on Nonparametric Inference ­ p.1/202 #12;Outline Measurement Error Inverse Probems Classification Nonparametric Bayes Tutorial on Nonparametric Inference ­ p.2

Masci, Frank

5

Alligator and Python Struggle  

USGS Multimedia Gallery

An American alligator and a Burmese python locked in a struggle to prevail in Everglades National Park. This python appears to be losing, but snakes in similar situations have apparently escaped unharmed, and in other situations pythons have eaten alligators....

6

Python Calculus  

NSDL National Science Digital Library

Students analyze a cartoon of a Rube Goldberg machine and a Python programming language script to practice engineering analysis. In both cases, they study the examples to determine how the different systems operate and the function of each component. This exercise in juxtaposition enables students to see the parallels between a more traditional mechanical engineering design and computer programming. Students also gain practice in analyzing two very different systems to fully understand how they work, similar to how engineers analyze systems and determine how they function and how changes to the system might affect the system.

IMPART RET Program, College of Information Science & Technology,

7

Python and Turtle  

NSDL National Science Digital Library

Learn a little bit of Python as well as how it can be used to graph simple shapes and pictures. This lesson can be used to learn the basic concepts of programming and logical processes. $.beautyOfCode.init({ brushes: ['Xml', 'JScript', 'CSharp', 'Plain', 'Php','Python'] }); Python - Turtle This lesson will focus around simple python programming concepts. ...

kajigga

2009-09-21

8

Dilation for Sets of Probabilities Teddy Seidenfeld; Larry Wasserman  

E-print Network

Dilation for Sets of Probabilities Teddy Seidenfeld; Larry Wasserman The Annals of Statistics, Vol-5364%28199309%2921%3A3%3C1139%3ADFSOP%3E2.0.CO%3B2-V The Annals of Statistics is currently published by Institute://www.jstor.org Tue Mar 4 10:39:53 2008 #12;The Annals of Statistics 1993, Vol. 21, No. 3, 1139-1154 DILATION FOR SETS

Spirtes, Peter

9

Python bindings for libcloudph++  

E-print Network

This technical note introduces the Python bindings for libcloudph++. The libcloudph++ is a C++ library of algorithms for representing atmospheric cloud microphysics in numerical models. The bindings expose the complete functionality of the library to the Python users. The bindings are implemented using the Boost.Python C++ library and use NumPy arrays. This note includes listings with Python scripts exemplifying the use of selected library components. An example solution for using the Python bindings to access libcloudph++ from Fortran is presented.

Jarecka, Dorota; Del Vento, Davide

2015-01-01

10

Python Programming Language  

NSDL National Science Digital Library

Python is often compared to Tcl, Perl, Scheme or Java and runs on many brands of UNIX, on Windows, OS/2, Mac, Amiga, and many other platforms. The most recent version of Python is available for free from this website. Also included are Python 2.3.3 Documentation (released December 19, 2003), the interpreter program that reads Python programs and carries out their instructions, tutorials for non-programmers and programmers, some examples and sample code, information for developers, and links to the programming community user groups. "The Python implementation is copyrighted but freely usable and distributable, even for commercial use."

11

Python Script Analysis  

NSDL National Science Digital Library

Working in small groups, students complete and run functioning Python codes. They begin by determining the missing commands in a sample piece of Python code that doubles all the elements of a given input and sums the resulting values. Then students modify more advanced Python code, which numerically computes the slope of a tangent line by finding the slopes of progressively closer secant lines; to this code they add explanatory comments to describe the function of each line of code. This requires students to understand the logic employed in the Python code. Finally, students make modifications to the code in order to find the slopes of tangents to a variety of functions.

IMPART RET Program, College of Information Science & Technology,

12

Teaching and Learning Python  

NSDL National Science Digital Library

Python is an introductory programming language considered ideal for learning the basic concepts of programming. This website offers examples of educational uses and lesson plans for Python. "LiveWires" is used to teach Python to children ages 12-15 at a summer camp in Britain. The lesson materials are free online and include a series of worksheets, reference sheets and game sheets for use with the LiveWires package (Python modules). Also on this website are a list of possible activities that will be offered in the 2004 summer session, interviews with current and former LiveWires people, and a typical timetable.

13

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)....

14

Python to learn programming  

NASA Astrophysics Data System (ADS)

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.

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

2013-04-01

15

NEURON and Python  

PubMed Central

The NEURON simulation program now allows Python to be used, alone or in combination with NEURON's traditional Hoc interpreter. Adding Python to NEURON has the immediate benefit of making available a very extensive suite of analysis tools written for engineering and science. It also catalyzes NEURON software development by offering users a modern programming tool that is recognized for its flexibility and power to create and maintain complex programs. At the same time, nothing is lost because all existing models written in Hoc, including graphical user interface tools, continue to work without change and are also available within the Python context. An example of the benefits of Python availability is the use of the xml module in implementing NEURON's Import3D and CellBuild tools to read MorphML and NeuroML model specifications. PMID:19198661

Hines, Michael L.; Davison, Andrew P.; Muller, Eilif

2008-01-01

16

Monte Python: Monte Carlo code for CLASS in Python  

NASA Astrophysics Data System (ADS)

Monte Python is a parameter inference code which combines the flexibility of the python language and the robustness of the cosmological code CLASS into a simple and easy to manipulate Monte Carlo Markov Chain code.

Audren, Benjamin; Lesgourgues, Julien; Benabed, Karim; Prunet, Simon

2013-07-01

17

Biologists Remove Python from Everglades  

USGS Multimedia Gallery

This 16 1/2-foot python, being removed from the wild by USGS and NPS personnel, was captured in a thicket in Everglades National Park in May 2012. The python was equipped with a radio-transmitter and an accelerometer as part of one of the Burmese python projects led by USGS to learn more about the b...

18

Invent with Python  

NSDL National Science Digital Library

Have you ever wanted to learn how to make your own computer games? This is now a possibility, and all one needs to do is look over the excellent "Invent Your Own Computer Games with Python" site. The guide has been written to be understood by people as young as 10 and each chapter gives users the complete source guide, then teaches the programming concepts from the example. There are twenty chapters here, including "Using the Debugger," "Hangman," "Tic Tac Toe," and "Installing Python." Each chapter includes graphics and flow-charts designed to help neophytes get acclimated to the entire experience and process. The work is rounded out by the inclusion of four appendices, including "Common Error Messages in Python."

Sweigart, Al

19

Arsenic and manganese exposure and children's intellectual function Gail A. Wasserman a,b,  

E-print Network

Arsenic and manganese exposure and children's intellectual function Gail A. Wasserman a neurobehavioral function and exposure to arsenic (As) via drinking water or industrial sources (Calderon et al Accepted 21 March 2011 Available online 29 March 2011 Keywords: Arsenic Manganese Children Water Bangladesh

van Geen, Alexander

20

Investigating interaction-induced chaos using time-dependent density-functional theory Adam Wasserman,1  

E-print Network

Wasserman,1 Neepa T. Maitra,2 and Eric J. Heller1,3 1 Department of Chemistry and Chemical Biology, HarvardRevA.77.042503 PACS number s : 31.15.E , 31.10. z, 05.45.Mt, 73.21.La I. INTRODUCTION The study

21

Reprinted from Calcium Binding Proteins and Calcium Function (R.H. Wasserman et al., eds.).  

E-print Network

185 Reprinted from Calcium Binding Proteins and Calcium Function (R.H. Wasserman et al., eds . It is generally accepted that depolarization at the transverse tubular membrane initiates the release of calcium (SDS-PAGE). The procedure of Fairbanks11 was used for glycopro- tein staining. SR calcium oxalate

Campbell, Kevin P.

22

Algorithms for Designing Pop-Up Cards Zachary Abel1  

E-print Network

Algorithms for Designing Pop-Up Cards Zachary Abel1 , Erik D. Demaine2 , Martin L. Demaine2 , Sarah for delivering babies. Dean & Sons' Little Red Riding Hood (1850) is the first known movable book where a flat ­ Leibniz-Zentrum für Informatik, Dagstuhl Publishing, Germany #12;2 Algorithms for Designing Pop-Up Cards

Demaine, Erik

23

Development of Prototype UrbanSim Models Zachary Patterson  

E-print Network

. This reputation makes many potential users think twice before developing an UrbanSim model. We believe the onlyDevelopment of Prototype UrbanSim Models Zachary Patterson Michel Bierlaire 18 August 2008 Report Engineering Ecole Polytechnique F´ed´erale de Lausanne transp-or.epfl.ch Abstract UrbanSim is an integrated

Bierlaire, Michel

24

Python fiber optic seal  

Microsoft Academic Search

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

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

1993-01-01

25

Python and computer vision  

SciTech Connect

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.

Doak, J. E. (Justin E.); Prasad, Lakshman

2002-01-01

26

Simulation Programming with Python  

E-print Network

of the examples in Chap. 3 can be programmed using Python and the SimPy simulation library[1]. The goals of the chapter are to introduce SimPy, and to hint at the experiment design and analysis issues the conventions and patterns enabled by the SimPy library. 4.1 SimPy Overview SimPy is an object-oriented, process

Nelson, Barry L.

27

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

USGS Publications Warehouse

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.

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

2011-01-01

28

Pyro Python Robotics  

NSDL National Science Digital Library

Pyro stands for Python Robotics. The goal of the project is to provide a programming environment for easily exploring advanced topics in artificial intelligence and robotics without having to worry about the low-level details of the underlying hardware. That is not to say that Pyro is just a toy. In fact, Pyro is used for real robotics research as well as courseware.This website is the 2005 recipient of Premier Award for Excellence in Engineering Education Courseware. It includes curriculum, tutorials, modules and documentations.

29

A comparison of C++, Java and Python  

E-print Network

and Embedding the Python Interpreter, http: //www. ida. liu. se/imported/python/python-ext/ext. html [18] Gvmo VAN RossvM, 1997, Python Tutorial, http: //www. wcmh. corn/uworld /archives/95/tutorial/005. html [19] BJARNE STROUSTRUP, 1995, The Design...

Chou, Ling

1997-01-01

30

Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis  

E-print Network

Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis-throughput a b s t r a c t We present the Python Materials Genomics (pymatgen) library, a robust, open

Ceder, Gerbrand

31

CSC326 Python Sequences CSC326 Python Sequences  

E-print Network

Matrics 12 13 Prime Number with List Comprehension 12 14 Quick Sort 12 15 Recap 12 #12;CSC326 Python return -1 · membership/subset test: in operator >>>> 'a' in 'banana' True >>>> 'seed' in 'banana' False

Zhu, Jianwen

32

PythonPhot: Simple DAOPHOT-type photometry in Python  

NASA Astrophysics Data System (ADS)

PythonPhot is a simple Python translation of DAOPHOT-type (ascl:1104.011) photometry procedures from the IDL AstroLib (Landsman 1993), including aperture and PSF-fitting algorithms, with a few modest additions to increase functionality and ease of use. These codes allow fast, easy, and reliable photometric measurements and are currently used in the Pan-STARRS supernova pipeline and the HST CLASH/CANDELS supernova analysis.

Jones, David O.; Scolnic, Daniel M.; Rodney, Steven A.

2015-01-01

33

Python fiber optic seal  

SciTech Connect

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.

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

1993-08-01

34

Pynamic: the Python Dynamic Benchmark  

SciTech Connect

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.

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

2007-07-10

35

Humoral regulation of heart rate during digestion in pythons (Python molurus and Python regius).  

PubMed

Pythons exhibit a doubling of heart rate when metabolism increases several times during digestion. Pythons, therefore, represent a promising model organism to study autonomic cardiovascular regulation during the postprandial state, and previous studies show that the postprandial tachycardia is governed by a release of vagal tone as well as a pronounced stimulation from nonadrenergic, noncholinergic (NANC) factors. Here we show that infusion of plasma from digesting donor pythons elicit a marked tachycardia in fasting snakes, demonstrating that the NANC factor resides in the blood. Injections of the gastrin and cholecystokinin receptor antagonist proglumide had no effect on double-blocked heart rate or blood pressure. Histamine has been recognized as a NANC factor in the early postprandial period in pythons, but the mechanism of its release has not been identified. Mast cells represent the largest repository of histamine in vertebrates, and it has been speculated that mast cells release histamine during digestion. Treatment with the mast cell stabilizer cromolyn significantly reduced postprandial heart rate in pythons compared with an untreated group but did not affect double-blocked heart rate. While this study indicates that histamine induces postprandial tachycardia in pythons, its release during digestion is not stimulated by gastrin or cholecystokinin nor is its release from mast cells a stimulant of postprandial tachycardia. PMID:22422667

Enok, Sanne; Simonsen, Lasse Stærdal; Pedersen, Signe Vesterskov; Wang, Tobias; Skovgaard, Nini

2012-05-15

36

Web Interfaces 1 Python Scripts in Browsers  

E-print Network

Web Interfaces 1 Python Scripts in Browsers the web server Apache processing forms with Python scripts Python code to write HTML 2 Web Interfaces for the Determinant dynamic interactive forms passing, 28 October 2013 Scientific Software (MCS 507 L-27) web interfaces 28 October 2013 1 / 42 #12;Web

Verschelde, Jan

37

Python programming --Debugging Finn Arup Nielsen  

E-print Network

DB Introspection with Paul Butler's debugging decorator Finn °Arup Nielsen 1 October 30, 2013 #12;Python debuggingPython programming -- Debugging Finn °Arup Nielsen DTU Compute Technical University of Denmark discover "real" bugs Finn °Arup Nielsen 2 October 30, 2013 #12;Python debugging Print print: While ok

38

Python programming --Semantic Web Finn Arup Nielsen  

E-print Network

Python programming -- Semantic Web Finn °Arup Nielsen DTU Compute Technical University of Denmark October 9, 2013 #12;Python programming -- Semantic Web What is Semantic Web? Semantic Web = Triple data 9, 2013 #12;Python programming -- Semantic Web Why the Semantic Web? IBM's Watson supercomputer

39

Accessing the VO with Python  

NASA Astrophysics Data System (ADS)

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.

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

2014-05-01

40

python-qucs: Python package for automating QUCS simulations  

NASA Astrophysics Data System (ADS)

Characterization of the frequency response of coherent radiometric receivers is a key element in estimating the flux of astrophysical emissions, since the measured signal depends on the convolution of the source spectral emission with the instrument band shape. Python-qucs automates the process of preparing input data, running simulations and exporting results of QUCS (Quasi Universal Circuit Simulator) simulations.

Zonca, Andrea

2015-01-01

41

Pybus -- A Python Software Bus  

SciTech Connect

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.

Lavrijsen, Wim T.L.P.

2004-10-14

42

Advanced Python Scripting Using Sherpa  

NASA Astrophysics Data System (ADS)

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.

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

2011-07-01

43

Building Facebook Application using Python  

E-print Network

Building Facebook Application using Python ECS-15, Fall 2010 Prantik Bhattacharyya #12;· Farmville ­ Game ­ 57.5m users ­ http://www.facebook.com/FarmVille · Causes ­ Issues/Movement ­ 26.4m users ­ http://www.facebook.com/causes Facebook Applications #12;Outline · Facebook Applications · Writing Applications · Development

Wu, S. Felix

44

Imagining a Stata / Python Combination  

NASA Technical Reports Server (NTRS)

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.

Fiedler, James

2012-01-01

45

Python on Ranger and Lonestar  

NSDL National Science Digital Library

While Python is a scripting language, it has plenty of facilities for high performance computing. This article covers some of its features and libraries that are particularly helpful when moving scientific code to a large cluster resource. It also includes specific recipes for compilation and execution on the TACC clusters.

46

Steering object-oriented computations with Python  

SciTech Connect

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.

Yang, T.-Y.B.; Dubois, P.F.; Furnish, G. [Lawrence Livermore National Lab., CA (United States); Beazley, D.M. [Utah Univ., Salt Lake City, UT (United States). Dept. of Computer Science

1996-10-01

47

A Burmese Python and an Alligator Encounter in South Florida  

USGS Multimedia Gallery

A Burmese python (Python molurus) peeks over the head of an alligator that holds the python's body in its mouth in Everglades National Park. Photo courtesy of Lori Oberhofer, National Park Service....

48

Pyomo : Python Optimization Modeling Objects.  

SciTech Connect

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.

Siirola, John; Laird, Carl Damon (Texas A& M University, College Station, TX); Hart, William Eugene; Watson, Jean-Paul

2010-11-01

49

PyFITS. a Python FITS Module  

NASA Astrophysics Data System (ADS)

PyFITS is a Python module for reading, writing, and manipulating FITS files. The module uses Python's object-oriented features to provide quick, easy, and efficient access to FITS files. The use of Python's array syntax enables immediate access to any FITS extension, header cards, or data items. The FITS module is written in Python for maintainability and portability and uses C-extension modules, Numeric and Record, for efficient access to the data. These and other features, and future developments are discussed in this paper.

Barrett, P. E.; Bridgman, W. T.

50

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

PubMed

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

Hedley, Joanna; Eatwell, Kevin; Schwarz, Tobias

2014-01-01

51

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

USGS Publications Warehouse

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 from tests of control tools for large cryptic predators such as Burmese pythons. ?? CSIRO 2011.

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

2011-01-01

52

Astropy: Community Python library for astronomy  

NASA Astrophysics Data System (ADS)

Astropy provides a common framework, core package of code, and affiliated packages for astronomy in Python. Development is actively ongoing, with major packages such as PyFITS, PyWCS, vo, and asciitable already merged in. Astropy is intended to contain much of the core functionality and some common tools needed for performing astronomy and astrophysics with Python.

Greenfield, Perry; Robitaille, Thomas; Tollerud, Erik; Aldcroft, Tom; Barbary, Kyle; Barrett, Paul; Bray, Erik; Crighton, Neil; Conley, Alex; Conseil, Simon; Davis, Matt; Deil, Christoph; Dencheva, Nadia; Droettboom, Michael; Ferguson, Henry; Ginsburg, Adam; Grollier, Frédéric; Moritz Günther, Hans; Hanley, Chris; Hsu, J. C.; Kerzendorf, Wolfgang; Kramer, Roban; Lian Lim, Pey; Muna, Demitri; Nair, Prasanth; Price-Whelan, Adrian; Shiga, David; Singer, Leo; Taylor, James; Turner, James; Woillez, Julien; Zabalza, Victor

2013-04-01

53

An Introduction to SNAP for Python  

E-print Network

An Introduction to Snap.py SNAP for Python Author: Rok Sosic Created: Sep 26, 2013 #12;Content Introduction to Snap.py Tutorial Plotting Q&A #12;What is SNAP Stanford Network Analysis Project (SNAP++ based Web site at http://snap.stanford.edu #12;What is Snap.py Snap.py: SNAP for Python Provides SNAP

Pratt, Vaughan

54

Why Python? 1) readable, compact, simple syntax  

E-print Network

Starting with Python #12;Why Python? 1) readable, compact, simple syntax 2) documented 3) memory -> your_file.py your_file.pyc Compiled to .pyc file 10/19/2010 3 #12;Basic syntax · No semicolon; · C-based syntax, less brackets · Control flow via indentation if danger : if smaller_than_you(): fight() else: run

Spang, Rainer

55

Python programming --Pandas Finn Arup Nielsen  

E-print Network

Python programming -- Pandas Finn °Arup Nielsen DTU Compute Technical University of Denmark October 5, 2013 #12;Pandas Overview Pandas? Reading data Summary statistics Indexing Merging, joining Group-by and cross-tabulation Statistical modeling Finn °Arup Nielsen 1 October 5, 2013 #12;Pandas Pandas? "Python

56

Alien Presence in the Home: The Design of Tableau Mario Romero, Zachary Pousman, Michael Mateas  

E-print Network

Alien Presence in the Home: The Design of Tableau Machine Mario Romero, Zachary Pousman, Michael a design strategy, alien presence, which combines work in Human-Computer Interaction, Artificial of daily activities. An alien presence actively interprets and characterizes daily activity and reflects

Mateas, Michael

57

Effects of Early-Life Experience on Learning Ability in Fruit Flies Zachary Durisko & Reuven Dukas  

E-print Network

Animal Behaviour Group, Department of Psychology, Neuroscience & Behaviour, McMaster University, Hamilton, Ontario, Canada Correspondence Zachary Durisko, Animal Behaviour Group, Department of Psychology, 2013 (D. Zeh) doi: 10.1111/eth.12168 Abstract Learning and memory require the development, modification

Dukas, Reuven

58

OPTIMAL INVESTMENT POLICIES FOR THE HORSE RACE MODEL Thomas S. Ferguson and C. Zachary Gilstein  

E-print Network

OPTIMAL INVESTMENT POLICIES FOR THE HORSE RACE MODEL Thomas S. Ferguson and C. Zachary Gilstein strategies for the log, power and exponential utility functions are derived. 1. Introduction. In Ferguson. Models with this utility function were considered in Ferguson [5] for investors whose main concern

Ferguson, Thomas S.

59

Sensitivity of soil respiration and microbial communities to altered snowfall Zachary T. Aanderud a,1  

E-print Network

snow-covered ecosystems Seed banks Sub-zero conditions Winter CO2 ux a b s t r a c t Winter respirationSensitivity of soil respiration and microbial communities to altered snowfall Zachary T. Aanderud a in winter soil respiration may be in uenced by the effects of snowfall on microbial communities

Fierer, Noah

60

Synthesizing Representative I/O Workloads Using Iterative Distillation Zachary Kurmas  

E-print Network

Synthesizing Representative I/O Workloads Using Iterative Distillation Zachary Kurmas College proper- ties are "key" for a given workload and storage system. We have developed a tool, the Distiller, that automati- cally identifies the key properties ("attribute-values") of the workload. The Distiller then uses

Kurmas, Zachary

61

Parallel, Distributed Scripting with Python  

SciTech Connect

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.

Miller, P J

2002-05-24

62

Reflection-Based Python-C++ Bindings  

SciTech Connect

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.

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

2004-10-14

63

Python based high-level synthesis compiler  

NASA Astrophysics Data System (ADS)

This paper presents a python based High-Level synthesis (HLS) compiler. The compiler interprets an algorithmic description of a desired behavior written in Python and map it to VHDL. FPGA combines many benefits of both software and ASIC implementations. Like software, the mapped circuit is flexible, and can be reconfigured over the lifetime of the system. FPGAs therefore 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. Creating parallel programs implemented in FPGAs is not trivial. This article describes design, implementation and first results of created Python based compiler.

Cieszewski, Rados?aw; Pozniak, Krzysztof; Romaniuk, Ryszard

2014-11-01

64

Development of hemipenes in the ball python snake Python regius.  

PubMed

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

Leal, Francisca; Cohn, Martin J

2015-01-01

65

Python fiber-optic seal  

SciTech Connect

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.

Ystesund, K.; Bartberger, J.; Brusseau, C.; Fleming, P.; Insch, K.; Tolk, K. [Sandia National Labs., Albuquerque, NM (United States)

1993-12-31

66

Astropy: Community Python Software for Astronomy  

NASA Astrophysics Data System (ADS)

The Astropy Project is a community effort to develop an open source Python package of common data structures and routines for use by other, more specialized astronomy software in Python in order to foster software interoperability in the astronomical community. The project encompasses Astropy's ”core” and ”affiliated" packages that adopt Astropy’s coding, testing and documentation standards. By doing so we aim to improve interoperability with other Python packages in astronomy, and help a broader community implement more Pythonic solutions to astronomy computing problems while minimizing duplication of effort. The project provides a template for other projects that use Astropy to reuse much of Astropy’s development framework without reinventing the wheel. Here we present an overview of the key features of the core package (existing and upcoming), current and planned affiliated packages, and how we manage a large open source project with a diverse community of contributors.

Greenfield, Perry; Tollerud, E. J.; Robitaille, T.; Developers, Astropy

2014-01-01

67

Constructive Artificial Intelligence Programming in Python  

E-print Network

Constructive Artificial Intelligence Programming in Python Daniel Polani School of Computer Science referenced. Constructive Artificial Intelligence #12;First Steps in Programming Example (Addition) a = 3 b becomes a, # simultaneously Constructive Artificial Intelligence #12;Conditions 1 A condition is specified

Polani, Daniel

68

Grid Programming in Java and Python  

NSDL National Science Digital Library

Introduction to Grids, the Globus Toolkit, and the Commodity Grid (CoG) Kit. Using and programming grids with the Java and Python CoG Kits, including secure access to remote resources, remote job submission and data access.

Gregor von Laszewski

69

PyMSES: Python modules for RAMSES  

NASA Astrophysics Data System (ADS)

PyMSES provides a python solution for getting data out of RAMSES (ascl:1011.007) astrophysical fluid dynamics simulations. It permits transparent manipulation of large simulations and interfaces with common Python libraries and existing code, and can serve as a post-processing toolbox for data analysis. It also does three-dimensional volume rendering with a specific algorithm optimized to work on RAMSES distributed data (Guillet et al. 2011 and Jones et a. 2011).

Guillet, Thomas; Chapon, Damien; Labadens, Marc

2013-10-01

70

Why do female ball pythons (Python regius) coil so tightly around their eggs?  

