GillesPy: A Python Package for Stochastic Model Building and Simulation

PubMed Central

Abel, John H.; Drawert, Brian; Hellander, Andreas; Petzold, Linda R.

2017-01-01

GillesPy is an open-source Python package for model construction and simulation of stochastic biochemical systems. GillesPy consists of a Python framework for model building and an interface to the StochKit2 suite of efficient simulation algorithms based on the Gillespie stochastic simulation algorithms (SSA). To enable intuitive model construction and seamless integration into the scientific Python stack, we present an easy to understand, action-oriented programming interface. Here, we describe the components of this package and provide a detailed example relevant to the computational biology community. PMID:28630888

Neuroimaging, Genetics, and Clinical Data Sharing in Python Using the CubicWeb Framework

PubMed Central

Grigis, Antoine; Goyard, David; Cherbonnier, Robin; Gareau, Thomas; Papadopoulos Orfanos, Dimitri; Chauvat, Nicolas; Di Mascio, Adrien; Schumann, Gunter; Spooren, Will; Murphy, Declan; Frouin, Vincent

2017-01-01

In neurosciences or psychiatry, the emergence of large multi-center population imaging studies raises numerous technological challenges. From distributed data collection, across different institutions and countries, to final data publication service, one must handle the massive, heterogeneous, and complex data from genetics, imaging, demographics, or clinical scores. These data must be both efficiently obtained and downloadable. We present a Python solution, based on the CubicWeb open-source semantic framework, aimed at building population imaging study repositories. In addition, we focus on the tools developed around this framework to overcome the challenges associated with data sharing and collaborative requirements. We describe a set of three highly adaptive web services that transform the CubicWeb framework into a (1) multi-center upload platform, (2) collaborative quality assessment platform, and (3) publication platform endowed with massive-download capabilities. Two major European projects, IMAGEN and EU-AIMS, are currently supported by the described framework. We also present a Python package that enables end users to remotely query neuroimaging, genetics, and clinical data from scripts. PMID:28360851

Neuroimaging, Genetics, and Clinical Data Sharing in Python Using the CubicWeb Framework.

PubMed

Grigis, Antoine; Goyard, David; Cherbonnier, Robin; Gareau, Thomas; Papadopoulos Orfanos, Dimitri; Chauvat, Nicolas; Di Mascio, Adrien; Schumann, Gunter; Spooren, Will; Murphy, Declan; Frouin, Vincent

2017-01-01

In neurosciences or psychiatry, the emergence of large multi-center population imaging studies raises numerous technological challenges. From distributed data collection, across different institutions and countries, to final data publication service, one must handle the massive, heterogeneous, and complex data from genetics, imaging, demographics, or clinical scores. These data must be both efficiently obtained and downloadable. We present a Python solution, based on the CubicWeb open-source semantic framework, aimed at building population imaging study repositories. In addition, we focus on the tools developed around this framework to overcome the challenges associated with data sharing and collaborative requirements. We describe a set of three highly adaptive web services that transform the CubicWeb framework into a (1) multi-center upload platform, (2) collaborative quality assessment platform, and (3) publication platform endowed with massive-download capabilities. Two major European projects, IMAGEN and EU-AIMS, are currently supported by the described framework. We also present a Python package that enables end users to remotely query neuroimaging, genetics, and clinical data from scripts.

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

PubMed

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

2013-12-15

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

EMPIRE and pyenda: Two ensemble-based data assimilation systems written in Fortran and Python

NASA Astrophysics Data System (ADS)

Geppert, Gernot; Browne, Phil; van Leeuwen, Peter Jan; Merker, Claire

2017-04-01

We present and compare the features of two ensemble-based data assimilation frameworks, EMPIRE and pyenda. Both frameworks allow to couple models to the assimilation codes using the Message Passing Interface (MPI), leading to extremely efficient and fast coupling between models and the data-assimilation codes. The Fortran-based system EMPIRE (Employing Message Passing Interface for Researching Ensembles) is optimized for parallel, high-performance computing. It currently includes a suite of data assimilation algorithms including variants of the ensemble Kalman and several the particle filters. EMPIRE is targeted at models of all kinds of complexity and has been coupled to several geoscience models, eg. the Lorenz-63 model, a barotropic vorticity model, the general circulation model HadCM3, the ocean model NEMO, and the land-surface model JULES. The Python-based system pyenda (Python Ensemble Data Assimilation) allows Fortran- and Python-based models to be used for data assimilation. Models can be coupled either using MPI or by using a Python interface. Using Python allows quick prototyping and pyenda is aimed at small to medium scale models. pyenda currently includes variants of the ensemble Kalman filter and has been coupled to the Lorenz-63 model, an advection-based precipitation nowcasting scheme, and the dynamic global vegetation model JSBACH.

Dr.LiTHO: a development and research lithography simulator

NASA Astrophysics Data System (ADS)

Fühner, Tim; Schnattinger, Thomas; Ardelean, Gheorghe; Erdmann, Andreas

2007-03-01

This paper introduces Dr.LiTHO, a research and development oriented lithography simulation environment developed at Fraunhofer IISB to flexibly integrate our simulation models into one coherent platform. We propose a light-weight approach to a lithography simulation environment: The use of a scripting (batch) language as an integration platform. Out of the great variety of different scripting languages, Python proved superior in many ways: It exhibits a good-natured learning-curve, it is efficient, available on virtually any platform, and provides sophisticated integration mechanisms for existing programs. In this paper, we will describe the steps, required to provide Python bindings for existing programs and to finally generate an integrated simulation environment. In addition, we will give a short introduction into selected software design demands associated with the development of such a framework. We will especially focus on testing and (both technical and user-oriented) documentation issues. Dr.LiTHO Python files contain not only all simulation parameter settings but also the simulation flow, providing maximum flexibility. In addition to relatively simple batch jobs, repetitive tasks can be pooled in libraries. And as Python is a full-blown programming language, users can add virtually any functionality, which is especially useful in the scope of simulation studies or optimization tasks, that often require masses of evaluations. Furthermore, we will give a short overview of the numerous existing Python packages. Several examples demonstrate the feasibility and productiveness of integrating Python packages into custom Dr.LiTHO scripts.

A computational framework for automation of point defect calculations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Goyal, Anuj; Gorai, Prashun; Peng, Haowei

We have developed a complete and rigorously validated open-source Python framework to automate point defect calculations using density functional theory. Furthermore, the framework provides an effective and efficient method for defect structure generation, and creation of simple yet customizable workflows to analyze defect calculations. This package provides the capability to compute widely-accepted correction schemes to overcome finite-size effects, including (1) potential alignment, (2) image-charge correction, and (3) band filling correction to shallow defects. Using Si, ZnO and In2O3 as test examples, we demonstrate the package capabilities and validate the methodology.

A computational framework for automation of point defect calculations

DOE PAGES

Goyal, Anuj; Gorai, Prashun; Peng, Haowei; ...

2017-01-13

We have developed a complete and rigorously validated open-source Python framework to automate point defect calculations using density functional theory. Furthermore, the framework provides an effective and efficient method for defect structure generation, and creation of simple yet customizable workflows to analyze defect calculations. This package provides the capability to compute widely-accepted correction schemes to overcome finite-size effects, including (1) potential alignment, (2) image-charge correction, and (3) band filling correction to shallow defects. Using Si, ZnO and In2O3 as test examples, we demonstrate the package capabilities and validate the methodology.

Introduction to Python for CMF Authority Users

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pritchett-Sheats, Lori A.

This talk is a very broad over view of Python that highlights key features in the language used in the Common Model Framework (CMF). I assume that the audience has some programming experience in a shell scripting language (C shell, Bash, PERL) or other high level language (C/C++/ Fortran). The talk will cover Python data types, classes (objects) and basic programming constructs. The talk concludes with slides describing how I developed the basic classes for a TITANS homework assignment.

Distributed Computing Framework for Synthetic Radar Application

NASA Technical Reports Server (NTRS)

Gurrola, Eric M.; Rosen, Paul A.; Aivazis, Michael

2006-01-01

We are developing an extensible software framework, in response to Air Force and NASA needs for distributed computing facilities for a variety of radar applications. The objective of this work is to develop a Python based software framework, that is the framework elements of the middleware that allows developers to control processing flow on a grid in a distributed computing environment. Framework architectures to date allow developers to connect processing functions together as interchangeable objects, thereby allowing a data flow graph to be devised for a specific problem to be solved. The Pyre framework, developed at the California Institute of Technology (Caltech), and now being used as the basis for next-generation radar processing at JPL, is a Python-based software framework. We have extended the Pyre framework to include new facilities to deploy processing components as services, including components that monitor and assess the state of the distributed network for eventual real-time control of grid resources.

Bifrost: a Modular Python/C++ Framework for Development of High-Throughput Data Analysis Pipelines

NASA Astrophysics Data System (ADS)

Cranmer, Miles; Barsdell, Benjamin R.; Price, Danny C.; Garsden, Hugh; Taylor, Gregory B.; Dowell, Jayce; Schinzel, Frank; Costa, Timothy; Greenhill, Lincoln J.

2017-01-01

Large radio interferometers have data rates that render long-term storage of raw correlator data infeasible, thus motivating development of real-time processing software. For high-throughput applications, processing pipelines are challenging to design and implement. Motivated by science efforts with the Long Wavelength Array, we have developed Bifrost, a novel Python/C++ framework that eases the development of high-throughput data analysis software by packaging algorithms as black box processes in a directed graph. This strategy to modularize code allows astronomers to create parallelism without code adjustment. Bifrost uses CPU/GPU ’circular memory’ data buffers that enable ready introduction of arbitrary functions into the processing path for ’streams’ of data, and allow pipelines to automatically reconfigure in response to astrophysical transient detection or input of new observing settings. We have deployed and tested Bifrost at the latest Long Wavelength Array station, in Sevilleta National Wildlife Refuge, NM, where it handles throughput exceeding 10 Gbps per CPU core.

USEEIO Framework Demo

EPA Science Inventory

The code base for creating versions of the USEEIO model and USEEIO-like models is called the USEEIO Modeling Framework. The framework is built in a combination of R and Python languages.This demonstration provides a brief overview and introduction into the framework.

Efficient and Flexible Climate Analysis with Python in a Cloud-Based Distributed Computing Framework

NASA Astrophysics Data System (ADS)

Gannon, C.

2017-12-01

As climate models become progressively more advanced, and spatial resolution further improved through various downscaling projects, climate projections at a local level are increasingly insightful and valuable. However, the raw size of climate datasets presents numerous hurdles for analysts wishing to develop customized climate risk metrics or perform site-specific statistical analysis. Four Twenty Seven, a climate risk consultancy, has implemented a Python-based distributed framework to analyze large climate datasets in the cloud. With the freedom afforded by efficiently processing these datasets, we are able to customize and continually develop new climate risk metrics using the most up-to-date data. Here we outline our process for using Python packages such as XArray and Dask to evaluate netCDF files in a distributed framework, StarCluster to operate in a cluster-computing environment, cloud computing services to access publicly hosted datasets, and how this setup is particularly valuable for generating climate change indicators and performing localized statistical analysis.

OpenSeesPy: Python library for the OpenSees finite element framework

NASA Astrophysics Data System (ADS)

Zhu, Minjie; McKenna, Frank; Scott, Michael H.

2018-01-01

OpenSees, an open source finite element software framework, has been used broadly in the earthquake engineering community for simulating the seismic response of structural and geotechnical systems. The framework allows users to perform finite element analysis with a scripting language and for developers to create both serial and parallel finite element computer applications as interpreters. For the last 15 years, Tcl has been the primary scripting language to which the model building and analysis modules of OpenSees are linked. To provide users with different scripting language options, particularly Python, the OpenSees interpreter interface was refactored to provide multi-interpreter capabilities. This refactoring, resulting in the creation of OpenSeesPy as a Python module, is accomplished through an abstract interface for interpreter calls with concrete implementations for different scripting languages. Through this approach, users are able to develop applications that utilize the unique features of several scripting languages while taking advantage of advanced finite element analysis models and algorithms.

The Python ARM Radar Toolkit (Py-ART), a library for working with weather radar data in the Python programming language

DOE Office of Scientific and Technical Information (OSTI.GOV)

Helmus, Jonathan J.; Collis, Scott M.

The Python ARM Radar Toolkit is a package for reading, visualizing, correcting and analysing data from weather radars. Development began to meet the needs of the Atmospheric Radiation Measurement Climate Research Facility and has since expanded to provide a general-purpose framework for working with data from weather radars in the Python programming language. The toolkit is built on top of libraries in the Scientific Python ecosystem including NumPy, SciPy, and matplotlib, and makes use of Cython for interfacing with existing radar libraries written in C and to speed up computationally demanding algorithms. As a result, the source code for themore » toolkit is available on GitHub and is distributed under a BSD license.« less

The Python ARM Radar Toolkit (Py-ART), a library for working with weather radar data in the Python programming language

DOE PAGES

Helmus, Jonathan J.; Collis, Scott M.

2016-07-18

The Python ARM Radar Toolkit is a package for reading, visualizing, correcting and analysing data from weather radars. Development began to meet the needs of the Atmospheric Radiation Measurement Climate Research Facility and has since expanded to provide a general-purpose framework for working with data from weather radars in the Python programming language. The toolkit is built on top of libraries in the Scientific Python ecosystem including NumPy, SciPy, and matplotlib, and makes use of Cython for interfacing with existing radar libraries written in C and to speed up computationally demanding algorithms. As a result, the source code for themore » toolkit is available on GitHub and is distributed under a BSD license.« less

CSB: a Python framework for structural bioinformatics.

PubMed

Kalev, Ivan; Mechelke, Martin; Kopec, Klaus O; Holder, Thomas; Carstens, Simeon; Habeck, Michael

2012-11-15

Computational Structural Biology Toolbox (CSB) is a cross-platform Python class library for reading, storing and analyzing biomolecular structures with rich support for statistical analyses. CSB is designed for reusability and extensibility and comes with a clean, well-documented API following good object-oriented engineering practice. Stable release packages are available for download from the Python Package Index (PyPI) as well as from the project's website http://csb.codeplex.com. ivan.kalev@gmail.com or michael.habeck@tuebingen.mpg.de

Unilateral microphthalmia or anophthalmia in eight pythons (Pythonidae).

PubMed

Da Silva, Mari-Ann O; Bertelsen, Mads F; Wang, Tobias; Pedersen, Michael; Lauridsen, Henrik; Heegaard, Steffen

2015-01-01

To provide morphological descriptions of microphthalmia or anophthalmia in eight pythons using microcomputerized tomography (μCT), magnetic resonance imaging (MRI), and histopathology. Seven Burmese pythons (Python bivittatus) and one ball python (P. regius) with clinically normal right eyes and an abnormal or missing left eye. At the time of euthanasia, four of the eight snakes underwent necropsy. Hereafter, the heads of two Burmese pythons and one ball python were examined using μCT, and another Burmese python was subjected to MRI. Following these procedures, the heads of these four pythons along with the heads of an additional three Burmese pythons were prepared for histology. All eight snakes had left ocular openings seen as dermal invaginations between 0.2 and 2.0 mm in diameter. They also had varying degrees of malformations of the orbital bones and a limited presence of nervous, glandular, and muscle tissue in the posterior orbit. Two individuals had small but identifiable eyes. Furthermore, remnants of the pigmented embryonic framework of the hyaloid vessels were found in the anophthalmic snakes. Necropsies revealed no other macroscopic anomalies. Eight pythons with unilateral left-sided microphthalmia or anophthalmia had one normal eye and a left orbit with malformed or incompletely developed ocular structures along with remnants of fetal structures. These cases lend further information to a condition that is often seen in snakes, but infrequently described. © 2014 American College of Veterinary Ophthalmologists.

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

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.

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 helps researchers to ensure the robustness of the algorithm, correctness of the results in edge cases as well as the detection of changes in results due to new development. For all modules GRASS GIS automatically creates standardized command line and graphical user interfaces and documentation. Finally, we will show how GRASS GIS can be used together with powerful Python tools such as the NumPy package and the IPython Notebook. References: Gebbert, S., Pebesma, E., 2014. A temporal GIS for field based environmental modeling. Environmental Modelling & Software 53, 1-12. Neteler, M., Bowman, M.H., Landa, M. and Metz, M., 2012. GRASS GIS: a multi-purpose Open Source GIS. Environmental Modelling & Software 31: 124-130. Petras, V., Gebbert, S., 2014. Testing framework for GRASS GIS: ensuring reproducibility of scientific geospatial computing. Poster presented at: AGU Fall Meeting, December 15-19, 2014, San Francisco, USA. Zambelli, P., Gebbert, S., Ciolli, M., 2013. Pygrass: An Object Oriented Python Application Programming Interface (API) for Geographic Resources Analysis Support System (GRASS) Geographic Information System (GIS). ISPRS International Journal of Geo-Information 2, 201-219.

Python Scripts for Automation of Current-Voltage Testing of Semiconductor Devices (FY17)

DTIC Science & Technology

2017-01-01

ARL-TR-7923 ● JAN 2017 US Army Research Laboratory Python Scripts for Automation of Current- Voltage Testing of Semiconductor...manual device-testing procedures is reduced or eliminated through automation. This technical report includes scripts written in Python , version 2.7, used ...nothing. 3.1.9 Exit Program The script exits the entire program. Line 505, sys.exit(), uses the sys package that comes with Python to exit system

RadVel: The Radial Velocity Modeling Toolkit

NASA Astrophysics Data System (ADS)

Fulton, Benjamin J.; Petigura, Erik A.; Blunt, Sarah; Sinukoff, Evan

2018-04-01

RadVel is an open-source Python package for modeling Keplerian orbits in radial velocity (RV) timeseries. RadVel provides a convenient framework to fit RVs using maximum a posteriori optimization and to compute robust confidence intervals by sampling the posterior probability density via Markov Chain Monte Carlo (MCMC). RadVel allows users to float or fix parameters, impose priors, and perform Bayesian model comparison. We have implemented real-time MCMC convergence tests to ensure adequate sampling of the posterior. RadVel can output a number of publication-quality plots and tables. Users may interface with RadVel through a convenient command-line interface or directly from Python. The code is object-oriented and thus naturally extensible. We encourage contributions from the community. Documentation is available at http://radvel.readthedocs.io.

Pybus -- A Python Software Bus

DOE Office of Scientific and Technical Information (OSTI.GOV)

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 conceptmore » 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.« less

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

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.

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

The Muon Ionization Cooling Experiment User Software

NASA Astrophysics Data System (ADS)

Dobbs, A.; Rajaram, D.; MICE Collaboration

2017-10-01

The Muon Ionization Cooling Experiment (MICE) is a proof-of-principle experiment designed to demonstrate muon ionization cooling for the first time. MICE is currently on Step IV of its data taking programme, where transverse emittance reduction will be demonstrated. The MICE Analysis User Software (MAUS) is the reconstruction, simulation and analysis framework for the MICE experiment. MAUS is used for both offline data analysis and fast online data reconstruction and visualization to serve MICE data taking. This paper provides an introduction to MAUS, describing the central Python and C++ based framework, the data structure and and the code management and testing procedures.

RAVE—a Detector-independent vertex reconstruction toolkit

NASA Astrophysics Data System (ADS)

Waltenberger, Wolfgang; Mitaroff, Winfried; Moser, Fabian

2007-10-01

A detector-independent toolkit for vertex reconstruction (RAVE ) is being developed, along with a standalone framework (VERTIGO ) for testing, analyzing and debugging. The core algorithms represent state of the art for geometric vertex finding and fitting by both linear (Kalman filter) and robust estimation methods. Main design goals are ease of use, flexibility for embedding into existing software frameworks, extensibility, and openness. The implementation is based on modern object-oriented techniques, is coded in C++ with interfaces for Java and Python, and follows an open-source approach. A beta release is available. VERTIGO = "vertex reconstruction toolkit and interface to generic objects".

Model Diagnostics for the Department of Energy's Accelerated Climate Modeling for Energy (ACME) Project

NASA Astrophysics Data System (ADS)

Smith, B.

2015-12-01

In 2014, eight Department of Energy (DOE) national laboratories, four academic institutions, one company, and the National Centre for Atmospheric Research combined forces in a project called Accelerated Climate Modeling for Energy (ACME) with the goal to speed Earth system model development for climate and energy. Over the planned 10-year span, the project will conduct simulations and modeling on DOE's most powerful high-performance computing systems at Oak Ridge, Argonne, and Lawrence Berkeley Leadership Compute Facilities. A key component of the ACME project is the development of an interactive test bed for the advanced Earth system model. Its execution infrastructure will accelerate model development and testing cycles. The ACME Workflow Group is leading the efforts to automate labor-intensive tasks, provide intelligent support for complex tasks and reduce duplication of effort through collaboration support. As part of this new workflow environment, we have created a diagnostic, metric, and intercomparison Python framework, called UVCMetrics, to aid in the testing-to-production execution of the ACME model. The framework exploits similarities among different diagnostics to compactly support diagnosis of new models. It presently focuses on atmosphere and land but is designed to support ocean and sea ice model components as well. This framework is built on top of the existing open-source software framework known as the Ultrascale Visualization Climate Data Analysis Tools (UV-CDAT). Because of its flexible framework design, scientists and modelers now can generate thousands of possible diagnostic outputs. These diagnostics can compare model runs, compare model vs. observation, or simply verify a model is physically realistic. Additional diagnostics are easily integrated into the framework, and our users have already added several. Diagnostics can be generated, viewed, and manipulated from the UV-CDAT graphical user interface, Python command line scripts and programs, and web browsers. The framework is designed to be scalable to large datasets, yet easy to use and familiar to scientists using previous tools. Integration in the ACME overall user interface facilitates data publication, further analysis, and quick feedback to model developers and scientists making component or coupled model runs.

