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Sample records for zachary wasserman python

  1. Python import replacement

    Energy Science and Technology Software Center (ESTSC)

    2011-10-01

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

  2. Fast retreat of Zachariæ Isstrøm, northeast Greenland.

    PubMed

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

    2015-12-11

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

  3. Fast retreat of Zachariæ Isstrøm, northeast Greenland

    NASA Astrophysics Data System (ADS)

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

    2015-12-01

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

  4. Python for Ecology

    EPA Science Inventory

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

  5. Python to learn programming

    NASA Astrophysics Data System (ADS)

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

    2013-04-01

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

  6. Python in Astronomy 2016

    NASA Astrophysics Data System (ADS)

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

    2016-04-01

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

  7. Python Data Visualization

    SciTech Connect

    2011-08-15

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

  8. Developing Sherpa with Python

    NASA Astrophysics Data System (ADS)

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

    2007-10-01

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

  9. Repast for Python Scripting.

    SciTech Connect

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

    2005-01-01

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

  10. Python Data Visualization

    Energy Science and Technology Software Center (ESTSC)

    2011-08-15

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

  11. Python/Lua Benchmarks

    SciTech Connect

    Busby, L.

    2014-08-01

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

  12. Python and computer vision

    SciTech Connect

    Doak, J. E.; Prasad, Lakshman

    2002-01-01

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

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

    USGS Publications Warehouse

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

    2011-01-01

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

  14. Learning Scientific Programming with Python

    NASA Astrophysics Data System (ADS)

    Hill, Christian

    2016-02-01

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

  15. Python fiber optic seal

    SciTech Connect

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

    1993-08-01

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

  16. Pynamic: the Python Dynamic Benchmark

    SciTech Connect

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

    2007-07-10

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

  17. Extension Modules for the Python Interpretive language

    Energy Science and Technology Software Center (ESTSC)

    2006-12-29

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

  18. Accessing the VO with Python

    NASA Astrophysics Data System (ADS)

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

    2014-05-01

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

  19. An Array Module for Python

    NASA Astrophysics Data System (ADS)

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

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

  20. Algorithmic synthesis using Python compiler

    NASA Astrophysics Data System (ADS)

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

    2015-09-01

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

  1. Python-ARM Radar Toolkit

    SciTech Connect

    Jonathan Helmus, Scott Collis

    2013-03-17

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

  2. Python in Astronomy 2016 Unproceedings

    NASA Astrophysics Data System (ADS)

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

    2016-03-01

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

  3. Imagining a Stata / Python Combination

    NASA Technical Reports Server (NTRS)

    Fiedler, James

    2012-01-01

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

  4. Advanced Python Scripting Using Sherpa

    NASA Astrophysics Data System (ADS)

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

    2011-07-01

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

  5. Pybus -- A Python Software Bus

    SciTech Connect

    Lavrijsen, Wim T.L.P.

    2004-10-14

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

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

    Federal Register 2010, 2011, 2012, 2013, 2014

    2012-01-23

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

  7. Pyomo : Python Optimization Modeling Objects.

    SciTech Connect

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

    2010-11-01

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

  8. Steering object-oriented computations with Python

    SciTech Connect

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

    1996-10-01

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

  9. BoF - Python in Astronomy

    NASA Astrophysics Data System (ADS)

    Barrett, P. E.

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

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

    NASA Astrophysics Data System (ADS)

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

    2014-12-01

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

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

    PubMed

    Hedley, Joanna; Eatwell, Kevin; Schwarz, Tobias

    2014-01-01

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

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

    USGS Publications Warehouse

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

    2011-01-01

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

  13. Parallel, Distributed Scripting with Python

    SciTech Connect

    Miller, P J

    2002-05-24

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

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

    PubMed

    Leal, Francisca; Cohn, Martin J

    2015-01-01

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

  15. Reflection-Based Python-C++ Bindings

    SciTech Connect

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

    2004-10-14

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

  16. Python fiber-optic seal

    SciTech Connect

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

    1993-12-31

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

  17. Python-Based Applications for Hydrogeological Modeling

    NASA Astrophysics Data System (ADS)

    Khambhammettu, P.