Microsoft Academic Search

Question: What benefits does brooding confer to offspring viability that outweigh its costs to the nest-attending female? Organisms: Thirty captive Python regius females and their clutches. Site: Vicinity of Lomé, Togo. Background: It has previously been shown that brooding enhances ball python hatching success by reducing desiccation of eggs. Methods: We captured wild, gravid females just before the time of

Fabien Aubret; Xavier Bonnet; Richard Shine; Stéphanie Maumelat

2005-01-01

71

Measuring the Length of a Captured Burmese Python  

USGS Multimedia Gallery

Skip Snow (National Park Service) measures the length of a captured Burmese python (Python molurus) at the South Florida Research Center, Everglades National Park. Photo courtesy of Lori Oberhofer, NPS. ...

72

Starkiller : a static type inferencer and compiler for Python  

E-print Network

Starkiller is a type inferencer and compiler for the dynamic language Python designed to generate fast native code. It analyzes Python source programs and converts them into equivalent C++ programs. Starkiller's type ...

Salib, Michael, 1978-

2004-01-01

73

Temporal and Spatial Complexity of Maternal Thermoregulation in Tropical Pythons  

E-print Network

219 Temporal and Spatial Complexity of Maternal Thermoregulation in Tropical Pythons examined the behavioral and physiological mechanisms by which female water pythons Liasis fuscus meet a widespread developmental need (thermoregulation) in a nat- ural setting. Although female L. fuscus were

Denardo, Dale

74

Gist: A scientific graphics package for Python  

SciTech Connect

{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.

Busby, L.E.

1996-05-08

75

The Python Interface to Antelope and Applications  

NASA Astrophysics Data System (ADS)

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.

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

2008-12-01

76

Building a programmable interface for physics codes using numeric python  

SciTech Connect

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.

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

1996-04-16

77

Implanting a Radio Transmitter in a Burmese Python  

USGS Multimedia Gallery

Researchers implant a radio transmitter in a 16-foot, 155-pound female Burmese python (Python molurus) at the South Florida Research Center, Everglades National Park. Radio-tracking builds understanding of where pythons spend their time and therefore where they can be controlled in practice. Photo c...

78

Consumption of bird eggs by invasive Burmese Pythons in Florida  

USGS Publications Warehouse

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.

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

2012-01-01

79

Python for Large-Scale Electrophysiology  

PubMed Central

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

Spacek, Martin; Blanche, Tim; Swindale, Nicholas

2008-01-01

80

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

USGS Publications Warehouse

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.

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

2011-01-01

81

APLpy: Astronomical Plotting Library in Python  

NASA Astrophysics Data System (ADS)

APLpy (the Astronomical Plotting Library in Python) is a Python module for producing publication-quality plots of astronomical imaging data in FITS format. The module uses Matplotlib, a powerful and interactive plotting package. It is capable of creating output files in several graphical formats, including EPS, PDF, PS, PNG, and SVG. Plots can be made interactively or by using scripts, and can generate co-aligned FITS cubes to make three-color RGB images. It also offers different overlay capabilities, including contour sets, markers with customizable symbols, and coordinate grids, and a range of other useful features.

Robitaille, Thomas; Bressert, Eli

2012-08-01

82

Python Ephemeris Module for Radio Astronomy  

NASA Astrophysics Data System (ADS)

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.

Kuiper, T. B.

2013-05-01

83

A New Python Library for Spectroscopic Analysis with MIDAS Style  

NASA Astrophysics Data System (ADS)

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.

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

2013-10-01

84

Extending Python: speed it upExtending Python: speed it up Python is great for rapid application development  

E-print Network

C / Fortran Programming · The good ­ High performance ­ Low-level systems programming ­ Available everywhere platform ­ Java: Sun's OO language: on-the-fly (just in time) compiliation of Byte code intepreter) ­ High level programming ­ Systems integration (gluing components together) #12;6 What Python Brings to C

Reluga, Tim

85

Identifying plausible scenarios for the establishment of invasive Burmese pythons ( Python molurus ) in Southern Florida  

Microsoft Academic Search

Successful invasions of secretive alien species often go unrecognized until spread has exceeded the point where control or\\u000a eradication is feasible. In such situations, understanding factors that contributed to establishment can be critical to preventing\\u000a subsequent introductions of previously-successful invaders or ecologically similar species. The Burmese python (Python molurus bivittatus), a native to Southeast Asia, is abundant in the pet

John D. WillsonMichael; Michael E. Dorcas; Raymond W. Snow

2011-01-01

86

Python programming --databasing Finn Arup Nielsen  

E-print Network

/value store -- Persistent storage of dictionary-like objects SQL -- Traditional relational databases NoSQL -- JSON-like storage Mongo -- A NoSQL database CouchDB -- Another NoSQL database Finn °Arup Nielsen 1Python programming -- databasing Finn °Arup Nielsen DTU Compute Technical University of Denmark

87

A Record-Breaking Invasive Burmese Python  

USGS Multimedia Gallery

This female Burmese python broke the records for her length - 17 feet, 7 inches - and the number of eggs she contained: 87. She was first captured in Everglades National Park by USGS researchers in the spring of 2012, when they followed a

88

PyXNAT: XNAT in Python  

PubMed Central

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

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

2012-01-01

89

Python Scripting in the Nengo Simulator  

PubMed Central

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

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

2008-01-01

90

A Record-Breaking Invasive Burmese Python  

USGS Multimedia Gallery

This female Burmese python broke the records for her length - 17 feet, 7 inches - and the number of eggs she contained: 87. She was first captured in Everglades National Park by USGS researchers in the spring of 2012, when they followed a

91

A Record-Breaking Invasive Burmese Python  

USGS Multimedia Gallery

This female Burmese python broke the records for her length -- 17 feet, 7 inches – and the number of eggs she contained: 87. She was first captured in Everglades National Park by USGS researchers in the spring of 2012, when they followed a

92

Burmese Python Caught in the Everglades  

USGS Multimedia Gallery

This large Burmese python, weighing 162 pounds and more than 15 feet long at the time of its capture in 2009, was caught alive in the Everglades and was found to have eaten an American alligator that measured about 6 feet in length. University of Florida researchers in the photo: Michael Rochford is...

93

Design of a Modular Snake Robot Cornell Wright, Aaron Johnson, Aaron Peck, Zachary McCord, Allison Naaktgeboren,  

E-print Network

Design of a Modular Snake Robot Cornell Wright, Aaron Johnson, Aaron Peck, Zachary McCord, Allison such as size, power, and weight constrain the design of modular snake robots. Meeting these constraints of the mechanical and electrical architectures results in a robust and versatile robot. I. INTRODUCTION Snake robots

Choset, Howie

94

Design, Analysis, and Learning Control of a Robotic Wind Turbine J. Zico Kolter, Zachary Jackowski, Russ Tedrake*  

E-print Network

Design, Analysis, and Learning Control of a Robotic Wind Turbine J. Zico Kolter, Zachary Jackowski, and improvements to wind turbine design and control can have a significant impact on energy sustainability a large impact on wind energy research. Pursuing this goal, in this paper we develop a small, fully

Jackson, Daniel

95

Visual analysis for live LIDAR battlefield change detection Thomas Butkiewicz, Remco Chang, Zachary Wartell, and William Ribarsky  

E-print Network

Visual analysis for live LIDAR battlefield change detection Thomas Butkiewicz, Remco Chang, Zachary of airborne LIDAR range data in a highly interactive visual interface. The system consists of three major, the cycle is completed by the generation of a goal map for the LIDAR collection hardware that instructs

Wartell, Zachary

96

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

PubMed Central

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

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

97

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

USGS Publications Warehouse

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.

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

2012-01-01

98

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

USGS Publications Warehouse

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.

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

2012-01-01

99

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

Federal Register 2010, 2011, 2012, 2013, 2014

...1018-AV68 Injurious Wildlife Species; Listing the Boa Constrictor, Four Python Species, and Four Anaconda...sebae), Southern African python (Python natalensis), boa constrictor (Boa constrictor), yellow anaconda (Eunectes...

2010-07-01

100

matplotlib -- A Portable Python Plotting Package  

NASA Astrophysics Data System (ADS)

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.

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

2005-12-01

101

Stimfit: quantifying electrophysiological data with Python.  

PubMed

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

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

2014-01-01

102

Stimfit: quantifying electrophysiological data with Python  

PubMed Central

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

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

2013-01-01

103

PyMidas: Interface from Python to Midas  

NASA Astrophysics Data System (ADS)

PyMidas is an interface between Python and MIDAS, the major ESO legacy general purpose data processing system. PyMidas allows a user to exploit both the rich legacy of MIDAS software and the power of Python scripting in a unified interactive environment. PyMidas also allows the usage of other Python-based astronomical analysis systems such as PyRAF.

Maisala, Sami; Oittinen, Tero

2014-01-01

104

PsychoPy—Psychophysics software in Python  

Microsoft Academic Search

The vast majority of studies into visual processing are conducted using computer display technology. The current paper describes a new free suite of software tools designed to make this task easier, using the latest advances in hardware and software. PsychoPy is a platform-independent experimental control system written in the Python interpreted language using entirely free libraries. PsychoPy scripts are designed

Jonathan W. Peirce

2007-01-01

105

Extensible message passing application development and debugging with Python  

SciTech Connect

The authors describe how they have parallelized Python, an interpreted object oriented scripting language, and used it to build an extensible message-passing C/C++ applications for the CM-5, Cray T3D, and Sun multiprocessor servers running MPI. Using a parallelized Python interpreter, it is possible to interact with large-scale parallel applications, rapidly prototype new features, and perform application specific debugging. It is even possible to write message passing programs in Python itself. The authors describe some of the tools they have developed to extend Python and applications of this approach.

Beazley, D.M. [Univ. of Utah, Salt Lake City, UT (United States). Dept. of Computer Science; Lomdahl, P.S. [Los Alamos National Lab., NM (United States). Theoretical Div.

1996-09-19

106

Re-imagining a Stata/Python Combination  

NASA Technical Reports Server (NTRS)

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.

Fiedler, James

2013-01-01

107

Leveraging Python Interoperability Tools to Improve Sapphire's Usability  

SciTech Connect

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.

Gezahegne, A; Love, N S

2007-12-10

108

Ureka: A Distribution of Python and IRAF Software for Astronomy  

NASA Astrophysics Data System (ADS)

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.

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

2014-05-01

109

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

PubMed

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

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

2015-01-01

110

Subspectacular Nematodiasis Caused by a Novel Serpentirhabdias species in Ball Pythons (Python regius).  

PubMed

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

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

2015-01-01

111

The Virtual Observatory for the Python Programmer  

NASA Astrophysics Data System (ADS)

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 archives. It also features support for SAMP, a protocol that can be used to drive other desktop tools such Topcat and Aladin from a Python script. Finally, it provides a framework for making compiled legacy software callable from Python.

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

2014-01-01

112

Python programming --Interfacing with other Finn Arup Nielsen  

E-print Network

University of Denmark September 2, 2013 #12;Interfacing Overview Hello-world examples with different September 2, 2013 #12;Interfacing Calling C++ from Python via boost Hello World example from Boost.Python tutorial with hello.cpp: #include char const* greet() { return "hello, world

113

The Python Development Project 5.1 Introduction  

E-print Network

development projects and the Python development team. The following methods and sources were used to collect of development at ICI and how it compares with the development plan. These methods generated data at severalChapter 5 The Python Development Project 5.1 Introduction This chapter describes the calibration

Ford, David N.

114

Python bite: an unusual cause of hand injury.  

PubMed

We report a patient that sustained a severe hand injury following a python bite. Python bite injuries are rare and we were unable to find guidelines in literature regarding the management of this injury. This report details our experience in managing this case and summarizes the available literature. PMID:24876681

Yak, Ryan Siqi; Lundin, Anna Carin; Peng, Yeong Pin; Sebastin, Sandeep Jacob

2014-06-01

115

Inference---A Python Package for Astrostatistics  

NASA Astrophysics Data System (ADS)

Python is an object-oriented ``very high level language'' that is easy to learn, actively supported, and freely available for a large variety of computing platforms. It possesses sophisticated scientific computing capabilities thanks to ongoing work by a community of scientists and engineers who maintain a suite of open source scientific packages. Key contributions come from the STScI group maintaining PyRAF, a Python environment for running IRAF tasks. Python's main scientific computing packages are the Numeric and numarray packages implementing efficient array and image processing, and the SciPy package implementing a wide variety of general-use algorithms including optimization, root finding, special functions, numerical integration, and basic statistical tasks. We describe the Inference package, a collection of tools for carrying out advanced astrostatistical analyses that is about to be released as a supplement to SciPy. The Inference package has two main parts. First is a Parametric Inference Engine that offers a unified environment for analysis of parametric models with a variety of methods, including minimum ?2, maximum likelihood, and Bayesian methods. Several common analysis tasks are available with simple syntax (e.g., optimization, multidimensional exploration and integration, simulation); its parameter syntax is remensicent of that of SHERPA. Second, the package includes a growing library of diverse, specialized astrostatistical methods in a variety of domains including time series, spectrum and survey analysis, and basic image analysis. Where possible, a variety of methods are available for a given problem, enabling users to explore alternative methods in a unified environment, with the guidance of significant documentation. The Inference project is supported by NASA AISRP grant NAG5-12082.

Loredo, T. J.; Connors, A.; Oliphant, T. E.

2004-08-01

116

scikit-image: image processing in Python.  

PubMed

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

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

2014-01-01

117

scikit-image: image processing in Python  

PubMed Central

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

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

2014-01-01

118

SunPy: Solar Physics in Python  

NASA Astrophysics Data System (ADS)

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.

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

2015-04-01

119

PyMOOSE: Interoperable Scripting in Python for MOOSE  

PubMed Central

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

Ray, Subhasis; Bhalla, Upinder S.

2008-01-01

120

drive-casa: Python interface for CASA scripting  

NASA Astrophysics Data System (ADS)

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.

Staley, Tim D.

2015-04-01

121

Implementation of quantum game theory simulations using Python  

NASA Astrophysics Data System (ADS)

This paper provides some examples about quantum games simulated in Python's programming language. The quantum games have been developed with the Sympy Python library, which permits solving quantum problems in a symbolic form. The application of these methods of quantum mechanics to game theory gives us more possibility to achieve results not possible before. To illustrate the results of these methods, in particular, there have been simulated the quantum battle of the sexes, the prisoner's dilemma and card games. These solutions are able to exceed the classic bottle neck and obtain optimal quantum strategies. In this form, python demonstrated that is possible to do more advanced and complicated quantum games algorithms.

Madrid S., A.

2013-05-01

122

A multi-organ transcriptome resource for the Burmese Python (Python molurus bivittatus)  

PubMed Central

Background Snakes provide a unique vertebrate system for studying a diversity of extreme adaptations, including those related to development, metabolism, physiology, and venom. Despite their importance as research models, genomic resources for snakes are few. Among snakes, the Burmese python is the premier model for studying extremes of metabolic fluctuation and physiological remodelling. In this species, the consumption of large infrequent meals can induce a 40-fold increase in metabolic rate and more than a doubling in size of some organs. To provide a foundation for research utilizing the python, our aim was to assemble and annotate a transcriptome reference from the heart and liver. To accomplish this aim, we used the 454-FLX sequencing platform to collect sequence data from multiple cDNA libraries. Results We collected nearly 1 million 454 sequence reads, and assembled these into 37,245 contigs with a combined length of 13,409,006 bp. To identify known genes, these contigs were compared to chicken and lizard gene sets, and to all Genbank sequences. A total of 13,286 of these contigs were annotated based on similarity to known genes or Genbank sequences. We used gene ontology (GO) assignments to characterize the types of genes in this transcriptome resource. The raw data, transcript contig assembly, and transcript annotations are made available online for use by the broader research community. Conclusion These data should facilitate future studies using pythons and snakes in general, helping to further contribute to the utilization of snakes as a model evolutionary and physiological system. This sequence collection represents a major genomic resource for the Burmese python, and the large number of transcript sequences characterized should contribute to future research in this and other snake species. PMID:21867488

2011-01-01

123

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

PubMed

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±0.05?m 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

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

2012-08-01

124

Reduction of blood oxygen levels enhances postprandial cardiac hypertrophy in Burmese python (Python bivittatus).  

PubMed

Physiological cardiac hypertrophy is characterized by reversible enlargement of cardiomyocytes and changes in chamber architecture, which increase stroke volume and via augmented convective oxygen transport. Cardiac hypertrophy is known to occur in response to repeated elevations of O2 demand and/or reduced O2 supply in several species of vertebrate ectotherms, including postprandial Burmese pythons (Python bivittatus). Recent data suggest postprandial cardiac hypertrophy in P. bivittatus is a facultative rather than obligatory response to digestion, though the triggers of this response are unknown. Here, we hypothesized that an O2 supply-demand mismatch stimulates postprandial cardiac enlargement in Burmese pythons. To test this hypothesis, we rendered animals anemic prior to feeding, essentially halving blood oxygen content during the postprandial period. Fed anemic animals had heart rates 126% higher than those of fasted controls, which, coupled with a 71% increase in mean arterial pressure, suggests fed anemic animals were experiencing significantly elevated cardiac work. We found significant cardiac hypertrophy in fed anemic animals, which exhibited ventricles 39% larger than those of fasted controls and 28% larger than in fed controls. These findings support our hypothesis that those animals with a greater magnitude of O2 supply-demand mismatch exhibit the largest hearts. The 'low O2 signal' stimulating postprandial cardiac hypertrophy is likely mediated by elevated ventricular wall stress associated with postprandial hemodynamics. PMID:24311803

Slay, Christopher E; Enok, Sanne; Hicks, James W; Wang, Tobias

2014-05-15

125

Astropy: A community Python package for astronomy  

NASA Astrophysics Data System (ADS)

We present the first public version (v0.2) of the open-source and community-developed Python package, Astropy. This package provides core astronomy-related functionality to the community, including support for domain-specific file formats such as flexible image transport system (FITS) files, Virtual Observatory (VO) tables, and common ASCII table formats, unit and physical quantity conversions, physical constants specific to astronomy, celestial coordinate and time transformations, world coordinate system (WCS) support, generalized containers for representing gridded as well as tabular data, and a framework for cosmological transformations and conversions. Significant functionality is under activedevelopment, such as a model fitting framework, VO client and server tools, and aperture and point spread function (PSF) photometry tools. The core development team is actively making additions and enhancements to the current code base, and we encourage anyone interested to participate in the development of future Astropy versions.

Astropy Collaboration; Robitaille, Thomas P.; Tollerud, Erik J.; Greenfield, Perry; Droettboom, Michael; Bray, Erik; Aldcroft, Tom; Davis, Matt; Ginsburg, Adam; Price-Whelan, Adrian M.; Kerzendorf, Wolfgang E.; Conley, Alexander; Crighton, Neil; Barbary, Kyle; Muna, Demitri; Ferguson, Henry; Grollier, Frédéric; Parikh, Madhura M.; Nair, Prasanth H.; Unther, Hans M.; Deil, Christoph; Woillez, Julien; Conseil, Simon; Kramer, Roban; Turner, James E. H.; Singer, Leo; Fox, Ryan; Weaver, Benjamin A.; Zabalza, Victor; Edwards, Zachary I.; Azalee Bostroem, K.; Burke, D. J.; Casey, Andrew R.; Crawford, Steven M.; Dencheva, Nadia; Ely, Justin; Jenness, Tim; Labrie, Kathleen; Lim, Pey Lian; Pierfederici, Francesco; Pontzen, Andrew; Ptak, Andy; Refsdal, Brian; Servillat, Mathieu; Streicher, Ole

2013-10-01

126

COSMOS: Python library for massively parallel workflows  

PubMed Central

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

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

127

PyEphem: Astronomical Ephemeris for Python  

NASA Astrophysics Data System (ADS)

PyEphem provides scientific-grade astronomical computations for the Python programming language. Given a date and location on the Earth’s surface, it can compute the positions of the Sun and Moon, of the planets and their moons, and of any asteroids, comets, or earth satellites whose orbital elements the user can provide. Additional functions are provided to compute the angular separation between two objects in the sky, to determine the constellation in which an object lies, and to find the times at which an object rises, transits, and sets on a particular day. The numerical routines that lie behind PyEphem are those from the wonderful XEphem astronomy application, whose author, Elwood Downey, generously gave permission for us to use them as the basis for PyEphem.

Rhodes, Brandon Craig

2011-12-01

128

Scripting the Virtual Observatory in Python  

NASA Astrophysics Data System (ADS)

The VOClient package from the US Virtual Astronomical Observatory (VAO) provides a desktop or client-side interface to the Virtual Observatory (VO). The VO integrates data and services from many archives into a single unified system, allowing development of research tools which combine data from multiple sources. VOClient provides both ready to use tools for finding and retrieving data from remote archives, as well as support for user scripting to build custom applications. In this paper we focus on the capabilities provided by VOClient for developing user scripts or other applications which access remote data and services via the VO framework. A companion paper (Fitzpatrick et. al.) describes the user tools provided by VOClient. Initial support for application development using VOClient emphasizes Python scripting. Integration with high level environments such as IRAF and CASA is also provided.

Tody, Douglas; Fitzpatrick, M. J.; Graham, M.; Young, W.

2013-01-01

129

Neutron Scattering Experiment Automation with Python  

SciTech Connect

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.

Zolnierczuk, Piotr A [ORNL] [ORNL; Riedel, Richard A [ORNL] [ORNL

2010-01-01

130

PyMC: Bayesian Stochastic Modelling in Python  

PubMed Central

This user guide describes a Python package, PyMC, that allows users to efficiently code a probabilistic model and draw samples from its posterior distribution using Markov chain Monte Carlo techniques. PMID:21603108

Patil, Anand; Huard, David; Fonnesbeck, Christopher J.

2010-01-01

131

Rabacus: A Python Package for Analytic Cosmological Radiative Transfer Calculations  

E-print Network

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 a...

Altay, Gabriel

2015-01-01

132

Enrico: Python package to simplify Fermi-LAT analysis  

NASA Astrophysics Data System (ADS)

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.

Sanchez, David; Deil, Christoph

2015-01-01

133

PyBDSM: Python Blob Detection and Source Measurement  

NASA Astrophysics Data System (ADS)

PyBDSM (Python Blob Detection and Source Measurement) decomposes radio interferometry images into sources and makes their properties available for further use. PyBDSM can decompose an image into a set of Gaussians, shapelets, or wavelets as well as calculate spectral indices and polarization properties of sources and measure the psf variation across an image. PyBDSM uses an interactive environment based on CASA (ascl:1107.013); PyBDSM may also be used in Python scripts.

Mohan, Niruj; Rafferty, David

2015-02-01

134

Rapid Development of Interferometric Software Using MIRIAD and Python  

NASA Astrophysics Data System (ADS)

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.

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

2012-06-01

135

MEG and EEG data analysis with MNE-Python  

PubMed Central

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

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

136

MEG and EEG data analysis with MNE-Python.  

PubMed

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

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

137

Ecological correlates of invasion impact for Burmese pythons in Florida  

USGS Publications Warehouse

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.

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

2012-01-01

138

Pythons metabolize prey to fuel the response to feeding.  

PubMed Central

We investigated the energy source fuelling the post-feeding metabolic upregulation (specific dynamic action, SDA) in pythons (Python regius). Our goal was to distinguish between two alternatives: (i) snakes fuel SDA by metabolizing energy depots from their tissues; or (ii) snakes fuel SDA by metabolizing their prey. To characterize the postprandial response of pythons we used transcutaneous ultrasonography to measure organ-size changes and respirometry to record oxygen consumption. To discriminate unequivocally between the two hypotheses, we enriched mice (= prey) with the stable isotope of carbon (13C). For two weeks after feeding we quantified the CO2 exhaled by pythons and determined its isotopic 13C/12C signature. Ultrasonography and respirometry showed typical postprandial responses in pythons. After feeding, the isotope ratio of the exhaled breath changed rapidly to values that characterized enriched mouse tissue, followed by a very slow change towards less enriched values over a period of two weeks after feeding. We conclude that pythons metabolize their prey to fuel SDA. The slowly declining delta13C values indicate that less enriched tissues (bone, cartilage and collagen) from the mouse become available after several days of digestion. PMID:15255044

Starck, J. Matthias; Moser, Patrick; Werner, Roland A.; Linke, Petra

2004-01-01

139

Discrimination of integumentary prey chemicals and strike-induced chemosensory searching in the ball python, Python regius  

Microsoft Academic Search

Experimental tests show that the ball python (Python regius) has the ability to discriminate prey chemicals from control substances by tongue-flicking and exhibits a poststrike elevation\\u000a in tongue-flicking rate (PETF). Prey chemical discrimination was revealed by significantly higher number of tongue-flicks\\u000a and tongue-flick attack score in response to integumental chemicals from mice than to cologne or distilled water and by

1991-01-01

140

The effects of UV light on calcium metabolism in ball pythons (Python regius).  