Detection of nidoviruses in live pythons and boas.

PubMed

Marschang, Rachel E; Kolesnik, Ekaterina

2017-02-09

Nidoviruses have recently been described as a putative cause of severe respiratory disease in pythons in the USA and Europe. The objective of this study was to establish the use of a conventional PCR for the detection of nidoviruses in samples from live animals and to extend the list of susceptible species. A PCR targeting a portion of ORF1a of python nidoviruses was used to detect nidoviruses in diagnostic samples from live boas and pythons. A total of 95 pythons, 84 boas and 22 snakes of unknown species were included in the study. Samples tested included oral swabs and whole blood. Nidoviruses were detected in 27.4% of the pythons and 2.4% of the boas tested. They were most commonly detected in ball pythons (Python [P.] regius) and Indian rock pythons (P. molurus), but were also detected for the first time in other python species, including Morelia spp. and Boa constrictor. Oral swabs were most commonly tested positive. The PCR described here can be used for the detection of nidoviruses in oral swabs from live snakes. These viruses appear to be relatively common among snakes in captivity in Europe and screening for these viruses should be considered in the clinical work-up. Nidoviruses are believed to be an important cause of respiratory disease in pythons, but can also infect boas. Detection of these viruses in live animals is now possible and can be of interest both in diseased animals as well as in quarantine situations.

pyGeno: A Python package for precision medicine and proteogenomics.

PubMed

Daouda, Tariq; Perreault, Claude; Lemieux, Sébastien

2016-01-01

pyGeno is a Python package mainly intended for precision medicine applications that revolve around genomics and proteomics. It integrates reference sequences and annotations from Ensembl, genomic polymorphisms from the dbSNP database and data from next-gen sequencing into an easy to use, memory-efficient and fast framework, therefore allowing the user to easily explore subject-specific genomes and proteomes. Compared to a standalone program, pyGeno gives the user access to the complete expressivity of Python, a general programming language. Its range of application therefore encompasses both short scripts and large scale genome-wide studies.

pyGeno: A Python package for precision medicine and proteogenomics

PubMed Central

Daouda, Tariq; Perreault, Claude; Lemieux, Sébastien

2016-01-01

pyGeno is a Python package mainly intended for precision medicine applications that revolve around genomics and proteomics. It integrates reference sequences and annotations from Ensembl, genomic polymorphisms from the dbSNP database and data from next-gen sequencing into an easy to use, memory-efficient and fast framework, therefore allowing the user to easily explore subject-specific genomes and proteomes. Compared to a standalone program, pyGeno gives the user access to the complete expressivity of Python, a general programming language. Its range of application therefore encompasses both short scripts and large scale genome-wide studies. PMID:27785359

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

PubMed Central

Pecevski, Dejan; Natschläger, Thomas; Schuch, Klaus

2008-01-01

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

Status of parallel Python-based implementation of UEDGE

NASA Astrophysics Data System (ADS)

Umansky, M. V.; Pankin, A. Y.; Rognlien, T. D.; Dimits, A. M.; Friedman, A.; Joseph, I.

2017-10-01

The tokamak edge transport code UEDGE has long used the code-development and run-time framework Basis. However, with the support for Basis expected to terminate in the coming years, and with the advent of the modern numerical language Python, it has become desirable to move UEDGE to Python, to ensure its long-term viability. Our new Python-based UEDGE implementation takes advantage of the portable build system developed for FACETS. The new implementation gives access to Python's graphical libraries and numerical packages for pre- and post-processing, and support of HDF5 simplifies exchanging data. The older serial version of UEDGE has used for time-stepping the Newton-Krylov solver NKSOL. The renovated implementation uses backward Euler discretization with nonlinear solvers from PETSc, which has the promise to significantly improve the UEDGE parallel performance. We will report on assessment of some of the extended UEDGE capabilities emerging in the new implementation, and will discuss the future directions. Work performed for U.S. DOE by LLNL under contract DE-AC52-07NA27344.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Veseli, S.

As the number of sites deploying and adopting EPICS Version 4 grows, so does the need to support PV Access from multiple languages. Especially important are the widely used scripting languages that tend to reduce both software development time and the learning curve for new users. In this paper we describe PvaPy, a Python API for the EPICS PV Access protocol and its accompanying structured data API. Rather than implementing the protocol itself in Python, PvaPy wraps the existing EPICS Version 4 C++ libraries using the Boost.Python framework. This approach allows us to benefit from the existing code base andmore » functionality, and to significantly reduce the Python API development effort. PvaPy objects are based on Python dictionaries and provide users with the ability to access even the most complex of PV Data structures in a relatively straightforward way. Its interfaces are easy to use, and include support for advanced EPICS Version 4 features such as implementation of client and server Remote Procedure Calls (RPC).« less

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

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.

ssbio: a Python framework for structural systems biology.

PubMed

Mih, Nathan; Brunk, Elizabeth; Chen, Ke; Catoiu, Edward; Sastry, Anand; Kavvas, Erol; Monk, Jonathan M; Zhang, Zhen; Palsson, Bernhard O

2018-06-15

Working with protein structures at the genome-scale has been challenging in a variety of ways. Here, we present ssbio, a Python package that provides a framework to easily work with structural information in the context of genome-scale network reconstructions, which can contain thousands of individual proteins. The ssbio package provides an automated pipeline to construct high quality genome-scale models with protein structures (GEM-PROs), wrappers to popular third-party programs to compute associated protein properties, and methods to visualize and annotate structures directly in Jupyter notebooks, thus lowering the barrier of linking 3D structural data with established systems workflows. ssbio is implemented in Python and available to download under the MIT license at http://github.com/SBRG/ssbio. Documentation and Jupyter notebook tutorials are available at http://ssbio.readthedocs.io/en/latest/. Interactive notebooks can be launched using Binder at https://mybinder.org/v2/gh/SBRG/ssbio/master?filepath=Binder.ipynb. Supplementary data are available at Bioinformatics online.

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

PubMed

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

2010-01-01

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

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

PubMed Central

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

2010-01-01

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

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. © 2016 Wiley Periodicals, Inc.

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

PubMed

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

2014-01-01

pyOpenMS is an open-source, Python-based interface to the C++ OpenMS library, providing facile access to a feature-rich, open-source algorithm library for MS-based proteomics analysis. It contains Python bindings that allow raw access to the data structures and algorithms implemented in OpenMS, specifically those for file access (mzXML, mzML, TraML, mzIdentML among others), basic signal processing (smoothing, filtering, de-isotoping, and peak-picking) and complex data analysis (including label-free, SILAC, iTRAQ, and SWATH analysis tools). pyOpenMS thus allows fast prototyping and efficient workflow development in a fully interactive manner (using the interactive Python interpreter) and is also ideally suited for researchers not proficient in C++. In addition, our code to wrap a complex C++ library is completely open-source, allowing other projects to create similar bindings with ease. The pyOpenMS framework is freely available at https://pypi.python.org/pypi/pyopenms while the autowrap tool to create Cython code automatically is available at https://pypi.python.org/pypi/autowrap (both released under the 3-clause BSD licence). © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Scalable Decision Support at the Point of Care: A Substitutable Electronic Health Record App for Monitoring Medication Adherence

PubMed Central

Mandel, Joshua; Jonikas, Magdalena; Ramoni, Rachel Badovinac; Kohane, Isaac S; Mandl, Kenneth D

2013-01-01

Background Non-adherence to prescribed medications is a serious health problem in the United States, costing an estimated $100 billion per year. While poor adherence should be addressable with point of care health information technology, integrating new solutions with existing electronic health records (EHR) systems require customization within each organization, which is difficult because of the monolithic software design of most EHR products. Objective The objective of this study was to create a published algorithm for predicting medication adherence problems easily accessible at the point of care through a Web application that runs on the Substitutable Medical Apps, Reusuable Technologies (SMART) platform. The SMART platform is an emerging framework that enables EHR systems to behave as “iPhone like platforms” by exhibiting an application programming interface for easy addition and deletion of third party apps. The app is presented as a point of care solution to monitoring medication adherence as well as a sufficiently general, modular application that may serve as an example and template for other SMART apps. Methods The widely used, open source Django framework was used together with the SMART platform to create the interoperable components of this app. Django uses Python as its core programming language. This allows statistical and mathematical modules to be created from a large array of Python numerical libraries and assembled together with the core app to create flexible and sophisticated EHR functionality. Algorithms that predict individual adherence are derived from a retrospective study of dispensed medication claims from a large private insurance plan. Patients’ prescription fill information is accessed through the SMART framework and the embedded algorithms compute adherence information, including predicted adherence one year after the first prescription fill. Open source graphing software is used to display patient medication information and the results of statistical prediction of future adherence on a clinician-facing Web interface. Results The user interface allows the physician to quickly review all medications in a patient record for potential non-adherence problems. A gap-check and current medication possession ratio (MPR) threshold test are applied to all medications in the record to test for current non-adherence. Predictions of 1-year non-adherence are made for certain drug classes for which external data was available. Information is presented graphically to indicate present non-adherence, or predicted non-adherence at one year, based on early prescription fulfillment patterns. The MPR Monitor app is installed in the SMART reference container as the “MPR Monitor”, where it is publically available for use and testing. MPR is an acronym for Medication Possession Ratio, a commonly used measure of adherence to a prescribed medication regime. This app may be used as an example for creating additional functionality by replacing statistical and display algorithms with new code in a cycle of rapid prototyping and implementation or as a framework for a new SMART app. Conclusions The MPR Monitor app is a useful pilot project for monitoring medication adherence. It also provides an example that integrates several open source software components, including the Python-based Django Web framework and python-based graphics, to build a SMART app that allows complex decision support methods to be encapsulated to enhance EHR functionality. PMID:23876796

Scalable decision support at the point of care: a substitutable electronic health record app for monitoring medication adherence.

PubMed

Bosl, William; Mandel, Joshua; Jonikas, Magdalena; Ramoni, Rachel Badovinac; Kohane, Isaac S; Mandl, Kenneth D

2013-07-22

Non-adherence to prescribed medications is a serious health problem in the United States, costing an estimated $100 billion per year. While poor adherence should be addressable with point of care health information technology, integrating new solutions with existing electronic health records (EHR) systems require customization within each organization, which is difficult because of the monolithic software design of most EHR products. The objective of this study was to create a published algorithm for predicting medication adherence problems easily accessible at the point of care through a Web application that runs on the Substitutable Medical Apps, Reusuable Technologies (SMART) platform. The SMART platform is an emerging framework that enables EHR systems to behave as "iPhone like platforms" by exhibiting an application programming interface for easy addition and deletion of third party apps. The app is presented as a point of care solution to monitoring medication adherence as well as a sufficiently general, modular application that may serve as an example and template for other SMART apps. The widely used, open source Django framework was used together with the SMART platform to create the interoperable components of this app. Django uses Python as its core programming language. This allows statistical and mathematical modules to be created from a large array of Python numerical libraries and assembled together with the core app to create flexible and sophisticated EHR functionality. Algorithms that predict individual adherence are derived from a retrospective study of dispensed medication claims from a large private insurance plan. Patients' prescription fill information is accessed through the SMART framework and the embedded algorithms compute adherence information, including predicted adherence one year after the first prescription fill. Open source graphing software is used to display patient medication information and the results of statistical prediction of future adherence on a clinician-facing Web interface. The user interface allows the physician to quickly review all medications in a patient record for potential non-adherence problems. A gap-check and current medication possession ratio (MPR) threshold test are applied to all medications in the record to test for current non-adherence. Predictions of 1-year non-adherence are made for certain drug classes for which external data was available. Information is presented graphically to indicate present non-adherence, or predicted non-adherence at one year, based on early prescription fulfillment patterns. The MPR Monitor app is installed in the SMART reference container as the "MPR Monitor", where it is publically available for use and testing. MPR is an acronym for Medication Possession Ratio, a commonly used measure of adherence to a prescribed medication regime. This app may be used as an example for creating additional functionality by replacing statistical and display algorithms with new code in a cycle of rapid prototyping and implementation or as a framework for a new SMART app. The MPR Monitor app is a useful pilot project for monitoring medication adherence. It also provides an example that integrates several open source software components, including the Python-based Django Web framework and python-based graphics, to build a SMART app that allows complex decision support methods to be encapsulated to enhance EHR functionality.

COMP Superscalar, an interoperable programming framework

NASA Astrophysics Data System (ADS)

Badia, Rosa M.; Conejero, Javier; Diaz, Carlos; Ejarque, Jorge; Lezzi, Daniele; Lordan, Francesc; Ramon-Cortes, Cristian; Sirvent, Raul

2015-12-01

COMPSs is a programming framework that aims to facilitate the parallelization of existing applications written in Java, C/C++ and Python scripts. For that purpose, it offers a simple programming model based on sequential development in which the user is mainly responsible for (i) identifying the functions to be executed as asynchronous parallel tasks and (ii) annotating them with annotations or standard Python decorators. A runtime system is in charge of exploiting the inherent concurrency of the code, automatically detecting and enforcing the data dependencies between tasks and spawning these tasks to the available resources, which can be nodes in a cluster, clouds or grids. In cloud environments, COMPSs provides scalability and elasticity features allowing the dynamic provision of resources.

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.

[Not Available].

PubMed

Pecevski, Dejan; Natschläger, Thomas; Schuch, Klaus

2009-01-01

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

A Data Management Framework for Real-Time Water Quality Monitoring

NASA Astrophysics Data System (ADS)

Mulyono, E.; Yang, D.; Craig, M.

2007-12-01

CSU East Bay operates two in-situ, near-real-time water quality monitoring stations in San Francisco Bay as a member of the Center for Integrative Coastal Ocean Observation, Research, and Education (CICORE) and the Central and Northern California Ocean Observing System (CeNCOOS). We have been operating stations at Dumbarton Pier and San Leandro Marina for the past two years. At each station, a sonde measures seven water quality parameters every six minutes. During the first year of operation, we retrieved data from the sondes every few weeks by visiting the sites and uploading data to a handheld logger. Last year we implemented a telemetry system utilizing a cellular CDMA modem to transfer data from the field to our data center on an hourly basis. Data from each station are initially stored in monthly files in native format. We import data from these files into a SQL database every hour. SQL is handled by Django, an open source web framework. Django provides a user- friendly web user interface (UI) to administer the data. We utilized parts of the Django UI for our database web- front, which allows users to access our database via the World Wide Web and perform basic queries. We also serve our data to other aggregating sites, including the central CICORE website and NOAA's National Data Buoy Center (NDBC). Since Django is written in Python, it allows us to integrate other Python modules into our software, such as the Matplot library for scientific graphics. We store our code in a Subversion repository, which keeps track of software revisions. Code is tested using Python's unittest and doctest modules within Django's testing facility, which warns us when our code modifications cause other parts of the software to break. During the past two years of data acquisition, we have incrementally updated our data model to accommodate changes in physical hardware, including equipment moves, instrument replacements, and sensor upgrades that affected data format.

A WPS Based Architecture for Climate Data Analytic Services (CDAS) at NASA

NASA Astrophysics Data System (ADS)

Maxwell, T. P.; McInerney, M.; Duffy, D.; Carriere, L.; Potter, G. L.; Doutriaux, C.

2015-12-01

Faced with unprecedented growth in the Big Data domain of climate science, NASA has developed the Climate Data Analytic Services (CDAS) framework. This framework enables scientists to execute trusted and tested analysis operations in a high performance environment close to the massive data stores at NASA. The data is accessed in standard (NetCDF, HDF, etc.) formats in a POSIX file system and processed using trusted climate data analysis tools (ESMF, CDAT, NCO, etc.). The framework is structured as a set of interacting modules allowing maximal flexibility in deployment choices. The current set of module managers include: Staging Manager: Runs the computation locally on the WPS server or remotely using tools such as celery or SLURM. Compute Engine Manager: Runs the computation serially or distributed over nodes using a parallelization framework such as celery or spark. Decomposition Manger: Manages strategies for distributing the data over nodes. Data Manager: Handles the import of domain data from long term storage and manages the in-memory and disk-based caching architectures. Kernel manager: A kernel is an encapsulated computational unit which executes a processor's compute task. Each kernel is implemented in python exploiting existing analysis packages (e.g. CDAT) and is compatible with all CDAS compute engines and decompositions. CDAS services are accessed via a WPS API being developed in collaboration with the ESGF Compute Working Team to support server-side analytics for ESGF. The API can be executed using either direct web service calls, a python script or application, or a javascript-based web application. Client packages in python or javascript contain everything needed to make CDAS requests. The CDAS architecture brings together the tools, data storage, and high-performance computing required for timely analysis of large-scale data sets, where the data resides, to ultimately produce societal benefits. It is is currently deployed at NASA in support of the Collaborative REAnalysis Technical Environment (CREATE) project, which centralizes numerous global reanalysis datasets onto a single advanced data analytics platform. This service permits decision makers to investigate climate changes around the globe, inspect model trends, compare multiple reanalysis datasets, and variability.

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

NASA Astrophysics Data System (ADS)

Sandner, Raimar; Vukics, András

2014-09-01

The v2 Milestone 10 release of C++QED is primarily a feature release, which also corrects some problems of the previous release, especially as regards the build system. The adoption of C++11 features has led to many simplifications in the codebase. A full doxygen-based API manual [1] is now provided together with updated user guides. A largely automated, versatile new testsuite directed both towards computational and physics features allows for quickly spotting arising errors. The states of trajectories are now savable and recoverable with full binary precision, allowing for trajectory continuation regardless of evolution method (single/ensemble Monte Carlo wave-function or Master equation trajectory). As the main new feature, the framework now presents Python bindings to the highest-level programming interface, so that actual simulations for given composite quantum systems can now be performed from Python. Catalogue identifier: AELU_v2_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AELU_v2_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: yes No. of lines in distributed program, including test data, etc.: 492422 No. of bytes in distributed program, including test data, etc.: 8070987 Distribution format: tar.gz Programming language: C++/Python. Computer: i386-i686, x86 64. Operating system: In principle cross-platform, as yet tested only on UNIX-like systems (including Mac OS X). RAM: The framework itself takes about 60MB, which is fully shared. The additional memory taken by the program which defines the actual physical system (script) is typically less than 1MB. The memory storing the actual data scales with the system dimension for state-vector manipulations, and the square of the dimension for density-operator manipulations. This might easily be GBs, and often the memory of the machine limits the size of the simulated system. Classification: 4.3, 4.13, 6.2. External routines: Boost C++ libraries, GNU Scientific Library, Blitz++, FLENS, NumPy, SciPy Catalogue identifier of previous version: AELU_v1_0 Journal reference of previous version: Comput. Phys. Comm. 183 (2012) 1381 Does the new version supersede the previous version?: Yes Nature of problem: Definition of (open) composite quantum systems out of elementary building blocks [2,3]. Manipulation of such systems, with emphasis on dynamical simulations such as Master-equation evolution [4] and Monte Carlo wave-function simulation [5]. Solution method: Master equation, Monte Carlo wave-function method Reasons for new version: The new version is mainly a feature release, but it does correct some problems of the previous version, especially as regards the build system. Summary of revisions: We give an example for a typical Python script implementing the ring-cavity system presented in Sec. 3.3 of Ref. [2]: Restrictions: Total dimensionality of the system. Master equation-few thousands. Monte Carlo wave-function trajectory-several millions. Unusual features: Because of the heavy use of compile-time algorithms, compilation of programs written in the framework may take a long time and much memory (up to several GBs). Additional comments: The framework is not a program, but provides and implements an application-programming interface for developing simulations in the indicated problem domain. We use several C++11 features which limits the range of supported compilers (g++ 4.7, clang++ 3.1) Documentation, http://cppqed.sourceforge.net/ Running time: Depending on the magnitude of the problem, can vary from a few seconds to weeks. References: [1] Entry point: http://cppqed.sf.net [2] A. Vukics, C++QEDv2: The multi-array concept and compile-time algorithms in the definition of composite quantum systems, Comp. Phys. Comm. 183(2012)1381. [3] A. Vukics, H. Ritsch, C++QED: an object-oriented framework for wave-function simulations of cavity QED systems, Eur. Phys. J. D 44 (2007) 585. [4] H. J. Carmichael, An Open Systems Approach to Quantum Optics, Springer, 1993. [5] J. Dalibard, Y. Castin, K. Molmer, Wave-function approach to dissipative processes in quantum optics, Phys. Rev. Lett. 68 (1992) 580.

A Computational Framework for Automation of Point Defect Calculations

NASA Astrophysics Data System (ADS)

Goyal, Anuj; Gorai, Prashun; Peng, Haowei; Lany, Stephan; Stevanovic, Vladan; National Renewable Energy Laboratory, Golden, Colorado 80401 Collaboration

A complete and rigorously validated open-source Python framework to automate point defect calculations using density functional theory has been developed. The framework provides an effective and efficient method for defect structure generation, and creation of simple yet customizable workflows to analyze defect calculations. The package provides the capability to compute widely accepted correction schemes to overcome finite-size effects, including (1) potential alignment, (2) image-charge correction, and (3) band filling correction to shallow defects. Using Si, ZnO and In2O3as test examples, we demonstrate the package capabilities and validate the methodology. We believe that a robust automated tool like this will enable the materials by design community to assess the impact of point defects on materials performance. National Renewable Energy Laboratory, Golden, Colorado 80401.

Resonant Frequency Control For the PIP-II Injector Test RFQ: Control Framework and Initial Results

DOE Office of Scientific and Technical Information (OSTI.GOV)

Edelen, A. L.; Biedron, S. G.; Milton, S. V.