    2013-12-01

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

  18. Julia and Python in Astronomy: Better Together

    NASA Astrophysics Data System (ADS)

    Barbary, Kyle

    2016-03-01

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

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

    PubMed

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

    2002-08-01

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

  20. Gist: A scientific graphics package for Python

    SciTech Connect

    Busby, L.E.

    1996-05-08

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

  1. SunPy—Python for solar physics

    NASA Astrophysics Data System (ADS)

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

    2015-01-01

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

  2. The Python Interface to Antelope and Applications

    NASA Astrophysics Data System (ADS)

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

    2008-12-01

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

  3. Charming Users into Scripting CIAO with Python

    NASA Astrophysics Data System (ADS)

    Burke, D. J.

    2011-07-01

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

  4. Python for Large-Scale Electrophysiology

    PubMed Central

    Spacek, Martin; Blanche, Tim; Swindale, Nicholas

    2008-01-01

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

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

    SciTech Connect

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

    1996-04-16

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

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

    USGS Publications Warehouse

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

    2011-01-01

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

  7. Python Bindings for the Common Pipeline Library

    NASA Astrophysics Data System (ADS)

    Streicher, O.; Weilbacher, P. M.

    2012-09-01

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

  8. Python Ephemeris Module for Radio Astronomy

    NASA Astrophysics Data System (ADS)

    Kuiper, T. B.

    2013-05-01

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

  9. THE PYTHON SHELL FOR THE ORBIT CODE

    SciTech Connect

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

    2009-01-01

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

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

    USGS Publications Warehouse

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

    2012-01-01

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

  11. Python scripting in the nengo simulator.

    PubMed

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

    2009-01-01

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

  12. Python Scripting in the Nengo Simulator

    PubMed Central

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

    2008-01-01

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

  13. PyXNAT: XNAT in Python

    PubMed Central

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

    2012-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2013-10-01

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

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

    PubMed Central

    Reed, Robert N.

    2015-01-01

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

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

    USGS Publications Warehouse

    Falk, Bryan; Reed, Robert N.

    2015-01-01

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

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

    NASA Astrophysics Data System (ADS)

    Poore, Geoffrey M.

    2015-01-01

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

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

    PubMed

    Penning, David A; Dartez, Schuyler F

    2016-03-01

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

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

    PubMed

    Falk, Bryan G; Reed, Robert N

    2015-01-01

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

  20. matplotlib -- A Portable Python Plotting Package

    NASA Astrophysics Data System (ADS)

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

    2005-12-01

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

  1. Stimfit: quantifying electrophysiological data with Python

    PubMed Central

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

    2013-01-01

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

  2. Simulation of Planetary Formation using Python

    NASA Astrophysics Data System (ADS)

    Bufkin, James; Bixler, David

    2015-03-01

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

  3. ParselTongue: AIPS Talking Python

    NASA Astrophysics Data System (ADS)

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

    2006-07-01

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

  4. Writing analytic element programs in Python.

    PubMed

    Bakker, Mark; Kelson, Victor A

    2009-01-01

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

  5. Meteor Shower Identification and Characterization with Python

    NASA Technical Reports Server (NTRS)

    Moorhead, Althea

    2015-01-01

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

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

    USGS Publications Warehouse

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

    2012-01-01

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

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

    USGS Publications Warehouse

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

    2012-01-01

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

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

    PubMed Central

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

    2014-01-01

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

  9. Python: a language for computational physics

    NASA Astrophysics Data System (ADS)

    Borcherds, P. H.

    2007-07-01

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

  10. POPPY: Physical Optics Propagation in PYthon

    NASA Astrophysics Data System (ADS)

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

    2016-02-01

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

  11. Python in gamma-ray astronomy

    NASA Astrophysics Data System (ADS)

    Deil, Christoph Deil

    2016-03-01

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

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

    PubMed

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

    2009-03-01

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

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

    PubMed

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

    2015-01-01

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

  14. The Virtual Observatory for the Python Programmer

    NASA Astrophysics Data System (ADS)

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

    2014-01-01

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

  15. Feeding a large-scale physics application to Python

    SciTech Connect

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

    1997-10-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2014-11-01

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

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

    PubMed

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

    2015-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2014-05-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2015-12-01

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

  20. Re-imagining a Stata/Python Combination

    NASA Technical Reports Server (NTRS)

    Fiedler, James

    2013-01-01

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

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

    SciTech Connect

    Gezahegne, A; Love, N S

    2007-12-10

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

  2. Rapid web development using AJAX and Python

    NASA Astrophysics Data System (ADS)

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

    2008-07-01

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

  3. scikit-image: image processing in Python

    PubMed Central

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

    2014-01-01

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

  4. TRIPPy: Python-based Trailed Source Photometry

    NASA Astrophysics Data System (ADS)

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

    2016-05-01

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

  5. scikit-image: image processing in Python.