PubMed

Despite the popularity of keeping snakes in captivity, there has been limited investigation into the effects of UV radiation on vitamin D levels in snakes. The aim of this study was to investigate the effects of UV-b radiation on plasma 25-hydroxyvitamin D3 levels and ionised calcium concentrations in ball pythons (Python regius). Blood samples were taken from 14 ball pythons, which had never been exposed to UV-b light, to obtain baseline 25-hydroxyvitamin D3 levels and ionised calcium concentrations. Blood samples were then taken again from the same snakes 70 days later after one group (Group 1, n=6 females) were exposed to UV-b radiation daily, and the other group (Group 2, n=5 males and 3 females) were exposed to no UV-b radiation. Mean±sd 25-hydroxyvitamin D3 levels on day 0 in Group 1 were 197±35 nmol/l, and on day 70 were 203.5±13.8 nmol/l. Mean±sd 25-hydroxyvitamin D3 levels in Group 2 on day 0 were 77.7±41.5 nmol/l, and on day 70 were 83.0±41.9 nmol/l. Mean±sd ionised calcium levels at day 0 were 1.84±0.05 mmol/l for Group 1, and on day 70 were 1.78±0.07 mmol/l. Mean±sd ionised calcium levels at day 0 were 1.79±0.07 mmol/l for Group 2, and on day 70 were 1.81±0.05 mmol/l. No association was demonstrated between exposure to UV-b radiation and plasma 25-hydroxyvitamin D3 and ionised calcium concentrations. These results may provide baseline parameters for future studies in this and other snake species to determine ability to utilise UV-b light for vitamin D production. PMID:24068697

Hedley, J; Eatwell, K

2013-10-12

141

Introducing Python tools for magnetotellurics: MTpy  

NASA Astrophysics Data System (ADS)

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 Australia. Workflow of MT data processing. Within the structural diagram, the MTpy sub-packages are shown in red (time series data processing), green (handling of EDI files and impedance tensor data), yellow (connection to modelling/inversion algorithms), black (impedance tensor interpretation, e.g. by Phase Tensor calculations), and blue (generation of visual representations, e.g pseudo sections or resistivity models).

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

2013-12-01

142

Python-Assisted MODFLOW Application and Code Development  

NASA Astrophysics Data System (ADS)

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.

Langevin, C.

2013-12-01

143

Bioinformatic pipelines in Python with Leaf  

PubMed Central

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

2013-01-01

144

AJAC: Atomic data calculation tool in Python  

NASA Astrophysics Data System (ADS)

In this work, new features and extensions of a currently used online atomic database management system are reported. A multiplatform flexible computation package is added to the present system, to allow the calculation of various atomic radiative and collisional processes, based on simplifying the use of some existing atomic codes adopted from the literature. The interaction between users and data is facilitated by a rather extensive Python graphical user interface working online and could be installed in personal computers of different classes. In particular, this study gives an overview of the use of one model of the package models (i.e., electron impact collisional excitation model). The accuracy of computing capability of the electron impact collisional excitation in the adopted model, which follows the distorted wave approximation approach, is enhanced by implementing the Dirac R-matrix approximation approach. The validity and utility of this approach are presented through a comparison of the current computed results with earlier available theoretical and experimental results. Finally, the source code is made available under the general public license and being distributed freely in the hope that it will be useful to a wide community of laboratory and astrophysical plasma diagnostics.

Amani, Tahat; Jordi, Marti; Kaher, Tahat; Ali, Khwaldeh

2013-04-01

145

PsychoPy—Psychophysics software in Python  

PubMed Central

The vast majority of studies into visual processing are conducted using computer display technology. The current paper describes a new free suite of software tools designed to make this task easier, using the latest advances in hardware and software. PsychoPy is a platform-independent experimental control system written in the Python interpreted language using entirely free libraries. PsychoPy scripts are designed to be extremely easy to read and write, while retaining complete power for the user to customize the stimuli and environment. Tools are provided within the package to allow everything from stimulus presentation and response collection (from a wide range of devices) to simple data analysis such as psychometric function fitting. Most importantly, PsychoPy is highly extensible and the whole system can evolve via user contributions. If a user wants to add support for a particular stimulus, analysis or hardware device they can look at the code for existing examples, modify them and submit the modifications back into the package so that the whole community benefits. PMID:17254636

Peirce, Jonathan W.

2007-01-01

146

PsychoPy--Psychophysics software in Python.  

PubMed

The vast majority of studies into visual processing are conducted using computer display technology. The current paper describes a new free suite of software tools designed to make this task easier, using the latest advances in hardware and software. PsychoPy is a platform-independent experimental control system written in the Python interpreted language using entirely free libraries. PsychoPy scripts are designed to be extremely easy to read and write, while retaining complete power for the user to customize the stimuli and environment. Tools are provided within the package to allow everything from stimulus presentation and response collection (from a wide range of devices) to simple data analysis such as psychometric function fitting. Most importantly, PsychoPy is highly extensible and the whole system can evolve via user contributions. If a user wants to add support for a particular stimulus, analysis or hardware device they can look at the code for existing examples, modify them and submit the modifications back into the package so that the whole community benefits. PMID:17254636

Peirce, Jonathan W

2007-05-15

147

MTpy: A Python toolbox for magnetotellurics  

NASA Astrophysics Data System (ADS)

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.

Krieger, Lars; Peacock, Jared R.

2014-11-01

148

Python algorithms in particle tracking microrheology  

PubMed Central

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

2012-01-01

149

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

PubMed

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

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

2014-05-01

150

Facile Synthesis of a Tungsten Alkylidyne Catalyst for Alkyne Zachary J. Tonzetich, Yan Choi Lam, Peter Muller, and Richard R. Schrock*  

E-print Network

Facile Synthesis of a Tungsten Alkylidyne Catalyst for Alkyne Metathesis Zachary J. Tonzetich, Yan cis double bonds. Tungsten alkylidyne trialkoxide alkyne metathesis catalysts were discovered in 1981 of cleavage of a tungsten-tungsten triple bond upon reaction with an alkyne or nitrile.9 Recent advances

Müller, Peter

151

A Tungsten(VI) Nitride Having a W2(-N)2 Core Zachary J. Tonzetich, Richard R. Schrock,* Keith M. Wampler, Brad C. Bailey,  

E-print Network

A Tungsten(VI) Nitride Having a W2(µ-N)2 Core Zachary J. Tonzetich, Richard R. Schrock,* Keith M-331, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139 Received September 27, 2007 The tungsten that the tungsten alkylidyne species W(C-t-Bu)(CH2-t-Bu)(OAr)2 (Ar ) 2,6-diisopropylphenyl) can be prepared readily

Müller, Peter

152

Stochastic spatio-temporal modelling with PCRaster Python  

NASA Astrophysics Data System (ADS)

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).

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

2012-04-01

153

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

SciTech Connect

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.

Kostka, Timothy D.

2013-01-01

154

ETE: a python Environment for Tree Exploration  

PubMed Central

Background Many bioinformatics analyses, ranging from gene clustering to phylogenetics, produce hierarchical trees as their main result. These are used to represent the relationships among different biological entities, thus facilitating their analysis and interpretation. A number of standalone programs are available that focus on tree visualization or that perform specific analyses on them. However, such applications are rarely suitable for large-scale surveys, in which a higher level of automation is required. Currently, many genome-wide analyses rely on tree-like data representation and hence there is a growing need for scalable tools to handle tree structures at large scale. Results Here we present the Environment for Tree Exploration (ETE), a python programming toolkit that assists in the automated manipulation, analysis and visualization of hierarchical trees. ETE libraries provide a broad set of tree handling options as well as specific methods to analyze phylogenetic and clustering trees. Among other features, ETE allows for the independent analysis of tree partitions, has support for the extended newick format, provides an integrated node annotation system and permits to link trees to external data such as multiple sequence alignments or numerical arrays. In addition, ETE implements a number of built-in analytical tools, including phylogeny-based orthology prediction and cluster validation techniques. Finally, ETE's programmable tree drawing engine can be used to automate the graphical rendering of trees with customized node-specific visualizations. Conclusions ETE provides a complete set of methods to manipulate tree data structures that extends current functionality in other bioinformatic toolkits of a more general purpose. ETE is free software and can be downloaded from http://ete.cgenomics.org. PMID:20070885

2010-01-01

155

Synthetic seismogram web service and Python tools  

NASA Astrophysics Data System (ADS)

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

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

2014-05-01

156

pyam: Python Implementation of YaM  

NASA Technical Reports Server (NTRS)

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.

Myint, Steven; Jain, Abhinandan

2012-01-01

157

A Python interface with Narcisse graphics  

SciTech Connect

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.

Motteler, Z.C.

1996-04-15

158

Cold-induced mortality of invasive Burmese pythons in south Florida  

USGS Publications Warehouse

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.

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

2011-01-01

159

Python robotics: an environment for exploring robotics beyond LEGOs  

Microsoft Academic Search

This paper describes Pyro, a robotics programming environment designed to allow inexperienced undergraduates to explore topics in advanced robotics. Pyro, which stands for Python Robotics, runs on a number of advanced robotics platforms. In addition, programs in Pyro can abstract away low-level details such that individual programs can work unchanged across very different robotics hardware. Results of using Pyro in

Douglas S. Blank; Lisa Meeden; Deepak Kumar

2003-01-01

160

SunPy: Python for Solar Physics Data Analysis  

NASA Astrophysics Data System (ADS)

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.

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

2012-05-01

161

ChiantiPy: Python package for the CHIANTI atomic database  

NASA Astrophysics Data System (ADS)

ChiantiPy is an object-orient Python package for calculating astrophysical spectra using the CHIANTI atomic database for astrophysical spectroscopy. It provides access to the database and the ability to calculate various physical quantities for the interpretation of astrophysical spectra.

Dere, Ken

2013-08-01

162

pynbody: N-Body/SPH analysis for python  

NASA Astrophysics Data System (ADS)

Pynbody is a lightweight, portable, format-transparent analysis package for astrophysical N-body and smooth particle hydrodynamic simulations supporting PKDGRAV/Gasoline, Gadget, N-Chilada, and RAMSES AMR outputs. Written in python, the core tools are accompanied by a library of publication-level analysis routines.

Pontzen, Andrew; Roškar, Rok; Stinson, Greg; Woods, Rory

2013-05-01

163

p3d – Python module for structural bioinformatics  

PubMed Central

Background High-throughput bioinformatic analysis tools are needed to mine the large amount of structural data via knowledge based approaches. The development of such tools requires a robust interface to access the structural data in an easy way. For this the Python scripting language is the optimal choice since its philosophy is to write an understandable source code. Results p3d is an object oriented Python module that adds a simple yet powerful interface to the Python interpreter to process and analyse three dimensional protein structure files (PDB files). p3d's strength arises from the combination of a) very fast spatial access to the structural data due to the implementation of a binary space partitioning (BSP) tree, b) set theory and c) functions that allow to combine a and b and that use human readable language in the search queries rather than complex computer language. All these factors combined facilitate the rapid development of bioinformatic tools that can perform quick and complex analyses of protein structures. Conclusion p3d is the perfect tool to quickly develop tools for structural bioinformatics using the Python scripting language. PMID:19698094

Fufezan, Christian; Specht, Michael

2009-01-01

164

Rabacus: A Python package for analytic cosmological radiative transfer calculations  

NASA Astrophysics Data System (ADS)

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.

Altay, G.; Wise, J. H.

2015-04-01

165

pyGadgetReader: GADGET snapshot reader for python  

NASA Astrophysics Data System (ADS)

pyGadgetReader is a universal GADGET snapshot reader for python that supports type-1, type-2, HDF5, and TIPSY (ascl:1111.015) binary formats. It additionally supports reading binary outputs from FoF_Special, P-StarGroupFinder, Rockstar (ascl:1210.008), and Rockstar-Galaxies.

Thompson, Robert

2014-11-01

166

Python for Unified Research in Econometrics and Statistics Roseline Bilina  

E-print Network

Python for Unified Research in Econometrics and Statistics Roseline Bilina Steve Lawford Cornell-alone applications in econometrics and statistics, and as a tool for gluing different applications together. (It methods and programming), C87 (Econometric software), C88 (Other computer software). Keywords: Object

Boyer, Edmond

167

Python Robotics: An Environment for Exploring Robotics Beyond LEGOs  

E-print Network

Python Robotics: An Environment for Exploring Robotics Beyond LEGOs Douglas Blank Computer Science Mawr, PA 19010 dkumar@cs.brynmawr.edu Abstract This paper describes Pyro, a robotics programming en­ vironment designed to allow inexperienced undergradu­ ates to explore topics in advanced robotics. Pyro

Blank, Douglas

168

Python Robotics: An Environment for Exploring Robotics Beyond LEGOs  

E-print Network

Python Robotics: An Environment for Exploring Robotics Beyond LEGOs Douglas Blank Computer Science Mawr, PA 19010 dkumar@cs.brynmawr.edu Abstract This paper describes Pyro, a robotics programming en- vironment designed to allow inexperienced undergradu- ates to explore topics in advanced robotics. Pyro

Blank, Douglas

169

A Python interface for SPICE-based simulations  

Microsoft Academic Search

This paper introduces a programming interface for integrated CMOS circuit design. It connects the SPICE netlist level to an easy to use programming language: Python [1]. After classifying the context of this tool the fundamental application for parametric circuit simulation and signal processing is shown. This is done at the example of a parameterizable netlist for an inverter. Further on,

Daniel Batas; Horst Fiedler

2010-01-01

170

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

PubMed Central

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

Jurica, Peter; van Leeuwen, Cees

2008-01-01

171

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

PubMed Central

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

Hunter, Margaret E.; Hart, Kristen M.

2013-01-01

172

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

PubMed

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

Hunter, Margaret E; Hart, Kristen M

2013-01-01

173

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

USGS Publications Warehouse

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.

Hunter, Margaret E.; Hart, Kristen M.

2013-01-01

174

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

PubMed Central

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

2011-01-01

175

Expyriment: a Python library for cognitive and neuroscientific experiments.  

PubMed

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

Krause, Florian; Lindemann, Oliver

2014-06-01

176

PyCraters: A Python framework for crater function analysis  

E-print Network

We introduce a Python framework designed to automate the most common tasks associated with the extraction and upscaling of the statistics of single-impact crater functions to inform coefficients of continuum equations describing surface morphology evolution. Designed with ease-of-use in mind, the framework allows users to extract meaningful statistical estimates with very short Python programs. Wrappers to interface with specific simulation packages, routines for statistical extraction of output, and fitting and differentiation libraries are all hidden behind simple, high-level user-facing functions. In addition, the framework is extensible, allowing advanced users to specify the collection of specialized statistics or the creation of customized plots. The framework is hosted on the BitBucket service under an open-source license, with the aim of helping non-specialists easily extract preliminary estimates of relevant crater function results associated with a particular experimental system.

Norris, Scott A

2014-01-01

177

Numbers and Formulas 1 Python as a Calculator  

E-print Network

Mathematical, Statistical and Scientific Software Jan Verschelde, 28 August 2013 Scientific Software (MCS 507 L, simplification, and evaluation Scientific Software (MCS 507 L-2) Numbers and Formulas 28 August 2013 2 / 27 #12 Python 2.7.3 (v2.7.3:70274d53c1dd, Apr 9 2012, 20:52: [GCC 4.2.1 (Apple Inc. build 5666) (dot 3

Verschelde, Jan

178

PyRAT - python radiography analysis tool (u)  

SciTech Connect

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.

Temple, Brian A [Los Alamos National Laboratory; Buescher, Kevin L [Los Alamos National Laboratory; Armstrong, Jerawan C [Los Alamos National Laboratory

2011-01-14

179

Hands-On Python A Tutorial Introduction for Beginners  

E-print Network

-Noncommercial-Share Alike 3.0 United States License http://creativecommons.org/licenses/by-nc-sa/3.0/us/ #12;#12;Contents 3.5. Further Topics to Consider 120 3.6. Summary 121 Chapter 4. Dynamic Web Pages 125 4.1. Web page Basics 125 4.2. Composing Web Pages in Python 127 4.3. CGI - Dynamic Web Pages 131 4.4. Summary 137 3 #12

Reluga, Tim

180

ObsPy: A Python Toolbox for Seismology  

NASA Astrophysics Data System (ADS)

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.

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

2013-12-01

181

Python as a Federation Tool for GENESIS 3.0  

PubMed Central

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. Employed in this way, the stand-alone software components of the GENESIS 3.0 simulator provide a framework for progressive federated software development in computational neuroscience. PMID:22276101

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

2012-01-01

182

A Community Python Library for Solar Physics (SunPy)  

NASA Astrophysics Data System (ADS)

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.

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

183

BioC implementations in Go, Perl, Python and Ruby.  

PubMed

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

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

2014-01-01

184

GAiN: Distributed Array Computation with Python  

SciTech Connect

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.

Daily, Jeffrey A.

2009-04-24

185

ACPYPE - AnteChamber PYthon Parser interfacE  

PubMed Central

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

2012-01-01

186

Parallel astronomical data processing with Python: Recipes for multicore machines  

NASA Astrophysics Data System (ADS)

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.

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

2013-08-01

187

BioC implementations in Go, Perl, Python and Ruby  

PubMed Central

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

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

2014-01-01

188

ELLIPT2D: A Flexible Finite Element Code Written Python  

SciTech Connect

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.

Pletzer, A.; Mollis, J.C.

2001-03-22

189

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

PubMed

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

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

2015-04-22

190

ObsPy - A Bridge to Utilize Python's Big Data Capabilities for Seismology  

NASA Astrophysics Data System (ADS)

Numerous packages for handling and analysing big data sets with Python surfaced in the last couple of years. Seismology has similarly seen a rapid increase in the amount of available data to the point where the tools commonly employed can no longer deal with them in a reasonable fashion. ObsPy is a community-driven, open-source project dedicated to provide a Python framework for seismological data enabling users to easily utilize Python 's aforementioned big data modules for their research. This contribution presents a short overview of the current landscape of Python's big data ecosystem along with selected applications in seismology.

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

2013-12-01

191

PyEEG: An Open Source Python Module for EEG/MEG Feature Extraction  

PubMed Central

Computer-aided diagnosis of neural diseases from EEG signals (or other physiological signals that can be treated as time series, e.g., MEG) is an emerging field that has gained much attention in past years. Extracting features is a key component in the analysis of EEG signals. In our previous works, we have implemented many EEG feature extraction functions in the Python programming language. As Python is gaining more ground in scientific computing, an open source Python module for extracting EEG features has the potential to save much time for computational neuroscientists. In this paper, we introduce PyEEG, an open source Python module for EEG feature extraction. PMID:21512582

Bao, Forrest Sheng; Liu, Xin; Zhang, Christina

2011-01-01

192

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

USGS Publications Warehouse

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.

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

2015-01-01

193

Pybedtools: a flexible Python library for manipulating genomic datasets and annotations  

PubMed Central

Summary: pybedtools is a flexible Python software library for manipulating and exploring genomic datasets in many common formats. It provides an intuitive Python interface that extends upon the popular BEDTools genome arithmetic tools. The library is well documented and efficient, and allows researchers to quickly develop simple, yet powerful scripts that enable complex genomic analyses. Availability: pybedtools is maintained under the GPL license. Stable versions of pybedtools as well as documentation are available on the Python Package Index at http://pypi.python.org/pypi/pybedtools. Contact: dalerr@niddk.nih.gov; arq5x@virginia.edu Supplementary Information: Supplementary data are available at Bioinformatics online. PMID:21949271

Dale, Ryan K.; Pedersen, Brent S.; Quinlan, Aaron R.

2011-01-01

194

PYTHON PROGRAMMING FOR PHYSICISTS OUR FIRST item of business is to learn how to write computer programs in  

E-print Network

installed as well. (If not, it is available as a free download from the web.3 ) How you start IDLE depends available on-line, so if there's something you want to do and it's not explained in this book, I encourage is the official Python website at www.python.org. 2.1 GETTING STARTED A Python program consists of a list

Newman, Mark

195

Climate Map for Northern African Python: Areas Potentially Suitable for Invasion  

USGS Multimedia Gallery

The Northern African Python ( Python sebae ) occurs naturally in a diverse collection of localities in central Africa. The climate-matched portions of the U.S. include peninsular Florida, extreme south Texas, Puerto Rico (right inset), Hawaii (left inset), and the other island territories (not shown...

196

IE 172 Laboratory 0: Setting Up Python and Eclipse Dr. T.K. Ralphs  

E-print Network

is an application for developing and debugging software applications. You have probably used the Eclipse IDE code for the class in one place. 5. Install the PyDev plug-in by choosing "Install New Software" from interpreter Python27\\App\\python.exe. 3. Type Lab0 as the project name and click on the Finish button to create

Ralphs, Ted

197

STATE OF THE ART OF SOAP LIBRARIES IN PYTHON AND RUBY  

E-print Network

STATE OF THE ART OF SOAP LIBRARIES IN PYTHON AND RUBY Pekka Kanerva Helsinki Institute for Information Technology August 6, 2007 HIIT TECHNICAL REPORT 2007-02 #12;State of the Art of SOAP Libraries also elsewhere. ii #12;State of the Art of SOAP Libraries in Python and Ruby Pekka Kanerva

Myllymäki, Petri

198

Fatty Acids Identified in the Burmese Python Promote Beneficial Cardiac Growth  

PubMed Central

Burmese pythons display a dramatic increase in heart mass after a large meal. We investigated the molecular mechanisms of this physiological heart growth, with the goal of applying this knowledge to the mammalian heart. We found that heart growth in pythons is characterized by myocyte hypertrophy in the absence of cell proliferation and by activation of PI3K/Akt/mTor signaling pathways. Despite high levels of circulating lipids, the postprandial python heart does not accumulate triglycerides or fatty acids. Instead, there is robust activation of pathways of fatty acid transport and oxidation combined with increased expression and activity of the cardioprotective enzyme, superoxide dismutase. Finally, we identified a combination of fatty acids in python plasma that promotes physiological heart growth when injected into either pythons or mice. PMID:22034436

Riquelme, Cecilia A.; Magida, Jason A.; Harrison, Brooke C.; Wall, Christopher E.; Marr, Thomas G.; Secor, Stephen M.; Leinwand, Leslie A.

2012-01-01

199

Pyteomics--a Python framework for exploratory data analysis and rapid software prototyping in proteomics.  

PubMed

Pyteomics is a cross-platform, open-source Python library providing a rich set of tools for MS-based proteomics. It provides modules for reading LC-MS/MS data, search engine output, protein sequence databases, theoretical prediction of retention times, electrochemical properties of polypeptides, mass and m/z calculations, and sequence parsing. Pyteomics is available under Apache license; release versions are available at the Python Package Index http://pypi.python.org/pyteomics, the source code repository at http://hg.theorchromo.ru/pyteomics, documentation at http://packages.python.org/pyteomics. Pyteomics.biolccc documentation is available at http://packages.python.org/pyteomics.biolccc/. Questions on installation and usage can be addressed to pyteomics mailing list: pyteomics@googlegroups.com. PMID:23292976

Goloborodko, Anton A; Levitsky, Lev I; Ivanov, Mark V; Gorshkov, Mikhail V

2013-02-01

200

Pyteomics—a Python Framework for Exploratory Data Analysis and Rapid Software Prototyping in Proteomics  

NASA Astrophysics Data System (ADS)

Pyteomics is a cross-platform, open-source Python library providing a rich set of tools for MS-based proteomics. It provides modules for reading LC-MS/MS data, search engine output, protein sequence databases, theoretical prediction of retention times, electrochemical properties of polypeptides, mass and m/z calculations, and sequence parsing. Pyteomics is available under Apache license; release versions are available at the Python Package Index http://pypi.python.org/pyteomics, the source code repository at http://hg.theorchromo.ru/pyteomics, documentation at http://packages.python.org/pyteomics. Pyteomics.biolccc documentation is available at http://packages.python.org/pyteomics.biolccc/. Questions on installation and usage can be addressed to pyteomics mailing list: pyteomics@googlegroups.com

Goloborodko, Anton A.; Levitsky, Lev I.; Ivanov, Mark V.; Gorshkov, Mikhail V.

2013-02-01

201

Pybel: a Python wrapper for the OpenBabel cheminformatics toolkit  

PubMed Central

Background Scripting languages such as Python are ideally suited to common programming tasks in cheminformatics such as data analysis and parsing information from files. However, for reasons of efficiency, cheminformatics toolkits such as the OpenBabel toolkit are often implemented in compiled languages such as C++. We describe Pybel, a Python module that provides access to the OpenBabel toolkit. Results Pybel wraps the direct toolkit bindings to simplify common tasks such as reading and writing molecular files and calculating fingerprints. Extensive use is made of Python iterators to simplify loops such as that over all the molecules in a file. A Pybel Molecule can be easily interconverted to an OpenBabel OBMol to access those methods or attributes not wrapped by Pybel. Conclusion Pybel allows cheminformaticians to rapidly develop Python scripts that manipulate chemical information. It is open source, available cross-platform, and offers the power of the OpenBabel toolkit to Python programmers. PMID:18328109

O'Boyle, Noel M; Morley, Chris; Hutchison, Geoffrey R

2008-01-01

202

MTpy - Python Tools for Magnetotelluric Data Processing and Analysis  

NASA Astrophysics Data System (ADS)

We present the Python package MTpy, which provides functions for the processing, analysis, and handling of magnetotelluric (MT) data sets. MT is a relatively immature and not widely applied geophysical method in comparison to other geophysical techniques such as seismology. As a result, the data processing within the academic MT community is not thoroughly standardised and is often based on a loose collection of software, adapted to the respective local specifications. We have developed MTpy to overcome problems that arise from missing standards, and to provide a simplification of the general handling of MT data. MTpy is written in Python, and the open-source code is freely available from a GitHub repository. The setup follows the modular approach of successful geoscience software packages such as GMT or Obspy. It contains sub-packages and modules for the various tasks within the standard work-flow of MT data processing and interpretation. In order to allow the inclusion of already existing and well established software, MTpy does not only provide pure Python classes and functions, but also wrapping command-line scripts to run standalone tools, e.g. modelling and inversion codes. Our aim is to provide a flexible framework, which is open for future dynamic extensions. MTpy has the potential to promote the standardisation of processing procedures and at same time be a versatile supplement for existing algorithms. Here, we introduce the concept and structure of MTpy, and we illustrate the workflow of MT data processing, interpretation, and visualisation utilising MTpy on example data sets collected over different regions of Australia and the USA.