For the PIP-II Injector Test (PI-Test) at Fermilab, a four-vane radio frequency quadrupole (RFQ) is designed to accelerate a 30-keV, 1-mA to 10-mA, H- beam to 2.1 MeV under both pulsed and continuous wave (CW) RF operation. The available headroom of the RF amplifiers limits the maximum allowable detuning to 3 kHz, and the detuning is controlled entirely via thermal regulation. Fine control over the detuning, minimal manual intervention, and fast trip recovery is desired. In addition, having active control over both the walls and vanes provides a wider tuning range. For this, we intend to use model predictive controlmore » (MPC). To facilitate these objectives, we developed a dedicated control framework that handles higher-level system decisions as well as executes control calculations. It is written in Python in a modular fashion for easy adjustments, readability, and portability. Here we describe the framework and present the first control results for the PI-Test RFQ under pulsed and CW operation.« less

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.

MADANALYSIS 5, a user-friendly framework for collider phenomenology

NASA Astrophysics Data System (ADS)

Conte, Eric; Fuks, Benjamin; Serret, Guillaume

2013-01-01

We present MADANALYSIS 5, a new framework for phenomenological investigations at particle colliders. Based on a C++ kernel, this program allows us to efficiently perform, in a straightforward and user-friendly fashion, sophisticated physics analyses of event files such as those generated by a large class of Monte Carlo event generators. MADANALYSIS 5 comes with two modes of running. The first one, easier to handle, uses the strengths of a powerful PYTHON interface in order to implement physics analyses by means of a set of intuitive commands. The second one requires one to implement the analyses in the C++ programming language, directly within the core of the analysis framework. This opens unlimited possibilities concerning the level of complexity which can be reached, being only limited by the programming skills and the originality of the user. Program summaryProgram title: MadAnalysis 5 Catalogue identifier: AENO_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AENO_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: Permission to use, copy, modify and distribute this program is granted under the terms of the GNU General Public License. No. of lines in distributed program, including test data, etc.: 31087 No. of bytes in distributed program, including test data, etc.: 399105 Distribution format: tar.gz Programming language: PYTHON, C++. Computer: All platforms on which Python version 2.7, Root version 5.27 and the g++ compiler are available. Compatibility with newer versions of these programs is also ensured. However, the Python version must be below version 3.0. Operating system: Unix, Linux and Mac OS operating systems on which the above-mentioned versions of Python and Root, as well as g++, are available. Classification: 11.1. External routines: ROOT (http://root.cern.ch/drupal/) Nature of problem: Implementing sophisticated phenomenological analyses in high-energy physics through a flexible, efficient and straightforward fashion, starting from event files such as those produced by Monte Carlo event generators. The event files can have been matched or not to parton-showering and can have been processed or not by a (fast) simulation of a detector. According to the sophistication level of the event files (parton-level, hadron-level, reconstructed-level), one must note that several input formats are possible. Solution method: We implement an interface allowing the production of predefined as well as user-defined histograms for a large class of kinematical distributions after applying a set of event selection cuts specified by the user. This therefore allows us to devise robust and novel search strategies for collider experiments, such as those currently running at the Large Hadron Collider at CERN, in a very efficient way. Restrictions: Unsupported event file format. Unusual features: The code is fully based on object representations for events, particles, reconstructed objects and cuts, which facilitates the implementation of an analysis. Running time: It depends on the purposes of the user and on the number of events to process. It varies from a few seconds to the order of the minute for several millions of events.

ESMPy and OpenClimateGIS: Python Interfaces for High Performance Grid Remapping and Geospatial Dataset Manipulation

NASA Astrophysics Data System (ADS)

O'Kuinghttons, Ryan; Koziol, Benjamin; Oehmke, Robert; DeLuca, Cecelia; Theurich, Gerhard; Li, Peggy; Jacob, Joseph

2016-04-01

The Earth System Modeling Framework (ESMF) Python interface (ESMPy) supports analysis and visualization in Earth system modeling codes by providing access to a variety of tools for data manipulation. ESMPy started as a Python interface to the ESMF grid remapping package, which provides mature and robust high-performance and scalable grid remapping between 2D and 3D logically rectangular and unstructured grids and sets of unconnected data. ESMPy now also interfaces with OpenClimateGIS (OCGIS), a package that performs subsetting, reformatting, and computational operations on climate datasets. ESMPy exposes a subset of ESMF grid remapping utilities. This includes bilinear, finite element patch recovery, first-order conservative, and nearest neighbor grid remapping methods. There are also options to ignore unmapped destination points, mask points on source and destination grids, and provide grid structure in the polar regions. Grid remapping on the sphere takes place in 3D Cartesian space, so the pole problem is not an issue as it can be with other grid remapping software. Remapping can be done between any combination of 2D and 3D logically rectangular and unstructured grids with overlapping domains. Grid pairs where one side of the regridding is represented by an appropriate set of unconnected data points, as is commonly found with observational data streams, is also supported. There is a developing interoperability layer between ESMPy and OpenClimateGIS (OCGIS). OCGIS is a pure Python, open source package designed for geospatial manipulation, subsetting, and computation on climate datasets stored in local NetCDF files or accessible remotely via the OPeNDAP protocol. Interfacing with OCGIS has brought GIS-like functionality to ESMPy (i.e. subsetting, coordinate transformations) as well as additional file output formats (i.e. CSV, ESRI Shapefile). ESMPy is distinguished by its strong emphasis on open source, community governance, and distributed development. The user base has grown quickly, and the package is integrating with several other software tools and frameworks. These include the Ultrascale Visualization Climate Data Analysis Tools (UV-CDAT), Iris, PyFerret, cfpython, and the Community Surface Dynamics Modeling System (CSDMS). ESMPy minimum requirements include Python 2.6, Numpy 1.6.1 and an ESMF installation. Optional dependencies include NetCDF and OCGIS-related dependencies: GDAL, Shapely, and Fiona. ESMPy is regression tested nightly, and supported on Darwin, Linux and Cray systems with the GNU compiler suite and MPI communications. OCGIS is supported on Linux, and also undergoes nightly regression testing. Both packages are installable from Anaconda channels. Upcoming development plans for ESMPy involve development of a higher order conservative grid remapping method. Future OCGIS development will focus on mesh and location stream interoperability and streamlined access to ESMPy's MPI implementation.

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

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

2011-11-01

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

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

PubMed

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

2008-01-01

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

A streamlined Python framework for AT-TPC data analysis

NASA Astrophysics Data System (ADS)

Taylor, J. Z.; Bradt, J.; Bazin, D.; Kuchera, M. P.

2017-09-01

User-friendly data analysis software has been developed for the Active-Target Time Projection Chamber (AT-TPC) experiment at the National Superconducting Cyclotron Laboratory at Michigan State University. The AT-TPC, commissioned in 2014, is a gas-filled detector that acts as both the detector and target for high-efficiency detection of low-intensity, exotic nuclear reactions. The pytpc framework is a Python package for analyzing AT-TPC data. The package was developed for the analysis of 46Ar(p, p) data. The existing software was used to analyze data produced by the 40Ar(p, p) experiment that ran in August, 2015. Usage of the package was documented in an analysis manual both to improve analysis steps and aid in the work of future AT-TPC users. Software features and analysis methods in the pytpc framework will be presented along with the 40Ar results.

Materials Knowledge Systems in Python - A Data Science Framework for Accelerated Development of Hierarchical Materials.

PubMed

Brough, David B; Wheeler, Daniel; Kalidindi, Surya R

2017-03-01

There is a critical need for customized analytics that take into account the stochastic nature of the internal structure of materials at multiple length scales in order to extract relevant and transferable knowledge. Data driven Process-Structure-Property (PSP) linkages provide systemic, modular and hierarchical framework for community driven curation of materials knowledge, and its transference to design and manufacturing experts. The Materials Knowledge Systems in Python project (PyMKS) is the first open source materials data science framework that can be used to create high value PSP linkages for hierarchical materials that can be leveraged by experts in materials science and engineering, manufacturing, machine learning and data science communities. This paper describes the main functions available from this repository, along with illustrations of how these can be accessed, utilized, and potentially further refined by the broader community of researchers.

Materials Knowledge Systems in Python - A Data Science Framework for Accelerated Development of Hierarchical Materials

PubMed Central

Brough, David B; Wheeler, Daniel; Kalidindi, Surya R.

2017-01-01

There is a critical need for customized analytics that take into account the stochastic nature of the internal structure of materials at multiple length scales in order to extract relevant and transferable knowledge. Data driven Process-Structure-Property (PSP) linkages provide systemic, modular and hierarchical framework for community driven curation of materials knowledge, and its transference to design and manufacturing experts. The Materials Knowledge Systems in Python project (PyMKS) is the first open source materials data science framework that can be used to create high value PSP linkages for hierarchical materials that can be leveraged by experts in materials science and engineering, manufacturing, machine learning and data science communities. This paper describes the main functions available from this repository, along with illustrations of how these can be accessed, utilized, and potentially further refined by the broader community of researchers. PMID:28690971

gadfly: A pandas-based Framework for Analyzing GADGET Simulation Data

NASA Astrophysics Data System (ADS)

Hummel, Jacob A.

2016-11-01

We present the first public release (v0.1) of the open-source gadget Dataframe Library: gadfly. The aim of this package is to leverage the capabilities of the broader python scientific computing ecosystem by providing tools for analyzing simulation data from the astrophysical simulation codes gadget and gizmo using pandas, a thoroughly documented, open-source library providing high-performance, easy-to-use data structures that is quickly becoming the standard for data analysis in python. Gadfly is a framework for analyzing particle-based simulation data stored in the HDF5 format using pandas DataFrames. The package enables efficient memory management, includes utilities for unit handling, coordinate transformations, and parallel batch processing, and provides highly optimized routines for visualizing smoothed-particle hydrodynamics data sets.

FRED 2: an immunoinformatics framework for Python

PubMed Central

Schubert, Benjamin; Walzer, Mathias; Brachvogel, Hans-Philipp; Szolek, András; Mohr, Christopher; Kohlbacher, Oliver

2016-01-01

Summary: Immunoinformatics approaches are widely used in a variety of applications from basic immunological to applied biomedical research. Complex data integration is inevitable in immunological research and usually requires comprehensive pipelines including multiple tools and data sources. Non-standard input and output formats of immunoinformatics tools make the development of such applications difficult. Here we present FRED 2, an open-source immunoinformatics framework offering easy and unified access to methods for epitope prediction and other immunoinformatics applications. FRED 2 is implemented in Python and designed to be extendable and flexible to allow rapid prototyping of complex applications. Availability and implementation: FRED 2 is available at http://fred-2.github.io Contact: schubert@informatik.uni-tuebingen.de Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27153717

FRED 2: an immunoinformatics framework for Python.

PubMed

Schubert, Benjamin; Walzer, Mathias; Brachvogel, Hans-Philipp; Szolek, András; Mohr, Christopher; Kohlbacher, Oliver

2016-07-01

Immunoinformatics approaches are widely used in a variety of applications from basic immunological to applied biomedical research. Complex data integration is inevitable in immunological research and usually requires comprehensive pipelines including multiple tools and data sources. Non-standard input and output formats of immunoinformatics tools make the development of such applications difficult. Here we present FRED 2, an open-source immunoinformatics framework offering easy and unified access to methods for epitope prediction and other immunoinformatics applications. FRED 2 is implemented in Python and designed to be extendable and flexible to allow rapid prototyping of complex applications. FRED 2 is available at http://fred-2.github.io schubert@informatik.uni-tuebingen.de Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

A lightweight messaging-based distributed processing and workflow execution framework for real-time and big data analysis

NASA Astrophysics Data System (ADS)

Laban, Shaban; El-Desouky, Aly

2014-05-01

To achieve a rapid, simple and reliable parallel processing of different types of tasks and big data processing on any compute cluster, a lightweight messaging-based distributed applications processing and workflow execution framework model is proposed. The framework is based on Apache ActiveMQ and Simple (or Streaming) Text Oriented Message Protocol (STOMP). ActiveMQ , a popular and powerful open source persistence messaging and integration patterns server with scheduler capabilities, acts as a message broker in the framework. STOMP provides an interoperable wire format that allows framework programs to talk and interact between each other and ActiveMQ easily. In order to efficiently use the message broker a unified message and topic naming pattern is utilized to achieve the required operation. Only three Python programs and simple library, used to unify and simplify the implementation of activeMQ and STOMP protocol, are needed to use the framework. A watchdog program is used to monitor, remove, add, start and stop any machine and/or its different tasks when necessary. For every machine a dedicated one and only one zoo keeper program is used to start different functions or tasks, stompShell program, needed for executing the user required workflow. The stompShell instances are used to execute any workflow jobs based on received message. A well-defined, simple and flexible message structure, based on JavaScript Object Notation (JSON), is used to build any complex workflow systems. Also, JSON format is used in configuration, communication between machines and programs. The framework is platform independent. Although, the framework is built using Python the actual workflow programs or jobs can be implemented by any programming language. The generic framework can be used in small national data centres for processing seismological and radionuclide data received from the International Data Centre (IDC) of the Preparatory Commission for the Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO). Also, it is possible to extend the use of the framework in monitoring the IDC pipeline. The detailed design, implementation,conclusion and future work of the proposed framework will be presented.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gamble, John; Jacobson, Noah Tobias; Baczewski, Andrew

EMTpY is an implementation of effective mass theory in python. It is designed to simulate semiconductor qubits within a non-perturbative, multi-valley effective mass theory framework using robust Gaussian basis sets.

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

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

2010-11-29

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

Respiratory disease in ball pythons (Python regius) experimentally infected with ball python nidovirus.

PubMed

Hoon-Hanks, Laura L; Layton, Marylee L; Ossiboff, Robert J; Parker, John S L; Dubovi, Edward J; Stenglein, Mark D

2018-04-01

Circumstantial evidence has linked a new group of nidoviruses with respiratory disease in pythons, lizards, and cattle. We conducted experimental infections in ball pythons (Python regius) to test the hypothesis that ball python nidovirus (BPNV) infection results in respiratory disease. Three ball pythons were inoculated orally and intratracheally with cell culture isolated BPNV and two were sham inoculated. Antemortem choanal, oroesophageal, and cloacal swabs and postmortem tissues of infected snakes were positive for viral RNA, protein, and infectious virus by qRT-PCR, immunohistochemistry, western blot and virus isolation. Clinical signs included oral mucosal reddening, abundant mucus secretions, open-mouthed breathing, and anorexia. Histologic lesions included chronic-active mucinous rhinitis, stomatitis, tracheitis, esophagitis and proliferative interstitial pneumonia. Control snakes remained negative and free of clinical signs throughout the experiment. Our findings establish a causal relationship between nidovirus infection and respiratory disease in ball pythons and shed light on disease progression and transmission. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

The Earth Data Analytic Services (EDAS) Framework

NASA Astrophysics Data System (ADS)

Maxwell, T. P.; Duffy, D.

2017-12-01

Faced with unprecedented growth in earth data volume and demand, NASA has developed the Earth Data Analytic Services (EDAS) framework, a high performance big data analytics framework built on Apache Spark. This framework enables scientists to execute data processing workflows combining common analysis operations close to the massive data stores at NASA. The data is accessed in standard (NetCDF, HDF, etc.) formats in a POSIX file system and processed using vetted earth data analysis tools (ESMF, CDAT, NCO, etc.). EDAS utilizes a dynamic caching architecture, a custom distributed array framework, and a streaming parallel in-memory workflow for efficiently processing huge datasets within limited memory spaces with interactive response times. EDAS services are accessed via a WPS API being developed in collaboration with the ESGF Compute Working Team to support server-side analytics for ESGF. The API can be accessed using direct web service calls, a Python script, a Unix-like shell client, or a JavaScript-based web application. New analytic operations can be developed in Python, Java, or Scala (with support for other languages planned). Client packages in Python, Java/Scala, or JavaScript contain everything needed to build and submit EDAS requests. The EDAS architecture brings together the tools, data storage, and high-performance computing required for timely analysis of large-scale data sets, where the data resides, to ultimately produce societal benefits. It is is currently deployed at NASA in support of the Collaborative REAnalysis Technical Environment (CREATE) project, which centralizes numerous global reanalysis datasets onto a single advanced data analytics platform. This service enables decision makers to compare multiple reanalysis datasets and investigate trends, variability, and anomalies in earth system dynamics around the globe.

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. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

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

A general spectral method for the numerical simulation of one-dimensional interacting fermions

NASA Astrophysics Data System (ADS)

Clason, Christian; von Winckel, Gregory

2012-08-01

This software implements a general framework for the direct numerical simulation of systems of interacting fermions in one spatial dimension. The approach is based on a specially adapted nodal spectral Galerkin method, where the basis functions are constructed to obey the antisymmetry relations of fermionic wave functions. An efficient Matlab program for the assembly of the stiffness and potential matrices is presented, which exploits the combinatorial structure of the sparsity pattern arising from this discretization to achieve optimal run-time complexity. This program allows the accurate discretization of systems with multiple fermions subject to arbitrary potentials, e.g., for verifying the accuracy of multi-particle approximations such as Hartree-Fock in the few-particle limit. It can be used for eigenvalue computations or numerical solutions of the time-dependent Schrödinger equation. The new version includes a Python implementation of the presented approach. New version program summaryProgram title: assembleFermiMatrix Catalogue identifier: AEKO_v1_1 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEKO_v1_1.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 332 No. of bytes in distributed program, including test data, etc.: 5418 Distribution format: tar.gz Programming language: MATLAB/GNU Octave, Python Computer: Any architecture supported by MATLAB, GNU Octave or Python Operating system: Any supported by MATLAB, GNU Octave or Python RAM: Depends on the data Classification: 4.3, 2.2. External routines: Python 2.7+, NumPy 1.3+, SciPy 0.10+ Catalogue identifier of previous version: AEKO_v1_0 Journal reference of previous version: Comput. Phys. Commun. 183 (2012) 405 Does the new version supersede the previous version?: Yes Nature of problem: The direct numerical solution of the multi-particle one-dimensional Schrödinger equation in a quantum well is challenging due to the exponential growth in the number of degrees of freedom with increasing particles. Solution method: A nodal spectral Galerkin scheme is used where the basis functions are constructed to obey the antisymmetry relations of the fermionic wave function. The assembly of these matrices is performed efficiently by exploiting the combinatorial structure of the sparsity patterns. Reasons for new version: A Python implementation is now included. Summary of revisions: Added a Python implementation; small documentation fixes in Matlab implementation. No change in features of the package. Restrictions: Only one-dimensional computational domains with homogeneous Dirichlet or periodic boundary conditions are supported. Running time: Seconds to minutes.

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

PubMed

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

2015-01-15

A large choice of tools exists for many standard tasks in the analysis of high-throughput sequencing (HTS) data. However, once a project deviates from standard workflows, custom scripts are needed. We present HTSeq, a Python library to facilitate the rapid development of such scripts. HTSeq offers parsers for many common data formats in HTS projects, as well as classes to represent data, such as genomic coordinates, sequences, sequencing reads, alignments, gene model information and variant calls, and provides data structures that allow for querying via genomic coordinates. We also present htseq-count, a tool developed with HTSeq that preprocesses RNA-Seq data for differential expression analysis by counting the overlap of reads with genes. HTSeq is released as an open-source software under the GNU General Public Licence and available from http://www-huber.embl.de/HTSeq or from the Python Package Index at https://pypi.python.org/pypi/HTSeq. © The Author 2014. Published by Oxford University Press.

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

NASA Astrophysics Data System (ADS)

Lin, J. W.

2008-12-01

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

KMCLib: A general framework for lattice kinetic Monte Carlo (KMC) simulations

NASA Astrophysics Data System (ADS)

Leetmaa, Mikael; Skorodumova, Natalia V.

2014-09-01

KMCLib is a general framework for lattice kinetic Monte Carlo (KMC) simulations. The program can handle simulations of the diffusion and reaction of millions of particles in one, two, or three dimensions, and is designed to be easily extended and customized by the user to allow for the development of complex custom KMC models for specific systems without having to modify the core functionality of the program. Analysis modules and on-the-fly elementary step diffusion rate calculations can be implemented as plugins following a well-defined API. The plugin modules are loosely coupled to the core KMCLib program via the Python scripting language. KMCLib is written as a Python module with a backend C++ library. After initial compilation of the backend library KMCLib is used as a Python module; input to the program is given as a Python script executed using a standard Python interpreter. We give a detailed description of the features and implementation of the code and demonstrate its scaling behavior and parallel performance with a simple one-dimensional A-B-C lattice KMC model and a more complex three-dimensional lattice KMC model of oxygen-vacancy diffusion in a fluorite structured metal oxide. KMCLib can keep track of individual particle movements and includes tools for mean square displacement analysis, and is therefore particularly well suited for studying diffusion processes at surfaces and in solids. Catalogue identifier: AESZ_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AESZ_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: GNU General Public License, version 3 No. of lines in distributed program, including test data, etc.: 49 064 No. of bytes in distributed program, including test data, etc.: 1 575 172 Distribution format: tar.gz Programming language: Python and C++. Computer: Any computer that can run a C++ compiler and a Python interpreter. Operating system: Tested on Ubuntu 12.4 LTS, CentOS release 5.9, Mac OSX 10.5.8 and Mac OSX 10.8.2, but should run on any system that can have a C++ compiler, MPI and a Python interpreter. Has the code been vectorized or parallelized?: Yes. From one to hundreds of processors depending on the type of input and simulation. RAM: From a few megabytes to several gigabytes depending on input parameters and the size of the system to simulate. Classification: 4.13, 16.13. External routines: KMCLib uses an external Mersenne Twister pseudo random number generator that is included in the code. A Python 2.7 interpreter and a standard C++ runtime library are needed to run the serial version of the code. For running the parallel version an MPI implementation is needed, such as e.g. MPICH from http://www.mpich.org or Open-MPI from http://www.open-mpi.org. SWIG (obtainable from http://www.swig.org/) and CMake (obtainable from http://www.cmake.org/) are needed for building the backend module, Sphinx (obtainable from http://sphinx-doc.org) for building the documentation and CPPUNIT (obtainable from http://sourceforge.net/projects/cppunit/) for building the C++ unit tests. Nature of problem: Atomic scale simulation of slowly evolving dynamics is a great challenge in many areas of computational materials science and catalysis. When the rare-events dynamics of interest is orders of magnitude slower than the typical atomic vibrational frequencies a straight-forward propagation of the equations of motions for the particles in the simulation cannot reach time scales of relevance for modeling the slow dynamics. Solution method: KMCLib provides an implementation of the kinetic Monte Carlo (KMC) method that solves the slow dynamics problem by utilizing the separation of time scales between fast vibrational motion and the slowly evolving rare-events dynamics. Only the latter is treated explicitly and the system is simulated as jumping between fully equilibrated local energy minima on the slow-dynamics potential energy surface. Restrictions: KMCLib implements the lattice KMC method and is as such restricted to geometries that can be expressed on a grid in space. Unusual features: KMCLib has been designed to be easily customized, to allow for user-defined functionality and integration with other codes. The user can define her own on-the-fly rate calculator via a Python API, so that site-specific elementary process rates, or rates depending on long-range interactions or complex geometrical features can easily be included. KMCLib also allows for on-the-fly analysis with user-defined analysis modules. KMCLib can keep track of individual particle movements and includes tools for mean square displacement analysis, and is therefore particularly well suited for studying diffusion processes at surfaces and in solids. Additional comments: The full documentation of the program is distributed with the code and can also be found at http://www.github.com/leetmaa/KMCLib/manual Running time: rom a few seconds to several days depending on the type of simulation and input parameters.