    PubMed

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

    2014-01-01

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

  6. Pcigale: Porting Code Investigating Galaxy Emission to Python

    NASA Astrophysics Data System (ADS)

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

    2014-05-01

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

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

    SciTech Connect

    Dubois, P F

    1998-07-14

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

  8. Python Program to Select HII Region Models

    NASA Astrophysics Data System (ADS)

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

    2016-01-01

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

  9. SunPy: Solar Physics in Python

    NASA Astrophysics Data System (ADS)

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

    2015-04-01

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

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

    PubMed

    Tsuji, Yamato; Prayitno, Bambang; Suryobroto, Bambang

    2016-04-01

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

  11. PyMOOSE: Interoperable Scripting in Python for MOOSE

    PubMed Central

    Ray, Subhasis; Bhalla, Upinder S.

    2008-01-01

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

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

    PubMed

    Brashears, Jake; DeNardo, Dale F

    2015-08-01

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

  13. DMTCP: bringing interactive checkpoint-restart to Python

    NASA Astrophysics Data System (ADS)

    Arya, Kapil; Cooperman, Gene

    2015-01-01

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

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

    Federal Register 2010, 2011, 2012, 2013, 2014

    2010-07-01

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

  15. COSMOS: Python library for massively parallel workflows

    PubMed Central

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

    2014-01-01

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

  16. ScrumPy: metabolic modelling with Python.

    PubMed

    Poolman, M G

    2006-09-01

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

  17. Neutron Scattering Experiment Automation with Python

    SciTech Connect

    Zolnierczuk, Piotr A; Riedel, Richard A

    2010-01-01

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

  18. galpy: A python LIBRARY FOR GALACTIC DYNAMICS

    SciTech Connect

    Bovy, Jo

    2015-02-01

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

  19. galpy: A python Library for Galactic Dynamics

    NASA Astrophysics Data System (ADS)

    Bovy, Jo

    2015-02-01

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

  20. drive-casa: Python interface for CASA scripting

    NASA Astrophysics Data System (ADS)

    Staley, Tim D.

    2015-04-01

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

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

    NASA Astrophysics Data System (ADS)

    Collis, S. M.; Helmus, J.

    2015-12-01

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

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

    NASA Astrophysics Data System (ADS)

    Sanchez, David; Deil, Christoph

    2015-01-01

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

  3. Adapting the BIMA Image Pipeline for Miriad Using Python

    NASA Astrophysics Data System (ADS)

    Mehringer, D. M.; Plante, R.

    2004-07-01

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

  4. MEG and EEG data analysis with MNE-Python

    PubMed Central

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

    2013-01-01

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

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

    PubMed

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

    2013-12-26

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

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

    PubMed Central

    Diamond, Steven; Boyd, Stephen

    2016-01-01

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

  7. PyMultiNest: Python interface for MultiNest

    NASA Astrophysics Data System (ADS)

    Buchner, Johannes

    2016-06-01

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

  8. Rapid Development of Interferometric Software Using MIRIAD and Python

    NASA Astrophysics Data System (ADS)

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

    2012-06-01

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

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

    USGS Publications Warehouse

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

    2012-01-01

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

  10. Using Python to Develop Graphical Interfaces to Scientific Data

    SciTech Connect

    MacFarland, L; Streletz, G J

    1999-09-24

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

  11. Introducing Python tools for magnetotellurics: MTpy

    NASA Astrophysics Data System (ADS)

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

    2013-12-01

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

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

    PubMed

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

    2016-05-01

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

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

    PubMed

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

    2015-10-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2015-04-01

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

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

    PubMed

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

    2012-08-01

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

  16. Python algorithms in particle tracking microrheology

    PubMed Central

    2012-01-01

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

  17. MTpy: A Python toolbox for magnetotellurics

    NASA Astrophysics Data System (ADS)

    Krieger, Lars; Peacock, Jared R.