Krieger, Lars; Peacock, Jared; Thiel, Stephan; Inverarity, Kent; Kirkby, Alison; Robertson, Kate; Soeffky, Paul; Didana, Yohannes

2014-05-01

203

Isolation and characterisation of crocodile and python ovotransferrins.  

PubMed

Transferrins play a major role in iron homeostasis and metabolism. In vertebrates, these proteins are synthesised in the liver and dispersed within the organism by the bloodstream. In oviparous vertebrates additional expression is observed in the oviduct and the synthesised protein is deposited in egg white as ovotransferrin. Most research on ovotransferrin has been performed on the chicken protein. There is a limited amount of information on other bird transferrins, and until our previous paper on red-eared turtle protein there was no data on the isolation, sequencing and biochemical properties of reptilian ovotransferrins. Recently our laboratory deposited ten new sequences of reptilian transferrins in the EMBL database. A comparative analysis of these sequences indicates a possibility of different mechanisms of iron release among crocodile and snake transferrin. In the present paper we follow with the purification and analysis of the basic biochemical properties of two crocodile (Crocodilus niloticus, C. rhombifer) and one snake (Python molurus bivittatus) ovotransferrins. The proteins were purified by anion exchange and hydrophobic chromatography, and their N-terminal amino-acid sequences, molecular mass and isoelectric points were determined. All three proteins are glycosylated and their N-glycan chromatographic profiles show the largest contribution of neutral oligosaccharides in crocodile and disialylated glycans in python ovotransferrin. The absorption spectra of iron-saturated transferrins were analysed. Iron release from these proteins is pH-dependent, showing a biphasic character in crocodile ovotransferrins and a monophasic type in the python protein. The reason for the different types of iron release is discussed. PMID:17351671

Ciuraszkiewicz, Justyna; Olczak, Mariusz; Watorek, Wies?aw

2007-01-01

204

PyVO: Python access to the Virtual Observatory  

NASA Astrophysics Data System (ADS)

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.

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

2014-02-01

205

The fast azimuthal integration Python library: pyFAI  

PubMed Central

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

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

2015-01-01

206

PIAO: Python spherIcAl Overdensity code  

NASA Astrophysics Data System (ADS)

PIAO is an efficient memory-controlled Python code that uses the standard spherical overdensity (SO) algorithm to identify halos. PIAO employs two additional parameters besides the overdensity ?c. The first is the mesh-box size, which splits the whole simulation box into smaller ones then analyzes them one-by-one, thereby overcoming a possible memory limitation problem that can occur when dealing with high-resolution, large-volume simulations. The second is the smoothed particle hydrodynamics (SPH) neighbors number, which is used for the SPH density calculation.

Cui, Weiguang

2014-12-01

207

Pythran: enabling static optimization of scientific Python programs  

NASA Astrophysics Data System (ADS)

Pythran is an open source static compiler that turns modules written in a subset of Python language into native ones. Assuming that scientific modules do not rely much on the dynamic features of the language, it trades them for powerful, possibly inter-procedural, optimizations. These optimizations include detection of pure functions, temporary allocation removal, constant folding, Numpy ufunc fusion and parallelization, explicit thread-level parallelism through OpenMP annotations, false variable polymorphism pruning, and automatic vector instruction generation such as AVX or SSE. In addition to these compilation steps, Pythran provides a C++ runtime library that leverages the C++ STL to provide generic containers, and the Numeric Template Toolbox for Numpy support. It takes advantage of modern C++11 features such as variadic templates, type inference, move semantics and perfect forwarding, as well as classical idioms such as expression templates. Unlike the Cython approach, Pythran input code remains compatible with the Python interpreter. Output code is generally as efficient as the annotated Cython equivalent, if not more, but without the backward compatibility loss.

Guelton, Serge; Brunet, Pierrick; Amini, Mehdi; Merlini, Adrien; Corbillon, Xavier; Raynaud, Alan

2015-01-01

208

Conservative constraints on early cosmology with MONTE PYTHON  

SciTech Connect

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 ?CDM 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.

Audren, Benjamin; Lesgourgues, Julien [Institut de Théorie des Phénomènes Physiques, École Polytechnique Fédérale de Lausanne, CH-1015, Lausanne (Switzerland); Benabed, Karim; Prunet, Simon, E-mail: benjamin.audren@epfl.ch, E-mail: Julien.Lesgourgues@cern.ch, E-mail: benabed@iap.fr, E-mail: prunet@iap.fr [Institute d'Astrophysique de Paris, 98 Bd Arago, F-75014 Paris (France)

2013-02-01

209

Hardware-accelerated interactive data visualization for neuroscience in Python.  

PubMed

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

Rossant, Cyrille; Harris, Kenneth D

2013-01-01

210

SCoT: a Python toolbox for EEG source connectivity  

PubMed Central

Analysis of brain connectivity has become an important research tool in neuroscience. Connectivity can be estimated between cortical sources reconstructed from the electroencephalogram (EEG). Such analysis often relies on trial averaging to obtain reliable results. However, some applications such as brain-computer interfaces (BCIs) require single-trial estimation methods. In this paper, we present SCoT—a source connectivity toolbox for Python. This toolbox implements routines for blind source decomposition and connectivity estimation with the MVARICA approach. Additionally, a novel extension called CSPVARICA is available for labeled data. SCoT estimates connectivity from various spectral measures relying on vector autoregressive (VAR) models. Optionally, these VAR models can be regularized to facilitate ill posed applications such as single-trial fitting. We demonstrate basic usage of SCoT on motor imagery (MI) data. Furthermore, we show simulation results of utilizing SCoT for feature extraction in a BCI application. These results indicate that CSPVARICA and correct regularization can significantly improve MI classification. While SCoT was mainly designed for application in BCIs, it contains useful tools for other areas of neuroscience. SCoT is a software package that (1) brings combined source decomposition and connectivtiy estimation to the open Python platform, and (2) offers tools for single-trial connectivity estimation. The source code is released under the MIT license and is available online at github.com/SCoT-dev/SCoT. PMID:24653694

Billinger, Martin; Brunner, Clemens; Müller-Putz, Gernot R.

2014-01-01

211

Programming biological models in Python using PySB.  

PubMed

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

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

2013-01-01

212

SCoT: a Python toolbox for EEG source connectivity.  

PubMed

Analysis of brain connectivity has become an important research tool in neuroscience. Connectivity can be estimated between cortical sources reconstructed from the electroencephalogram (EEG). Such analysis often relies on trial averaging to obtain reliable results. However, some applications such as brain-computer interfaces (BCIs) require single-trial estimation methods. In this paper, we present SCoT-a source connectivity toolbox for Python. This toolbox implements routines for blind source decomposition and connectivity estimation with the MVARICA approach. Additionally, a novel extension called CSPVARICA is available for labeled data. SCoT estimates connectivity from various spectral measures relying on vector autoregressive (VAR) models. Optionally, these VAR models can be regularized to facilitate ill posed applications such as single-trial fitting. We demonstrate basic usage of SCoT on motor imagery (MI) data. Furthermore, we show simulation results of utilizing SCoT for feature extraction in a BCI application. These results indicate that CSPVARICA and correct regularization can significantly improve MI classification. While SCoT was mainly designed for application in BCIs, it contains useful tools for other areas of neuroscience. SCoT is a software package that (1) brings combined source decomposition and connectivtiy estimation to the open Python platform, and (2) offers tools for single-trial connectivity estimation. The source code is released under the MIT license and is available online at github.com/SCoT-dev/SCoT. PMID:24653694

Billinger, Martin; Brunner, Clemens; Müller-Putz, Gernot R

2014-01-01

213

CMCpy: Genetic Code-Message Coevolution Models in Python.  

PubMed

Code-message coevolution (CMC) models represent coevolution of a genetic code and a population of protein-coding genes ("messages"). Formally, CMC models are sets of quasispecies coupled together for fitness through a shared genetic code. Although CMC models display plausible explanations for the origin of multiple genetic code traits by natural selection, useful modern implementations of CMC models are not currently available. To meet this need we present CMCpy, an object-oriented Python API and command-line executable front-end that can reproduce all published results of CMC models. CMCpy implements multiple solvers for leading eigenpairs of quasispecies models. We also present novel analytical results that extend and generalize applications of perturbation theory to quasispecies models and pioneer the application of a homotopy method for quasispecies with non-unique maximally fit genotypes. Our results therefore facilitate the computational and analytical study of a variety of evolutionary systems. CMCpy is free open-source software available from http://pypi.python.org/pypi/CMCpy/. PMID:23532367

Becich, Peter J; Stark, Brian P; Bhat, Harish S; Ardell, David H

2013-01-01

214

Novel divergent nidovirus in a python with pneumonia.  

PubMed

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

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

215

SClib, a hack for straightforward embedded C functions in Python  

E-print Network

We present SClib, a simple hack that allows easy and straightforward evaluation of C functions within Python code, boosting flexibility for better trade-off between computation power and feature availability, such as visualization and existing computation routines in SciPy. We also present two cases were SClib has been used. In the first set of applications we use SClib to write a port to Python of a Schr\\"odinger equation solver that has been extensively used the literature, the resulting script presents a speed-up of about 150x with respect to the original one. A review of the situations where the speeded-up script has been used is presented. We also describe the solution to the related problem of solving a set of coupled Schr\\"odinger-like equations where SClib is used to implement the speed-critical parts of the code. We argue that when using SClib within IPython we can use NumPy and Matplotlib for the manipulation and visualization of the solutions in an interactive environment with no performance compro...

Fuentes, Esteban

2014-01-01

216

Hardware-accelerated interactive data visualization for neuroscience in Python  

PubMed Central

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

Rossant, Cyrille; Harris, Kenneth D.

2013-01-01

217

CMCpy: Genetic Code-Message Coevolution Models in Python  

PubMed Central

Code-message coevolution (CMC) models represent coevolution of a genetic code and a population of protein-coding genes (“messages”). Formally, CMC models are sets of quasispecies coupled together for fitness through a shared genetic code. Although CMC models display plausible explanations for the origin of multiple genetic code traits by natural selection, useful modern implementations of CMC models are not currently available. To meet this need we present CMCpy, an object-oriented Python API and command-line executable front-end that can reproduce all published results of CMC models. CMCpy implements multiple solvers for leading eigenpairs of quasispecies models. We also present novel analytical results that extend and generalize applications of perturbation theory to quasispecies models and pioneer the application of a homotopy method for quasispecies with non-unique maximally fit genotypes. Our results therefore facilitate the computational and analytical study of a variety of evolutionary systems. CMCpy is free open-source software available from http://pypi.python.org/pypi/CMCpy/. PMID:23532367

Becich, Peter J.; Stark, Brian P.; Bhat, Harish S.; Ardell, David H.

2013-01-01

218

Programming biological models in Python using PySB  

PubMed Central

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

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

2013-01-01

219

COBRApy: COnstraints-Based Reconstruction and Analysis for Python  

PubMed Central

Background COnstraint-Based Reconstruction and Analysis (COBRA) methods are widely used for genome-scale modeling of metabolic networks in both prokaryotes and eukaryotes. Due to the successes with metabolism, there is an increasing effort to apply COBRA methods to reconstruct and analyze integrated models of cellular processes. The COBRA Toolbox for MATLAB is a leading software package for genome-scale analysis of metabolism; however, it was not designed to elegantly capture the complexity inherent in integrated biological networks and lacks an integration framework for the multiomics data used in systems biology. The openCOBRA Project is a community effort to promote constraints-based research through the distribution of freely available software. Results Here, we describe COBRA for Python (COBRApy), a Python package that provides support for basic COBRA methods. COBRApy is designed in an object-oriented fashion that facilitates the representation of the complex biological processes of metabolism and gene expression. COBRApy does not require MATLAB to function; however, it includes an interface to the COBRA Toolbox for MATLAB to facilitate use of legacy codes. For improved performance, COBRApy includes parallel processing support for computationally intensive processes. Conclusion COBRApy is an object-oriented framework designed to meet the computational challenges associated with the next generation of stoichiometric constraint-based models and high-density omics data sets. Availability http://opencobra.sourceforge.net/ PMID:23927696

2013-01-01

220

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

PubMed Central

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

2010-01-01

221

Myiasis by Megaselia scalaris (Diptera: Phoridae) in a python affected by pulmonitis.  

PubMed

Myiases are caused by the presence of maggots in vertebrate tissues and organs. Myiases have been studied widely in humans, farm animals, and pets, whereas reports of myiasis in reptiles are scarce. We describe a case of myiasis caused by the Megaselia scalaris (Loew) in an Indian python (Python molurus bivittatus, Kuhl) (Ophida: Boidae). The python, 15 yr old, born and reared in a terrarium in the mainland of Venice (Italy), was affected by diffuse, purulent pneumonia caused by Burkholderia cepacia. The severe infestation of maggots found in the lungs during an autopsy indicated at a myiasis. PMID:23427672

Vanin, S; Mazzariol, S; Menandro, M L; Lafisca, A; Turchetto, M

2013-01-01

222

PyORBIT: A Python Shell For ORBIT  

SciTech Connect

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.

Jean-Francois Ostiguy; Jeffrey Holmes

2003-07-01

223

Python package for model STructure ANalysis (pySTAN)  

NASA Astrophysics Data System (ADS)

The selection and identification of a suitable hydrological model structure is more than fitting parameters of a model structure to reproduce a measured hydrograph. The procedure is highly dependent on various criteria, i.e. the modelling objective, the characteristics and the scale of the system under investigation as well as the available data. Rigorous analysis of the candidate model structures is needed to support and objectify the selection of the most appropriate structure for a specific case (or eventually justify the use of a proposed ensemble of structures). This holds both in the situation of choosing between a limited set of different structures as well as in the framework of flexible model structures with interchangeable components. Many different methods to evaluate and analyse model structures exist. This leads to a sprawl of available methods, all characterized by different assumptions, changing conditions of application and various code implementations. Methods typically focus on optimization, sensitivity analysis or uncertainty analysis, with backgrounds from optimization, machine-learning or statistics amongst others. These methods also need an evaluation metric (objective function) to compare the model outcome with some observed data. However, for current methods described in literature, implementations are not always transparent and reproducible (if available at all). No standard procedures exist to share code and the popularity (and amount of applications) of the methods is sometimes more dependent on the availability than the merits of the method. Moreover, new implementations of existing methods are difficult to verify and the different theoretical backgrounds make it difficult for environmental scientists to decide about the usefulness of a specific method. A common and open framework with a large set of methods can support users in deciding about the most appropriate method. Hence, it enables to simultaneously apply and compare different 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.

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

2013-04-01

224

Intraspecific scaling of arterial blood pressure in the Burmese python.  

PubMed

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 ?gh (where ? 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

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

2014-07-01

225

A cross-validation package driving Netica with python  

USGS Publications Warehouse

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).

Fienen, Michael N.; Plant, Nathaniel G.

2014-01-01

226

A pseudo-parallel Python environment for database curation  

E-print Network

One of the major challenges providing large databases like the WFCAM Science Archive (WSA) is to minimize ingest times for pixel/image metadata and catalogue data. In this article we describe how the pipeline processed data are ingested into the database as the first stage in building a release database which will be succeeded by advanced processing (source merging, seaming, detection quality flagging etc.). To accomplish the ingestion procedure as fast as possible we use a mixed Python/C++ environment and run the required tasks in a simple parallel modus operandi where the data are split into daily chunks and then processed on different computers. The created data files can be ingested into the database immediately as they are available. This flexible way of handling the data allows the most usage of the available CPUs as the comparison with sequential processing shows.

Eckhard Sutorius; Johann Bryant; Ross Collins; Nicholas Cross; Nigel Hambly; Mike Read

2007-11-13

227

PyRAT (python radiography analysis tool): overview  

SciTech Connect

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.

Armstrong, Jerawan C [Los Alamos National Laboratory; Temple, Brian A [Los Alamos National Laboratory; Buescher, Kevin L [Los Alamos National Laboratory

2011-01-14

228

SunPy: Python for Solar Physics. An implementation for local correlation tracking  

NASA Astrophysics Data System (ADS)

Python programming language has experienced a great progress and growing use in the scientific community in the last years as well as a direct impact on solar physics. Python is a very mature language and almost any fundamental feature you might want to do is already implemented in a library or module. SunPy is a common effort of, using the advantages of Python, developing tools to be applied for processing and analysis of solar data. In this work we present a particular development, based on Python, for the analysis of proper motions in time series of images through the local correlation tracking algorithm. A graphic user interface allows to select different parameters for the computations, visualization and analysis of flow fields.

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

229

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

USGS Publications Warehouse

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.

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

2014-01-01

230

6.189 A Gentle Introduction to Programming Using Python, January IAP 2010  

E-print Network

This 6-unit P/D/F course will provide a gentle introduction to programming using Python for highly motivated students with little or no prior experience in programming computers over the first two weeks of IAP. The course ...

Canelake, Sarina

2010-01-01

231

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

Federal Register 2010, 2011, 2012, 2013, 2014

...Enforcement Management Information System (LEMIS) data, we estimate that...have been found in Burmese python digestive systems in Florida. However, of greater...Detection and Distribution Mapping System, University of Georgia, in...

2012-01-23

232

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

PubMed Central

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

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

233

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

USGS Publications Warehouse

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. Generic sampling design and terminology are proposed to standardize and clarify interpretations of eDNA-based occupancy models.

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

234

Obtaining and processing Daymet data using Python and ArcGIS  

USGS Publications Warehouse

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.

Bohms, Stefanie

2013-01-01

235

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

PubMed Central

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. Generic sampling design and terminology are proposed to standardize and clarify interpretations of eDNA-based occupancy models. PMID:25874630

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

236

Parallel Simulations of Manufacturing Processing using Simpy, a Python-Based Discrete Event Simulation Tool  

Microsoft Academic Search

SimPy is a Python-based, interpreted simulation tool that offers the power and convenience of Python. It is able to launch processes and sub-processes using generators, which act autonomously and may interact using interrupts. SimPy offers other advantages over competing commercial codes in that it allows for modular development, use of a version control system such as CVS, can be made

V. Castillo

2006-01-01

237

Parallel simulations of manufacturing processing using simpy, a python-based discrete event simulation tool  

Microsoft Academic Search

SimPy is a Python-based, interpreted simulation tool that offers the power and convenience of Python. It is able to launch processes and sub-processes using generators, which act autonomously and may interact using interrupts. SimPy offers other advantages over competing commercial codes in that it allows for modular development, use of a version control system such as CVS, can be made

Victor Castillo

2006-01-01

238

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

PubMed

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

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

239

Molecular identification of python species: Development and validation of a novel assay for forensic investigations.  

PubMed

Python snake species are often encountered in illegal activities and the question of species identity can be pertinent to such criminal investigations. Morphological identification of species of pythons can be confounded by many issues and molecular examination by DNA analysis can provide an alternative and objective means of identification. Our paper reports on the development and validation of a PCR primer pair that amplifies a segment of the mitochondrial cytochrome b gene that has been suggested previously as a good candidate locus for differentiating python species. We used this DNA region to perform species identification of pythons, even when the template DNA was of poor quality, as might be the case with forensic evidentiary items. Validation tests are presented to demonstrate the characteristics of the assay. Tests involved the cross-species amplification of this marker in non-target species, minimum amount of DNA template required, effects of degradation on product amplification and a blind trial to simulate a casework scenario that provided 100% correct identity. Our results demonstrate that this assay performs reliably and robustly on pythons and can be applied directly to forensic investigations where the presence of a species of python is in question. PMID:25541013

Ciavaglia, Sherryn A; Tobe, Shanan S; Donnellan, Stephen C; Henry, Julianne M; Linacre, Adrian M T

2015-05-01

240

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

USGS Publications Warehouse

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.

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

241

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

PubMed Central

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

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

242

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

PubMed

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

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

2014-01-01

243

GOGrapher: A Python library for GO graph representation and analysis  

PubMed Central

Background The Gene Ontology is the most commonly used controlled vocabulary for annotating proteins. The concepts in the ontology are organized as a directed acyclic graph, in which a node corresponds to a biological concept and a directed edge denotes the parent-child semantic relationship between a pair of terms. A large number of protein annotations further create links between proteins and their functional annotations, reflecting the contemporary knowledge about proteins and their functional relationships. This leads to a complex graph consisting of interleaved biological concepts and their associated proteins. What is needed is a simple, open source library that provides tools to not only create and view the Gene Ontology graph, but to analyze and manipulate it as well. Here we describe the development and use of GOGrapher, a Python library that can be used for the creation, analysis, manipulation, and visualization of Gene Ontology related graphs. Findings An object-oriented approach was adopted to organize the hierarchy of the graphs types and associated classes. An Application Programming Interface is provided through which different types of graphs can be pragmatically created, manipulated, and visualized. GOGrapher has been successfully utilized in multiple research projects, e.g., a graph-based multi-label text classifier for protein annotation. Conclusion The GOGrapher project provides a reusable programming library designed for the manipulation and analysis of Gene Ontology graphs. The library is freely available for the scientific community to use and improve. PMID:19583843

Muller, Brian; Richards, Adam J; Jin, Bo; Lu, Xinghua

2009-01-01

244

Screening_mgmt: a Python module for managing screening data.  

PubMed

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

Helfenstein, Andreas; Tammela, Päivi

2015-02-01

245

AstroML: Python-powered Machine Learning for Astronomy  

NASA Astrophysics Data System (ADS)

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.

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

2014-01-01

246

SIMA: Python software for analysis of dynamic fluorescence imaging data  

PubMed Central

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

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

2014-01-01

247

SunPy - Python for Solar Physics, Version 0.4  

NASA Astrophysics Data System (ADS)

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.

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

2014-06-01

248

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

PubMed

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. PMID:12135085

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

2002-05-01

249

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

NASA Astrophysics Data System (ADS)

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.

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

2002-04-01

250

The role of temperature and humidity in python nest site selection Z. R. Stahlschmidt*, J. Brashears, D. F. DeNardo  

E-print Network

childreni Children's python oviposition site selection parental care snake thermoregulation water balance multiple developmental variables (e.g. embryonic water balance and thermoregulation). Pythons have recently to their cool-nesting counterparts, female water pythons, Liasis fuscus, that choose to oviposit in warm nest

Denardo, Dale

251

Installation Instructions The first thing to ensure is you have python, most linux distributions will have it. Galaxy requires  

E-print Network

--------------------------------------------------------------------------------------------------------------------------- The first thing to ensure is you have python, most linux distributions will have it. Galaxy requires Python --------------------------------------------------------------------------------------------------------------------------- Once you have all this, you are ready to go ahead and setup Galaxy. (If you have a galaxy instance running you can skip this part) #12;Get the latest copy of the Galaxy server from https://bitbucket.org/galaxy/galaxy

Kissinger, Jessica

252

PeptideBuilder: A simple Python library to generate model peptides  

PubMed Central

We present a simple Python library to construct models of polypeptides from scratch. The intended use case is the generation of peptide models with pre-specified backbone angles. For example, using our library, one can generate a model of a set of amino acids in a specific conformation using just a few lines of python code. We do not provide any tools for energy minimization or rotamer packing, since powerful tools are available for these purposes. Instead, we provide a simple Python interface that enables one to add residues to a peptide chain in any desired conformation. Bond angles and bond lengths can be manipulated if so desired, and reasonable values are used by default. PMID:23717802

Tien, Matthew Z.; Sydykova, Dariya K.; Meyer, Austin G.

2013-01-01

253

PeptideBuilder: A simple Python library to generate model peptides.  

PubMed

We present a simple Python library to construct models of polypeptides from scratch. The intended use case is the generation of peptide models with pre-specified backbone angles. For example, using our library, one can generate a model of a set of amino acids in a specific conformation using just a few lines of python code. We do not provide any tools for energy minimization or rotamer packing, since powerful tools are available for these purposes. Instead, we provide a simple Python interface that enables one to add residues to a peptide chain in any desired conformation. Bond angles and bond lengths can be manipulated if so desired, and reasonable values are used by default. PMID:23717802

Tien, Matthew Z; Sydykova, Dariya K; Meyer, Austin G; Wilke, Claus O

2013-01-01

254

interPopula: a Python API to access the HapMap Project dataset  

PubMed Central

Background The HapMap project is a publicly available catalogue of common genetic variants that occur in humans, currently including several million SNPs across 1115 individuals spanning 11 different populations. This important database does not provide any programmatic access to the dataset, furthermore no standard relational database interface is provided. Results interPopula is a Python API to access the HapMap dataset. interPopula provides integration facilities with both the Python ecology of software (e.g. Biopython and matplotlib) and other relevant human population datasets (e.g. Ensembl gene annotation and UCSC Known Genes). A set of guidelines and code examples to address possible inconsistencies across heterogeneous data sources is also provided. Conclusions interPopula is a straightforward and flexible Python API that facilitates the construction of scripts and applications that require access to the HapMap dataset. PMID:21210977

2010-01-01

255

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

NASA Astrophysics Data System (ADS)

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.