Python erythrocytes are resistant to α-hemolysin from Escherichia coli.

PubMed

Larsen, Casper K; Skals, Marianne; Wang, Tobias; Cheema, Muhammad U; Leipziger, Jens; Praetorius, Helle A

2011-12-01

α-Hemolysin (HlyA) from Escherichia coli lyses mammalian erythrocytes by creating nonselective cation pores in the membrane. Pore insertion triggers ATP release and subsequent P2X receptor and pannexin channel activation. Blockage of either P2X receptors or pannexin channels reduces HlyA-induced hemolysis. We found that erythrocytes from Python regius and Python molurus are remarkably resistant to HlyA-induced hemolysis compared to human and Trachemys scripta erythrocytes. HlyA concentrations that induced maximal hemolysis of human erythrocytes did not affect python erythrocytes, but increasing the HlyA concentration 40-fold did induce hemolysis. Python erythrocytes were more resistant to osmotic stress than human erythrocytes, but osmotic stress tolerance per se did not confer HlyA resistance. Erythrocytes from T. scripta, which showed higher osmotic resistance than python erythrocytes, were as susceptible to HlyA as human erythrocytes. Therefore, we tested whether python erythrocytes lack the purinergic signalling known to amplify HlyA-induced hemolysis in human erythrocytes. P. regius erythrocytes increased intracellular Ca²⁺ concentration and reduced cell volume when exposed to 3 mM ATP, indicating the presence of a P2X₇-like receptor. In addition, scavenging extracellular ATP or blocking P2 receptors or pannexin channels reduced the HlyA-induced hemolysis. We tested whether the low HlyA sensitivity resulted from low affinity of HlyA to the python erythrocyte membrane. We found comparable incorporation of HlyA into human and python erythrocyte membranes. Taken together, the remarkable HlyA resistance of python erythrocytes was not explained by increased osmotic resistance, lack of purinergic hemolysis amplification, or differences in HlyA affinity.

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.

A python framework for environmental model uncertainty analysis

USGS Publications Warehouse

White, Jeremy; Fienen, Michael N.; Doherty, John E.

2016-01-01

We have developed pyEMU, a python framework for Environmental Modeling Uncertainty analyses, open-source tool that is non-intrusive, easy-to-use, computationally efficient, and scalable to highly-parameterized inverse problems. The framework implements several types of linear (first-order, second-moment (FOSM)) and non-linear uncertainty analyses. The FOSM-based analyses can also be completed prior to parameter estimation to help inform important modeling decisions, such as parameterization and objective function formulation. Complete workflows for several types of FOSM-based and non-linear analyses are documented in example notebooks implemented using Jupyter that are available in the online pyEMU repository. Example workflows include basic parameter and forecast analyses, data worth analyses, and error-variance analyses, as well as usage of parameter ensemble generation and management capabilities. These workflows document the necessary steps and provides insights into the results, with the goal of educating users not only in how to apply pyEMU, but also in the underlying theory of applied uncertainty quantification.

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

PubMed

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

2015-05-01

Python snake species are often encountered in illegal activities and the question of species identity can be pertinent to such criminal investigations. Morphological identification of species of pythons can be confounded by many issues and molecular examination by DNA analysis can provide an alternative and objective means of identification. Our paper reports on the development and validation of a PCR primer pair that amplifies a segment of the mitochondrial cytochrome b gene that has been suggested previously as a good candidate locus for differentiating python species. We used this DNA region to perform species identification of pythons, even when the template DNA was of poor quality, as might be the case with forensic evidentiary items. Validation tests are presented to demonstrate the characteristics of the assay. Tests involved the cross-species amplification of this marker in non-target species, minimum amount of DNA template required, effects of degradation on product amplification and a blind trial to simulate a casework scenario that provided 100% correct identity. Our results demonstrate that this assay performs reliably and robustly on pythons and can be applied directly to forensic investigations where the presence of a species of python is in question. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Note: Tormenta: An open source Python-powered control software for camera based optical microscopy.

PubMed

Barabas, Federico M; Masullo, Luciano A; Stefani, Fernando D

2016-12-01

Until recently, PC control and synchronization of scientific instruments was only possible through closed-source expensive frameworks like National Instruments' LabVIEW. Nowadays, efficient cost-free alternatives are available in the context of a continuously growing community of open-source software developers. Here, we report on Tormenta, a modular open-source software for the control of camera-based optical microscopes. Tormenta is built on Python, works on multiple operating systems, and includes some key features for fluorescence nanoscopy based on single molecule localization.

Note: Tormenta: An open source Python-powered control software for camera based optical microscopy

NASA Astrophysics Data System (ADS)

Barabas, Federico M.; Masullo, Luciano A.; Stefani, Fernando D.

2016-12-01

Until recently, PC control and synchronization of scientific instruments was only possible through closed-source expensive frameworks like National Instruments' LabVIEW. Nowadays, efficient cost-free alternatives are available in the context of a continuously growing community of open-source software developers. Here, we report on Tormenta, a modular open-source software for the control of camera-based optical microscopes. Tormenta is built on Python, works on multiple operating systems, and includes some key features for fluorescence nanoscopy based on single molecule localization.

XIMPOL: a new x-ray polarimetry observation-simulation and analysis framework

NASA Astrophysics Data System (ADS)

Omodei, Nicola; Baldini, Luca; Pesce-Rollins, Melissa; di Lalla, Niccolò

2017-08-01

We present a new simulation framework, XIMPOL, based on the python programming language and the Scipy stack, specifically developed for X-ray polarimetric applications. XIMPOL is not tied to any specific mission or instrument design and is meant to produce fast and yet realistic observation-simulations, given as basic inputs: (i) an arbitrary source model including morphological, temporal, spectral and polarimetric information, and (ii) the response functions of the detector under study, i.e., the effective area, the energy dispersion, the point-spread function and the modulation factor. The format of the response files is OGIP compliant, and the framework has the capability of producing output files that can be directly fed into the standard visualization and analysis tools used by the X-ray community, including XSPEC which make it a useful tool not only for simulating physical systems, but also to develop and test end-to-end analysis chains.

The InSAR Scientific Computing Environment (ISCE): An Earth Science SAR Processing Framework, Toolbox, and Foundry

NASA Astrophysics Data System (ADS)

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

2016-12-01

The InSAR Scientific Computing Environment (ISCE) provides both a modular, flexible, and extensible framework for building software components and applications that work together seamlessly as well as a toolbox for processing InSAR data into higher level geodetic image products from a diverse array of radar satellites and aircraft. ISCE easily scales to serve as the SAR processing engine at the core of the NASA JPL Advanced Rapid Imaging and Analysis (ARIA) Center for Natural Hazards as well as a software toolbox for individual scientists working with SAR data. ISCE is planned as the foundational element in processing NISAR data, enabling a new class of analyses that take greater advantage of the long time and large spatial scales of these data. ISCE in ARIA is also a SAR Foundry for development of new processing components and workflows to meet the needs of both large processing centers and individual users. The ISCE framework contains object-oriented Python components layered to construct Python InSAR components that manage legacy Fortran/C InSAR programs. The Python user interface enables both command-line deployment of workflows as well as an interactive "sand box" (the Python interpreter) where scientists can "play" with the data. Recent developments in ISCE include the addition of components to ingest Sentinel-1A SAR data (both stripmap and TOPS-mode) and a new workflow for processing the TOPS-mode data. New components are being developed to exploit polarimetric-SAR data to provide the ecosystem and land-cover/land-use change communities with rigorous and efficient tools to perform multi-temporal, polarimetric and tomographic analyses in order to generate calibrated, geocoded and mosaicked Level-2 and Level-3 products (e.g., maps of above-ground biomass or forest disturbance). ISCE has been downloaded by over 200 users by a license for WinSAR members through the Unavco.org website. Others may apply directly to JPL for a license at download.jpl.nasa.gov.

GammaLib and ctools. A software framework for the analysis of astronomical gamma-ray data

NASA Astrophysics Data System (ADS)

Knödlseder, J.; Mayer, M.; Deil, C.; Cayrou, J.-B.; Owen, E.; Kelley-Hoskins, N.; Lu, C.-C.; Buehler, R.; Forest, F.; Louge, T.; Siejkowski, H.; Kosack, K.; Gerard, L.; Schulz, A.; Martin, P.; Sanchez, D.; Ohm, S.; Hassan, T.; Brau-Nogué, S.

2016-08-01

The field of gamma-ray astronomy has seen important progress during the last decade, yet to date no common software framework has been developed for the scientific analysis of gamma-ray telescope data. We propose to fill this gap by means of the GammaLib software, a generic library that we have developed to support the analysis of gamma-ray event data. GammaLib was written in C++ and all functionality is available in Python through an extension module. Based on this framework we have developed the ctools software package, a suite of software tools that enables flexible workflows to be built for the analysis of Imaging Air Cherenkov Telescope event data. The ctools are inspired by science analysis software available for existing high-energy astronomy instruments, and they follow the modular ftools model developed by the High Energy Astrophysics Science Archive Research Center. The ctools were written in Python and C++, and can be either used from the command line via shell scripts or directly from Python. In this paper we present the GammaLib and ctools software versions 1.0 that were released at the end of 2015. GammaLib and ctools are ready for the science analysis of Imaging Air Cherenkov Telescope event data, and also support the analysis of Fermi-LAT data and the exploitation of the COMPTEL legacy data archive. We propose using ctools as the science tools software for the Cherenkov Telescope Array Observatory.

The GBT Dynamic Scheduling System: Powered by the Web

NASA Astrophysics Data System (ADS)

Marganian, P.; Clark, M.; McCarty, M.; Sessoms, E.; Shelton, A.

2009-09-01

The web technologies utilized for the Robert C. Byrd Green Bank Telescope's (GBT) new Dynamic Scheduling System are discussed, focusing on languages, frameworks, and tools. We use a popular Python web framework, TurboGears, to take advantage of the extensive web services the system provides. TurboGears is a model-view-controller framework, which aggregates SQLAlchemy, Genshi, and CherryPy respectively. On top of this framework, Javascript (Prototype, script.aculo.us, and JQuery) and cascading style sheets (Blueprint) are used for desktop-quality web pages.

An integrated open framework for thermodynamics of reactions that combines accuracy and coverage.

PubMed

Noor, Elad; Bar-Even, Arren; Flamholz, Avi; Lubling, Yaniv; Davidi, Dan; Milo, Ron

2012-08-01

The laws of thermodynamics describe a direct, quantitative relationship between metabolite concentrations and reaction directionality. Despite great efforts, thermodynamic data suffer from limited coverage, scattered accessibility and non-standard annotations. We present a framework for unifying thermodynamic data from multiple sources and demonstrate two new techniques for extrapolating the Gibbs energies of unmeasured reactions and conditions. Both methods account for changes in cellular conditions (pH, ionic strength, etc.) by using linear regression over the ΔG(○) of pseudoisomers and reactions. The Pseudoisomeric Reactant Contribution method systematically infers compound formation energies using measured K' and pK(a) data. The Pseudoisomeric Group Contribution method extends the group contribution method and achieves a high coverage of unmeasured reactions. We define a continuous index that predicts the reversibility of a reaction under a given physiological concentration range. In the characteristic physiological range 3μM-3mM, we find that roughly half of the reactions in Escherichia coli's metabolism are reversible. These new tools can increase the accuracy of thermodynamic-based models, especially in non-standard pH and ionic strengths. The reversibility index can help modelers decide which reactions are reversible in physiological conditions. Freely available on the web at: http://equilibrator.weizmann.ac.il. Website implemented in Python, MySQL, Apache and Django, with all major browsers supported. The framework is open-source (code.google.com/p/milo-lab), implemented in pure Python and tested mainly on Linux. ron.milo@weizmann.ac.il Supplementary data are available at Bioinformatics online.

An integrated open framework for thermodynamics of reactions that combines accuracy and coverage

PubMed Central

Noor, Elad; Bar-Even, Arren; Flamholz, Avi; Lubling, Yaniv; Davidi, Dan; Milo, Ron

2012-01-01

Motivation: The laws of thermodynamics describe a direct, quantitative relationship between metabolite concentrations and reaction directionality. Despite great efforts, thermodynamic data suffer from limited coverage, scattered accessibility and non-standard annotations. We present a framework for unifying thermodynamic data from multiple sources and demonstrate two new techniques for extrapolating the Gibbs energies of unmeasured reactions and conditions. Results: Both methods account for changes in cellular conditions (pH, ionic strength, etc.) by using linear regression over the ΔG○ of pseudoisomers and reactions. The Pseudoisomeric Reactant Contribution method systematically infers compound formation energies using measured K′ and pKa data. The Pseudoisomeric Group Contribution method extends the group contribution method and achieves a high coverage of unmeasured reactions. We define a continuous index that predicts the reversibility of a reaction under a given physiological concentration range. In the characteristic physiological range 3μM–3mM, we find that roughly half of the reactions in Escherichia coli's metabolism are reversible. These new tools can increase the accuracy of thermodynamic-based models, especially in non-standard pH and ionic strengths. The reversibility index can help modelers decide which reactions are reversible in physiological conditions. Availability: Freely available on the web at: http://equilibrator.weizmann.ac.il. Website implemented in Python, MySQL, Apache and Django, with all major browsers supported. The framework is open-source (code.google.com/p/milo-lab), implemented in pure Python and tested mainly on Linux. Contact: ron.milo@weizmann.ac.il Supplementary Information: Supplementary data are available at Bioinformatics online. PMID:22645166

Identification and Characterization of Two Closely Related Unclassifiable Endogenous Retroviruses in Pythons (Python molurus and Python curtus)

PubMed Central

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

2002-01-01

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

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

NASA Astrophysics Data System (ADS)

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

2015-12-01

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

COBRApy: COnstraints-Based Reconstruction and Analysis for Python.

PubMed

Ebrahim, Ali; Lerman, Joshua A; Palsson, Bernhard O; Hyduke, Daniel R

2013-08-08

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

GPU-powered model analysis with PySB/cupSODA.

PubMed

Harris, Leonard A; Nobile, Marco S; Pino, James C; Lubbock, Alexander L R; Besozzi, Daniela; Mauri, Giancarlo; Cazzaniga, Paolo; Lopez, Carlos F

2017-11-01

A major barrier to the practical utilization of large, complex models of biochemical systems is the lack of open-source computational tools to evaluate model behaviors over high-dimensional parameter spaces. This is due to the high computational expense of performing thousands to millions of model simulations required for statistical analysis. To address this need, we have implemented a user-friendly interface between cupSODA, a GPU-powered kinetic simulator, and PySB, a Python-based modeling and simulation framework. For three example models of varying size, we show that for large numbers of simulations PySB/cupSODA achieves order-of-magnitude speedups relative to a CPU-based ordinary differential equation integrator. The PySB/cupSODA interface has been integrated into the PySB modeling framework (version 1.4.0), which can be installed from the Python Package Index (PyPI) using a Python package manager such as pip. cupSODA source code and precompiled binaries (Linux, Mac OS/X, Windows) are available at github.com/aresio/cupSODA (requires an Nvidia GPU; developer.nvidia.com/cuda-gpus). Additional information about PySB is available at pysb.org. paolo.cazzaniga@unibg.it or c.lopez@vanderbilt.edu. Supplementary data are available at Bioinformatics online. © The Author(s) 2017. Published by Oxford University Press.

A New Effort for Atmospherical Forecast: Meteorological Image Processing Software (MIPS) for Astronomical Observations

NASA Astrophysics Data System (ADS)

Shameoni Niaei, M.; Kilic, Y.; Yildiran, B. E.; Yüzlükoglu, F.; Yesilyaprak, C.

2016-12-01

We have described a new software (MIPS) about the analysis and image processing of the meteorological satellite (Meteosat) data for an astronomical observatory. This software will be able to help to make some atmospherical forecast (cloud, humidity, rain) using meteosat data for robotic telescopes. MIPS uses a python library for Eumetsat data that aims to be completely open-source and licenced under GNU/General Public Licence (GPL). MIPS is a platform independent and uses h5py, numpy, and PIL with the general-purpose and high-level programming language Python and the QT framework.

The Computational Science Environment (CSE)

DTIC Science & Technology

2009-08-01

supported CSE platforms. Developers can also build against different versions of a particular package (e.g., Python-2.4 vs . Python-2.5) via a...8.2.1 TK Testing Error and Workaround It has been found that TK tends to produces more testing errors when using KDE , and in some instances, the test...suite freezes when reaching the TK select test. These issues have not been seen when using Gnome . 8.2.2 VTK Testing Error and Workaround VTK test

Web-based application for inverting one-dimensional magnetotelluric data using Python

NASA Astrophysics Data System (ADS)

Suryanto, Wiwit; Irnaka, Theodosius Marwan

2016-11-01

One-dimensional modeling of magnetotelluric (MT) data has been performed using an online application on a web-based virtual private server. The application was developed with the Python language using the Django framework with HTML and CSS components. The input data, including the apparent resistivity and phase as a function of period or frequency with standard deviation, can be entered through an interactive web page that can be freely accessed at https://komputasi.geofisika.ugm.ac.id. The subsurface models, represented by resistivity as a function of depth, are iteratively improved by changing the model parameters, such as the resistivity and the layer depth, based on the observed apparent resistivity and phase data. The output of the application displayed on the screen presents resistivity as a function of depth and includes the RMS error for each iteration. Synthetic and real data were used in comparative tests of the application's performance, and it is shown that the application developed accurate subsurface resistivity models. Hence, this application can be used for practical one-dimensional modeling of MT data.

Python Turboprop Prepared for a Test in the Altitude Wind Tunnel

NASA Image and Video Library

1949-08-21

A 3670-horsepower Armstrong-Siddeley Python turboprop being prepared for tests in the Altitude Wind Tunnel at the National Advisory Committee for Aeronautics (NACA) Lewis Flight Propulsion Laboratory. In 1947 Lewis researcher Walter Olsen led a group of representatives from the military, industry, and the NACA on a fact finding mission to investigate the technological progress of British turbojet manufacturers. Afterwards several British engines, including the Python, were brought to Cleveland for testing in Lewis’s altitude facilities. The Python was a 14-stage axial-flow compressor turboprop with a fixed-area nozzle and contra-rotating propellers. Early turboprops combined the turbojet and piston engine technologies. They could move large quantities of air so required less engine speed and thus less fuel. This was very appealing to the military for some applications. The military asked the NACA to compare the Python’s performance at sea to that at high altitudes. The NACA researchers studied the Python in the Altitude Wind Tunnel from July 1949 through January 1950. It was the first time the tunnel was used to study an engine with the sole purpose of learning about, not improving it. They analyzed the engine’s dynamic response using a frequency response method at altitudes between 10,000 to 30,000 feet. Lewis researchers found that they could predict the dynamic response characteristics at any altitude from the data obtained from any other specific altitude. This portion of the testing was completed during a single test run.

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.

The NOvA software testing framework

NASA Astrophysics Data System (ADS)

Tamsett, M.; C Group

2015-12-01

The NOvA experiment at Fermilab is a long-baseline neutrino experiment designed to study vε appearance in a vμ beam. NOvA has already produced more than one million Monte Carlo and detector generated files amounting to more than 1 PB in size. This data is divided between a number of parallel streams such as far and near detector beam spills, cosmic ray backgrounds, a number of data-driven triggers and over 20 different Monte Carlo configurations. Each of these data streams must be processed through the appropriate steps of the rapidly evolving, multi-tiered, interdependent NOvA software framework. In total there are greater than 12 individual software tiers, each of which performs a different function and can be configured differently depending on the input stream. In order to regularly test and validate that all of these software stages are working correctly NOvA has designed a powerful, modular testing framework that enables detailed validation and benchmarking to be performed in a fast, efficient and accessible way with minimal expert knowledge. The core of this system is a novel series of python modules which wrap, monitor and handle the underlying C++ software framework and then report the results to a slick front-end web-based interface. This interface utilises modern, cross-platform, visualisation libraries to render the test results in a meaningful way. They are fast and flexible, allowing for the easy addition of new tests and datasets. In total upwards of 14 individual streams are regularly tested amounting to over 70 individual software processes, producing over 25 GB of output files. The rigour enforced through this flexible testing framework enables NOvA to rapidly verify configurations, results and software and thus ensure that data is available for physics analysis in a timely and robust manner.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bovy, Jo, E-mail: bovy@ias.edu

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 axisymmetricmore » 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.« less

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

The Profile Envision and Splicing Tool (PRESTO): Developing an Atmospheric Wind Analysis Tool for Space Launch Vehicles Using Python

NASA Technical Reports Server (NTRS)

Orcutt, John M.; Barbre, Robert E., Jr.; Brenton, James C.; Decker, Ryan K.