    2014-11-01

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

  18. TRIPPy: Trailed Image Photometry in Python

    NASA Astrophysics Data System (ADS)

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

    2016-06-01

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

  19. Bioinformatic pipelines in Python with Leaf

    PubMed Central

    2013-01-01

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

  20. Python-Assisted MODFLOW Application and Code Development

    NASA Astrophysics Data System (ADS)

    Langevin, C.

    2013-12-01

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

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

    PubMed

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

    2014-05-01

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

  2. pyam: Python Implementation of YaM

    NASA Technical Reports Server (NTRS)

    Myint, Steven; Jain, Abhinandan

    2012-01-01

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

  3. A Python interface with Narcisse graphics

    SciTech Connect

    Motteler, Z.C.

    1996-04-15

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

  4. Synthetic seismogram web service and Python tools

    NASA Astrophysics Data System (ADS)

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

    2014-05-01

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

  5. Scripting MODFLOW model development using Python and FloPy

    USGS Publications Warehouse

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

    2016-01-01

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

  6. Stochastic spatio-temporal modelling with PCRaster Python

    NASA Astrophysics Data System (ADS)

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

    2012-04-01

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

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

    PubMed

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

    1998-02-01

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

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

    PubMed Central

    Rutllant, Josep

    2016-01-01

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

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

    PubMed

    Irizarry, Kristopher J L; Rutllant, Josep

    2016-01-01

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

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

    PubMed

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

    2004-03-01

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

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

    NASA Astrophysics Data System (ADS)

    Mortensen, Mikael; Langtangen, Hans Petter

    2016-06-01

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

  12. Advanced PANIC quick-look tool using Python

    NASA Astrophysics Data System (ADS)

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

    2012-09-01

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

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

    SciTech Connect

    Kostka, Timothy D.

    2013-01-01

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

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

    USGS Publications Warehouse

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

    2012-01-01

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

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

    PubMed

    Jurica, Peter; van Leeuwen, Cees

    2009-01-01

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

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

    PubMed

    Meyer, Robert; Obermayer, Klaus

    2016-01-01

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

  17. PyKrige: Development of a Kriging Toolkit for Python

    NASA Astrophysics Data System (ADS)

    Murphy, B. S.

    2014-12-01

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

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

    PubMed Central

    Jurica, Peter; van Leeuwen, Cees

    2008-01-01

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

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

    PubMed Central

    Meyer, Robert; Obermayer, Klaus

    2016-01-01

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

  20. SunPy: Python for Solar Physics Data Analysis

    NASA Astrophysics Data System (ADS)

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

    2012-05-01

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

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

    NASA Astrophysics Data System (ADS)

    Zingale, Michael

    2015-07-01

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

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

    PubMed

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

    2015-10-01

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

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

    NASA Astrophysics Data System (ADS)

    Altay, G.; Wise, J. H.

    2015-04-01

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

  4. Flexible Environmental Modeling with Python and Open - GIS

    NASA Astrophysics Data System (ADS)

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

    2015-04-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2014-12-01

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

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

    USGS Publications Warehouse

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

    2011-01-01

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

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

    PubMed Central

    2011-01-01

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

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

    USGS Publications Warehouse

    Hunter, Margaret E.; Hart, Kristen M.

    2013-01-01

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

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

    USGS Publications Warehouse

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

    2016-01-01

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

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

    PubMed

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

    2015-09-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2015-12-01

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

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

    PubMed Central

    Spielman, Stephanie J.; Wilke, Claus O.

    2015-01-01

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

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

    PubMed

    Spielman, Stephanie J; Wilke, Claus O

    2015-01-01

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

  14. Developing PYTHON Codes for the Undergraduate ALFALFA Team

    NASA Astrophysics Data System (ADS)

    Troischt, Parker; Ryan, Nicholas; Alfalfa Team

    2016-03-01

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

  15. From Start to Finish: Python for Space Missions

    NASA Astrophysics Data System (ADS)

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

    2010-12-01

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

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

    PubMed

    Krause, Florian; Lindemann, Oliver

    2014-06-01

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

  17. Python as a Federation Tool for GENESIS 3.0

    PubMed Central

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

    2012-01-01

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

  18. PyRAT - python radiography analysis tool (u)

    SciTech Connect

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

    2011-01-14

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

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

    Energy Science and Technology Software Center (ESTSC)