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

2014-01-01

256

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

NASA Astrophysics Data System (ADS)

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.

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

2015-04-01

257

Zachary Gagnon Jason Gordon  

E-print Network

Article Bovine red blood cell starvation age discrimination through a glutaraldehyde- amplified reaction that allows for sensitive di- electrophoretic analysis and discrimination of bovine red blood is because younger (reduced star- vation time) cells possess more amino groups that the reaction can release

Chang, Hsueh-Chia

258

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

PubMed

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

Chen, Weiliang; De Schutter, Erik

2014-01-01

259

Python-based geometry preparation and simulation visualization toolkits for STEPS  

PubMed Central

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

Chen, Weiliang; De Schutter, Erik

2014-01-01

260

STEPS: Modeling and Simulating Complex Reaction-Diffusion Systems with Python  

PubMed Central

We describe how the use of the Python language improved the user interface of the program STEPS. STEPS is a simulation platform for modeling and stochastic simulation of coupled reaction-diffusion systems with complex 3-dimensional boundary conditions. Setting up such models is a complicated process that consists of many phases. Initial versions of STEPS relied on a static input format that did not cleanly separate these phases, limiting modelers in how they could control the simulation and becoming increasingly complex as new features and new simulation algorithms were added. We solved all of these problems by tightly integrating STEPS with Python, using SWIG to expose our existing simulation code. PMID:19623245

Wils, Stefan; Schutter, Erik De

2008-01-01

261

STEPS: Modeling and Simulating Complex Reaction-Diffusion Systems with Python.  

PubMed

We describe how the use of the Python language improved the user interface of the program STEPS. STEPS is a simulation platform for modeling and stochastic simulation of coupled reaction-diffusion systems with complex 3-dimensional boundary conditions. Setting up such models is a complicated process that consists of many phases. Initial versions of STEPS relied on a static input format that did not cleanly separate these phases, limiting modelers in how they could control the simulation and becoming increasingly complex as new features and new simulation algorithms were added. We solved all of these problems by tightly integrating STEPS with Python, using SWIG to expose our existing simulation code. PMID:19623245

Wils, Stefan; De Schutter, Erik

2009-01-01

262

Light and electron microscopic observations of the life cycle of Sarcocystis orientalis sp. n. in the rat ( Rattus norvegicus ) and the Malaysian reticulated python ( Python reticulatus )  

Microsoft Academic Search

A light and electron microscopic study of Sarcocystis orientalis sp. n. was made. The life cycle of this parasite is in two hosts. Gametogony is in the intestinal epithelial cells of a predator, Python reticulatus. Isospora-like oocysts developed. Sporocysts average 9.1 by 7.7 µm. Rats (Rattus norvegicus) were infected with sporocysts and asexual stages developed. Ten days after infection large

V. Zaman; Frederick C. Colley

1975-01-01

263

Expression of venom gene homologs in diverse python tissues suggests a new model for the evolution of snake venom.  

PubMed

Snake venom gene evolution has been studied intensively over the past several decades, yet most previous studies have lacked the context of complete snake genomes and the full context of gene expression across diverse snake tissues. We took a novel approach to studying snake venom evolution by leveraging the complete genome of the Burmese python, including information from tissue-specific patterns of gene expression. We identified the orthologs of snake venom genes in the python genome, and conducted detailed analysis of gene expression of these venom homologs to identify patterns that differ between snake venom gene families and all other genes. We found that venom gene homologs in the python are expressed in many different tissues outside of oral glands, which illustrates the pitfalls of using transcriptomic data alone to define "venom toxins." We hypothesize that the python may represent an ancestral state prior to major venom development, which is supported by our finding that the expansion of venom gene families is largely restricted to highly venomous caenophidian snakes. Therefore, the python provides insight into biases in which genes were recruited for snake venom systems. Python venom homologs are generally expressed at lower levels, have higher variance among tissues, and are expressed in fewer organs compared with all other python genes. We propose a model for the evolution of snake venoms in which venom genes are recruited preferentially from genes with particular expression profile characteristics, which facilitate a nearly neutral transition toward specialized venom system expression. PMID:25338510

Reyes-Velasco, Jacobo; Card, Daren C; Andrew, Audra L; Shaney, Kyle J; Adams, Richard H; Schield, Drew R; Casewell, Nicholas R; Mackessy, Stephen P; Castoe, Todd A

2015-01-01

264

Close-Up of a Radio Transmitter on an Invasive Burmese Python  

USGS Multimedia Gallery

This close-up is of the radio-transmitter on a 16 1/2-foot python. The snake, being removed from the wild by USGS and NPS personnel, was re-captured in a thicket in Everglades National Park in April 2012. After its first capture, the snake was equipped with a radio-transmitter and an accelerometer a...

265

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

SciTech Connect

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.

Enkovaara, J.; Romero, N.; Shende, S.; Mortensen, J. (LCF)

2011-01-01

266

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

SciTech Connect

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.

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

2010-11-29

267

PIANO Reference Manual Python Interface for Assimilation in NemO  

E-print Network

PIANO Reference Manual Python Interface for Assimilation in NemO Claire CHAUVIN 3 décembre 2010 Table des matières 1 First steps with PIANO 2 1.1 Description of the directory piano . . . . . . . . . . . . . . . . . 2 1.2 Running piano . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.2.1 Running piano

Boyer, Edmond

268

Python Environment for Bayesian Learning: Inferring the Structure of Bayesian Networks from Knowledge and Data  

PubMed Central

Summary In this paper, we introduce pebl, a Python library and application for learning Bayesian network structure from data and prior knowledge that provides features unmatched by alternative software packages: the ability to use interventional data, flexible specification of structural priors, modeling with hidden variables and exploitation of parallel processing. PMID:20161541

Shah, Abhik; Woolf, Peter

2009-01-01

269

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

USGS Publications Warehouse

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.

Reed, Robert N.; Snow, Ray W.

2014-01-01

270

MissionEngine: Multi-system integration using Python in the Tactical Language Project  

E-print Network

pronunciation (assessed using a speech recognition interface) and history. Learners must then practiceMissionEngine: Multi-system integration using Python in the Tactical Language Project Hannes California ­ Information Sciences Institute {hannes, samtani}@isi.edu ABSTRACT The Tactical Language Training

Vilhjálmsson, Hannes Högni

271

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

ERIC Educational Resources Information Center

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)

Murrell, Elizabeth

1998-01-01

272

Using Python Packages in 6D (Py)Ferret: EOF Analysis, OPeNDAP Sequence Data  

NASA Astrophysics Data System (ADS)

PyFerret was designed to provide the easy methods of access, analysis, and display of data found in the Ferret under the simple yet powerful Python scripting/programming language. This has enabled PyFerret to take advantage of a large and expanding collection of third-party scientific Python modules. Furthermore, ensemble and forecast axes have been added to Ferret and PyFerret for creating and working with collections of related data in Ferret's delayed-evaluation and minimal-data-access mode of operation. These axes simplify processing and visualization of these collections of related data. As one example, an empirical orthogonal function (EOF) analysis Python module was developed, taking advantage of the linear algebra module and other standard functionality in NumPy for efficient numerical array processing. This EOF analysis module is used in a Ferret function to provide an ensemble of levels of data explained by each EOF and Time Amplitude Function (TAF) product. Another example makes use of the PyDAP Python module to provide OPeNDAP sequence data for use in Ferret with minimal data access characteristic of Ferret.

Smith, K. M.; Manke, A.; Hankin, S. C.

2012-12-01

273

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

ERIC Educational Resources Information Center

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…

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

2007-01-01

274

A MythTV Python API to complement the JustPlay Network  

E-print Network

In this thesis, I developed an API to control a MythTV backend. This API is called PyMythTV. It allows one to develop software that can take advantage of a PVR device, MythTV. The API was written in Python, which allows ...

Deonier, Christian

2008-01-01

275

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

PubMed

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

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

2014-01-01

276

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

PubMed Central

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

Pecevski, Dejan; Natschläger, Thomas; Schuch, Klaus

2008-01-01

277

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

PubMed Central

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

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

2014-01-01

278

"Python First": A Lab-Based Digital Introduction to Computer Science  

E-print Network

"Python First": A Lab-Based Digital Introduction to Computer Science Atanas Radenski Chapman, OOP, CS2, Java, online study pack, self-guided lab 1. THE NEED Today, the majority of introductory commercial languages in CS1 has established the perception of computer science as a dry and technically

Radenski, Atanas

279

The Python Papers Monograph, Vol. 1 (2009) Available online at http://ojs.pythonpapers.org/index.php/tppm  

E-print Network

is hard, but that's just peanuts to programming. Here's what Donald Knuth says [6]: In fact, my main;Introductory Programming with Python 3 9. Files and modules: files, file processing, directories, creating

McCane, Brendan

280

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

NASA Astrophysics Data System (ADS)

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 project homepage [7]. Running time: Problem dependent (a simulation with real valued electromagnetic field takes typically about 0.16 ?s per Yee cell per time-step.) SWIG, http://www.swig.org. Cython, http://www.cython.org. NumPy, http://numpy.scipy.org. SciPy, http://www.scipy.org. matplotlib, http://matplotlib.sourceforge.net. MPI for Python, http://mpi4py.scipy.org. GMES, http://sourceforge.net/projects/gmes.

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

2013-04-01

281

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

PubMed Central

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

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

2013-01-01

282

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

NASA Technical Reports Server (NTRS)

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 larger regional or national picture of precipitating weather systems. Composites, horizontal and vertical crosssections, and combinations thereof are easily displayed using as little as one line of code. MMM-Py can also write to the native MRMS binary format, and sub-sectioning of tiles (or multiple stitched tiles) is anticipated to be in place by the time of this meeting. Thus, MMM-Py also can be used to power the creation of custom mosaics for targeted regional studies. Overlays of other data (e.g., lightning observations) are easily accomplished. Demonstrations of MMM-Py, including the creation of animations, will be shown. Finally, Marshall has done significant work to interface Python-based analysis routines with the U.S. Department of Energy's Py-ART software package for radar data ingest, processing, and analysis. One example of this is the Python Turbulence Detection Algorithm (PyTDA), an MSFC-based implementation of the National Center for Atmospheric Research (NCAR) Turbulence Detection Algorithm (NTDA) for the purposes of convective-scale analysis, situational awareness, and forensic meteorology. PyTDA exploits Py-ART's radar data ingest routines and data model to rapidly produce aviation-relevant turbulence estimates from Doppler radar data. Work toward processing speed optimization and better integration within the Py-ART framework will be highlighted. Python-based analysis within the Py-ART framework is also being done for new research related to intercomparison of ground-based radar data with satellite estimates of ocean winds, as well as research on the electrification of pyrocumulus clouds.

Lang, Timothy J.

2015-01-01

283

PLACE: an open-source python package for laboratory automation, control, and experimentation.  

PubMed

In modern laboratories, software can drive the full experimental process from data acquisition to storage, processing, and analysis. The automation of laboratory data acquisition is an important consideration for every laboratory. When implementing a laboratory automation scheme, important parameters include its reliability, time to implement, adaptability, and compatibility with software used at other stages of experimentation. In this article, we present an open-source, flexible, and extensible Python package for Laboratory Automation, Control, and Experimentation (PLACE). The package uses modular organization and clear design principles; therefore, it can be easily customized or expanded to meet the needs of diverse laboratories. We discuss the organization of PLACE, data-handling considerations, and then present an example using PLACE for laser-ultrasound experiments. Finally, we demonstrate the seamless transition to post-processing and analysis with Python through the development of an analysis module for data produced by PLACE automation. PMID:25304874

Johnson, Jami L; Tom Wörden, Henrik; van Wijk, Kasper

2015-02-01

284

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

PubMed

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

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

285

ObsPy: A Python Toolbox for Seismology and Seismological Observatories  

NASA Astrophysics Data System (ADS)

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.

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

2013-04-01

286

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

NASA Astrophysics Data System (ADS)

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).

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

2012-08-01

287

A tool stack for implementing Behaviour-Driven Development in Python Language  

E-print Network

This paper presents a tool stack for the implementation, specification and test of software following the practices of Behavior Driven Development (BDD) in Python language. The usage of this stack highlights the specification and validation of the software's expected behavior, reducing the error rate and improving documentation. Therefore, it is possible to produce code with much less defects at both functional and unit levels, in addition to better serving to stakeholders' expectations.

Tavares, Hugo Lopes; Santos, Vanderson Mota dos; Manhaes, Rodrigo Soares; de Carvalho, Rogerio Atem

2010-01-01

288

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

PubMed Central

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

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

289

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

PubMed Central

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

Pecevski, Dejan; Kappel, David; Jonke, Zeno

2014-01-01

290

A Python package for parsing, validating, mapping and formatting sequence variants using HGVS nomenclature  

PubMed Central

Summary: Biological sequence variants are commonly represented in scientific literature, clinical reports and databases of variation using the mutation nomenclature guidelines endorsed by the Human Genome Variation Society (HGVS). Despite the widespread use of the standard, no freely available and comprehensive programming libraries are available. Here we report an open-source and easy-to-use Python library that facilitates the parsing, manipulation, formatting and validation of variants according to the HGVS specification. The current implementation focuses on the subset of the HGVS recommendations that precisely describe sequence-level variation relevant to the application of high-throughput sequencing to clinical diagnostics. Availability and implementation: The package is released under the Apache 2.0 open-source license. Source code, documentation and issue tracking are available at http://bitbucket.org/hgvs/hgvs/. Python packages are available at PyPI (https://pypi.python.org/pypi/hgvs). Contact: reecehart@gmail.com Supplementary information: Supplementary data are available at Bioinformatics online. PMID:25273102

Hart, Reece K.; Rico, Rudolph; Hare, Emily; Garcia, John; Westbrook, Jody; Fusaro, Vincent A.

2015-01-01

291

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

PubMed

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

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

2013-11-25

292

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

NASA Astrophysics Data System (ADS)

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.

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

2013-12-01

293

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

PubMed

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

Pecevski, Dejan; Kappel, David; Jonke, Zeno

2014-01-01

294

A Python Analytical Pipeline to Identify Prohormone Precursors and Predict Prohormone Cleavage Sites  

PubMed Central

Neuropeptides and hormones are signaling molecules that support cell–cell communication in the central nervous system. Experimentally characterizing neuropeptides requires significant efforts because of the complex and variable processing of prohormone precursor proteins into neuropeptides and hormones. We demonstrate the power and flexibility of the Python language to develop components of an bioinformatic analytical pipeline to identify precursors from genomic data and to predict cleavage as these precursors are en route to the final bioactive peptides. We identified 75 precursors in the rhesus genome, predicted cleavage sites using support vector machines and compared the rhesus predictions to putative assignments based on homology to human sequences. The correct classification rate of cleavage using the support vector machines was over 97% for both human and rhesus data sets. The functionality of Python has been important to develop and maintain NeuroPred (http://neuroproteomics.scs.uiuc.edu/neuropred.html), a user-centered web application for the neuroscience community that provides cleavage site prediction from a wide range of models, precision and accuracy statistics, post-translational modifications, and the molecular mass of potential peptides. The combined results illustrate the suitability of the Python language to implement an all-inclusive bioinformatics approach to predict neuropeptides that encompasses a large number of interdependent steps, from scanning genomes for precursor genes to identification of potential bioactive neuropeptides. PMID:19169350

Southey, Bruce R.; Sweedler, Jonathan V.; Rodriguez-Zas, Sandra L.

2008-01-01

295

Food composition influences metabolism, heart rate and organ growth during digestion in Python regius.  

PubMed

Digestion in pythons is associated with a large increase in oxygen consumption (SDA), increased cardiac output and growth in visceral organs assisting in digestion. The processes leading to the large postprandial rise in metabolism in snakes is subject to opposing views. Gastric work, protein synthesis and organ growth have each been speculated to be major contributors to the SDA. To investigate the role of food composition on SDA, heart rate (HR) and organ growth, 48 ball pythons (Python regius) were fed meals of either fat, glucose, protein or protein combined with carbonate. Our study shows that protein, in the absence or presence of carbonate causes a large SDA response, while glucose caused a significantly smaller SDA response and digestion of fat failed to affect metabolism. Addition of carbonate to the diet to stimulate gastric acid secretion did not increase the SDA response. These results support protein synthesis as a major contributor to the SDA response and show that increased gastric acid secretion occurs at a low metabolic cost. The increase in metabolism was supported by tachycardia caused by altered autonomic regulation as well as an increased non-adrenergic, non-cholinergic (NANC) tone in response to all diets, except for the lipid meal. Organ growth only occurred in the small intestine and liver in snakes fed on a high protein diet. PMID:25553896

Henriksen, Poul Secher; Enok, Sanne; Overgaard, Johannes; Wang, Tobias

2015-05-01

296

HTSeq—a Python framework to work with high-throughput sequencing data  

PubMed Central

Motivation: A large choice of tools exists for many standard tasks in the analysis of high-throughput sequencing (HTS) data. However, once a project deviates from standard workflows, custom scripts are needed. Results: We present HTSeq, a Python library to facilitate the rapid development of such scripts. HTSeq offers parsers for many common data formats in HTS projects, as well as classes to represent data, such as genomic coordinates, sequences, sequencing reads, alignments, gene model information and variant calls, and provides data structures that allow for querying via genomic coordinates. We also present htseq-count, a tool developed with HTSeq that preprocesses RNA-Seq data for differential expression analysis by counting the overlap of reads with genes. Availability and implementation: HTSeq is released as an open-source software under the GNU General Public Licence and available from http://www-huber.embl.de/HTSeq or from the Python Package Index at https://pypi.python.org/pypi/HTSeq. Contact: sanders@fs.tum.de PMID:25260700

Anders, Simon; Pyl, Paul Theodor; Huber, Wolfgang

2015-01-01

297

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

PubMed

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

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

2014-01-01

298

R.N. Reed and G.H. Rodda (eds): Giant constrictors: biological and management profiles and an establishment risk assessment for nine large species of pythons, anacondas, and the boa constrictor  

Microsoft Academic Search

Giant Constrictors estimates ecological risks associated with colonization of the United States by the world’s four largest snakes (green anaconda, Indian or Burmese python, northern African python, and reticulated python) plus four very similar species (the southern African python, yellow anaconda, DeSchaunsee’s anaconda, and Beni anaconda) and the boa constrictor. Possible economic costs are also detailed but not quantified. This

Daniel Simberloff

2010-01-01

299

Tools for managing invasions: acceptance of non-toxic baits by juvenile Nile monitor lizards and Burmese pythons under laboratory conditions  

Microsoft Academic Search

Nile monitor lizards (Varanus niloticus) and Burmese pythons (Python molurus bivittatus) are large, invasive, predatory reptiles, which are now well established in south Florida. Acetaminophen was recently shown to be lethal to both animals and therefore has potential for inclusion in an integrated pest management effort to control these species. However, acceptable bait matrices for both species are still needed

Peter J. Savarie; Richard M. Engeman; Richard E. Mauldin; Tom Mathies; Kenneth L. Tope

2011-01-01

300

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

NASA Astrophysics Data System (ADS)

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-flow finite-difference transport simulations (MT3DMS). Results show more accurate simulation of pumping-well BTCs for a given grid cell size when using analytical solutions. The code base is extended to transient flow and BTCs are compared to results from MT3DMS simulations. Results show the particle-based solutions can resolve transient behavior using coarser model grids with far less computational effort than MT3DMS. The effect of simulation accuracy on parameter estimates (porosity) also is investigated. Porosity estimated using more accurate analytical solutions are less biased than in synthetic finite-difference transport simulations, which tend to be biased by coarseness of the grid. Eliminating the bias by using a finer grid comes at the expense of much larger computational effort. Finally, the code base was applied to an actual groundwater-flow model of Salt Lake Valley, Utah. Particle simulations using the Python code base compare well with finite-difference simulations, but with less computational effort, and have the added advantage of delineating flow paths, thus explicitly connecting solute source areas with receptors, and producing complete particle-age distributions. Knowledge of source areas and age distribution greatly enhances the analysis of dissolved solids data in Salt Lake Valley.

Starn, J. J.

2013-12-01

301

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

NASA Astrophysics Data System (ADS)

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.

Schreier, Franz; García, Sebastián Gimeno

2013-05-01

302

Development of Conceptual Benchmark Models to Evaluate Complex Hydrologic Model Calibration in Managed Basins Using Python  

NASA Astrophysics Data System (ADS)

For many numerical hydrologic models it is a challenge to quantitatively demonstrate that complex models are preferable to simpler models. Typically, a decision is made to develop and calibrate a complex model at the beginning of a study. The value of selecting a complex model over simpler models is commonly inferred from use of a model with fewer simplifications of the governing equations because it can be time consuming to develop another numerical code with data processing and parameter estimation functionality. High-level programming languages like Python can greatly reduce the effort required to develop and calibrate simple models that can be used to quantitatively demonstrate the increased value of a complex model. We have developed and calibrated a spatially-distributed surface-water/groundwater flow model for managed basins in southeast Florida, USA, to (1) evaluate the effect of municipal groundwater pumpage on surface-water/groundwater exchange, (2) investigate how the study area will respond to sea-level rise, and (3) explore combinations of these forcing functions. To demonstrate the increased value of this complex model, we developed a two-parameter conceptual-benchmark-discharge model for each basin in the study area. The conceptual-benchmark-discharge model includes seasonal scaling and lag parameters and is driven by basin rainfall. The conceptual-benchmark-discharge models were developed in the Python programming language and used weekly rainfall data. Calibration was implemented with the Broyden-Fletcher-Goldfarb-Shanno method available in the Scientific Python (SciPy) library. Normalized benchmark efficiencies calculated using output from the complex model and the corresponding conceptual-benchmark-discharge model indicate that the complex model has more explanatory power than the simple model driven only by rainfall.

Hughes, J. D.; White, J.

2013-12-01

303

Interfacing MATLAB and Python Optimizers to Black-Box Environmental Simulation Models  

NASA Astrophysics Data System (ADS)

A common approach for utilizing environmental models in a management or policy-analysis context is to incorporate them into a simulation-optimization framework - where an underlying process-based environmental model is linked with an optimization search algorithm. The optimization search algorithm iteratively adjusts various model inputs (i.e. parameters or design variables) in order to minimize an application-specific objective function computed on the basis of model outputs (i.e. response variables). Numerous optimization algorithms have been applied to the simulation-optimization of environmental systems and this research investigated the use of optimization libraries and toolboxes that are readily available in MATLAB and Python - two popular high-level programming languages. Inspired by model-independent calibration codes (e.g. PEST and UCODE), a small piece of interface software (known as PIGEON) was developed. PIGEON allows users to interface Python and MATLAB optimizers with arbitrary black-box environmental models without writing any additional interface code. An initial set of benchmark tests (involving more than 20 MATLAB and Python optimization algorithms) were performed to validate the interface software - results highlight the need to carefully consider such issues as numerical precision in output files and enforcement (or not) of parameter limits. Additional benchmark testing considered the problem of fitting isotherm expressions to laboratory data - with an emphasis on dual-mode expressions combining non-linear isotherms with a linear partitioning component. With respect to the selected isotherm fitting problems, derivative-free search algorithms significantly outperformed gradient-based algorithms. Attempts to improve gradient-based performance, via parameter tuning and also via several alternative multi-start approaches, were largely unsuccessful.

Matott, L. S.; Leung, K.; Tolson, B.

2009-12-01

304

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

NASA Astrophysics Data System (ADS)

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.

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

2013-12-01

305

Claims of Potential Expansion throughout the U.S. by Invasive Python Species Are Contradicted by Ecological Niche Models  

PubMed Central

Background Recent reports from the United States Geological Survey (USGS) suggested that invasive Burmese pythons in the Everglades may quickly spread into many parts of the U.S. due to putative climatic suitability. Additionally, projected trends of global warming were predicted to significantly increase suitable habitat and promote range expansion by these snakes. However, the ecological limitations of the Burmese python are not known and the possible effects of global warming on the potential expansion of the species are also unclear. Methodology/Principal Findings Here we show that a predicted continental expansion is unlikely based on the ecology of the organism and the climate of the U.S. Our ecological niche models, which include variables representing climatic extremes as well as averages, indicate that the only suitable habitat in the U.S. for Burmese pythons presently occurs in southern Florida and in extreme southern Texas. Models based on the current distribution of the snake predict suitable habitat in essentially the only region in which the snakes are found in the U.S. Future climate models based on global warming forecasts actually indicate a significant contraction in suitable habitat for Burmese pythons in the U.S. as well as in their native range. Conclusions/Significance The Burmese python is strongly limited to the small area of suitable environmental conditions in the United States it currently inhabits due to the ecological niche preferences of the snake. The ability of the Burmese python to expand further into the U.S. is severely limited by ecological constraints. Global warming is predicted to significantly reduce the area of suitable habitat worldwide, underscoring the potential negative effects of climate change for many species. PMID:18698351

Pyron, R. Alexander; Burbrink, Frank T.; Guiher, Timothy J.