2017-01-01

Launch vehicle programs require vertically complete atmospheric profiles. Many systems at the ER to make the necessary measurements, but all have different EVR, vertical coverage, and temporal coverage. MSFC Natural Environments Branch developed a tool to create a vertically complete profile from multiple inputs using Python. Forward work: Finish Formal Testing Acceptance Testing, End-to-End Testing. Formal Release

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

NASA Astrophysics Data System (ADS)

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

2013-04-01

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

Cosmic Microwave Background Anisotropy Measurement from Python V

NASA Astrophysics Data System (ADS)

Coble, K.; Dodelson, S.; Dragovan, M.; Ganga, K.; Knox, L.; Kovac, J.; Ratra, B.; Souradeep, T.

2003-02-01

We analyze observations of the microwave sky made with the Python experiment in its fifth year of operation at the Amundsen-Scott South Pole Station in Antarctica. After modeling the noise and constructing a map, we extract the cosmic signal from the data. We simultaneously estimate the angular power spectrum in eight bands ranging from large (l~40) to small (l~260) angular scales, with power detected in the first six bands. There is a significant rise in the power spectrum from large to smaller (l~200) scales, consistent with that expected from acoustic oscillations in the early universe. We compare this Python V map to a map made from data taken in the third year of Python. Python III observations were made at a frequency of 90 GHz and covered a subset of the region of the sky covered by Python V observations, which were made at 40 GHz. Good agreement is obtained both visually (with a filtered version of the map) and via a likelihood ratio test.

Integrating neuroinformatics tools in TheVirtualBrain.

PubMed

Woodman, M Marmaduke; Pezard, Laurent; Domide, Lia; Knock, Stuart A; Sanz-Leon, Paula; Mersmann, Jochen; McIntosh, Anthony R; Jirsa, Viktor

2014-01-01

TheVirtualBrain (TVB) is a neuroinformatics Python package representing the convergence of clinical, systems, and theoretical neuroscience in the analysis, visualization and modeling of neural and neuroimaging dynamics. TVB is composed of a flexible simulator for neural dynamics measured across scales from local populations to large-scale dynamics measured by electroencephalography (EEG), magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI), and core analytic and visualization functions, all accessible through a web browser user interface. A datatype system modeling neuroscientific data ties together these pieces with persistent data storage, based on a combination of SQL and HDF5. These datatypes combine with adapters allowing TVB to integrate other algorithms or computational systems. TVB provides infrastructure for multiple projects and multiple users, possibly participating under multiple roles. For example, a clinician might import patient data to identify several potential lesion points in the patient's connectome. A modeler, working on the same project, tests these points for viability through whole brain simulation, based on the patient's connectome, and subsequent analysis of dynamical features. TVB also drives research forward: the simulator itself represents the culmination of several simulation frameworks in the modeling literature. The availability of the numerical methods, set of neural mass models and forward solutions allows for the construction of a wide range of brain-scale simulation scenarios. This paper briefly outlines the history and motivation for TVB, describing the framework and simulator, giving usage examples in the web UI and Python scripting.

Integrating neuroinformatics tools in TheVirtualBrain

PubMed Central

Woodman, M. Marmaduke; Pezard, Laurent; Domide, Lia; Knock, Stuart A.; Sanz-Leon, Paula; Mersmann, Jochen; McIntosh, Anthony R.; Jirsa, Viktor

2014-01-01

TheVirtualBrain (TVB) is a neuroinformatics Python package representing the convergence of clinical, systems, and theoretical neuroscience in the analysis, visualization and modeling of neural and neuroimaging dynamics. TVB is composed of a flexible simulator for neural dynamics measured across scales from local populations to large-scale dynamics measured by electroencephalography (EEG), magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI), and core analytic and visualization functions, all accessible through a web browser user interface. A datatype system modeling neuroscientific data ties together these pieces with persistent data storage, based on a combination of SQL and HDF5. These datatypes combine with adapters allowing TVB to integrate other algorithms or computational systems. TVB provides infrastructure for multiple projects and multiple users, possibly participating under multiple roles. For example, a clinician might import patient data to identify several potential lesion points in the patient's connectome. A modeler, working on the same project, tests these points for viability through whole brain simulation, based on the patient's connectome, and subsequent analysis of dynamical features. TVB also drives research forward: the simulator itself represents the culmination of several simulation frameworks in the modeling literature. The availability of the numerical methods, set of neural mass models and forward solutions allows for the construction of a wide range of brain-scale simulation scenarios. This paper briefly outlines the history and motivation for TVB, describing the framework and simulator, giving usage examples in the web UI and Python scripting. PMID:24795617

Automatising the analysis of stochastic biochemical time-series

PubMed Central

2015-01-01

Background Mathematical and computational modelling of biochemical systems has seen a lot of effort devoted to the definition and implementation of high-performance mechanistic simulation frameworks. Within these frameworks it is possible to analyse complex models under a variety of configurations, eventually selecting the best setting of, e.g., parameters for a target system. Motivation This operational pipeline relies on the ability to interpret the predictions of a model, often represented as simulation time-series. Thus, an efficient data analysis pipeline is crucial to automatise time-series analyses, bearing in mind that errors in this phase might mislead the modeller's conclusions. Results For this reason we have developed an intuitive framework-independent Python tool to automate analyses common to a variety of modelling approaches. These include assessment of useful non-trivial statistics for simulation ensembles, e.g., estimation of master equations. Intuitive and domain-independent batch scripts will allow the researcher to automatically prepare reports, thus speeding up the usual model-definition, testing and refinement pipeline. PMID:26051821

When is the unfamiliar the uncanny? Meaning affirmation after exposure to absurdist literature, humor, and art.

PubMed

Proulx, Travis; Heine, Steven J; Vohs, Kathleen D

2010-06-01

The meaning maintenance model asserts that following a meaning threat, people will affirm any meaning frameworks that are available. Three experiments tested (a) whether people affirm alternative meaning frameworks after reading absurdist literature, (b) what role expectations play in determining whether absurdities are threatening, and (c) whether people have a heightened need for meaning following exposure to absurdist art. In Study 1, participants who read an absurd Kafka parable affirmed an alternative meaning framework more than did those who read a meaningful parable. In Study 2, participants who read an absurd Monty Python parody engaged in compensatory affirmation efforts only if they were led to expect a conventional story. In Study 3, participants who were exposed to absurdist art or reminders of their mortality, compared to participants exposed to representational or abstract art, reported higher scores on the Personal Need for Structure scale, suggesting that they experienced a heightened need for meaning.

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.

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.

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

Technical integration of hippocampus, Basal Ganglia and physical models for spatial navigation.

PubMed

Fox, Charles; Humphries, Mark; Mitchinson, Ben; Kiss, Tamas; Somogyvari, Zoltan; Prescott, Tony

2009-01-01

Computational neuroscience is increasingly moving beyond modeling individual neurons or neural systems to consider the integration of multiple models, often constructed by different research groups. We report on our preliminary technical integration of recent hippocampal formation, basal ganglia and physical environment models, together with visualisation tools, as a case study in the use of Python across the modelling tool-chain. We do not present new modeling results here. The architecture incorporates leaky-integrator and rate-coded neurons, a 3D environment with collision detection and tactile sensors, 3D graphics and 2D plots. We found Python to be a flexible platform, offering a significant reduction in development time, without a corresponding significant increase in execution time. We illustrate this by implementing a part of the model in various alternative languages and coding styles, and comparing their execution times. For very large-scale system integration, communication with other languages and parallel execution may be required, which we demonstrate using the BRAHMS framework's Python bindings.

Dshell++: A Component Based, Reusable Space System Simulation Framework

NASA Technical Reports Server (NTRS)

Lim, Christopher S.; Jain, Abhinandan

2009-01-01

This paper describes the multi-mission Dshell++ simulation framework for high fidelity, physics-based simulation of spacecraft, robotic manipulation and mobility systems. Dshell++ is a C++/Python library which uses modern script driven object-oriented techniques to allow component reuse and a dynamic run-time interface for complex, high-fidelity simulation of spacecraft and robotic systems. The goal of the Dshell++ architecture is to manage the inherent complexity of physicsbased simulations while supporting component model reuse across missions. The framework provides several features that support a large degree of simulation configurability and usability.

LSD: Large Survey Database framework

NASA Astrophysics Data System (ADS)

Juric, Mario

2012-09-01

The Large Survey Database (LSD) is a Python framework and DBMS for distributed storage, cross-matching and querying of large survey catalogs (>10^9 rows, >1 TB). The primary driver behind its development is the analysis of Pan-STARRS PS1 data. It is specifically optimized for fast queries and parallel sweeps of positionally and temporally indexed datasets. It transparently scales to more than >10^2 nodes, and can be made to function in "shared nothing" architectures.

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

NASA Technical Reports Server (NTRS)

Lang, Timothy J.

2015-01-01

At NASA Marshall Space Flight Center (MSFC), Python is used several different ways to analyze and visualize precipitating weather systems. A number of different Python-based software packages have been developed, which are available to the larger scientific community. The approach in all these packages is to utilize pre-existing Python modules as well as to be object-oriented and scalable. The first package that will be described and demonstrated is the Python Advanced Microwave Precipitation Radiometer (AMPR) Data Toolkit, or PyAMPR for short. PyAMPR reads geolocated brightness temperature data from any flight of the AMPR airborne instrument over its 25-year history into a common data structure suitable for user-defined analyses. It features rapid, simplified (i.e., one line of code) production of quick-look imagery, including Google Earth overlays, swath plots of individual channels, and strip charts showing multiple channels at once. These plotting routines are also capable of significant customization for detailed, publication-ready figures. Deconvolution of the polarization-varying channels to static horizontally and vertically polarized scenes is also available. Examples will be given of PyAMPR's contribution toward real-time AMPR data display during the Integrated Precipitation and Hydrology Experiment (IPHEx), which took place in the Carolinas during May-June 2014. The second software package is the Marshall Multi-Radar/Multi-Sensor (MRMS) Mosaic Python Toolkit, or MMM-Py for short. MMM-Py was designed to read, analyze, and display three-dimensional national mosaicked reflectivity data produced by the NOAA National Severe Storms Laboratory (NSSL). MMM-Py can read MRMS mosaics from either their unique binary format or their converted NetCDF format. It can also read and properly interpret the current mosaic design (4 regional tiles) as well as mosaics produced prior to late July 2013 (8 tiles). MMM-Py can easily stitch multiple tiles together to provide a larger regional or national picture of precipitating weather systems. Composites, horizontal and vertical crosssections, and combinations thereof are easily displayed using as little as one line of code. MMM-Py can also write to the native MRMS binary format, and sub-sectioning of tiles (or multiple stitched tiles) is anticipated to be in place by the time of this meeting. Thus, MMM-Py also can be used to power the creation of custom mosaics for targeted regional studies. Overlays of other data (e.g., lightning observations) are easily accomplished. Demonstrations of MMM-Py, including the creation of animations, will be shown. Finally, Marshall has done significant work to interface Python-based analysis routines with the U.S. Department of Energy's Py-ART software package for radar data ingest, processing, and analysis. One example of this is the Python Turbulence Detection Algorithm (PyTDA), an MSFC-based implementation of the National Center for Atmospheric Research (NCAR) Turbulence Detection Algorithm (NTDA) for the purposes of convective-scale analysis, situational awareness, and forensic meteorology. PyTDA exploits Py-ART's radar data ingest routines and data model to rapidly produce aviation-relevant turbulence estimates from Doppler radar data. Work toward processing speed optimization and better integration within the Py-ART framework will be highlighted. Python-based analysis within the Py-ART framework is also being done for new research related to intercomparison of ground-based radar data with satellite estimates of ocean winds, as well as research on the electrification of pyrocumulus clouds.

MAISIE: a multipurpose astronomical instrument simulator environment

NASA Astrophysics Data System (ADS)

O'Brien, Alan; Beard, Steven; Geers, Vincent; Klaassen, Pamela

2016-07-01

Astronomical instruments often need simulators to preview their data products and test their data reduction pipelines. Instrument simulators have tended to be purpose-built with a single instrument in mind, and at- tempting to reuse one of these simulators for a different purpose is often a slow and difficult task. MAISIE is a simulator framework designed for reuse on different instruments. An object-oriented design encourages reuse of functionality and structure, while offering the flexibility to create new classes with new functionality. MAISIE is a set of Python classes, interfaces and tools to help build instrument simulators. MAISIE can just as easily build simulators for single and multi-channel instruments, imagers and spectrometers, ground and space based instruments. To remain easy to use and to facilitate the sharing of simulators across teams, MAISIE is written in Python, a freely available and open-source language. New functionality can be created for MAISIE by creating new classes that represent optical elements. This approach allows new and novel instruments to add functionality and take advantage of the existing MAISIE classes. MAISIE has recently been used successfully to develop the simulator for the JWST/MIRI- Medium Resolution Spectrometer.

Generation of Test Questions from RDF Files Using PYTHON and SPARQL

NASA Astrophysics Data System (ADS)

Omarbekova, Assel; Sharipbay, Altynbek; Barlybaev, Alibek

2017-02-01

This article describes the development of the system for the automatic generation of test questions based on the knowledge base. This work has an applicable nature and provides detailed examples of the development of ontology and implementation the SPARQL queries in RDF-documents. Also it describes implementation of the program generating questions in the Python programming language including the necessary libraries while working with RDF-files.

LIVVkit: An extensible, python-based, land ice verification and validation toolkit for ice sheet models

NASA Astrophysics Data System (ADS)

Kennedy, Joseph H.; Bennett, Andrew R.; Evans, Katherine J.; Price, Stephen; Hoffman, Matthew; Lipscomb, William H.; Fyke, Jeremy; Vargo, Lauren; Boghozian, Adrianna; Norman, Matthew; Worley, Patrick H.

2017-06-01

To address the pressing need to better understand the behavior and complex interaction of ice sheets within the global Earth system, significant development of continental-scale, dynamical ice sheet models is underway. Concurrent to the development of the Community Ice Sheet Model (CISM), the corresponding verification and validation (V&V) process is being coordinated through a new, robust, Python-based extensible software package, the Land Ice Verification and Validation toolkit (LIVVkit). Incorporated into the typical ice sheet model development cycle, it provides robust and automated numerical verification, software verification, performance validation, and physical validation analyses on a variety of platforms, from personal laptops to the largest supercomputers. LIVVkit operates on sets of regression test and reference data sets, and provides comparisons for a suite of community prioritized tests, including configuration and parameter variations, bit-for-bit evaluation, and plots of model variables to indicate where differences occur. LIVVkit also provides an easily extensible framework to incorporate and analyze results of new intercomparison projects, new observation data, and new computing platforms. LIVVkit is designed for quick adaptation to additional ice sheet models via abstraction of model specific code, functions, and configurations into an ice sheet model description bundle outside the main LIVVkit structure. Ultimately, through shareable and accessible analysis output, LIVVkit is intended to help developers build confidence in their models and enhance the credibility of ice sheet models overall.

OpenDrift v1.0: a generic framework for trajectory modelling

NASA Astrophysics Data System (ADS)

Dagestad, Knut-Frode; Röhrs, Johannes; Breivik, Øyvind; Ådlandsvik, Bjørn

2018-04-01

OpenDrift is an open-source Python-based framework for Lagrangian particle modelling under development at the Norwegian Meteorological Institute with contributions from the wider scientific community. The framework is highly generic and modular, and is designed to be used for any type of drift calculations in the ocean or atmosphere. A specific module within the OpenDrift framework corresponds to a Lagrangian particle model in the traditional sense. A number of modules have already been developed, including an oil drift module, a stochastic search-and-rescue module, a pelagic egg module, and a basic module for atmospheric drift. The framework allows for the ingestion of an unspecified number of forcing fields (scalar and vectorial) from various sources, including Eulerian ocean, atmosphere and wave models, but also measurements or a priori values for the same variables. A basic backtracking mechanism is inherent, using sign reversal of the total displacement vector and negative time stepping. OpenDrift is fast and simple to set up and use on Linux, Mac and Windows environments, and can be used with minimal or no Python experience. It is designed for flexibility, and researchers may easily adapt or write modules for their specific purpose. OpenDrift is also designed for performance, and simulations with millions of particles may be performed on a laptop. Further, OpenDrift is designed for robustness and is in daily operational use for emergency preparedness modelling (oil drift, search and rescue, and drifting ships) at the Norwegian Meteorological Institute.

OpenNFT: An open-source Python/Matlab framework for real-time fMRI neurofeedback training based on activity, connectivity and multivariate pattern analysis.

PubMed

Koush, Yury; Ashburner, John; Prilepin, Evgeny; Sladky, Ronald; Zeidman, Peter; Bibikov, Sergei; Scharnowski, Frank; Nikonorov, Artem; De Ville, Dimitri Van

2017-08-01

Neurofeedback based on real-time functional magnetic resonance imaging (rt-fMRI) is a novel and rapidly developing research field. It allows for training of voluntary control over localized brain activity and connectivity and has demonstrated promising clinical applications. Because of the rapid technical developments of MRI techniques and the availability of high-performance computing, new methodological advances in rt-fMRI neurofeedback become possible. Here we outline the core components of a novel open-source neurofeedback framework, termed Open NeuroFeedback Training (OpenNFT), which efficiently integrates these new developments. This framework is implemented using Python and Matlab source code to allow for diverse functionality, high modularity, and rapid extendibility of the software depending on the user's needs. In addition, it provides an easy interface to the functionality of Statistical Parametric Mapping (SPM) that is also open-source and one of the most widely used fMRI data analysis software. We demonstrate the functionality of our new framework by describing case studies that include neurofeedback protocols based on brain activity levels, effective connectivity models, and pattern classification approaches. This open-source initiative provides a suitable framework to actively engage in the development of novel neurofeedback approaches, so that local methodological developments can be easily made accessible to a wider range of users. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

PyMVPA: A Unifying Approach to the Analysis of Neuroscientific Data

PubMed Central

Hanke, Michael; Halchenko, Yaroslav O.; Sederberg, Per B.; Olivetti, Emanuele; Fründ, Ingo; Rieger, Jochem W.; Herrmann, Christoph S.; Haxby, James V.; Hanson, Stephen José; Pollmann, Stefan

2008-01-01

The Python programming language is steadily increasing in popularity as the language of choice for scientific computing. The ability of this scripting environment to access a huge code base in various languages, combined with its syntactical simplicity, make it the ideal tool for implementing and sharing ideas among scientists from numerous fields and with heterogeneous methodological backgrounds. The recent rise of reciprocal interest between the machine learning (ML) and neuroscience communities is an example of the desire for an inter-disciplinary transfer of computational methods that can benefit from a Python-based framework. For many years, a large fraction of both research communities have addressed, almost independently, very high-dimensional problems with almost completely non-overlapping methods. However, a number of recently published studies that applied ML methods to neuroscience research questions attracted a lot of attention from researchers from both fields, as well as the general public, and showed that this approach can provide novel and fruitful insights into the functioning of the brain. In this article we show how PyMVPA, a specialized Python framework for machine learning based data analysis, can help to facilitate this inter-disciplinary technology transfer by providing a single interface to a wide array of machine learning libraries and neural data-processing methods. We demonstrate the general applicability and power of PyMVPA via analyses of a number of neural data modalities, including fMRI, EEG, MEG, and extracellular recordings. PMID:19212459

Detector Simulations with DD4hep

NASA Astrophysics Data System (ADS)

Petrič, M.; Frank, M.; Gaede, F.; Lu, S.; Nikiforou, N.; Sailer, A.

2017-10-01

Detector description is a key component of detector design studies, test beam analyses, and most of particle physics experiments that require the simulation of more and more different detector geometries and event types. This paper describes DD4hep, which is an easy-to-use yet flexible and powerful detector description framework that can be used for detector simulation and also extended to specific needs for a particular working environment. Linear collider detector concepts ILD, SiD and CLICdp as well as detector development collaborations CALICE and FCal have chosen to adopt the DD4hep geometry framework and its DDG4 pathway to Geant4 as its core simulation and reconstruction tools. The DDG4 plugins suite includes a wide variety of input formats, provides access to the Geant4 particle gun or general particles source and allows for handling of Monte Carlo truth information, eg. by linking hits and the primary particle that caused them, which is indispensable for performance and efficiency studies. An extendable array of segmentations and sensitive detectors allows the simulation of a wide variety of detector technologies. This paper shows how DD4hep allows to perform complex Geant4 detector simulations without compiling a single line of additional code by providing a palette of sub-detector components that can be combined and configured via compact XML files. Simulation is controlled either completely via the command line or via simple Python steering files interpreted by a Python executable. It also discusses how additional plugins and extensions can be created to increase the functionality.

MSNoise: Not Only dv/v! A Framework for Continuous Seismic Data Analysis

NASA Astrophysics Data System (ADS)

Mordret, A.; Lecocq, T.; De Plaen, R.; Caudron, C.; Brenguier, F.