    2008-08-14

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

  20. ObsPy: A Python Toolbox for Seismology

    NASA Astrophysics Data System (ADS)

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

    2013-12-01

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

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

    NASA Astrophysics Data System (ADS)

    Sommer, Philipp

    2016-04-01

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

  2. Python as a federation tool for GENESIS 3.0.

    PubMed

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

    2012-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2013-07-01

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

  4. AstroAsciiData: ASCII table Python module

    NASA Astrophysics Data System (ADS)

    Kümmel, Martin; Haase, Jonas

    2013-11-01

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

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

    PubMed

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

    2014-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2013-08-01

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

  7. ELLIPT2D: A Flexible Finite Element Code Written Python

    SciTech Connect

    Pletzer, A.; Mollis, J.C.

    2001-03-22

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

  8. DREAMTools: a Python package for scoring collaborative challenges

    PubMed Central

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

    2016-01-01

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

  9. User-friendly parallelization of GAUDI applications with Python

    NASA Astrophysics Data System (ADS)

    Mato, Pere; Smith, Eoin

    2010-04-01

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

  10. GAiN: Distributed Array Computation with Python

    SciTech Connect

    Daily, Jeffrey A.

    2009-05-01

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

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

    PubMed Central

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

    2014-01-01

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

  12. ACPYPE - AnteChamber PYthon Parser interfacE

    PubMed Central

    2012-01-01

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

  13. VPython: Python plus Animations in Stereo 3D

    NASA Astrophysics Data System (ADS)

    Sherwood, Bruce

    2004-03-01

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

  14. Python for Information Theoretic Analysis of Neural Data

    PubMed Central

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

    2008-01-01

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

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

    PubMed

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

    2015-04-22

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

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

    PubMed Central

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

    2015-01-01

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

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

    USGS Publications Warehouse

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

    2015-01-01

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

  18. PyVO: Python access to the Virtual Observatory

    NASA Astrophysics Data System (ADS)

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

    2014-02-01

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

  19. A Python-based IRAF Task Parameter Editor

    NASA Astrophysics Data System (ADS)

    de La Peña, M. D.

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

  20. The fast azimuthal integration Python library: pyFAI

    PubMed Central

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

    2015-01-01

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

  1. LASER STRIPPING COMPUTING WITH THE PYTHON ORBIT CODE

    SciTech Connect

    Gorlov, Timofey V; Shishlo, Andrei P

    2009-01-01

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

  2. Amateur Image Pipeline Processing using Python plus PyRAF

    NASA Astrophysics Data System (ADS)

    Green, Wayne

    2012-05-01

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

  3. An object oriented Python interface for atomistic simulations

    NASA Astrophysics Data System (ADS)

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

    2016-01-01

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

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

    NASA Astrophysics Data System (ADS)

    Kent, Brian R.

    2016-06-01

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

  5. Python GST Implementation v. 0.9 beta

    SciTech Connect

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

    2015-12-18

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

  6. Python GST Implementation v. 0.9 beta

    Energy Science and Technology Software Center (ESTSC)

    2015-12-18

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

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

    PubMed

    Secor, S M; Diamond, J

    1995-06-01

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

  8. New Python-based methods for data processing.

    PubMed

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

    2013-07-01

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

  9. New Python-based methods for data processing

    PubMed Central

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

    2013-01-01

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

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

    SciTech Connect

    Grant, Robert

    2015-11-23

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

  11. Programming biological models in Python using PySB

    PubMed Central

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

    2013-01-01

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

  12. Novel divergent nidovirus in a python with pneumonia.

    PubMed

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

    2014-11-01

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

  13. New Python-based methods for data processing

    SciTech Connect

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

    2013-07-01

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

  14. Conservative constraints on early cosmology with MONTE PYTHON

    SciTech Connect

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

    2013-02-01

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

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

    NASA Astrophysics Data System (ADS)

    Gerosa, Davide; Kesden, Michael

    2016-06-01

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

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

    PubMed Central

    Rossant, Cyrille; Harris, Kenneth D.