2008-01-01

306

SpacePy: Python-Based Tools for the Space Science Community  

NASA Astrophysics Data System (ADS)

SpacePy provides data analysis and visualization tools for the space science community. Written in Python, it builds on the capabilities of the NumPy and MatPlotLib packages to make basic data analysis, modeling and visualization easier. It contains modules for handling many complex time formats, obtaining data from the OMNI database, and accessing the powerful Onera library. It contains a library of commonly used empirical relationships, performs association analysis, coordinate transformations, radiation belt modeling, and CDF reading, and creates publication quality plots.

Morley, Steve; Koller, Josef; Welling, Dan; Larsen, Brian; Niehof, Jon

2014-01-01

307

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

PubMed

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

Alstott, Jeff; Bullmore, Ed; Plenz, Dietmar

2014-01-01

308

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

NASA Astrophysics Data System (ADS)

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 coordinates from the Python interface, and to return the forces and energy that are used to integrate the equations of motion. Restrictions: This code only deals with distinguishable particles. It does not include fermonic or bosonic exchanges between equivalent nuclei, which can become important at very low temperatures. Running time: Depends dramatically on the nature of the simulation being performed. A few minutes for short tests with empirical force fields, up to several weeks for production calculations with ab initio forces. The examples provided with the code run in less than an hour.

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

2014-03-01

309

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

PubMed Central

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

Alstott, Jeff; Bullmore, Ed; Plenz, Dietmar

2014-01-01

310

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

NASA Astrophysics Data System (ADS)

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 allows EarthWorm as well as SeisComP3 to access the data.

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

2010-05-01

311

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

PubMed Central

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.

2014-01-01

312

Python-based framework for coupled MC-TH reactor calculations  

NASA Astrophysics Data System (ADS)

We develop a set of Python packages to provide a modern programming interface to codes used for analysis of nuclear reactors. Currently implemented interfaces to the Monte Carlo (MC) neutronics code MCNP and thermo-hydraulic (TH) code SCF allow efficient description of calculation models and provide a framework for coupled calculations. In this paper we illustrate how these interfaces can be used to describe a pin model, and report results of coupled MCNP-SCF calculations performed for a PWR fuel assembly, organized by means of the interfaces.

Travleev, Anton A.; Molitor, Richard; Sanchez, Victor

2014-06-01

313

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

USGS Publications Warehouse

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).

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

2011-01-01

314

A World Without Referees Larry Wasserman1  

E-print Network

peer review is an authoritarian system resembling a priesthood or a guild. It made sense in the 1600's and democratize our approach to scientific publishing. 1 Introduction The peer review system that we use, in 1665 (see http://en.wikipedia.org/ wiki/Peer_review). We are using a refereeing system that is almost

315

Automatic Parallelization of Numerical Python Applications using the Global Arrays Toolkit  

SciTech Connect

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.

Daily, Jeffrey A.; Lewis, Robert R.

2011-11-30

316

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

NASA Astrophysics Data System (ADS)

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.

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

2012-12-01

317

Complete mitochondrial genome sequence from an endangered Indian snake, Python molurus molurus (Serpentes, Pythonidae).  

PubMed

This paper reports the complete mitochondrial genome sequence of an endangered Indian snake, Python molurus molurus (Indian Rock Python). A typical snake mitochondrial (mt) genome of 17258 bp length comprising of 37 genes including the 13 protein coding genes, 22 tRNA genes, and 2 ribosomal RNA genes along with duplicate control regions is described herein. The P. molurus molurus mt. genome is relatively similar to other snake mt. genomes with respect to gene arrangement, composition, tRNA structures and skews of AT/GC bases. The nucleotide composition of the genome shows that there are more A-C % than T-G% on the positive strand as revealed by positive AT and CG skews. Comparison of individual protein coding genes, with other snake genomes suggests that ATP8 and NADH3 genes have high divergence rates. Codon usage analysis reveals a preference of NNC codons over NNG codons in the mt. genome of P. molurus. Also, the synonymous and non-synonymous substitution rates (ka/ks) suggest that most of the protein coding genes are under purifying selection pressure. The phylogenetic analyses involving the concatenated 13 protein coding genes of P. molurus molurus conformed to the previously established snake phylogeny. PMID:22331485

Dubey, Bhawna; Meganathan, P R; Haque, Ikramul

2012-07-01

318

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

PubMed Central

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

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

2009-01-01

319

Nmrglue: an open source Python package for the analysis of multidimensional NMR data.  

PubMed

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

Helmus, Jonathan J; Jaroniec, Christopher P

2013-04-01

320

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

PubMed

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

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

2014-09-01

321

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

PubMed Central

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

Helmus, Jonathan J.; Jaroniec, Christopher P.

2013-01-01

322

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

PubMed

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

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

2004-03-01

323

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

PubMed Central

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

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

2010-01-01

324

Python for Development of OpenMP and CUDA Kernels for Multidimensional Data  

SciTech Connect

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.

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

2011-01-01

325

Whole transcriptome analysis of the fasting and fed Burmese python heart: insights into extreme physiological cardiac adaptation  

PubMed Central

The infrequently feeding Burmese python (Python molurus) experiences significant and rapid postprandial cardiac hypertrophy followed by regression as digestion is completed. To begin to explore the molecular mechanisms of this response, we have sequenced and assembled the fasted and postfed Burmese python heart transcriptomes with Illumina technology using the chicken (Gallus gallus) genome as a reference. In addition, we have used RNA-seq analysis to identify differences in the expression of biological processes and signaling pathways between fasted, 1 day postfed (DPF), and 3 DPF hearts. Out of a combined transcriptome of ?2,800 mRNAs, 464 genes were differentially expressed. Genes showing differential expression at 1 DPF compared with fasted were enriched for biological processes involved in metabolism and energetics, while genes showing differential expression at 3 DPF compared with fasted were enriched for processes involved in biogenesis, structural remodeling, and organization. Moreover, we present evidence for the activation of physiological and not pathological signaling pathways in this rapid, novel model of cardiac growth in pythons. Together, our data provide the first comprehensive gene expression profile for a reptile heart. PMID:21045117

Wall, Christopher E.; Cozza, Steven; Riquelme, Cecilia A.; McCombie, W. Richard; Heimiller, Joseph K.; Marr, Thomas G.

2011-01-01

326

Effect of water deprivation on baseline and stress-induced corticosterone levels in the Children's python (Antaresia childreni).  

PubMed

Corticosterone (CORT) secretion is influenced by endogenous factors (e.g., physiological status) and environmental stressors (e.g., ambient temperature). Heretofore, the impact of water deprivation on CORT plasma levels has not been thoroughly investigated. However, both baseline CORT and stress-induced CORT are expected to respond to water deprivation not only because of hydric stress per se, but also because CORT is an important mineralocorticoid in vertebrates. We assessed the effects of water deprivation on baseline CORT and stress-induced CORT, in Children's pythons (Antaresia childreni), a species that experiences seasonal droughts in natural conditions. We imposed a 52-day water deprivation on a group of unfed Children's pythons (i.e., water-deprived treatment) and provided water ad libitum to another group (i.e., control treatment). We examined body mass variations throughout the experiment, and baseline CORT and stress-induced CORT at the end of the treatments. Relative body mass loss averaged ~10% in pythons without water, a value 2 to 4 times higher compared to control snakes. Following re-exposition to water, pythons from the water-deprived treatment drank readily and abundantly and attained a body mass similar to pythons from the control treatment. Together, these results suggest a substantial dehydration as a consequence of water deprivation. Interestingly, stress-induced but not baseline CORT level was significantly higher in water-deprived snakes, suggesting that baseline CORT might not respond to this degree of dehydration. Therefore, possible mineralocorticoid role of CORT needs to be clarified in snakes. Because dehydration usually induces adjustments (reduced movements, lowered body temperature) to limit water loss, and decreases locomotor performances, elevated stress-induced CORT in water-deprived snakes might therefore compensate for altered locomotor performances. Future studies should test this hypothesis. PMID:24231466

Dupoué, Andréaz; Angelier, Frédéric; Lourdais, Olivier; Bonnet, Xavier; Brischoux, François

2014-02-01

327

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

PubMed

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

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

2014-01-01

328

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

PubMed Central

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

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

2014-01-01

329

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

SciTech Connect

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 analysis methods employed in scientific studies and will give access to advanced tools to all space scientists regardless of affiliation or circumstance.

Morley, Steven K [Los Alamos National Laboratory; Welling, Daniel T [Los Alamos National Laboratory; Koller, Josef [Los Alamos National Laboratory; Larsen, Brian A [Los Alamos National Laboratory; Henderson, Michael G [Los Alamos National Laboratory

2010-01-01

330

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

NASA Astrophysics Data System (ADS)

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 earthquakes from a wide variety of instrumentation with no modifications by instrument type or sample rate. We demonstrate the robustness and effectiveness of this picker by comparing manual picked earthquake phase arrivals with those obtained from this picker. In addition, because the picker picks both P- and S-phase arrivals, pick association algorithms can be enhanced by the additional phase arrival picks. We demonstrate the effectiveness of a local earthquake phase associator algorithm written in Python.

Chen, C.; Holland, A. A.

2013-12-01

331

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

NASA Astrophysics Data System (ADS)

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 inversion and appropriate solution schemes in escript. We will also give a brief introduction into escript's open framework for defining and solving geophysical inversion problems. Finally we will show some benchmark results to demonstrate the computational scalability of the inversion method across a large number of cores and compute nodes in a parallel computing environment. References: - L. Gross et al. (2013): Escript Solving Partial Differential Equations in Python Version 3.4, The University of Queensland, https://launchpad.net/escript-finley - L. Gross and C. Kemp (2013) Large Scale Joint Inversion of Geophysical Data using the Finite Element Method in escript. ASEG Extended Abstracts 2013, http://dx.doi.org/10.1071/ASEG2013ab306 - T. Poulet, L. Gross, D. Georgiev, J. Cleverley (2012): escript-RT: Reactive transport simulation in Python using escript, Computers & Geosciences, Volume 45, 168-176. http://dx.doi.org/10.1016/j.cageo.2011.11.005.

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

2014-05-01

332

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

NASA Astrophysics Data System (ADS)

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

Lecocq, Thomas; Caudron, Corentin; Brenguier, Florent

2014-05-01

333

Modular Toolkit for Data Processing (MDP): A Python Data Processing Framework  

PubMed Central

Modular toolkit for Data Processing (MDP) is a data processing framework written in Python. From the user's perspective, MDP is a collection of supervised and unsupervised learning algorithms and other data processing units that can be combined into data processing sequences and more complex feed-forward network architectures. Computations are performed efficiently in terms of speed and memory requirements. From the scientific developer's perspective, MDP is a modular framework, which can easily be expanded. The implementation of new algorithms is easy and intuitive. The new implemented units are then automatically integrated with the rest of the library. MDP has been written in the context of theoretical research in neuroscience, but it has been designed to be helpful in any context where trainable data processing algorithms are used. Its simplicity on the user's side, the variety of readily available algorithms, and the reusability of the implemented units make it also a useful educational tool. PMID:19169361

Zito, Tiziano; Wilbert, Niko; Wiskott, Laurenz; Berkes, Pietro

2008-01-01

334

Biopython: freely available Python tools for computational molecular biology and bioinformatics  

PubMed Central

Summary: The Biopython project is a mature open source international collaboration of volunteer developers, providing Python libraries for a wide range of bioinformatics problems. Biopython includes modules for reading and writing different sequence file formats and multiple sequence alignments, dealing with 3D macro molecular structures, interacting with common tools such as BLAST, ClustalW and EMBOSS, accessing key online databases, as well as providing numerical methods for statistical learning. Availability: Biopython is freely available, with documentation and source code at www.biopython.org under the Biopython license. Contact: All queries should be directed to the Biopython mailing lists, see www.biopython.org/wiki/_Mailing_listspeter.cock@scri.ac.uk. PMID:19304878

Cock, Peter J. A.; Antao, Tiago; Chang, Jeffrey T.; Chapman, Brad A.; Cox, Cymon J.; Dalke, Andrew; Friedberg, Iddo; Hamelryck, Thomas; Kauff, Frank; Wilczynski, Bartek; de Hoon, Michiel J. L.

2009-01-01

335

FragBuilder: an efficient Python library to setup quantum chemistry calculations on peptides models.  

PubMed

We present a powerful Python library to quickly and efficiently generate realistic peptide model structures. The library makes it possible to quickly set up quantum mechanical calculations on model peptide structures. It is possible to manually specify a specific conformation of the peptide. Additionally the library also offers sampling of backbone conformations and side chain rotamer conformations from continuous distributions. The generated peptides can then be geometry optimized by the MMFF94 molecular mechanics force field via convenient functions inside the library. Finally, it is possible to output the resulting structures directly to files in a variety of useful formats, such as XYZ or PDB formats, or directly as input files for a quantum chemistry program. FragBuilder is freely available at https://github.com/jensengroup/fragbuilder/ under the terms of the BSD open source license. PMID:24688855

Christensen, Anders S; Hamelryck, Thomas; Jensen, Jan H

2014-01-01

336

FragBuilder: an efficient Python library to setup quantum chemistry calculations on peptides models  

PubMed Central

We present a powerful Python library to quickly and efficiently generate realistic peptide model structures. The library makes it possible to quickly set up quantum mechanical calculations on model peptide structures. It is possible to manually specify a specific conformation of the peptide. Additionally the library also offers sampling of backbone conformations and side chain rotamer conformations from continuous distributions. The generated peptides can then be geometry optimized by the MMFF94 molecular mechanics force field via convenient functions inside the library. Finally, it is possible to output the resulting structures directly to files in a variety of useful formats, such as XYZ or PDB formats, or directly as input files for a quantum chemistry program. FragBuilder is freely available at https://github.com/jensengroup/fragbuilder/ under the terms of the BSD open source license. PMID:24688855

Hamelryck, Thomas; Jensen, Jan H.

2014-01-01

337

ABC-SysBio—approximate Bayesian computation in Python with GPU support  

PubMed Central

Motivation: The growing field of systems biology has driven demand for flexible tools to model and simulate biological systems. Two established problems in the modeling of biological processes are model selection and the estimation of associated parameters. A number of statistical approaches, both frequentist and Bayesian, have been proposed to answer these questions. Results: Here we present a Python package, ABC-SysBio, that implements parameter inference and model selection for dynamical systems in an approximate Bayesian computation (ABC) framework. ABC-SysBio combines three algorithms: ABC rejection sampler, ABC SMC for parameter inference and ABC SMC for model selection. It is designed to work with models written in Systems Biology Markup Language (SBML). Deterministic and stochastic models can be analyzed in ABC-SysBio. Availability: http://abc-sysbio.sourceforge.net Contact: christopher.barnes@imperial.ac.uk; ttoni@imperial.ac.uk PMID:20591907

Liepe, Juliane; Barnes, Chris; Cule, Erika; Erguler, Kamil; Kirk, Paul; Toni, Tina; Stumpf, Michael P.H.

2010-01-01

338

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

PubMed

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

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

2012-01-01

339

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

NASA Astrophysics Data System (ADS)

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.

Zabo?otny, Wojciech M.; Byszuk, Adrian; Chernyshova, Maryna; Cieszewski, Rados?aw; Czarski, Tomasz; Dalley, Simon; Hogben, Colin; Jakubowska, Katarzyna L.; Kasprowicz, Grzegorz; Po?niak, Krzysztof; Rzadkiewicz, Jacek; Scholz, Marek; Shumack, Amy

2014-11-01

340

Modular Toolkit for Data Processing (MDP): A Python Data Processing Framework.  

PubMed

Modular toolkit for Data Processing (MDP) is a data processing framework written in Python. From the user's perspective, MDP is a collection of supervised and unsupervised learning algorithms and other data processing units that can be combined into data processing sequences and more complex feed-forward network architectures. Computations are performed efficiently in terms of speed and memory requirements. From the scientific developer's perspective, MDP is a modular framework, which can easily be expanded. The implementation of new algorithms is easy and intuitive. The new implemented units are then automatically integrated with the rest of the library. MDP has been written in the context of theoretical research in neuroscience, but it has been designed to be helpful in any context where trainable data processing algorithms are used. Its simplicity on the user's side, the variety of readily available algorithms, and the reusability of the implemented units make it also a useful educational tool. PMID:19169361

Zito, Tiziano; Wilbert, Niko; Wiskott, Laurenz; Berkes, Pietro

2008-01-01

341

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

SciTech Connect

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.

Woodruff, David L. (University of California, Davis); Watson, Jean-Paul

2010-08-01

342

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

PubMed Central

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

Hull, Michael J.; Willshaw, David J.

2014-01-01

343

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

PubMed

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

Hull, Michael J; Willshaw, David J

2013-01-01

344

Wrapping Python around MODFLOW/MT3DMS based groundwater models  

NASA Astrophysics Data System (ADS)

Numerical models that simulate groundwater flow and solute transport require a great amount of input data that is often organized into different files. A large proportion of the input data consists of spatially-distributed model parameters. The model output consists of a variety data such as heads, fluxes and concentrations. Typically all files have different formats. Consequently, preparing input and managing output is a complex and error-prone task. Proprietary software tools are available that facilitate the preparation of input files and analysis of model outcomes. The use of such software may be limited if it does not support all the features of the groundwater model or when the costs of such tools are prohibitive. Therefore a Python library was developed that contains routines to generate input files and process output files of MODFLOW/MT3DMS based models. The library is freely available and has an open structure so that the routines can be customized and linked into other scripts and libraries. The current set of functions supports the generation of input files for MODFLOW and MT3DMS, including the capability to read spatially-distributed input parameters (e.g. hydraulic conductivity) from PNG files. Both ASCII and binary output files can be read efficiently allowing for visualization of, for example, solute concentration patterns in contour plots with superimposed flow vectors using matplotlib. Series of contour plots are then easily saved as an animation. The subroutines can also be used within scripts to calculate derived quantities such as the mass of a solute within a particular region of the model domain. Using Python as a wrapper around groundwater models provides an efficient and flexible way of processing input and output data, which is not constrained by limitations of third-party products.

Post, V.

2008-12-01

345

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

USGS Publications Warehouse

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.

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

2009-01-01

346

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

PubMed Central

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

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

2014-01-01

347

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)

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.

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

1993-07-01

348

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

NASA Astrophysics Data System (ADS)

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.

Chemin, Yann

2013-04-01

349

Enabling grand-canonical Monte Carlo: extending the flexibility of GROMACS through the GromPy python interface module.  

PubMed

We report on a python interface to the GROMACS molecular simulation package, GromPy (available at https://github.com/GromPy). This application programming interface (API) uses the ctypes python module that allows function calls to shared libraries, for example, written in C. To the best of our knowledge, this is the first reported interface to the GROMACS library that uses direct library calls. GromPy can be used for extending the current GROMACS simulation and analysis modes. In this work, we demonstrate that the interface enables hybrid Monte-Carlo/molecular dynamics (MD) simulations in the grand-canonical ensemble, a simulation mode that is currently not implemented in GROMACS. For this application, the interplay between GromPy and GROMACS requires only minor modifications of the GROMACS source code, not affecting the operation, efficiency, and performance of the GROMACS applications. We validate the grand-canonical application against MD in the canonical ensemble by comparison of equations of state. The results of the grand-canonical simulations are in complete agreement with MD in the canonical ensemble. The python overhead of the grand-canonical scheme is only minimal. PMID:22370965

Pool, René; Heringa, Jaap; Hoefling, Martin; Schulz, Roland; Smith, Jeremy C; Feenstra, K Anton

2012-05-01

350

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

NASA Astrophysics Data System (ADS)

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 or Git, since these systems do not perform well for large (binary) model data files. For this reason, a new concept of parameterization and data splitting has been implemented. Each file, or set of files, is uniquely labeled as a parameter, and for this parameter metadata is maintained by Subversion. The metadata data contains file hashes to identify data content and the location where the actual bulk data are stored that can be reached by FTP. The NHI model control script is a command-line driven Python script for pre-processing, running, and post-processing the NHI model and uses one single configuration file for all computational kernels. This configuration file is an easy-to-use, keyword-driven, Windows INI-file, having separate sections for all the kernels. It also includes a FileSync data section where the user can specify version controlled model data to be used as input. The NHI control script keeps all the data consistent during the pre-processing. Furthermore, this script is able to do model state handling when the NHI model is used for ensemble forecasting.

Verkaik, J.

2013-12-01

351

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

NASA Astrophysics Data System (ADS)

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 the oblique slip regimes. This approximation is similar to the Johnston et al. (1994) classification; with 7 classes of earthquakes: 1) Normal; 2) Normal - Strike-slip; 3) Strike-slip - Normal; 4) Strike-slip; 5) Strike-slip - Reverse; 6) Reverse - strike-slip and 7) Reverse. FMC uses by default this classification in the resulting diagram, based on the Kaverina et al. (1996) projection, which improves the Frohlich and Apperson (1992) ternary diagram.

Álvarez-Gómez, José A.

2014-05-01

352

A Python module to normalize microarray data by the quantile adjustment method  

PubMed Central

Microarray technology is widely used for gene expression research targeting the development of new drug treatments. In the case of a two-color microarray, the process starts with labeling DNA samples with fluorescent markers (cyanine 635 or Cy5 and cyanine 532 or Cy3), then mixing and hybridizing them on a chemically treated glass printed with probes, or fragments of genes. The level of hybridization between a strand of labeled DNA and a probe present on the array is measured by scanning the fluorescence of spots in order to quantify the expression based on the quality and number of pixels for each spot. The intensity data generated from these scans are subject to errors due to differences in fluorescence efficiency between Cy5 and Cy3, as well as variation in human handling and quality of the sample. Consequently, data have to be normalized to correct for variations which are not related to the biological phenomena under investigation. Among many existing normalization procedures, we have implemented the quantile adjustment method using the python computer language, and produced a module which can be run via an HTML dynamic form. This module is composed of different functions for data files reading, intensity and ratio computations and visualization. The current version of the HTML form allows the user to visualize the data before and after normalization. It also gives the option to subtract background noise before normalizing the data. The output results of this module are in agreement with the results of other normalization tools. PMID:20970526

Baber, Ibrahima; Tamby, Jean Philippe; Manoukis, Nicholas C.; Sangaré, Djibril; Doumbia, Seydou; Traoré, Sekou F.; Maiga, Mohamed S.; Dembélé, Doulaye

2010-01-01

353

PyMix - The Python mixture package - a tool for clustering of heterogeneous biological data  

PubMed Central

Background Cluster analysis is an important technique for the exploratory analysis of biological data. Such data is often high-dimensional, inherently noisy and contains outliers. This makes clustering challenging. Mixtures are versatile and powerful statistical models which perform robustly for clustering in the presence of noise and have been successfully applied in a wide range of applications. Results PyMix - the Python mixture package implements algorithms and data structures for clustering with basic and advanced mixture models. The advanced models include context-specific independence mixtures, mixtures of dependence trees and semi-supervised learning. PyMix is licenced under the GNU General Public licence (GPL). PyMix has been successfully used for the analysis of biological sequence, complex disease and gene expression data. Conclusions PyMix is a useful tool for cluster analysis of biological data. Due to the general nature of the framework, PyMix can be applied to a wide range of applications and data sets. PMID:20053276

2010-01-01

354

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

NASA Astrophysics Data System (ADS)

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.

Lecocq, T.; Caudron, C.; Brenguier, F.

2013-12-01

355

PyPLIF: Python-based Protein-Ligand Interaction Fingerprinting  

PubMed Central

Structure-based virtual screening (SBVS) methods often rely on docking score. The docking score is an over-simplification of the actual ligand-target binding. Its capability to model and predict the actual binding reality is limited. Recently, interaction fingerprinting (IFP) has come and offered us an alternative way to model reality. IFP provides us an alternate way to examine protein-ligand interactions. The docking score indicates the approximate affinity and IFP shows the interaction specificity. IFP is a method to convert three dimensional (3D) protein-ligand interactions into one dimensional (1D) bitstrings. The bitstrings are subsequently employed to compare the protein-ligand interaction predicted by the docking tool against the reference ligand. These comparisons produce scores that can be used to enhance the quality of SBVS campaigns. However, some IFP tools are either proprietary or using a proprietary library, which limits the access to the tools and the development of customized IFP algorithm. Therefore, we have developed PyPLIF, a Python-based open source tool to analyze IFP. In this article, we describe PyPLIF and its application to enhance the quality of SBVS in order to identify antagonists for estrogen ? receptor (ER?). Availability PyPLIF is freely available at http://code.google.com/p/pyplif PMID:23559752

Radifar, Muhammad; Yuniarti, Nunung; Istyastono, Enade Perdana

2013-01-01

356

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

PubMed Central

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

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

2014-01-01

357

ProDaMa: an open source Python library to generate protein structure datasets  

PubMed Central

Background The huge difference between the number of known sequences and known tertiary structures has justified the use of automated methods for protein analysis. Although a general methodology to solve these problems has not been yet devised, researchers are engaged in developing more accurate techniques and algorithms whose training plays a relevant role in determining their performance. From this perspective, particular importance is given to the training data used in experiments, and researchers are often engaged in the generation of specialized datasets that meet their requirements. Findings To facilitate the task of generating specialized datasets we devised and implemented ProDaMa, an open source Python library than provides classes for retrieving, organizing, updating, analyzing, and filtering protein data. Conclusion ProDaMa has been used to generate specialized datasets useful for secondary structure prediction and to develop a collaborative web application aimed at generating and sharing protein structure datasets. The library, the related database, and the documentation are freely available at the URL . PMID:19799773

Armano, Giuliano; Manconi, Andrea

2009-01-01

358

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

PubMed

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

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

2014-01-01

359

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

SciTech Connect

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.