2015-12-01

MSNoise is an Open and Free Python package known to be the only complete integrated workflow designed to analyse ambient seismic noise and study relative velocity changes (dv/v) in the crust. It is based on state of the art and well maintained Python modules, among which ObsPy plays an important role. To our knowledge, it is officially used for continuous monitoring at least in three notable places: the Observatory of the Piton de la Fournaise volcano (OVPF, France), the Auckland Volcanic Field (New Zealand) and on the South Napa earthquake (Berkeley, USA). It is also used by many researchers to process archive data, e.g. focussing on fault zones, intraplate Europe, geothermal exploitations or Antarctica. We first present the general working of MSNoise, originally written in 2010 to automatically scan data archives and process seismic data in order to produce dv/v time series. We demonstrate that its modularity provides a new potential to easily test new algorithms for each processing step. For example, to experiment new methods of cross-correlation (done by default in the frequency domain), stacking (default is linear stacking, averaging), or dt/t or dv/v estimation (default is moving window cross-spectrum "MWCS", so-called "doublet"), etc. Finally, we present the last major evolution of MSNoise, from a "single workflow: data archive to dv/v" to a framework system that allows plugins and modules to be developed and integrated into the MSNoise ecosystem. Examples of plugins in development such as continuous PPSD (à la McNamarra & Buland) or continuous RSAM/SSAM (Endo & Murray, Stephens) will be presented.

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

The selection and identification of a suitable hydrological model structure is more than fitting parameters of a model structure to reproduce a measured hydrograph. The procedure is highly dependent on various criteria, i.e. the modelling objective, the characteristics and the scale of the system under investigation as well as the available data. Rigorous analysis of the candidate model structures is needed to support and objectify the selection of the most appropriate structure for a specific case (or eventually justify the use of a proposed ensemble of structures). This holds both in the situation of choosing between a limited set of different structures as well as in the framework of flexible model structures with interchangeable components. Many different methods to evaluate and analyse model structures exist. This leads to a sprawl of available methods, all characterized by different assumptions, changing conditions of application and various code implementations. Methods typically focus on optimization, sensitivity analysis or uncertainty analysis, with backgrounds from optimization, machine-learning or statistics amongst others. These methods also need an evaluation metric (objective function) to compare the model outcome with some observed data. However, for current methods described in literature, implementations are not always transparent and reproducible (if available at all). No standard procedures exist to share code and the popularity (and amount of applications) of the methods is sometimes more dependent on the availability than the merits of the method. Moreover, new implementations of existing methods are difficult to verify and the different theoretical backgrounds make it difficult for environmental scientists to decide about the usefulness of a specific method. A common and open framework with a large set of methods can support users in deciding about the most appropriate method. Hence, it enables to simultaneously apply and compare different methods on a fair basis. We developed and present pySTAN (python framework for STructure Analysis), a python package containing a set of functions for model structure evaluation to provide the analysis of (hydrological) model structures. A selected set of algorithms for optimization, uncertainty and sensitivity analysis is currently available, together with a set of evaluation (objective) functions and input distributions to sample from. The methods are implemented model-independent and the python language provides the wrapper functions to apply administer external model codes. Different objective functions can be considered simultaneously with both statistical metrics and more hydrology specific metrics. By using so-called reStructuredText (sphinx documentation generator) and Python documentation strings (docstrings), the generation of manual pages is semi-automated and a specific environment is available to enhance both the readability and transparency of the code. It thereby enables a larger group of users to apply and compare these methods and to extend the functionalities.

An Interactive and Comprehensive Working Environment for High-Energy Physics Software with Python and Jupyter Notebooks

NASA Astrophysics Data System (ADS)

Braun, N.; Hauth, T.; Pulvermacher, C.; Ritter, M.

2017-10-01

Today’s analyses for high-energy physics (HEP) experiments involve processing a large amount of data with highly specialized algorithms. The contemporary workflow from recorded data to final results is based on the execution of small scripts - often written in Python or ROOT macros which call complex compiled algorithms in the background - to perform fitting procedures and generate plots. During recent years interactive programming environments, such as Jupyter, became popular. Jupyter allows to develop Python-based applications, so-called notebooks, which bundle code, documentation and results, e.g. plots. Advantages over classical script-based approaches is the feature to recompute only parts of the analysis code, which allows for fast and iterative development, and a web-based user frontend, which can be hosted centrally and only requires a browser on the user side. In our novel approach, Python and Jupyter are tightly integrated into the Belle II Analysis Software Framework (basf2), currently being developed for the Belle II experiment in Japan. This allows to develop code in Jupyter notebooks for every aspect of the event simulation, reconstruction and analysis chain. These interactive notebooks can be hosted as a centralized web service via jupyterhub with docker and used by all scientists of the Belle II Collaboration. Because of its generality and encapsulation, the setup can easily be scaled to large installations.

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

NASA Astrophysics Data System (ADS)

White, J.; Brakefield, L. K.

2015-12-01

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

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

NASA Astrophysics Data System (ADS)

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

2013-12-01

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

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

NASA Astrophysics Data System (ADS)

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

2012-12-01

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

User interfaces for computational science: A domain specific language for OOMMF embedded in Python

NASA Astrophysics Data System (ADS)

Beg, Marijan; Pepper, Ryan A.; Fangohr, Hans

2017-05-01

Computer simulations are used widely across the engineering and science disciplines, including in the research and development of magnetic devices using computational micromagnetics. In this work, we identify and review different approaches to configuring simulation runs: (i) the re-compilation of source code, (ii) the use of configuration files, (iii) the graphical user interface, and (iv) embedding the simulation specification in an existing programming language to express the computational problem. We identify the advantages and disadvantages of different approaches and discuss their implications on effectiveness and reproducibility of computational studies and results. Following on from this, we design and describe a domain specific language for micromagnetics that is embedded in the Python language, and allows users to define the micromagnetic simulations they want to carry out in a flexible way. We have implemented this micromagnetic simulation description language together with a computational backend that executes the simulation task using the Object Oriented MicroMagnetic Framework (OOMMF). We illustrate the use of this Python interface for OOMMF by solving the micromagnetic standard problem 4. All the code is publicly available and is open source.

Oasis: A high-level/high-performance open source Navier-Stokes solver

NASA Astrophysics Data System (ADS)

Mortensen, Mikael; Valen-Sendstad, Kristian

2015-03-01

Oasis is a high-level/high-performance finite element Navier-Stokes solver written from scratch in Python using building blocks from the FEniCS project (fenicsproject.org). The solver is unstructured and targets large-scale applications in complex geometries on massively parallel clusters. Oasis utilizes MPI and interfaces, through FEniCS, to the linear algebra backend PETSc. Oasis advocates a high-level, programmable user interface through the creation of highly flexible Python modules for new problems. Through the high-level Python interface the user is placed in complete control of every aspect of the solver. A version of the solver, that is using piecewise linear elements for both velocity and pressure, is shown to reproduce very well the classical, spectral, turbulent channel simulations of Moser et al. (1999). The computational speed is strongly dominated by the iterative solvers provided by the linear algebra backend, which is arguably the best performance any similar implicit solver using PETSc may hope for. Higher order accuracy is also demonstrated and new solvers may be easily added within the same framework.

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

USGS Publications Warehouse

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

2011-01-01

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

OzPythonPlex: An optimised forensic STR multiplex assay set for the Australasian carpet python (Morelia spilota).

PubMed

Ciavaglia, Sherryn; Linacre, Adrian

2018-05-01

Reptile species, and in particular snakes, are protected by national and international agreements yet are commonly handled illegally. To aid in the enforcement of such legislation, we report on the development of three 11-plex assays from the genome of the carpet python to type 24 loci of tetra-nucleotide and penta-nucleotide repeat motifs (pure, compound and complex included). The loci range in size between 70 and 550 bp. Seventeen of the loci are newly characterised with the inclusion of seven previously developed loci to facilitate cross-comparison with previous carpet python genotyping studies. Assays were optimised in accordance with human forensic profiling kits using one nanogram template DNA. Three loci are included in all three of the multiplex reactions as quality assurance markers, to ensure sample identity and genotyping accuracy is maintained across the three profiling assays. Allelic ladders have been developed for the three assays to ensure consistent and precise allele designation. A DNA reference database of allele frequencies is presented based on 249 samples collected from throughout the species native range. A small number of validation tests are conducted to demonstrate the utility of these multiplex assays. We suggest further appropriate validation tests that should be conducted prior to the application of the multiplex assays in criminal investigations involving carpet pythons. Copyright © 2018 Elsevier B.V. All rights reserved.

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

... Python Species, and Four Anaconda Species as Injurious Reptiles AGENCY: Fish and Wildlife Service... regulations to add Indian python (Python molurus, including Burmese python Python molurus bivittatus), reticulated python (Broghammerus reticulatus or Python reticulatus), Northern African python (Python sebae...

Status on the Development of a Modeling and Simulation Framework for the Economic Assessment of Nuclear Hybrid Energy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Epiney, Aaron Simon; Chen, Jun; Rabiti, Cristian

Continued effort to design and build a modeling and simulation framework to assess the economic viability of Nuclear Hybrid Energy Systems (NHES) was undertaken in fiscal year (FY) 2016. The purpose of this report is to document the various tasks associated with the development of such a framework and to provide a status of their progress. Several tasks have been accomplished. First, a synthetic time history generator has been developed in RAVEN, which consists of Fourier series and autoregressive moving average model. The former is used to capture the seasonal trend in historical data, while the latter is to characterizemore » the autocorrelation in residue time series (e.g., measurements with seasonal trends subtracted). As demonstration, both synthetic wind speed and grid demand are generated, showing matching statistics with database. In order to build a design and operations optimizer in RAVEN, a new type of sampler has been developed with highly object-oriented design. In particular, simultaneous perturbation stochastic approximation algorithm is implemented. The optimizer is capable to drive the model to optimize a scalar objective function without constraint in the input space, while the constraints handling is a work in progress and will be implemented to improve the optimization capability. Furthermore, a simplified cash flow model of the performance of an NHES in the electric market has been developed in Python and used as external model in RAVEN to confirm expectations on the analysis capability of RAVEN to provide insight into system economics and to test the capability of RAVEN to identify limit surfaces. Finally, an example calculation is performed that shows the integration and proper data passing in RAVEN of the synthetic time history generator, the cash flow model and the optimizer. It has been shown that the developed Python models external to RAVEN are able to communicate with RAVEN and each other through the newly developed RAVEN capability called “EnsembleModel”.« less

Large Survey Database: A Distributed Framework for Storage and Analysis of Large Datasets

NASA Astrophysics Data System (ADS)

Juric, Mario

2011-01-01

The Large Survey Database (LSD) is a Python framework and DBMS for distributed storage, cross-matching and querying of large survey catalogs (>10^9 rows, >1 TB). The primary driver behind its development is the analysis of Pan-STARRS PS1 data. It is specifically optimized for fast queries and parallel sweeps of positionally and temporally indexed datasets. It transparently scales to more than >10^2 nodes, and can be made to function in "shared nothing" architectures. An LSD database consists of a set of vertically and horizontally partitioned tables, physically stored as compressed HDF5 files. Vertically, we partition the tables into groups of related columns ('column groups'), storing together logically related data (e.g., astrometry, photometry). Horizontally, the tables are partitioned into partially overlapping ``cells'' by position in space (lon, lat) and time (t). This organization allows for fast lookups based on spatial and temporal coordinates, as well as data and task distribution. The design was inspired by the success of Google BigTable (Chang et al., 2006). Our programming model is a pipelined extension of MapReduce (Dean and Ghemawat, 2004). An SQL-like query language is used to access data. For complex tasks, map-reduce ``kernels'' that operate on query results on a per-cell basis can be written, with the framework taking care of scheduling and execution. The combination leverages users' familiarity with SQL, while offering a fully distributed computing environment. LSD adds little overhead compared to direct Python file I/O. In tests, we sweeped through 1.1 Grows of PanSTARRS+SDSS data (220GB) less than 15 minutes on a dual CPU machine. In a cluster environment, we achieved bandwidths of 17Gbits/sec (I/O limited). Based on current experience, we believe LSD should scale to be useful for analysis and storage of LSST-scale datasets. It can be downloaded from http://mwscience.net/lsd.

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. © 2015 Wiley Periodicals, Inc.

PyMT: A Python package for model-coupling in the Earth sciences

NASA Astrophysics Data System (ADS)

Hutton, E.

2016-12-01

The current landscape of Earth-system models is not only broad in scientific scope, but also broad in type. On the one hand, the large variety of models is exciting, as it provides fertile ground for extending or linking models together in novel ways to answer new scientific questions. However, the heterogeneity in model type acts to inhibit model coupling, model development, or even model use. Existing models are written in a variety of programming languages, operate on different grids, use their own file formats (both for input and output), have different user interfaces, have their own time steps, etc. Each of these factors become obstructions to scientists wanting to couple, extend - or simply run - existing models. For scientists whose main focus may not be computer science these barriers become even larger and become significant logistical hurdles. And this is all before the scientific difficulties of coupling or running models are addressed. The CSDMS Python Modeling Toolkit (PyMT) was developed to help non-computer scientists deal with these sorts of modeling logistics. PyMT is the fundamental package the Community Surface Dynamics Modeling System uses for the coupling of models that expose the Basic Modeling Interface (BMI). It contains: Tools necessary for coupling models of disparate time and space scales (including grid mappers) Time-steppers that coordinate the sequencing of coupled models Exchange of data between BMI-enabled models Wrappers that automatically load BMI-enabled models into the PyMT framework Utilities that support open-source interfaces (UGRID, SGRID,CSDMS Standard Names, etc.) A collection of community-submitted models, written in a variety of programminglanguages, from a variety of process domains - but all usable from within the Python programming language A plug-in framework for adding additional BMI-enabled models to the framework In this presentation we intoduce the basics of the PyMT as well as provide an example of coupling models of different domains and grid types.

LIVVkit: An extensible, python-based, land ice verification and validation toolkit for ice sheet models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kennedy, Joseph H.; Bennett, Andrew R.; Evans, Katherine J.

To address the pressing need to better understand the behavior and complex interaction of ice sheets within the global Earth system, significant development of continental-scale, dynamical ice sheet models is underway. Concurrent to the development of the Community Ice Sheet Model (CISM), the corresponding verification and validation (V&V) process is being coordinated through a new, robust, Python-based extensible software package, the Land Ice Verification and Validation toolkit (LIVVkit). Incorporated into the typical ice sheet model development cycle, it provides robust and automated numerical verification, software verification, performance validation, and physical validation analyses on a variety of platforms, from personal laptopsmore » to the largest supercomputers. LIVVkit operates on sets of regression test and reference data sets, and provides comparisons for a suite of community prioritized tests, including configuration and parameter variations, bit-for-bit evaluation, and plots of model variables to indicate where differences occur. LIVVkit also provides an easily extensible framework to incorporate and analyze results of new intercomparison projects, new observation data, and new computing platforms. LIVVkit is designed for quick adaptation to additional ice sheet models via abstraction of model specific code, functions, and configurations into an ice sheet model description bundle outside the main LIVVkit structure. Furthermore, through shareable and accessible analysis output, LIVVkit is intended to help developers build confidence in their models and enhance the credibility of ice sheet models overall.« less

LIVVkit: An extensible, python-based, land ice verification and validation toolkit for ice sheet models

DOE PAGES

Kennedy, Joseph H.; Bennett, Andrew R.; Evans, Katherine J.; ...

2017-03-23

To address the pressing need to better understand the behavior and complex interaction of ice sheets within the global Earth system, significant development of continental-scale, dynamical ice sheet models is underway. Concurrent to the development of the Community Ice Sheet Model (CISM), the corresponding verification and validation (V&V) process is being coordinated through a new, robust, Python-based extensible software package, the Land Ice Verification and Validation toolkit (LIVVkit). Incorporated into the typical ice sheet model development cycle, it provides robust and automated numerical verification, software verification, performance validation, and physical validation analyses on a variety of platforms, from personal laptopsmore » to the largest supercomputers. LIVVkit operates on sets of regression test and reference data sets, and provides comparisons for a suite of community prioritized tests, including configuration and parameter variations, bit-for-bit evaluation, and plots of model variables to indicate where differences occur. LIVVkit also provides an easily extensible framework to incorporate and analyze results of new intercomparison projects, new observation data, and new computing platforms. LIVVkit is designed for quick adaptation to additional ice sheet models via abstraction of model specific code, functions, and configurations into an ice sheet model description bundle outside the main LIVVkit structure. Furthermore, through shareable and accessible analysis output, LIVVkit is intended to help developers build confidence in their models and enhance the credibility of ice sheet models overall.« less

A Python object-oriented framework for the CMS alignment and calibration data

NASA Astrophysics Data System (ADS)

Dawes, Joshua H.; CMS Collaboration

2017-10-01

The Alignment, Calibrations and Databases group at the CMS Experiment delivers Alignment and Calibration Conditions Data to a large set of workflows which process recorded event data and produce simulated events. The current infrastructure for releasing and consuming Conditions Data was designed in the two years of the first LHC long shutdown to respond to use cases from the preceding data-taking period. During the second run of the LHC, new use cases were defined. For the consumption of Conditions Metadata, no common interface existed for the detector experts to use in Python-based custom scripts, resulting in many different querying and transaction management patterns. A new framework has been built to address such use cases: a simple object-oriented tool that detector experts can use to read and write Conditions Metadata when using Oracle and SQLite databases, that provides a homogeneous method of querying across all services. The tool provides mechanisms for segmenting large sets of conditions while releasing them to the production database, allows for uniform error reporting to the client-side from the server-side and optimizes the data transfer to the server. The architecture of the new service has been developed exploiting many of the features made available by the metadata consumption framework to implement the required improvements. This paper presents the details of the design and implementation of the new metadata consumption and data upload framework, as well as analyses of the new upload service’s performance as the server-side state varies.

The Climate Data Analytic Services (CDAS) Framework.

NASA Astrophysics Data System (ADS)

Maxwell, T. P.; Duffy, D.

2016-12-01

Faced with unprecedented growth in climate data volume and demand, NASA has developed the Climate Data Analytic Services (CDAS) framework. This framework enables scientists to execute data processing workflows combining common analysis operations in a high performance environment close to the massive data stores at NASA. The data is accessed in standard (NetCDF, HDF, etc.) formats in a POSIX file system and processed using vetted climate data analysis tools (ESMF, CDAT, NCO, etc.). A dynamic caching architecture enables interactive response times. CDAS utilizes Apache Spark for parallelization and a custom array framework for processing huge datasets within limited memory spaces. CDAS services are accessed via a WPS API being developed in collaboration with the ESGF Compute Working Team to support server-side analytics for ESGF. The API can be accessed using either direct web service calls, a python script, a unix-like shell client, or a javascript-based web application. Client packages in python, scala, or javascript contain everything needed to make CDAS requests. The CDAS architecture brings together the tools, data storage, and high-performance computing required for timely analysis of large-scale data sets, where the data resides, to ultimately produce societal benefits. It is is currently deployed at NASA in support of the Collaborative REAnalysis Technical Environment (CREATE) project, which centralizes numerous global reanalysis datasets onto a single advanced data analytics platform. This service permits decision makers to investigate climate changes around the globe, inspect model trends and variability, and compare multiple reanalysis datasets.

pyRMSD: a Python package for efficient pairwise RMSD matrix calculation and handling.

PubMed

Gil, Víctor A; Guallar, Víctor

2013-09-15

We introduce pyRMSD, an open source standalone Python package that aims at offering an integrative and efficient way of performing Root Mean Square Deviation (RMSD)-related calculations of large sets of structures. It is specially tuned to do fast collective RMSD calculations, as pairwise RMSD matrices, implementing up to three well-known superposition algorithms. pyRMSD provides its own symmetric distance matrix class that, besides the fact that it can be used as a regular matrix, helps to save memory and increases memory access speed. This last feature can dramatically improve the overall performance of any Python algorithm using it. In addition, its extensibility, testing suites and documentation make it a good choice to those in need of a workbench for developing or testing new algorithms. The source code (under MIT license), installer, test suites and benchmarks can be found at https://pele.bsc.es/ under the tools section. victor.guallar@bsc.es Supplementary data are available at Bioinformatics online.

FluxPyt: a Python-based free and open-source software for 13C-metabolic flux analyses.

PubMed

Desai, Trunil S; Srivastava, Shireesh

2018-01-01

13 C-Metabolic flux analysis (MFA) is a powerful approach to estimate intracellular reaction rates which could be used in strain analysis and design. Processing and analysis of labeling data for calculation of fluxes and associated statistics is an essential part of MFA. However, various software currently available for data analysis employ proprietary platforms and thus limit accessibility. We developed FluxPyt, a Python-based truly open-source software package for conducting stationary 13 C-MFA data analysis. The software is based on the efficient elementary metabolite unit framework. The standard deviations in the calculated fluxes are estimated using the Monte-Carlo analysis. FluxPyt also automatically creates flux maps based on a template for visualization of the MFA results. The flux distributions calculated by FluxPyt for two separate models: a small tricarboxylic acid cycle model and a larger Corynebacterium glutamicum model, were found to be in good agreement with those calculated by a previously published software. FluxPyt was tested in Microsoft™ Windows 7 and 10, as well as in Linux Mint 18.2. The availability of a free and open 13 C-MFA software that works in various operating systems will enable more researchers to perform 13 C-MFA and to further modify and develop the package.

FluxPyt: a Python-based free and open-source software for 13C-metabolic flux analyses

PubMed Central

Desai, Trunil S.