    2013-01-01

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

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

    SciTech Connect

    Hart, William Eugene

    2010-11-01

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

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

    PubMed Central

    2010-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2006-07-01

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

  20. Python package for model STructure ANalysis (pySTAN)

    NASA Astrophysics Data System (ADS)

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

    2013-04-01

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

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

    PubMed

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

    2012-12-01

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

  2. NIFTY: A versatile Python library for signal inference

    NASA Astrophysics Data System (ADS)

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

    2013-02-01

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

  3. PyRAT (python radiography analysis tool): overview

    SciTech Connect

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

    2011-01-14

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

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

    NASA Astrophysics Data System (ADS)

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

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

  5. PyORBIT: A Python Shell For ORBIT

    SciTech Connect

    Jean-Francois Ostiguy; Jeffrey Holmes

    2003-07-01

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

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

    PubMed

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

    2014-07-01

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

  7. A cross-validation package driving Netica with python

    USGS Publications Warehouse

    Fienen, Michael N.; Plant, Nathaniel G.

    2014-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2016-03-01

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

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

    NASA Astrophysics Data System (ADS)

    Parviainen, Hannu

    2015-07-01

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

  10. PyGSM: Python interface to the Global Sky Model

    NASA Astrophysics Data System (ADS)

    Price, Danny C.

    2016-03-01

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

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

    Energy Science and Technology Software Center (ESTSC)

    2012-02-22

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

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

    SciTech Connect

    Grote, D. P.

    2012-02-22

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

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

    USGS Publications Warehouse

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

    2014-01-01

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

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

    PubMed

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

    2014-03-01

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

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

    PubMed Central

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

    2014-01-01

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

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

    SciTech Connect

    Spotz, William F.

    2012-01-01

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

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

    PubMed

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

    2015-01-01

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

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

    PubMed Central

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

    2015-01-01

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

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

    PubMed

    Sharma, Jai Bhagwan

    2016-01-01

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

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

    PubMed Central

    Sharma, Jai Bhagwan

    2016-01-01

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

  1. Obtaining and processing Daymet data using Python and ArcGIS

    USGS Publications Warehouse

    Bohms, Stefanie

    2013-01-01

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

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

    USGS Publications Warehouse

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

    2015-01-01

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

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

    PubMed Central

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

    2012-01-01

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

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

    PubMed

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

    2012-02-14

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

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

    NASA Astrophysics Data System (ADS)

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

    2011-03-01

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

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

    USGS Publications Warehouse

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

    2012-01-01

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

  7. The Programming Language Python In Earth System Simulations

    NASA Astrophysics Data System (ADS)

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

    2004-12-01

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

  8. AstroML: Python-powered Machine Learning for Astronomy

    NASA Astrophysics Data System (ADS)

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

    2014-01-01

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

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

    PubMed

    Helfenstein, Andreas; Tammela, Päivi

    2015-02-01

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

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

    PubMed

    Li, Pengfei; Merz, Kenneth M

    2016-04-25

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

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

    PubMed Central

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

    2015-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2015-01-01

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

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

    PubMed Central

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

    2014-01-01

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

  14. batman: BAsic Transit Model cAlculatioN in Python

    NASA Astrophysics Data System (ADS)

    Kreidberg, Laura

    2015-11-01

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

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

    PubMed

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

    2015-01-01

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

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

    PubMed

    Fobian, Dannie; Overgaard, Johannes; Wang, Tobias

    2014-11-15

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

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

    NASA Astrophysics Data System (ADS)

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

    2002-04-01

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

  18. SunPy - Python for Solar Physics, Version 0.4

    NASA Astrophysics Data System (ADS)

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

    2014-06-01

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

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

    PubMed

    Esquerré, Damien; Scott Keogh, J

    2016-07-01

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

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

    PubMed

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

    2015-12-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2014-01-01

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

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

    PubMed

    Yesylevskyy, Semen O

    2015-07-15

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

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

    NASA Astrophysics Data System (ADS)

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

    2015-04-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2015-04-01

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

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

    PubMed Central

    Chen, Weiliang; De Schutter, Erik

    2014-01-01

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

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

    PubMed

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

    2016-06-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2015-09-01

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

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

    NASA Astrophysics Data System (ADS)

    Houska, Tobias; Kraft, Philipp; Breuer, Lutz

    2015-04-01

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

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

    PubMed

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

    2016-05-01

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

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

    PubMed

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

    2014-01-01

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