Turk, M.; /KIPAC, Menlo Park

2008-09-30

360

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

SciTech Connect

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.

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

2011-11-01

361

A python-based docking program utilizing a receptor bound ligand shape: PythDock.  

PubMed

PythDock is a heuristic docking program that uses Python programming language with a simple scoring function and a population based search engine. The scoring function considers electrostatic and dispersion/repulsion terms. The search engine utilizes a particle swarm optimization algorithm. A grid potential map is generated using the shape information of a bound ligand within the active site. Therefore, the searching area is more relevant to the ligand binding. To evaluate the docking performance of PythDock, two well-known docking programs (AutoDock and DOCK) were also used with the same data. The accuracy of docked results were measured by the difference of the ligand structure between x-ray structure, and docked pose, i.e., average root mean squared deviation values of the bound ligand were compared for fourteen protein-ligand complexes. Since the number of ligands' rotational flexibility is an important factor affecting the accuracy of a docking, the data set was chosen to have various degrees of flexibility. Although PythDock has a scoring function simpler than those of other programs (AutoDock and DOCK), our results showed that PythDock predicted more accurate poses than both AutoDock4.2 and DOCK6.2. This indicates that PythDock could be a useful tool to study ligand-receptor interactions and could also be beneficial in structure based drug design. PMID:21975806

Chung, Jae Yoon; Cho, Seung Joo; Hah, Jung-Mi

2011-09-01

362

ScalPy: A Python Package For Late Time Scalar Field Cosmology  

E-print Network

We present a python package "ScalPy" for studying the late time scalar field cosmology for a wide variety of scalar field models, namely the quintessence, tachyon and Galileon model. The package solves the autonomous system of equations for power law and exponential potential. But it can be easily generalized to add more complicated potential. For completeness, we also include the standard parameterization for dark energy models, e.g. the $\\Lambda$CDM, $w$CDM, $w_{0}w_{a}$CDM as well as the GCG parameterization. The package also solves the linear growth equation for matter perturbations on sub-horizon scales. All the important observables related to background universe as well as to the perturbed universe, e.g. luminosity distance ($D_{L}(z)$), angular diameter distance ($D_{A}(z)$), normalized Hubble parameter ($h(z)$), lookback time ($t_{L}$), equation of state for the dark energy ($w(z)$), growth rate ($f=\\frac{d \\ln\\delta}{d \\ln a}$), linear matter power spectra ($P(k)$), and its normalization $\\sigma_{8}...

Kumar, Sumit; Sen, Anjan A

2015-01-01

363

A Python Pipeline for the Mercury N-body Code With First-Order GR Effects  

NASA Astrophysics Data System (ADS)

We present a pipeline for use with the Mercury N-body code (Chambers 1999), which we make publicly available on github. We have modified the standard Mercury integrator to include first-order numerical relativistic effects and a smooth stellar potential for use in the near-Keplerian potential around a massive black hole. Python scripts generate the input files and perform analysis on hundreds of stars, including those in a disk around Sgr A* and in highly-eccentric remnants of disrupted binaries.We use this code to simulate the dynamical effects of an intermediate-mass black hole on the stars in the Galactic center. Preliminary results indicate significant effects on the semi-major axis and eccentricity distribution. Using the h-statistic (Madigan et al. 2014) as a proxy for eccentricity, this should be observable in current observational data, allowing us to constrain the remaining parameter space available to an intermediate-mass black hole in the Galactic center (Gualandris & Merritt, 2009).

Wieland, Christopher AM; Madigan, Ann-Marie

2015-01-01

364

Open-Source Python Modules to Estimate Level Ice Thickness from Ice Charts  

NASA Astrophysics Data System (ADS)

A collaborative research effort between the University of Delaware (UD) and National Ice Center (NIC) addresses the task of providing open-source translations of sea ice stage-of-development into level ice thickness estimates on a 4km grid for the Interactive Multisensor Snow and Ice Mapping System (IMS). The characteristics for stage-of-development are quantified from remote sensing imagery with estimates of level ice thickness categories originating from World Meteorological Organization (WMO) egg coded ice charts codified since the 1970s. Conversions utilize Python scripting modules which transform electronic ice charts with WMO egg code characteristics into five level ice thickness categories, in centimeters, (0-10, 10-30, 30-70, 70-120, >120cm) and five ice types (open water, first year pack ice, fast ice, multiyear ice, and glacial ice with a reserve slot for deformed ice fractions). Both level ice thickness categories and ice concentration fractions are reported with uncertainties propagated based on WMO ice stage ranges which serve as proxy estimates for standard deviation. These products are in preparation for use by NCEP, CMC, and NAVO by 2014 based on their modeling requirements for daily products in near-real time. In addition to development, continuing research tests the value of these estimated products against in situ observations to improve both value and uncertainty estimates.

Geiger, C. A.; Deliberty, T. L.; Bernstein, E. R.; Helfrich, S.

2012-12-01

365

Brainlab: A Python Toolkit to Aid in the Design, Simulation, and Analysis of Spiking Neural Networks with the NeoCortical Simulator  

PubMed Central

Neuroscience modeling experiments often involve multiple complex neural network and cell model variants, complex input stimuli and input protocols, followed by complex data analysis. Coordinating all this complexity becomes a central difficulty for the experimenter. The Python programming language, along with its extensive library packages, has emerged as a leading “glue” tool for managing all sorts of complex programmatic tasks. This paper describes a toolkit called Brainlab, written in Python, that leverages Python's strengths for the task of managing the general complexity of neuroscience modeling experiments. Brainlab was also designed to overcome the major difficulties of working with the NCS (NeoCortical Simulator) environment in particular. Brainlab is an integrated model-building, experimentation, and data analysis environment for the powerful parallel spiking neural network simulator system NCS. PMID:19506707

Drewes, Rich; Zou, Quan; Goodman, Philip H.

2008-01-01

366

Weighing empirical and hypothetical evidence for assessing potential invasive species range limits: a review of the case of Burmese pythons in the USA.  

PubMed

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

Engeman, Richard; Avery, Michael L; Jacobson, Elliott

2014-10-01

367

pyLIDEM: A Python-Based Tool to Delineate Coastal Watersheds Using LIDAR Data  

NASA Astrophysics Data System (ADS)

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-resolution Light Detection and Ranging (LIDAR) data, generates fine scale DEMs, and delineates watershed boundaries for a given pour point. Because LIDAR data are typically distributed in large sets of predefined tiles, our tool is capable of combining only the minimum number of bare earth LIDAR tiles required to delineate a watershed of interest. Our tool then processes the LIDAR data into Triangulated Irregular Networks, generates DEMs at user- specified cell sizes, and creates the required files needed to delineate watersheds within ArcGIS. To make pyLIDEM more accessible to the modeling community, we have bundled it within an ArcGIS toolbox, which also allows users to run it directly from an ArcGIS platform. We assess pyLIDEM functionality and accuracy by delineating several impaired small coastal watersheds in the Newport River Estuary in Eastern North Carolina using LIDAR data collected for the North Carolina Flood Mapping Program. We then compare the pyLIDAR-based watershed boundaries with those generated manually and with those generated using the 30-meter DEMs, and find that the pyLIDAR-based boundaries are more accurate than the 30-meter DEMs, and provide a significant time savings compared to manual delineation, particularly in cases where multiple watersheds need to be delineated for a single project.

O'Banion, R.; Alameddine, I.; Gronewold, A.; Reckhow, K.

2008-12-01

368

Topographica: Building and Analyzing Map-Level Simulations from Python, C/C++, MATLAB, NEST, or NEURON Components  

PubMed Central

Many neural regions are arranged into two-dimensional topographic maps, such as the retinotopic maps in mammalian visual cortex. Computational simulations have led to valuable insights about how cortical topography develops and functions, but further progress has been hindered by the lack of appropriate tools. It has been particularly difficult to bridge across levels of detail, because simulators are typically geared to a specific level, while interfacing between simulators has been a major technical challenge. In this paper, we show that the Python-based Topographica simulator makes it straightforward to build systems that cross levels of analysis, as well as providing a common framework for evaluating and comparing models implemented in other simulators. These results rely on the general-purpose abstractions around which Topographica is designed, along with the Python interfaces becoming available for many simulators. In particular, we present a detailed, general-purpose example of how to wrap an external spiking PyNN/NEST simulation as a Topographica component using only a dozen lines of Python code, making it possible to use any of the extensive input presentation, analysis, and plotting tools of Topographica. Additional examples show how to interface easily with models in other types of simulators. Researchers simulating topographic maps externally should consider using Topographica's analysis tools (such as preference map, receptive field, or tuning curve measurement) to compare results consistently, and for connecting models at different levels. This seamless interoperability will help neuroscientists and computational scientists to work together to understand how neurons in topographic maps organize and operate. PMID:19352443

Bednar, James A.

2008-01-01

369

Application of MATLAB and Python optimizers to two case studies involving groundwater flow and contaminant transport modeling  

NASA Astrophysics Data System (ADS)

One approach for utilizing geoscience models for management or policy analysis is via a simulation-based optimization framework—where an underlying model is linked with an optimization search algorithm. In this regard, MATLAB and Python are high-level programming languages that implement numerous optimization routines, including gradient-based, heuristic, and direct-search optimizers. The ever-expanding number of available algorithms makes it challenging for practitioners to identify optimizers that deliver good performance when applied to problems of interest. Thus, the primary contribution of this paper is to present a series of numerical experiments that investigated the performance of various MATLAB and Python optimizers. The experiments considered two simulation-based optimization case studies involving groundwater flow and contaminant transport. One case study examined the design of a pump-and-treat system for groundwater remediation, while the other considered least-squares calibration of a model of strontium (Sr) transport. Using these case studies, the performance of 12 different MATLAB and Python optimizers was compared. Overall, the Hooke-Jeeves direct search algorithm yielded the best performance in terms of identifying least-cost and best-fit solutions to the design and calibration problems, respectively. The IFFCO (implicit filtering for constrained optimization) direct search algorithm and the dynamically dimensioned search (DDS) heuristic algorithm also consistently yielded good performance and were up to 80% more efficient than Hooke-Jeeves when applied to the pump-and-treat problem. These results provide empirical evidence that, relative to gradient- and population-based alternatives, direct search algorithms and heuristic variants, such as DDS, are good choices for application to simulation-based optimization problems involving groundwater management.

Matott, L. Shawn; Leung, Kenny; Sim, Junyoung

2011-11-01

370

HDDM: Hierarchical Bayesian estimation of the Drift-Diffusion Model in Python  

PubMed Central

The diffusion model is a commonly used tool to infer latent psychological processes underlying decision-making, and to link them to neural mechanisms based on response times. Although efficient open source software has been made available to quantitatively fit the model to data, current estimation methods require an abundance of response time measurements to recover meaningful parameters, and only provide point estimates of each parameter. In contrast, hierarchical Bayesian parameter estimation methods are useful for enhancing statistical power, allowing for simultaneous estimation of individual subject parameters and the group distribution that they are drawn from, while also providing measures of uncertainty in these parameters in the posterior distribution. Here, we present a novel Python-based toolbox called HDDM (hierarchical drift diffusion model), which allows fast and flexible estimation of the the drift-diffusion model and the related linear ballistic accumulator model. HDDM requires fewer data per subject/condition than non-hierarchical methods, allows for full Bayesian data analysis, and can handle outliers in the data. Finally, HDDM supports the estimation of how trial-by-trial measurements (e.g., fMRI) influence decision-making parameters. This paper will first describe the theoretical background of the drift diffusion model and Bayesian inference. We then illustrate usage of the toolbox on a real-world data set from our lab. Finally, parameter recovery studies show that HDDM beats alternative fitting methods like the ?2-quantile method as well as maximum likelihood estimation. The software and documentation can be downloaded at: http://ski.clps.brown.edu/hddm_docs/ PMID:23935581

Wiecki, Thomas V.; Sofer, Imri; Frank, Michael J.

2013-01-01

371

HDDM: Hierarchical Bayesian estimation of the Drift-Diffusion Model in Python.  

PubMed

The diffusion model is a commonly used tool to infer latent psychological processes underlying decision-making, and to link them to neural mechanisms based on response times. Although efficient open source software has been made available to quantitatively fit the model to data, current estimation methods require an abundance of response time measurements to recover meaningful parameters, and only provide point estimates of each parameter. In contrast, hierarchical Bayesian parameter estimation methods are useful for enhancing statistical power, allowing for simultaneous estimation of individual subject parameters and the group distribution that they are drawn from, while also providing measures of uncertainty in these parameters in the posterior distribution. Here, we present a novel Python-based toolbox called HDDM (hierarchical drift diffusion model), which allows fast and flexible estimation of the the drift-diffusion model and the related linear ballistic accumulator model. HDDM requires fewer data per subject/condition than non-hierarchical methods, allows for full Bayesian data analysis, and can handle outliers in the data. Finally, HDDM supports the estimation of how trial-by-trial measurements (e.g., fMRI) influence decision-making parameters. This paper will first describe the theoretical background of the drift diffusion model and Bayesian inference. We then illustrate usage of the toolbox on a real-world data set from our lab. Finally, parameter recovery studies show that HDDM beats alternative fitting methods like the ?(2)-quantile method as well as maximum likelihood estimation. The software and documentation can be downloaded at: http://ski.clps.brown.edu/hddm_docs/ PMID:23935581

Wiecki, Thomas V; Sofer, Imri; Frank, Michael J

2013-01-01

372

Python script development for analyzing aquarius salinity data in the Southern Ocean  

NASA Astrophysics Data System (ADS)

With the Aquarius mission having completed its second full year of acquiring global sea surface salinity (SSS) measurements, many corrections were accounted for and biases were removed. However, some biases remain, keeping the mission from achieving its goal of +/- 0.2 psu accuracy for monthly products (150 km pixel size). Uncertainties in the Southern Ocean (among other biases) not only keep the mission from attaining such accuracy globally, but it also forces continued reliance on in situ point data sources. A Python script package is developed to process the Level 2 data for use, allowing users to target specific variables and to prepare ship and buoy data for analysis with the Aquarius data. To test the application of the scripting package, multiple assessments are completed. (1) The relationship between Aquarius brightness temperatures (Tb) and the percentage of ice and land cover is analyzed. Exponential and linear increases in Tb are observed with increasing ice and land, respectively. Little to no effect on Tb is found when there is less than 1% ice or land cover. (2) In situ SSS, in situ sea surface temperature (SST), and Aquarius Tb within a Response Surface Model are used to generate an equation to predict SSS using only Tb and SST as inputs. SSS is found strongly relying on SST, nearly removing the need for Aquarius T b. While this does not assist in converting Aquarius Tb into SSS, the use of SST alone proved a significantly more accurate method in predicting SSS over current Aquarius estimations for the Southern Ocean. This is not to say that SST should be used to predict SSS, but rather that the two are highly linked. Discrepancies in the relationships between SSS, SST, and T b require

Mueller, Chase

373

Structural and performance costs of reproduction in a pure capital breeder, the Children's python Antaresia childreni.  

PubMed

Females often manage the high energy demands associated with reproduction by accumulating and storing energy in the form of fat before initiating their reproductive effort. However, fat stores cannot satisfy all reproductive resource demands, which include considerable investment of amino acids (e.g., for the production of yolk proteins or gluconeogenesis). Because capital breeders generally do not eat during reproduction, these amino acids must come from internal resources, typically muscle proteins. Although the energetic costs of reproduction have been fairly well studied, there are limited data on structural and performance costs associated with the muscle degradation required to meet amino acid demands. Thus, we examined structural changes (epaxial muscle width) and performance costs (constriction and strength) over the course of reproduction in a pure capital breeder, the children's python (Antaresia childreni). We found that both egg production (i.e., direct resource allocation) and maternal care (egg brooding) induce muscle catabolism and affect performance of the female. Although epaxial muscle loss was minimal in nonreproductive females, it reached up to 22% (in females after oviposition) and 34% (in females after brooding) of initial muscle width. Interestingly, we found that individuals with higher initial muscular condition allocated more of their muscle into reproduction. The amount of muscle loss was significantly linked to clutch mass, underscoring the role of structural protein in egg production. Egg brooding significantly increased proteolysis and epaxial loss despite no direct allocation to the offspring. Muscle loss was linked to a significant reduction in performance in postreproductive females. Overall, these results demonstrate that capital-breeding females experience dramatic costs that consume structural resources and jeopardize performance. PMID:23434777

Lourdais, Olivier; Lorioux, Sophie; DeNardo, Dale F

2013-01-01

374

Nipype: A Flexible, Lightweight and Extensible Neuroimaging Data Processing Framework in Python  

PubMed Central

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 reproducible research. PMID:21897815

Gorgolewski, Krzysztof; Burns, Christopher D.; Madison, Cindee; Clark, Dav; Halchenko, Yaroslav O.; Waskom, Michael L.; Ghosh, Satrajit S.

2011-01-01

375

Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in python.  

PubMed

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 reproducible research. PMID:21897815

Gorgolewski, Krzysztof; Burns, Christopher D; Madison, Cindee; Clark, Dav; Halchenko, Yaroslav O; Waskom, Michael L; Ghosh, Satrajit S

2011-01-01

376

CCMR: Creating Elemental Arrays in the Python Numeric Module and Band Gaps of High Temperature Superconductor Crystals  

NSDL National Science Digital Library

Numeric allows one to enter many different types of data in an array, ranging from integers and floating points to Python objects. Unfortunately, other arrays could not be assigned as data elements of an array. This project was to modify the C code for Numeric so that it would gain this functionality. The problem with the original code was that if one was given an array a of dimension 2x3, and a new array b was created: b = array([[a,a],[a,a

Friesen, Michael

2005-08-17

377

Combustion-chamber Performance Characteristics of a Python Turbine-propeller Engine Investigated in Altitude Wind Tunnel  

NASA Technical Reports Server (NTRS)

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.

Campbell, Carl E

1951-01-01

378

A Python Code for the Emmanoulopoulos et al. [arXiv:1305.0304] Light Curve Simulation Algorithm  

E-print Network

I have created, for public use, a Python code allowing the simulation of light curves with any given power spectral density and any probability density function, following the algorithm described in Emmanoulopoulos et al. 2013. The simulated products have exactly the same variability and statistical properties as the observed light curves. The code and its documentation are available at: https://github.com/samconnolly/DELightcurveSimulation Note that a Mathematica code of the algorithm is given in Emmanoulopoulos et al. [arXiv:1305.0304

Connolly, S D

2015-01-01

379

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

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 constrictor populations. There is great uncertainty about many aspects of the risk assessment; the level of uncertainty is estimated separately for each risk component. Overall risk was judged to be high for five of the giant constrictors studied, and medium for the other four species. Because all nine species shared a large number of natural history traits that promote invasiveness or impede population control, none of the species was judged to be of low risk.

Reed, Robert N.; Rodda, Gordon H.

2009-01-01

380

Graph-based active learning of agglomeration (GALA): a Python library to segment 2D and 3D neuroimages.  

PubMed

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

Nunez-Iglesias, Juan; Kennedy, Ryan; Plaza, Stephen M; Chakraborty, Anirban; Katz, William T

2014-01-01

381

Graph-based active learning of agglomeration (GALA): a Python library to segment 2D and 3D neuroimages  

PubMed Central

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

Nunez-Iglesias, Juan; Kennedy, Ryan; Plaza, Stephen M.; Chakraborty, Anirban; Katz, William T.

2014-01-01

382

Novel phospholipase A2 inhibitors from python serum are potent peptide antibiotics.  

PubMed

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 ?g/disc). Among the tested peptides, phospholipase A2 inhibitory peptide (PIP)-18[59-76], ?-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 ?g/ml), with a remarkable activity noted against S. aureus at 6.8 ?g/ml dose within 6 h of incubation. Determination of minimum inhibitory concentrations (MICs) by a micro-broth dilution method at 100-3.125 ?g/ml revealed that PIP-18[59-76], ?-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 ?g/ml), while a much less inhibitory potency (MICs 12.5 ?g/ml) was noted for ?-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 ?g/disc). When the two most active peptides, PIP-18[59-76] and ?-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 ?-Asp65-PIP[59-67]) modulates NF-kB mediated wound repair in mice with relatively little haemolytic (100-1.56 ?g/ml) and cytotoxic (1000-3.125 ?g/ml) effects evident on human cells in vitro. PMID:25583073

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

383

A Python Plug-in Based Computational Framework for Spatially Distributed Environmental and Earth Sciences Modelling  

NASA Astrophysics Data System (ADS)

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 geoscience modelling frameworks such as CSDMS.

Willgoose, G. R.

2009-12-01

384

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

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.…

McFalls, Joseph A.; And Others

1986-01-01

385

Using Python for scientific computing: Efficient and flexible evaluation of the statistical characteristics of functions with multivariate random inputs  

NASA Astrophysics Data System (ADS)

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 integration. Running time: Depending on the number of random variables the time needed for the numerical estimation of the mean value of a function with a sufficiently low level of numerical error varies. For orientation, the time needed for two included examples: examples/fiber_tt_2p/fiber_tt_2p.py with 2 random variables: few milliseconds examples/fiber_po_8p/fiber_po_8p.py with 8 random variables: few seconds

Chudoba, R.; Sadílek, V.; Rypl, R.; Vo?echovský, M.

2013-02-01

386

Development of a 3D Potential Field Forward Modelling System in Python  

NASA Astrophysics Data System (ADS)

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 the field shape, merely the amplitude of the anomaly. Therefore, it is not necessary to recalculate the entire field if one of these parameters is changed. The interface to the program works similar to a paint program. The model is simply drawn into the side views or top views of the volume of interest. Relevant voxels are either activated or deactivated in this way. The software has proved to be extremely successful. It has enabled faster modelling of anomalies in a non-complex manner - implying little or no training to prospective users. References Blakely, R.J., Potential Theory in Gravity and Magnetic Applications (1996), pp 200 - 201 Singh, B., Guptasarma, D., (2001). New method for fast computation of gravity and magnetic anomalies from arbitrary polyhedral, Geophysics, 66, pp. 521 - 526

Cole, P.

2012-12-01

387

Python-based finite element code used as a universal and modular tool for electronic structure calculation  

NASA Astrophysics Data System (ADS)

Ab-initio calculations of electronic states within the density-functional framework has been performed by means of the open source finite element package SfePy (Simple Finite Elements in Python, http://sfepy.org). We describe a new robust ab-initio real-space code based on (i) density functional theory, (ii) finite element method and (iii) environment-reflecting pseudopotentials. This approach brings a new quality to solving Kohn-Sham equations, calculating electronic states, total energy, Hellmann-Feynman forces and material properties particularly for non-crystalline, non-periodic structures. The main asset of the above approach is an efficient combination of excellent convergence control of standard, universal basis used in industrially proved finite-element method, high precision of ab-initio environment-reflecting pseudopotentials, and applicability not restricted to electrically neutral periodic environment. We present also numerical examples illustrating the outputs of the method.

Cimrman, Robert; T?ma, Miroslav; Novák, Matyáš; ?ertík, Ond?ej; Plešek, Ji?í; Vacká?, Ji?í

2013-10-01

388

Wilber 3: A Python-Django Web Application For Acquiring Large-scale Event-oriented Seismic Data  

NASA Astrophysics Data System (ADS)

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.

Newman, R. L.; Clark, A.; Trabant, C. M.; Karstens, R.; Hutko, A. R.; Casey, R. E.; Ahern, T. K.

2013-12-01

389

QuTiP: An open-source Python framework for the dynamics of open quantum systems  

NASA Astrophysics Data System (ADS)

We present an object-oriented open-source framework for solving the dynamics of open quantum systems written in Python. Arbitrary Hamiltonians, including time-dependent systems, may be built up from operators and states defined by a quantum object class, and then passed on to a choice of master equation or Monte Carlo solvers. We give an overview of the basic structure for the framework before detailing the numerical simulation of open system dynamics. Several examples are given to illustrate the build up to a complete calculation. Finally, we measure the performance of our library against that of current implementations. The framework described here is particularly well suited to the fields of quantum optics, superconducting circuit devices, nanomechanics, and trapped ions, while also being ideal for use in classroom instruction. Catalogue identifier: AEMB_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEMB_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.: 16 482 No. of bytes in distributed program, including test data, etc.: 213 438 Distribution format: tar.gz Programming language: Python Computer: i386, x86-64 Operating system: Linux, Mac OSX, Windows RAM: 2+ Gigabytes Classification: 7 External routines: NumPy (http://numpy.scipy.org/), SciPy (http://www.scipy.org/), Matplotlib (http://matplotlib.sourceforge.net/) Nature of problem: Dynamics of open quantum systems. Solution method: Numerical solutions to Lindblad master equation or Monte Carlo wave function method. Restrictions: Problems must meet the criteria for using the master equation in Lindblad form. Running time: A few seconds up to several tens of minutes, depending on size of underlying Hilbert space.