2018-01-01

13C-Metabolic flux analysis (MFA) is a powerful approach to estimate intracellular reaction rates which could be used in strain analysis and design. Processing and analysis of labeling data for calculation of fluxes and associated statistics is an essential part of MFA. However, various software currently available for data analysis employ proprietary platforms and thus limit accessibility. We developed FluxPyt, a Python-based truly open-source software package for conducting stationary 13C-MFA data analysis. The software is based on the efficient elementary metabolite unit framework. The standard deviations in the calculated fluxes are estimated using the Monte-Carlo analysis. FluxPyt also automatically creates flux maps based on a template for visualization of the MFA results. The flux distributions calculated by FluxPyt for two separate models: a small tricarboxylic acid cycle model and a larger Corynebacterium glutamicum model, were found to be in good agreement with those calculated by a previously published software. FluxPyt was tested in Microsoft™ Windows 7 and 10, as well as in Linux Mint 18.2. The availability of a free and open 13C-MFA software that works in various operating systems will enable more researchers to perform 13C-MFA and to further modify and develop the package. PMID:29736347

PyMCT: A Very High Level Language Coupling Tool For Climate System Models

NASA Astrophysics Data System (ADS)

Tobis, M.; Pierrehumbert, R. T.; Steder, M.; Jacob, R. L.

2006-12-01

At the Climate Systems Center of the University of Chicago, we have been examining strategies for applying agile programming techniques to complex high-performance modeling experiments. While the "agile" development methodology differs from a conventional requirements process and its associated milestones, the process remain a formal one. It is distinguished by continuous improvement in functionality, large numbers of small releases, extensive and ongoing testing strategies, and a strong reliance on very high level languages (VHLL). Here we report on PyMCT, which we intend as a core element in a model ensemble control superstructure. PyMCT is a set of Python bindings for MCT, the Fortran-90 based Model Coupling Toolkit, which forms the infrastructure for the inter-component communication in the Community Climate System Model (CCSM). MCT provides a scalable model communication infrastructure. In order to take maximum advantage of agile software development methodologies, we exposed MCT functionality to Python, a prominent VHLL. We describe how the scalable architecture of MCT allows us to overcome the relatively weak runtime performance of Python, so that the performance of the combined system is not severely impacted. To demonstrate these advantages, we reimplemented the CCSM coupler in Python. While this alone offers no new functionality, it does provide a rigorous test of PyMCT functionality and performance. We reimplemented the CPL6 library, presenting an interesting case study of the comparison between conventional Fortran-90 programming and the higher abstraction level provided by a VHLL. The powerful abstractions provided by Python will allow much more complex experimental paradigms. In particular, we hope to build on the scriptability of our coupling strategy to enable systematic sensitivity tests. Our most ambitious objective is to combine our efforts with Bayesian inverse modeling techniques toward objective tuning at the highest level, across model architectures.

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

Isolation and identification of bacterial populations of zoonotic importance from captive non-venomous snakes in Malaysia.

PubMed

Abba, Yusuf; Ilyasu, Yusuf Maina; Noordin, Mustapha Mohamed

2017-07-01

Captivity of non-venomous snakes such as python and boa are common in zoos, aquariums and as pets in households. Poor captivity conditions expose these reptiles to numerous pathogens which may result in disease conditions. The purpose of this study was to investigate the common bacteria isolated from necropsied captive snakes in Malaysia over a five year period. A total of 27 snake carcasses presented for necropsy at the Universiti Putra Malaysia (UPM) were used in this survey. Samples were aseptically obtained at necropsy from different organs/tissues (lung, liver, heart, kindey, oesophagus, lymph node, stomach, spinal cord, spleen, intestine) and cultured onto 5% blood and McConkey agar, respectively. Gram staining, morphological evaluation and biochemical test such as oxidase, catalase and coagulase were used to tentatively identify the presumptive bacterial isolates. Pythons had the highest number of cases (81.3%) followed by anaconda (14.8%) and boa (3.7%). Mixed infection accounted for 81.5% in all snakes and was highest in pythons (63%). However, single infection was only observed in pythons (18.5%). A total of 82.7%, 95.4% and 100% of the bacterial isolates from python, anaconda and boa, respectively were gram negative. Aeromonas spp was the most frequently isolated bacteria in pythons and anaconda with incidences of 25 (18%) and 8 (36.6%) with no difference (p > 0.05) in incidence, respectively, while Salmonella spp was the most frequently isolated in boa and significantly higher (p < 0.05) than in python and anaconda. Bacteria species were most frequently isolated from the kidney of pythons 35 (25.2%), intestines of anacondas 11 (50%) and stomach of boa 3 (30%). This study showed that captive pythons harbored more bacterial species than anaconda or boa. Most of the bacterial species isolated from these snakes have public health importance and have been incriminated in human infections worldwide. Copyright © 2017 Elsevier Ltd. All rights reserved.

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

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

Food consumption increases cell proliferation in the python brain.

PubMed

Habroun, Stacy S; Schaffner, Andrew A; Taylor, Emily N; Strand, Christine R

2018-04-06

Pythons are model organisms for investigating physiological responses to food intake. While systemic growth in response to food consumption is well documented, what occurs in the brain is currently unexplored. In this study, male ball pythons ( Python regius ) were used to test the hypothesis that food consumption stimulates cell proliferation in the brain. We used 5-bromo-12'-deoxyuridine (BrdU) as a cell-birth marker to quantify and compare cell proliferation in the brain of fasted snakes and those at 2 and 6 days after a meal. Throughout the telencephalon, cell proliferation was significantly increased in the 6 day group, with no difference between the 2 day group and controls. Systemic postprandial plasticity occurs quickly after a meal is ingested, during the period of active digestion; however, the brain displays a surge of cell proliferation after most digestion and absorption is complete. © 2018. Published by The Company of Biologists Ltd.

Predicting size limit of wild blood python (python brongersmai stull, 1938) harvesting in north sumatera

NASA Astrophysics Data System (ADS)

Mangantar Pardamean Sianturi, Markus; Jumilawaty, Erni; Delvian; Hartanto, Adrian

2018-03-01

Blood python (Python brongersmai Stull, 1938) is one of heavily exploited wildlife in Indonesia. The high demands on its skin trade have made its harvesting regulated under quota-based setting by the government to prevent over-harvesting. To gain understanding on the sustainability of P. brongersmai in the wild, biological characters of wild-caught specimens were studied. Samples were collected from two slaughterhouses from Rantau Prapat and Langkat. Parameters measured were morphological (Snout-vent length (SVL), body mass, abdomen width) and anatomical characters (Fat classes). Total samples of P. brongersmai in this research were 541 with 269 male and 272 female snakes. Female snakes had the highest proportion of individuals with the best quality of abdominal fat reserves (Class 3). Linear models are built and tested for its significance in relation between fat classes as anatomical characters and morphological characters. All tested morphological characters were significant in female snakes. By using linear equation models, we generate size limit to prioritize harvesting in the future. We suggest the use of SVL and stomach width ranging between 139,7 – 141,5 cm and 24,72 – 25,71 cm respectively to achieve sustainability of P. brongersmai in the wild.

PEITH(Θ): perfecting experiments with information theory in Python with GPU support.

PubMed

Dony, Leander; Mackerodt, Jonas; Ward, Scott; Filippi, Sarah; Stumpf, Michael P H; Liepe, Juliane

2018-04-01

Different experiments provide differing levels of information about a biological system. This makes it difficult, a priori, to select one of them beyond mere speculation and/or belief, especially when resources are limited. With the increasing diversity of experimental approaches and general advances in quantitative systems biology, methods that inform us about the information content that a given experiment carries about the question we want to answer, become crucial. PEITH(Θ) is a general purpose, Python framework for experimental design in systems biology. PEITH(Θ) uses Bayesian inference and information theory in order to derive which experiments are most informative in order to estimate all model parameters and/or perform model predictions. https://github.com/MichaelPHStumpf/Peitho. m.stumpf@imperial.ac.uk or juliane.liepe@mpibpc.mpg.de.

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

NASA Astrophysics Data System (ADS)

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

2015-12-01

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

GoCxx: a tool to easily leverage C++ legacy code for multicore-friendly Go libraries and frameworks

NASA Astrophysics Data System (ADS)

Binet, Sébastien

2012-12-01

Current HENP libraries and frameworks were written before multicore systems became widely deployed and used. From this environment, a ‘single-thread’ processing model naturally emerged but the implicit assumptions it encouraged are greatly impairing our abilities to scale in a multicore/manycore world. Writing scalable code in C++ for multicore architectures, while doable, is no panacea. Sure, C++11 will improve on the current situation (by standardizing on std::thread, introducing lambda functions and defining a memory model) but it will do so at the price of complicating further an already quite sophisticated language. This level of sophistication has probably already strongly motivated analysis groups to migrate to CPython, hoping for its current limitations with respect to multicore scalability to be either lifted (Grand Interpreter Lock removal) or for the advent of a new Python VM better tailored for this kind of environment (PyPy, Jython, …) Could HENP migrate to a language with none of the deficiencies of C++ (build time, deployment, low level tools for concurrency) and with the fast turn-around time, simplicity and ease of coding of Python? This paper will try to make the case for Go - a young open source language with built-in facilities to easily express and expose concurrency - being such a language. We introduce GoCxx, a tool leveraging gcc-xml's output to automatize the tedious work of creating Go wrappers for foreign languages, a critical task for any language wishing to leverage legacy and field-tested code. We will conclude with the first results of applying GoCxx to real C++ code.

Pyro: A Python-Based Versatile Programming Environment for Teaching Robotics

ERIC Educational Resources Information Center

Blank, Douglas; Kumar, Deepak; Meeden, Lisa; Yanco, Holly

2004-01-01

In this article we describe a programming framework called Pyro, which provides a set of abstractions that allows students to write platform-independent robot programs. This project is unique because of its focus on the pedagogical implications of teaching mobile robotics via a top-down approach. We describe the background of the project, its…

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

Current Development Status of an Integrated Tool for Modeling Quasi-static Deformation in the Solid Earth

NASA Astrophysics Data System (ADS)

Williams, C. A.; Dicaprio, C.; Simons, M.

2003-12-01

With the advent of projects such as the Plate Boundary Observatory and future InSAR missions, spatially dense geodetic data of high quality will provide an increasingly detailed picture of the movement of the earth's surface. To interpret such information, powerful and easily accessible modeling tools are required. We are presently developing such a tool that we feel will meet many of the needs for evaluating quasi-static earth deformation. As a starting point, we begin with a modified version of the finite element code TECTON, which has been specifically designed to solve tectonic problems involving faulting and viscoelastic/plastic earth behavior. As our first priority, we are integrating the code into the GeoFramework, which is an extension of the Python-based Pyre modeling framework. The goal of this framework is to provide simplified user interfaces for powerful modeling codes, to provide easy access to utilities such as meshers and visualization tools, and to provide a tight integration between different modeling tools so they can interact with each other. The initial integration of the code into this framework is essentially complete, and a more thorough integration, where Python-based drivers control the entire solution, will be completed in the near future. We have an evolving set of priorities that we expect to solidify as we receive more input from the modeling community. Current priorities include the development of linear and quadratic tetrahedral elements, the development of a parallelized version of the code using the PETSc libraries, the addition of more complex rheologies, realistic fault friction models, adaptive time stepping, and spherical geometries. In this presentation we describe current progress toward our various priorities, briefly describe the structure of the code within the GeoFramework, and demonstrate some sample applications.

A web service framework for astronomical remote observation in Antarctica by using satellite link

NASA Astrophysics Data System (ADS)

Jia, M.-h.; Chen, Y.-q.; Zhang, G.-y.; Jiang, P.; Zhang, H.; Wang, J.

2018-07-01

Many telescopes are deployed in Antarctica as it offers excellent astronomical observation conditions. However, because Antarctica's environment is harsh to humans, remote operation of telescope is necessary for observation. Furthermore, communication to devices in Antarctica through satellite link with low bandwidth and high latency limits the effectiveness of remote observation. This paper introduces a web service framework for remote astronomical observation in Antarctica. The framework is based on Python Tornado. RTS2-HTTPD and REDIS are used as the access interface to the telescope control system in Antarctica. The web service provides real-time updates through WebSocket. To improve user experience and control effectiveness under the poor satellite link condition, an agent server is deployed in the mainland to synchronize the Antarctic server's data and send it to domestic users in China. The agent server will forward the request of domestic users to the Antarctic master server. The web service was deployed and tested on Bright Star Survey Telescope (BSST) in Antarctica. Results show that the service meets the demands of real-time, multiuser remote observation and domestic users have a better experience of remote operation.

Orchid: a novel management, annotation and machine learning framework for analyzing cancer mutations.

PubMed

Cario, Clinton L; Witte, John S

2018-03-15

As whole-genome tumor sequence and biological annotation datasets grow in size, number and content, there is an increasing basic science and clinical need for efficient and accurate data management and analysis software. With the emergence of increasingly sophisticated data stores, execution environments and machine learning algorithms, there is also a need for the integration of functionality across frameworks. We present orchid, a python based software package for the management, annotation and machine learning of cancer mutations. Building on technologies of parallel workflow execution, in-memory database storage and machine learning analytics, orchid efficiently handles millions of mutations and hundreds of features in an easy-to-use manner. We describe the implementation of orchid and demonstrate its ability to distinguish tissue of origin in 12 tumor types based on 339 features using a random forest classifier. Orchid and our annotated tumor mutation database are freely available at https://github.com/wittelab/orchid. Software is implemented in python 2.7, and makes use of MySQL or MemSQL databases. Groovy 2.4.5 is optionally required for parallel workflow execution. JWitte@ucsf.edu. Supplementary data are available at Bioinformatics online.

microMS: A Python Platform for Image-Guided Mass Spectrometry Profiling

NASA Astrophysics Data System (ADS)

Comi, Troy J.; Neumann, Elizabeth K.; Do, Thanh D.; Sweedler, Jonathan V.

2017-09-01

Image-guided mass spectrometry (MS) profiling provides a facile framework for analyzing samples ranging from single cells to tissue sections. The fundamental workflow utilizes a whole-slide microscopy image to select targets of interest, determine their spatial locations, and subsequently perform MS analysis at those locations. Improving upon prior reported methodology, a software package was developed for working with microscopy images. microMS, for microscopy-guided mass spectrometry, allows the user to select and profile diverse samples using a variety of target patterns and mass analyzers. Written in Python, the program provides an intuitive graphical user interface to simplify image-guided MS for novice users. The class hierarchy of instrument interactions permits integration of new MS systems while retaining the feature-rich image analysis framework. microMS is a versatile platform for performing targeted profiling experiments using a series of mass spectrometers. The flexibility in mass analyzers greatly simplifies serial analyses of the same targets by different instruments. The current capabilities of microMS are presented, and its application for off-line analysis of single cells on three distinct instruments is demonstrated. The software has been made freely available for research purposes. [Figure not available: see fulltext.

microMS: A Python Platform for Image-Guided Mass Spectrometry Profiling.

PubMed

Comi, Troy J; Neumann, Elizabeth K; Do, Thanh D; Sweedler, Jonathan V

2017-09-01

Image-guided mass spectrometry (MS) profiling provides a facile framework for analyzing samples ranging from single cells to tissue sections. The fundamental workflow utilizes a whole-slide microscopy image to select targets of interest, determine their spatial locations, and subsequently perform MS analysis at those locations. Improving upon prior reported methodology, a software package was developed for working with microscopy images. microMS, for microscopy-guided mass spectrometry, allows the user to select and profile diverse samples using a variety of target patterns and mass analyzers. Written in Python, the program provides an intuitive graphical user interface to simplify image-guided MS for novice users. The class hierarchy of instrument interactions permits integration of new MS systems while retaining the feature-rich image analysis framework. microMS is a versatile platform for performing targeted profiling experiments using a series of mass spectrometers. The flexibility in mass analyzers greatly simplifies serial analyses of the same targets by different instruments. The current capabilities of microMS are presented, and its application for off-line analysis of single cells on three distinct instruments is demonstrated. The software has been made freely available for research purposes. Graphical Abstract ᅟ.

Parameter optimization of a hydrologic model in a snow-dominated basin using a modular Python framework

NASA Astrophysics Data System (ADS)

Volk, J. M.; Turner, M. A.; Huntington, J. L.; Gardner, M.; Tyler, S.; Sheneman, L.

2016-12-01

Many distributed models that simulate watershed hydrologic processes require a collection of multi-dimensional parameters as input, some of which need to be calibrated before the model can be applied. The Precipitation Runoff Modeling System (PRMS) is a physically-based and spatially distributed hydrologic model that contains a considerable number of parameters that often need to be calibrated. Modelers can also benefit from uncertainty analysis of these parameters. To meet these needs, we developed a modular framework in Python to conduct PRMS parameter optimization, uncertainty analysis, interactive visual inspection of parameters and outputs, and other common modeling tasks. Here we present results for multi-step calibration of sensitive parameters controlling solar radiation, potential evapo-transpiration, and streamflow in a PRMS model that we applied to the snow-dominated Dry Creek watershed in Idaho. We also demonstrate how our modular approach enables the user to use a variety of parameter optimization and uncertainty methods or easily define their own, such as Monte Carlo random sampling, uniform sampling, or even optimization methods such as the downhill simplex method or its commonly used, more robust counterpart, shuffled complex evolution.

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 resulted in a good outcome.

qtcm 0.1.2: A Python Implementation of the Neelin-Zeng Quasi-Equilibrium Tropical Circulation model

NASA Astrophysics Data System (ADS)

Lin, J. W.-B.

2008-10-01

Historically, climate models have been developed incrementally and in compiled languages like Fortran. While the use of legacy compiled languages results in fast, time-tested code, the resulting model is limited in its modularity and cannot take advantage of functionality available with modern computer languages. Here we describe an effort at using the open-source, object-oriented language Python to create more flexible climate models: the package qtcm, a Python implementation of the intermediate-level Neelin-Zeng Quasi-Equilibrium Tropical Circulation model (QTCM1) of the atmosphere. The qtcm package retains the core numerics of QTCM1, written in Fortran to optimize model performance, but uses Python structures and utilities to wrap the QTCM1 Fortran routines and manage model execution. The resulting "mixed language" modeling package allows order and choice of subroutine execution to be altered at run time, and model analysis and visualization to be integrated in interactively with model execution at run time. This flexibility facilitates more complex scientific analysis using less complex code than would be possible using traditional languages alone, and provides tools to transform the traditional "formulate hypothesis → write and test code → run model → analyze results" sequence into a feedback loop that can be executed automatically by the computer.

qtcm 0.1.2: a Python implementation of the Neelin-Zeng Quasi-Equilibrium Tropical Circulation Model

NASA Astrophysics Data System (ADS)

Lin, J. W.-B.

2009-02-01

Historically, climate models have been developed incrementally and in compiled languages like Fortran. While the use of legacy compiled languages results in fast, time-tested code, the resulting model is limited in its modularity and cannot take advantage of functionality available with modern computer languages. Here we describe an effort at using the open-source, object-oriented language Python to create more flexible climate models: the package qtcm, a Python implementation of the intermediate-level Neelin-Zeng Quasi-Equilibrium Tropical Circulation model (QTCM1) of the atmosphere. The qtcm package retains the core numerics of QTCM1, written in Fortran to optimize model performance, but uses Python structures and utilities to wrap the QTCM1 Fortran routines and manage model execution. The resulting "mixed language" modeling package allows order and choice of subroutine execution to be altered at run time, and model analysis and visualization to be integrated in interactively with model execution at run time. This flexibility facilitates more complex scientific analysis using less complex code than would be possible using traditional languages alone, and provides tools to transform the traditional "formulate hypothesis → write and test code → run model → analyze results" sequence into a feedback loop that can be executed automatically by the computer.

Evolution of extreme ontogenetic allometric diversity and heterochrony in pythons, a clade of giant and dwarf snakes.

PubMed

Esquerré, Damien; Sherratt, Emma; Keogh, J Scott

2017-12-01

Ontogenetic allometry, how species change with size through their lives, and heterochony, a decoupling between shape, size, and age, are major contributors to biological diversity. However, macroevolutionary allometric and heterochronic trends remain poorly understood because previous studies have focused on small groups of closely related species. Here, we focus on testing hypotheses about the evolution of allometry and how allometry and heterochrony drive morphological diversification at the level of an entire species-rich and diverse clade. Pythons are a useful system due to their remarkably diverse and well-adapted phenotypes and extreme size disparity. We collected detailed phenotype data on 40 of the 44 species of python from 1191 specimens. We used a suite of analyses to test for shifts in allometric trajectories that modify morphological diversity. Heterochrony is the main driver of initial divergence within python clades, and shifts in the slopes of allometric trajectories make exploration of novel phenotypes possible later in divergence history. We found that allometric coefficients are highly evolvable and there is an association between ontogenetic allometry and ecology, suggesting that allometry is both labile and adaptive rather than a constraint on possible phenotypes. © 2017 The Author(s). Evolution © 2017 The Society for the Study of Evolution.

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. © 2016 John Wiley & Sons Ltd/CNRS.

DOE Office of Scientific and Technical Information (OSTI.GOV)

TESP combines existing domain simulators in the electric power grid, with new transactive agents, growth models and evaluation scripts. The existing domain simulators include GridLAB-D for the distribution grid and single-family residential buildings, MATPOWER for transmission and bulk generation, and EnergyPlus for large buildings. More are planned for subsequent versions of TESP. The new elements are: TEAgents - simulate market participants and transactive systems for market clearing. Some of this functionality was extracted from GridLAB-D and implemented in Python for customization by PNNL and others; Growth Model - a means for simulating system changes over a multiyear period, including bothmore » normal load growth and specific investment decisions. Customizable in Python code; and Evaluation Script - a means of evaluating different transactive systems through customizable post-processing in Python code. TESP provides a method for other researchers and vendors to design transactive systems, and test them in a virtual environment. It allows customization of the key components by modifying Python code.« less

The role of python eggshell permeability dynamics in a respiration-hydration trade-off.