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

2012-08-01

390

Processing of terabytes of data for seismic noise analysis with the Python codes of the Whisper Suite. (Invited)  

NASA Astrophysics Data System (ADS)

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 what are the changes in the elastic properties of the crust at large scale, including the co-seismic non linear response of the shallow layers and the signatures of the different processes affecting the crust at depth, such as postseismic slip and viscoelastic relaxation.

Briand, X.; Campillo, M.; Brenguier, F.; Boue, P.; Poli, P.; Roux, P.; Takeda, T.

2013-12-01

391

NERSC XT3/XT4 Benchmarking Harvey Wasserman  

E-print Network

vendors to design systems better suited to scientific computation by supplying analyses of algorithm mentioned here are from Jaguar XT4 and Cray internal system. #12;NATIONAL ENERGY RESEARCH SCIENTIFIC by the Director, Office of Science, Office of Advanced Scientific Computing Research of the U.S. Department

392

NMRbot: Python scripts enable high-throughput data collection on current Bruker BioSpin NMR spectrometers.  

PubMed

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

Clos, Lawrence J; Jofre, M Fransisca; Ellinger, James J; Westler, William M; Markley, John L

2013-06-01

393

Autonomic control of heart rate during orthostasis and the importance of orthostatic-tachycardia in the snake Python molurus.  

PubMed

Orthostasis dramatically influences the hemodynamics of terrestrial vertebrates, especially large and elongated animals such as snakes. When these animals assume a vertical orientation, gravity tends to reduce venous return, cardiac filling, cardiac output and blood pressure to the anterior regions of the body. The hypotension triggers physiological responses, which generally include vasomotor adjustments and tachycardia to normalize blood pressure. While some studies have focused on understanding the regulation of these vasomotor adjustments in ectothermic vertebrates, little is known about regulation and the importance of heart rate in these animals during orthostasis. We acquired heart rate and carotid pulse pressure (P PC) in pythons in their horizontal position, and during 30 and 60° inclinations while the animals were either untreated (control) or upon muscarinic cholinoceptor blockade and a double autonomic blockade. Double autonomic blockade completely eradicated the orthostatic-tachycardia, and without this adjustment, the P PC reduction caused by the tilts became higher than that which was observed in untreated animals. On the other hand, post-inclinatory vasomotor adjustments appeared to be of negligible importance in counterbalancing the hemodynamic effects of gravity. Finally, calculations of cardiac autonomic tones at each position revealed that the orthostatic-tachycardia is almost completely elicited by a withdrawal of vagal drive. PMID:25017862

Armelin, Vinicius Araújo; da Silva Braga, Victor Hugo; Abe, Augusto Shinya; Rantin, Francisco Tadeu; Florindo, Luiz Henrique

2014-10-01

394

Influence of temperature on the corticosterone stress-response: an experiment in the Children's python (Antaresia childreni).  

PubMed

To cope with environmental challenges, organisms have to adjust their behaviours and their physiology to the environmental conditions they face (i.e. allostasis). In vertebrates, such adjustments are often mediated through the secretion of glucocorticoids (GCs) that are well-known to activate and/or inhibit specific physiological and behavioural traits. In ectothermic species, most processes are temperature-dependent and according to previous studies, low external temperatures should be associated with low GC concentrations (both baseline and stress-induced concentrations). In this study, we experimentally tested this hypothesis by investigating the short term influence of temperature on the GC stress response in a squamate reptile, the Children's python (Antaresia childreni). Snakes were maintained in contrasting conditions (warm and cold groups), and their corticosterone (CORT) stress response was measured (baseline and stress-induced CORT concentrations), within 48h of treatment. Contrary to our prediction, baseline and stress-induced CORT concentrations were higher in the cold versus the warm treatment. In addition, we found a strong negative relationship between CORT concentrations (baseline and stress-induced) and temperature within the cold treatment. Although it remains unclear how cold temperatures can mechanistically result in increased CORT concentrations, we suggest that, at suboptimal temperature, high CORT concentrations may help the organism to maintain an alert state. PMID:23948369

Dupoué, Andréaz; Brischoux, François; Lourdais, Olivier; Angelier, Frédéric

2013-11-01

395

Application of python-based Abaqus preprocess and postprocess technique in analysis of gearbox vibration and noise reduction  

NASA Astrophysics Data System (ADS)

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.

Yi, Guilian; Sui, Yunkang; Du, Jiazheng

2011-06-01

396

C++QEDv2 Milestone 10: A C++/Python application-programming framework for simulating open quantum dynamics  

NASA Astrophysics Data System (ADS)

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++ libraries, GNU Scientific Library, Blitz++, FLENS, NumPy, SciPy Catalogue identifier of previous version: AELU_v1_0 Journal reference of previous version: Comput. Phys. Comm. 183 (2012) 1381 Does the new version supersede the previous version?: Yes Nature of problem: Definition of (open) composite quantum systems out of elementary building blocks [2,3]. Manipulation of such systems, with emphasis on dynamical simulations such as Master-equation evolution [4] and Monte Carlo wave-function simulation [5]. Solution method: Master equation, Monte Carlo wave-function method Reasons for new version: The new version is mainly a feature release, but it does correct some problems of the previous version, especially as regards the build system. Summary of revisions: We give an example for a typical Python script implementing the ring-cavity system presented in Sec. 3.3 of Ref. [2]: Restrictions: Total dimensionality of the system. Master equation-few thousands. Monte Carlo wave-function trajectory-several millions. Unusual features: Because of the heavy use of compile-time algorithms, compilation of programs written in the framework may take a long time and much memory (up to several GBs). Additional comments: The framework is not a program, but provides and implements an application-programming interface for developing simulations in the indicated problem domain. We use several C++11 features which limits the range of supported compilers (g++ 4.7, clang++ 3.1) Documentation, http://cppqed.sourceforge.net/ Running time: Depending on the magnitude of the problem, can vary from a few seconds to weeks. References: [1] Entry point: http://cppqed.sf.net [2] A. Vukics, C++QEDv2: The multi-array concept and compile-time algorithms in the definition of composite quantum systems, Comp. Phys. Comm. 183(2012)1381. [3] A. Vukics, H. Ritsch, C++QED: an object-oriented framework for wave-function simulations of cavity QED systems, Eur. Phys. J. D 44 (2007) 585. [4] H. J. Carmichael, An Open Systems Approach to Quantum Optics, Springer, 1993. [5] J. Dalibard, Y. Castin, K. Molmer, Wave-f

Sandner, Raimar; Vukics, András

2014-09-01

397

Molecular identification of Sarcocystis spp. helped to define the origin of green pythons (Morelia viridis) confiscated in Germany.  

PubMed

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

Moré, Gastón; Pantchev, Nikola; Herrmann, Daland C; Vrhovec, Majda Globokar; Öfner, Sabine; Conraths, Franz J; Schares, Gereon

2014-04-01

398

2010. 3. 28 324 . 1 Zachary  

E-print Network

. . . . 2001 Domingos Richardson (collaborative filtering) [2]. Amazon;2010. 3. 28 326 . . Facebook, Cyworld, Flickr, Twitter, Myspace . . . Twitter . Twitter 140 . Twitter follow follow . follow ( tweet

Moon, Sue B.

399

MAGNETIC FUSION ENERGY Zachary S Hartwig  

E-print Network

A Podpaly National Institute of Standards and Technology Revision : February 2014 #12;Preface The guiding feature transforms the Formulary into a gateway to a deeper understanding of the critical equations. Contributors The following is a list of people who have contributed their time, effort, and expertise

400

Constraint Network Analysis (CNA): a Python software package for efficiently linking biomacromolecular structure, flexibility, (thermo-)stability, and function.  

PubMed

For deriving maximal advantage from information on biomacromolecular flexibility and rigidity, results from rigidity analyses must be linked to biologically relevant characteristics of a structure. Here, we describe the Python-based software package Constraint Network Analysis (CNA) developed for this task. CNA functions as a front- and backend to the graph-based rigidity analysis software FIRST. CNA goes beyond the mere identification of flexible and rigid regions in a biomacromolecule in that it (I) provides a refined modeling of thermal unfolding simulations that also considers the temperature-dependence of hydrophobic tethers, (II) allows performing rigidity analyses on ensembles of network topologies, either generated from structural ensembles or by using the concept of fuzzy noncovalent constraints, and (III) computes a set of global and local indices for quantifying biomacromolecular stability. This leads to more robust results from rigidity analyses and extends the application domain of rigidity analyses in that phase transition points ("melting points") and unfolding nuclei ("structural weak spots") are determined automatically. Furthermore, CNA robustly handles small-molecule ligands in general. Such advancements are important for applying rigidity analysis to data-driven protein engineering and for estimating the influence of ligand molecules on biomacromolecular stability. CNA maintains the efficiency of FIRST such that the analysis of a single protein structure takes a few seconds for systems of several hundred residues on a single core. These features make CNA an interesting tool for linking biomacromolecular structure, flexibility, (thermo-)stability, and function. CNA is available from http://cpclab.uni-duesseldorf.de/software for nonprofit organizations. PMID:23517329

Pfleger, Christopher; Rathi, Prakash Chandra; Klein, Doris L; Radestock, Sebastian; Gohlke, Holger

2013-04-22

401

A biomolecular electrostatics solver using Python, GPUs and boundary elements that can handle solvent-filled cavities and Stern layers  

NASA Astrophysics Data System (ADS)

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.

Cooper, Christopher D.; Bardhan, Jaydeep P.; Barba, L. A.

2014-03-01

402

A biomolecular electrostatics solver using Python, GPUs and boundary elements that can handle solvent-filled cavities and Stern layers  

PubMed Central

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

Cooper, Christopher D.; Bardhan, Jaydeep P.; Barba, L. A.

2014-01-01

403

A biomolecular electrostatics solver using Python, GPUs and boundary elements that can handle solvent-filled cavities and Stern layers.  

PubMed

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

Cooper, Christopher D; Bardhan, Jaydeep P; Barba, L A

2014-03-01

404

Pancreatitis, very early compared with normal start of enteral feeding (PYTHON trial): design and rationale of a randomised controlled multicenter trial  

PubMed Central

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

2011-01-01

405

metaseq: a Python package for integrative genome-wide analysis reveals relationships between chromatin insulators and associated nuclear mRNA.  

PubMed

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

Dale, Ryan K; Matzat, Leah H; Lei, Elissa P

2014-08-01

406

eMZed: an open source framework in Python for rapid and interactive development of LC/MS data analysis workflows  

PubMed Central

Summary: The Python-based, open-source eMZed framework was developed for mass spectrometry (MS) users to create tailored workflows for liquid chromatography (LC)/MS data analysis. The goal was to establish a unique framework with comprehensive basic functionalities that are easy to apply and allow for the extension and modification of the framework in a straightforward manner. eMZed supports the iterative development and prototyping of individual evaluation strategies by providing a computing environment and tools for inspecting and modifying underlying LC/MS data. The framework specifically addresses non-expert programmers, as it requires only basic knowledge of Python and relies largely on existing successful open-source software, e.g. OpenMS. Availability: The framework eMZed and its documentation are freely available at http://emzed.biol.ethz.ch/. eMZed is published under the GPL 3.0 license, and an online discussion group is available at https://groups.google.com/group/emzed-users. Contact: kiefer@micro.biol.ethz.ch Supplementary information: Supplementary data are available at Bioinformatics online. PMID:23418185

Kiefer, Patrick; Schmitt, Uwe; Vorholt, Julia A.

2013-01-01

407

metaseq: a Python package for integrative genome-wide analysis reveals relationships between chromatin insulators and associated nuclear mRNA  

PubMed Central

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

Dale, Ryan K.; Matzat, Leah H.; Lei, Elissa P.

2014-01-01

408

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)

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. Based on the multi-segments fiber geometries and aforementioned techniques, smooth curved fiber geometries depicted by cubic B-spline polynomial interpolation are obtained and different types of RaFC RVEs with high fiber filament aspect ratio (AR>3000:1) and high RVE volume fraction (VF>40.29%) are simulated by ABAQUS scripting language PYTHON programming.

Jin, BoCheng

2011-12-01

409

Honorable Zachary J. Lemnios The Honorable Zachary J. Lemnios was sworn in as Director, Defense Research  

E-print Network

Research Projects Agency (DARPA) Microsystems Technology Office (MTO) as well as the Deputy Director organizations and represented DARPA on various national committees. Mr. Lemnios' also served as a Senior Staff Manager at DARPA. Additionally, he held various positions within industry at Hughes Aircraft Company

410

Pool-hmm: a Python program for estimating the allele frequency spectrum and detecting selective sweeps from next generation sequencing of pooled samples  

PubMed Central

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

Boitard, Simon; Kofler, Robert; Françoise, Pierre; Robelin, David; Schlötterer, Christian; Futschik, Andreas

2013-01-01

411

Pool-hmm: a Python program for estimating the allele frequency spectrum and detecting selective sweeps from next generation sequencing of pooled samples.  

PubMed

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

Boitard, Simon; Kofler, Robert; Françoise, Pierre; Robelin, David; Schlötterer, Christian; Futschik, Andreas

2013-03-01

412

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)

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.

Meyer, Carl L; Johnson, Lavern A

1952-01-01

413

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)

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 tools can provide the additional advantage of enhancing cohesion and communication across specific research areas, and reducing research obstacles in a range of disciplines.

Mayorga, E.

2013-12-01

414

Detecting an elusive invasive species: a diagnostic PCR to detect Burmese python in Florida waters and an assessment of persistence of environmental DNA.  

PubMed

Recent studies have demonstrated that detection of environmental DNA (eDNA) from aquatic vertebrates in water bodies is possible. The Burmese python, Python bivittatus, is a semi-aquatic, invasive species in Florida where its elusive nature and cryptic coloration make its detection difficult. Our goal was to develop a diagnostic PCR to detect P. bivittatus from water-borne eDNA, which could assist managers in monitoring this invasive species. First, we used captive P. bivittatus to determine whether reptilian DNA could be isolated and amplified from water samples. We also evaluated the efficacy of two DNA isolation methods and two DNA extraction kits commonly used in eDNA preparation. A fragment of the mitochondrial cytochrome b gene from P. bivittatus was detected in all water samples isolated with the sodium acetate precipitate and the QIAamp DNA Micro Kit. Next, we designed P. bivittatus-specific primers and assessed the degradation rate of eDNA in water. Our primers did not amplify DNA from closely related species, and we found that P. bivittatus DNA was consistently detectable up to 96 h. Finally, we sampled water from six field sites in south Florida. Samples from five sites, where P. bivittatus has been observed, tested positive for eDNA. The final site was negative and had no prior documented evidence of P. bivittatus. This study shows P. bivittatus eDNA can be isolated from water samples; thus, this method is a new and promising technique for the management of invasive reptiles. PMID:24119154

Piaggio, Antoinette J; Engeman, Richard M; Hopken, Matthew W; Humphrey, John S; Keacher, Kandy L; Bruce, William E; Avery, Michael L

2014-03-01

415

CMB Anisotropy Constraints on Flat-Lambda and Open CDM Cosmogonies from DMR, UCSB South Pole, Python, ARGO, MAX, White Dish, OVRO, and SuZIE Data  

E-print Network

We use joint likelihood analyses of combinations of fifteen cosmic microwave background (CMB) anisotropy data sets from the DMR, UCSB South Pole 1994, Python I--III, ARGO, MAX 4 and 5, White Dish, OVRO, and SuZIE experiments to constrain cosmogonies. We consider open and spatially-flat-Lambda cold dark matter cosmogonies, with nonrelativistic-mass density parameter Omega_0 in the range 0.1--1, baryonic-mass density parameter Omega_B in the range (0.005--0.029) h^{-2}, and age of the universe t_0 in the range (10--20) Gyr. Marginalizing over all parameters but Omega_0, the data favor Omega_0 \\simeq 0.9--1 (0.4--0.6) flat-Lambda (open) models. The range in deduced Omega_0 values is partially a consequence of the different combinations of smaller-angular-scale CMB anisotropy data sets used in the analyses, but more significantly a consequence of whether the DMR quadrupole moment is accounted for or ignored in the analysis. For both flat-Lambda and open models, after marginalizing over all other parameters, a lower Omega_B h^2 \\simeq 0.005--0.009 is favored. This is also marginally at odds with estimates from more recent CMB anisotropy data and some estimates from standard nucleosynthesis theory and observed light element abundances. For both sets of models a younger universe with t_0 \\simeq 12--15 Gyr is favored, consistent with other recent non-CMB indicators. We emphasize that since we consider only a small number of data sets, these results are tentative. More importantly, the analyses here do not rule out the currently favored flat-Lambda model with Omega_0 \\sim 0.3, nor the larger Omega_B h^2 values favored by some other data.

Pia Mukherjee; Ken Ganga; Bharat Ratra; Graca Rocha; Tarun Souradeep; Naoshi Sugiyama; Krzysztof M. Górski

2002-09-26

416

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

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'.

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

2011-01-01

417

Creative Computing with Landlab: Open-Source Python Software for Building and Exploring 2D Models of Earth-Surface Dynamics  

NASA Astrophysics Data System (ADS)

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.

Tucker, G. E.; Hobley, D. E.; Gasparini, N. M.; Hutton, E.; Istanbulluoglu, E.; Nudurupati, S.; Adams, J. M.

2013-12-01

418

Zachary Zwerko Senior Analyst, Financial Evaluation and Analysis  

E-print Network

for providing valuation analysis and financial due diligence for Merck's global Mergers & Acquisition, Licensing in their Sigma Finance group, focusing on financial analysis process improvement surrounding the Schering merger actuals to senior managment. Zack was later promoted to Manager of Sales and Marketing Finance. During

Lin, Xiaodong

419

Diseases and pests of honey bees Zachary Huang  

E-print Network

2001 2002 0 20 40 60 80 100 120 140 Natural mitefall No traps Traps Data from T. Webster 8. Using Drone-Trapping to reduce mites 1. Drone trapping method (works, labor-intensive, ~60%)\\ 2. Mitezapper 9. Stock 1. Use.Hygienic assay 3.Drone stock 4.Control of mating #12;4 Life cycle of Nosema apis · Transmitted by spores

Huang, Zachary

420

Leonard Lipshitz* and Zachary Robinson* November 11, 1996  

E-print Network

Abstract. Let K be an algebraically closed eld of any characteristic, complete with respect ... rings of separated power series in m variables of the rst sort and n variables of the. second sort ..... even at points where the Aj (x) are analytic. Let P

421

THE LOUISIANA OYSTER INDUSTRY. By F. C. ZACHARIE.  

E-print Network

and Pontchartrain, and following the shore line southward and westward around the mouths of the Mississippi River flavor, can not be excelled the world over. East of the Mississippi River these natural beds are still

422

Zachary D. Barker: Final DHS HS-STEM Report  

SciTech Connect

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.

Barker, Z D

2008-08-14

423

Python Programming: Lecture 1 Introduction  

E-print Network

Module system Just plain awesome #12;Introduction Java public class Hello { public static void main(String[] args) { System.out.println("Hello, world!"); } } #12;Introduction C++ #include int main() { std::cout Hello World!" hello world" #12

Plotkin, Joshua B.

424

Python Optimization Modeling Objects (Pyomo)  

E-print Network

Dec 29, 2009 ... to manage costs associated with the deployment of optimization ... supporting open standards and avoiding being locked in to a single vendor. ...... types and associated production rates, production limits, and inventory hold-.

2009-12-29

425

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 ...

426

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)

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 solution with daily time steps. Solute transport is described using a mixing cell concept in the unsaturated zone, and by numerically solving the 2D solute transport equation in the groundwater. Denitrification in soil and groundwater is described as a first order process. Mineralisation of organic N and P in the top soil of every grid cell is modelled according to the principles of the RothC model and by assigning C:N and C:P ratios to organic matter pools. As mineralization is a slow process, it is modelled with monthly rather than daily time steps. Soil erosion and N and P transport with sediment flow is modelled in line with the WaTEM/SEDEM spatially distributed soil erosion and sediment delivery model, also with monthly time steps. The performance of the model was evaluated with discharge and water quality time series from small catchments in Flanders.

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

427

Continuum states from time-dependent density functional theory Adam Wasserman  

E-print Network

.g., Ref. 13 . Since continuum states are the current-carrying states in mo- lecular electronic devices, we interacting electrons with that of its ground-state KS analog, s r,r ; .6 In operator form indicates spatial-dependent density functional theory is used to study low-lying electronic continuum states of targets that can bind

428

Python Java Classes, Objects, and Methods  

E-print Network

, all functions are in objects or classes so they are all called methods. Hello World def main(): print "Hello World" ---------------------------------------------------------------- public class HelloPrinter { public static void main(String[] args) { System.out.println("Hello World"); } } public class Hello

Parker, Gary B.

429

GaPP: Gaussian Processes in Python  

NASA Astrophysics Data System (ADS)

The algorithm Gaussian processes can reconstruct a function from a sample of data without assuming a parameterization of the function. The GaPP code can be used on any dataset to reconstruct a function. It handles individual error bars on the data and can be used to determine the derivatives of the reconstructed function. The data sample can consist of observations of the function and of its first derivative.

Seikel, Marina; Clarkson, Chris; Smith, Mathew

2013-03-01

430

PyGFit: Python Galaxy Fitter  

NASA Astrophysics Data System (ADS)

PyGFit measures PSF-matched photometry from images with disparate pixel scales and PSF sizes; its primary purpose is to extract robust spectral energy distributions (SEDs) from crowded images. It fits blended sources in crowded, low resolution images with models generated from a higher resolution image, thus minimizing the impact of crowding and also yielding consistently measured fluxes in different filters which minimizes systematic uncertainty in the final SEDs.

Mancone, Conor L.; Gonzalez, Anthony H.; Moustakas, Leonidas A.; Price, Andrew

2014-02-01

431

SunPy: Python for Solar Physicists  

NASA Astrophysics Data System (ADS)

SunPy is a community-developed free and open-source software package for solar physics and is an alternative to the SolarSoft data analysis environment. SunPy provides data structures for representing the most common solar data types (images, lightcurves, and spectra) and integration with the Virtual Solar Observatory (VSO) and the Heliophysics Event Knowledgebase (HEK) for data acquisition.

SunPy Development Team

2014-01-01

432

PyDrizzle: Python version of Drizzle  

NASA Astrophysics Data System (ADS)

PyDrizzle provides a semi-automated interface for computing the parameters necessary for running Drizzle. PyDrizzle performs the task of determining the parameters necessary for aligning images based on the WCS information in the input image headers, as well as any supplemental alignment information provided in shift files, and combines the images onto the same WCS. Though it does not identify cosmic rays, it has the ability to ignore pixels flagged as bad, such as pixels identified by other programs as affected by cosmic rays.

Hack, Warren; Blakeslee, John; Meurer, Gerhardt; Hook, Richard

2014-01-01

433

Medieval Romances: "Perceval" to "Monty Python."  

ERIC Educational Resources Information Center

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…

Jehle, Dorothy M.

434

Python programming --course introduction Finn Arup Nielsen  

E-print Network

, statistics, ma- chine learning) Some overlap with 02805 (So- cial graphs and interaction), 02806 Social data analysis and visualization, 02821 (Web og social interaktion) and 02822 (Social data modeller- ing). If you (usually clearer than Perl), interactive, many li- braries, runs on many platforms, e.g., Nokia smartphones

435

Python Engine Installed in Altitude Wind Tunnel  

NASA Technical Reports Server (NTRS)

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.

1949-01-01

436

Python programming --Web serving Finn Arup Nielsen  

E-print Network

.Template, tornado.template, . . . Called the "template" in Django. Controller translates URLs into actions using System Simple CherryPy Django Tornado Web server Apache CherryPy Apache Tornado Model e.g. pysqlite e.g. sqlobject django.db tornado.database View print e.g. Cheetah django.template tornado.template Controller

437

Python Programming: Lecture 3 Lili Dworkin  

E-print Network

, vegetable='broccoli'): print fruit1, fruit2, vegetable >>> l = ['kiwi', 'kumquat'] >>> d = {'vegetable': 'cauliflower'} >>> foo(*l, **d) kiwi kumquat cauliflower #12;Args and Kwargs Functions can take a variable([str(x) for x in args]) >>> foo(1, 2, 3) 1 2 3 >>> foo('a', 'b', 'c', 'd', 'e') a b c d e >>> foo(*l) kiwi

Plotkin, Joshua B.

438

Python Programming: Lecture 2 Lili Dworkin  

E-print Network

"] >>> a[1] = "pear" >>> a ['apple', 'pear', 'banana'] >>> a.insert(2, "kiwi") >>> a ['apple', 'orange', 'kiwi', 'banana'] #12;Lists: Concatenation >>> a = [1, 2, 3] >>> b = [4, 5, 6] >>> a + b [1, 2, 3, 4, 5

Plotkin, Joshua B.

439

MTpy: A Python toolbox for magnetotellurics  

USGS Publications Warehouse

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.

Krieger, Lars; Peacock, Jared R.

2014-01-01

440