PubMed

Stahlschmidt, Zachary R; Heulin, Benoit; DeNardo, Dale F

2010-01-01

Parental care is taxonomically widespread because it improves developmental conditions and thus fitness of offspring. Although relatively simplistic compared with parental behaviors of other taxa, python egg-brooding behavior exemplifies parental care because it mediates a trade-off between embryonic respiration and hydration. However, because egg brooding increases gas-exchange resistance between embryonic and nest environments and because female pythons do not adjust their brooding behavior in response to the increasing metabolic requirements of developing offspring, python egg brooding imposes hypoxic costs on embryos during the late stages of incubation. We conducted a series of experiments to determine whether eggshells coadapted with brooding behavior to minimize the negative effects of developmental hypoxia. We tested the hypotheses that python eggshells (1) increase permeability over time to accommodate increasing embryonic respiration and (2) exhibit permeability plasticity in response to chronic hypoxia. Over incubation, we serially measured the atomic and structural components of Children's python (Antaresia childreni) eggshells as well as in vivo and in vitro gas exchange across eggshells. In support of our first hypothesis, A. childreni eggshells exhibited a reduced fibrous layer, became more permeable, and facilitated greater gas exchange as incubation progressed. Our second hypothesis was not supported, as incubation O(2) concentration did not affect the shells' permeabilities to O(2) and H(2)O vapor. Our results suggest that python eggshell permeability changes during incubation but that the alterations over time are fixed and independent of environmental conditions. These findings are of broad evolutionary interest because they demonstrate that, even in relatively simple parental-care models, successful parent-offspring relationships depend on adjustments made by both the parent (i.e., egg-brooding behavioral shifts) and the offspring (i.e., changes in eggshell permeability).

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

NASA Astrophysics Data System (ADS)

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

2015-12-01

The InSAR Scientific Computing Environment (ISCE, funded by NASA ESTO) provides a modern computing framework for geodetic image processing of InSAR data from a diverse array of radar satellites and aircraft. ISCE is both a modular, flexible, and extensible framework for building software components and applications as well as a toolbox of applications for processing raw or focused InSAR and Polarimetric InSAR data. The ISCE framework contains object-oriented Python components layered to construct Python InSAR components that manage legacy Fortran/C InSAR programs. Components are independently configurable in a layered manner to provide maximum control. Polymorphism is used to define a workflow in terms of abstract facilities for each processing step that are realized by specific components at run-time. This enables a single workflow to work on either raw or focused data from all sensors. ISCE can serve as the core of a production center to process Level-0 radar data to Level-3 products, but is amenable to interactive processing approaches that allow scientists to experiment with data to explore new ways of doing science with InSAR data. The NASA-ISRO SAR (NISAR) Mission will deliver data of unprecedented quantity and quality, making possible global-scale studies in climate research, natural hazards, and Earth's ecosystems. ISCE is planned as the foundational element in processing NISAR data, enabling a new class of analyses that take greater advantage of the long time and large spatial scales of these new data. NISAR will be but one mission in a constellation of radar satellites in the future delivering such data. ISCE currently supports all publicly available strip map mode space-borne SAR data since ERS and is expected to include support for upcoming missions. ISCE has been incorporated into two prototype cloud-based systems that have demonstrated its elasticity in addressing larger data processing problems in a "production" context and its ability to be controlled by individual science users on the cloud for large data problems. ISCE has been downloaded by over 200 users by a license for WinSAR members through the Unavco.org website. Others may apply directly to JPL for a license at download.jpl.nasa.gov.

76 FR 40082 - Semiannual Regulatory Agenda

Federal Register 2010, 2011, 2012, 2013, 2014

2011-07-07

...; Constrictor Species From Python, Boa, and Eunectes Genera. Bureau of Ocean Energy Management, Regulation, and... Wildlife Evaluation; Constrictor Species from Python, Boa, and Eunectes Genera Legal Authority: 18 U.S.C... are: Indian python (including Burmese python), reticulated python, Northern African python, Southern...

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

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

PESO - The Python Based Control System of the Ondrejov 2m Telescope

NASA Astrophysics Data System (ADS)

Skoda, P.; Fuchs, J.; Honsa, J.

2005-12-01

Python has been gaining a good reputation and respectability in many areas of software development. We have chosen Python after getting the new CCD detector for the coudé spectrograph of Ondřejov observatory 2m telescope. The VersArray detector from Roper Scientific came only with the closed source library PVCAM of low-level camera control functions for Linux, so we had to write the whole astronomical data acquisition system from scratch and integrate it with the current spectrograph and telescope control systems. The final result of our effort, PESO (Python Exposure System for Ondřejov) is a highly comfortable GUI-based environment allowing the observer to change the spectrograph configuration, choose the detector acquisition mode, select the exposure parameters, and monitor the exposure progress. All of the relevant information from the control computers is written into the FITS headers by the PyFITS module, and the acquired CCD frame is immediately displayed in an SAO DS9 window using XPA calls. The GTK-based front end design was drawn in the Glade visual development tool, giving the shape and position of all widgets in single XML file, which is used in Python by a simple call of the PyGlade module. We describe our experience with the design and implementation of PESO, stressing the easiness of quick changes of the GUI, together with the capability of separate testing of every module using the Python debugger, IPython.

Graphical programming interface: A development environment for MRI methods.

PubMed

Zwart, Nicholas R; Pipe, James G

2015-11-01

To introduce a multiplatform, Python language-based, development environment called graphical programming interface for prototyping MRI techniques. The interface allows developers to interact with their scientific algorithm prototypes visually in an event-driven environment making tasks such as parameterization, algorithm testing, data manipulation, and visualization an integrated part of the work-flow. Algorithm developers extend the built-in functionality through simple code interfaces designed to facilitate rapid implementation. This article shows several examples of algorithms developed in graphical programming interface including the non-Cartesian MR reconstruction algorithms for PROPELLER and spiral as well as spin simulation and trajectory visualization of a FLORET example. The graphical programming interface framework is shown to be a versatile prototyping environment for developing numeric algorithms used in the latest MR techniques. © 2014 Wiley Periodicals, Inc.

MTpy - Python Tools for Magnetotelluric Data Processing and Analysis

NASA Astrophysics Data System (ADS)

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

2014-05-01

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

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

PubMed Central

Bednar, James A.

2008-01-01

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

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

NASA Astrophysics Data System (ADS)

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

2013-12-01

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

Multiscale Simulations of Magnetic Island Coalescence

NASA Technical Reports Server (NTRS)

Dorelli, John C.

2010-01-01

We describe a new interactive parallel Adaptive Mesh Refinement (AMR) framework written in the Python programming language. This new framework, PyAMR, hides the details of parallel AMR data structures and algorithms (e.g., domain decomposition, grid partition, and inter-process communication), allowing the user to focus on the development of algorithms for advancing the solution of a systems of partial differential equations on a single uniform mesh. We demonstrate the use of PyAMR by simulating the pairwise coalescence of magnetic islands using the resistive Hall MHD equations. Techniques for coupling different physics models on different levels of the AMR grid hierarchy are discussed.

AGAMA: Action-based galaxy modeling framework

NASA Astrophysics Data System (ADS)

Vasiliev, Eugene

2018-05-01

The AGAMA library models galaxies. It computes gravitational potential and forces, performs orbit integration and analysis, and can convert between position/velocity and action/angle coordinates. It offers a framework for finding best-fit parameters of a model from data and self-consistent multi-component galaxy models, and contains useful auxiliary utilities such as various mathematical routines. The core of the library is written in C++, and there are Python and Fortran interfaces. AGAMA may be used as a plugin for the stellar-dynamical software packages galpy (ascl:1411.008), AMUSE (ascl:1107.007), and NEMO (ascl:1010.051).

exocartographer: Constraining surface maps orbital parameters of exoplanets

NASA Astrophysics Data System (ADS)

Farr, Ben; Farr, Will M.; Cowan, Nicolas B.; Haggard, Hal M.; Robinson, Tyler

2018-05-01

exocartographer solves the exo-cartography inverse problem. This flexible forward-modeling framework, written in Python, retrieves the albedo map and spin geometry of a planet based on time-resolved photometry; it uses a Markov chain Monte Carlo method to extract albedo maps and planet spin and their uncertainties. Gaussian Processes use the data to fit for the characteristic length scale of the map and enforce smooth maps.

Providing Web Interfaces to the NSF EarthScope USArray Transportable Array

NASA Astrophysics Data System (ADS)

Vernon, Frank; Newman, Robert; Lindquist, Kent

2010-05-01

Since April 2004 the EarthScope USArray seismic network has grown to over 850 broadband stations that stream multi-channel data in near real-time to the Array Network Facility in San Diego. Providing secure, yet open, access to real-time and archived data for a broad range of audiences is best served by a series of platform agnostic low-latency web-based applications. We present a framework of tools that mediate between the world wide web and Boulder Real Time Technologies Antelope Environmental Monitoring System data acquisition and archival software. These tools provide comprehensive information to audiences ranging from network operators and geoscience researchers, to funding agencies and the general public. This ranges from network-wide to station-specific metadata, state-of-health metrics, event detection rates, archival data and dynamic report generation over a station's two year life span. Leveraging open source web-site development frameworks for both the server side (Perl, Python and PHP) and client-side (Flickr, Google Maps/Earth and jQuery) facilitates the development of a robust extensible architecture that can be tailored on a per-user basis, with rapid prototyping and development that adheres to web-standards. Typical seismic data warehouses allow online users to query and download data collected from regional networks, without the scientist directly visually assessing data coverage and/or quality. Using a suite of web-based protocols, we have recently developed an online seismic waveform interface that directly queries and displays data from a relational database through a web-browser. Using the Python interface to Datascope and the Python-based Twisted network package on the server side, and the jQuery Javascript framework on the client side to send and receive asynchronous waveform queries, we display broadband seismic data using the HTML Canvas element that is globally accessible by anyone using a modern web-browser. We are currently creating additional interface tools to create a rich-client interface for accessing and displaying seismic data that can be deployed to any system running the Antelope Real Time System. The software is freely available from the Antelope contributed code Git repository (http://www.antelopeusersgroup.org).

Using OPeNDAP's Data-Services Framework to Lift Mash-Ups above Blind Dates

NASA Astrophysics Data System (ADS)

Gallagher, J. H. R.; Fulker, D. W.

2015-12-01

OPeNDAP's data-as-service framework (Hyrax) matches diverse sources with many end-user tools and contexts. Keys to its flexibility include: A data model embracing tabular data alongside n-dim arrays and other structures useful in geoinformatics. A REST-like protocol that supports—via suffix notation—a growing set of output forms (netCDF, XML, etc.) plus a query syntax for subsetting. Subsetting applies (via constraints on column values) to tabular data or (via constraints on indices or coordinates) to array-style data . A handler-style architecture that admits a growing set of input types. Community members may contribute handlers, making Hyrax effective as middleware, where N sources are mapped to M outputs with order N+M effort (not NxM). Hyrax offers virtual aggregations of source data, enabling granularity aimed at users, not data-collectors. OPeNDAP-access libraries exist in multiple languages, including Python, Java, and C++. Recent enhancements are increasing this framework's interoperability (i.e., its mash-up) potential. Extensions implemented as servlets—running adjacent to Hyrax—are enriching the forms of aggregation and enabling new protocols: User-specified aggregations, namely, applying a query to (huge) lists of source granules, and receiving one (large) table or zipped netCDF file. OGC (Open Geospatial Consortium) protocols, WMS and WCS. A Webification (W10n) protocol that returns JavaScript Object Notation (JSON). Extensions to OPeNDAP's query language are reducing transfer volumes and enabling new forms of inspection. Advances underway include: Functions that, for triangular-mesh sources, return sub-meshes spec'd via geospatial bounding boxes. Functions that, for data from multiple, satellite-borne sensors (with differing orbits), select observations based on coincidence. Calculations of means, histograms, etc. that greatly reduce output volumes.. Paths for communities to contribute new server functions (in Python, e.g.) that data providers may incorporate into Hyrax via installation parameters. One could say Hyrax itself is a mash-up, but we suggest it as an instrument for a mash-up artist's toolbox. This instrument can support mash-ups built on netCDF files, OGC protocols, JavaScript Web pages, and/or programs written in Python, Java, C or C++.

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.

Interactions between the invasive Burmese python, Python bivittatus Kuhl, and the local mosquito community in Florida, USA

PubMed Central

Krysko, Kenneth L.; Gillett-Kaufman, Jennifer L.; Kawahara, Akito Y.; Connelly, C. Roxanne

2018-01-01

The Burmese python, Python bivittatus Kuhl, is a well-established invasive species in the greater Everglades ecosystem of southern Florida, USA. Most research on its ecological impacts focuses on its role as a predator and its trophic interactions with native vertebrate species, particularly mammals. Beyond predation, there is little known about the ecological interactions between P. bivittatus and native faunal communities. It is likely that established populations of P. bivittatus in southern Florida serve as hosts for native mosquito communities. To test this concept, we used mitochondrial cytochrome c oxidase subunit I DNA barcoding to determine the hosts of blood fed mosquitoes collected at a research facility in northern Florida where captive P. bivittatus and Argentine black and white tegu, Salvator merianae (Duméril and Bibron), are maintained in outdoor enclosures, accessible to local mosquitoes. We recovered python DNA from the blood meals of three species of Culex mosquitoes: Culex erraticus (Dyar and Knab), Culex quinquefasciatus Say, and Culex pilosus (Dyar and Knab). Culex erraticus conclusively (P = 0.001; Fisher’s Exact Test) took more blood meals from P. bivittatus than from any other available host. While the majority of mosquito blood meals in our sample were derived from P. bivittatus, only one was derived from S. merianae. These results demonstrate that local mosquitoes will feed on invasive P. bivittatus, a recently introduced host. If these interactions also occur in southern Florida, P. bivittatus may be involved in the transmission networks of mosquito-vectored pathogens. Our results also illustrate the potential of detecting the presence of P. bivittatus in the field through screening mosquito blood meals for their DNA. PMID:29342169

Interactions between the invasive Burmese python, Python bivittatus Kuhl, and the local mosquito community in Florida, USA.

PubMed

Reeves, Lawrence E; Krysko, Kenneth L; Avery, Michael L; Gillett-Kaufman, Jennifer L; Kawahara, Akito Y; Connelly, C Roxanne; Kaufman, Phillip E

2018-01-01

The Burmese python, Python bivittatus Kuhl, is a well-established invasive species in the greater Everglades ecosystem of southern Florida, USA. Most research on its ecological impacts focuses on its role as a predator and its trophic interactions with native vertebrate species, particularly mammals. Beyond predation, there is little known about the ecological interactions between P. bivittatus and native faunal communities. It is likely that established populations of P. bivittatus in southern Florida serve as hosts for native mosquito communities. To test this concept, we used mitochondrial cytochrome c oxidase subunit I DNA barcoding to determine the hosts of blood fed mosquitoes collected at a research facility in northern Florida where captive P. bivittatus and Argentine black and white tegu, Salvator merianae (Duméril and Bibron), are maintained in outdoor enclosures, accessible to local mosquitoes. We recovered python DNA from the blood meals of three species of Culex mosquitoes: Culex erraticus (Dyar and Knab), Culex quinquefasciatus Say, and Culex pilosus (Dyar and Knab). Culex erraticus conclusively (P = 0.001; Fisher's Exact Test) took more blood meals from P. bivittatus than from any other available host. While the majority of mosquito blood meals in our sample were derived from P. bivittatus, only one was derived from S. merianae. These results demonstrate that local mosquitoes will feed on invasive P. bivittatus, a recently introduced host. If these interactions also occur in southern Florida, P. bivittatus may be involved in the transmission networks of mosquito-vectored pathogens. Our results also illustrate the potential of detecting the presence of P. bivittatus in the field through screening mosquito blood meals for their DNA.

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.

Data processing with Pymicra, the Python tool for Micrometeorological Analyses

NASA Astrophysics Data System (ADS)

Chor, T. L.; Dias, N. L.

2017-12-01

With the ever-increasing capability of instrumentation of collecting high-frequency turbulence data, micrometeorological experiments are now generating significant amounts of data. Clearly, data processing -- and not data collection anymore -- has become the limiting factor for those very large data sets. The ability of extracting useful scientific information from those experiments, therefore, hinges on tools that (i) are able to process those data effectively and accurately, (ii) are flexible enough to be adapted to the specific requirements of each investigation, and (iii) are robust enough to make data analysis easily reproducible over different sets of large data sets. We have developed a framework for micrometeorological data analysis called Pymicra which does deliver such capabilities while maintaining proximity of the investigator with the data. It is fully written in an open-source, very high level language, Python, which has been gaining widespread acceptance as a scientific tool. It follows the philosophy of "not reinventing the wheel" and, as a result, relies on existing well-established open-source Python packages such as Numpy and Pandas. Thus, minimum effort is needed to program statistics, array processing, Fourier analysis, etc. Among the things that Pymicra does are reading and organizing data from virtually any format, applying common quality control procedures, extracting fluctuations in a number of ways, correcting for sensor drift, automatic calculation of fluid properties (such as air and dry air density), handling of units, calculation of cross-spectra, calculation of turbulent fluxes and scales, and all other features already provided by Pandas (interpolation, statistical tests, handling of missing data, etc.). Pymicra is freely available on Github and the fact that it makes heavy use of high-level programming makes adding and modifying code considerably easy for any scientific programmer, making it straightforward for other scientists to contribute with new functionality and point out room for improvements. Because of that, Pymicra is a candidate to be a community-developed code in the future and to centralize part of the data processing aimed at micrometeorology.

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

NASA Astrophysics Data System (ADS)

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

2013-04-01

This paper describes GMES, a free Python package for solving Maxwell’s equations using the finite-difference time-domain (FDTD) method. The design of GMES follows the object-oriented programming (OOP) approach and adopts a unique design strategy where the voxels in the computational domain are grouped and then updated according to its material type. This piecewise updating scheme ensures that GMES can adopt OOP without losing its simple structure and time-stepping speed. The users can easily add various material types, sources, and boundary conditions into their code using the Python programming language. The key design features, along with the supported material types, excitation sources, boundary conditions and parallel calculations employed in GMES are also described in detail. Catalog identifier: AEOK_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEOK_v1_0.html Program obtainable from: CPC Program Library, Queen’s University, Belfast, N. Ireland Licensing provisions: GNU General Public License v3.0 No. of lines in distributed program, including test data, etc.: 17700 No. of bytes in distributed program, including test data, etc.: 89878 Distribution format: tar.gz Programming language: C++, Python. Computer: Any computer with a Unix-like system with a C++ compiler, and a Python interpreter; developed on 2.53 GHz Intel CoreTM i3. Operating system: Any Unix-like system; developed under Ubuntu 12.04 LTS 64 bit. Has the code been vectorized or parallelized?: Yes. Parallelized with MPI directives (optional). RAM: Problem dependent (a simulation with real valued electromagnetic field uses roughly 0.18 KB per Yee cell.) Classification: 10. External routines: SWIG [1], Cython [2], NumPy [3], SciPy [4], matplotlib [5], MPI for Python [6] Nature of problem: Classical electrodynamics Solution method: Finite-difference time-domain (FDTD) method Additional comments: This article describes version 0.9.5. The most recent version can be downloaded at the GMES project homepage [7]. Running time: Problem dependent (a simulation with real valued electromagnetic field takes typically about 0.16 μs per Yee cell per time-step.) SWIG, http://www.swig.org. Cython, http://www.cython.org. NumPy, http://numpy.scipy.org. SciPy, http://www.scipy.org. matplotlib, http://matplotlib.sourceforge.net. MPI for Python, http://mpi4py.scipy.org. GMES, http://sourceforge.net/projects/gmes.

The LSST metrics analysis framework (MAF)

NASA Astrophysics Data System (ADS)

Jones, R. L.; Yoachim, Peter; Chandrasekharan, Srinivasan; Connolly, Andrew J.; Cook, Kem H.; Ivezic, Željko; Krughoff, K. S.; Petry, Catherine; Ridgway, Stephen T.

2014-07-01

We describe the Metrics Analysis Framework (MAF), an open-source python framework developed to provide a user-friendly, customizable, easily-extensible set of tools for analyzing data sets. MAF is part of the Large Synoptic Survey Telescope (LSST) Simulations effort. Its initial goal is to provide a tool to evaluate LSST Operations Simulation (OpSim) simulated surveys to help understand the effects of telescope scheduling on survey performance, however MAF can be applied to a much wider range of datasets. The building blocks of the framework are Metrics (algorithms to analyze a given quantity of data), Slicers (subdividing the overall data set into smaller data slices as relevant for each Metric), and Database classes (to access the dataset and read data into memory). We describe how these building blocks work together, and provide an example of using MAF to evaluate different dithering strategies. We also outline how users can write their own custom Metrics and use these within the framework.

Deterministic Design Optimization of Structures in OpenMDAO Framework

NASA Technical Reports Server (NTRS)

Coroneos, Rula M.; Pai, Shantaram S.

2012-01-01

Nonlinear programming algorithms play an important role in structural design optimization. Several such algorithms have been implemented in OpenMDAO framework developed at NASA Glenn Research Center (GRC). OpenMDAO is an open source engineering analysis framework, written in Python, for analyzing and solving Multi-Disciplinary Analysis and Optimization (MDAO) problems. It provides a number of solvers and optimizers, referred to as components and drivers, which users can leverage to build new tools and processes quickly and efficiently. Users may download, use, modify, and distribute the OpenMDAO software at no cost. This paper summarizes the process involved in analyzing and optimizing structural components by utilizing the framework s structural solvers and several gradient based optimizers along with a multi-objective genetic algorithm. For comparison purposes, the same structural components were analyzed and optimized using CometBoards, a NASA GRC developed code. The reliability and efficiency of the OpenMDAO framework was compared and reported in this report.

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

PubMed

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

2009-01-01

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

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

PubMed Central

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

2009-01-01

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

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.

Corral framework: Trustworthy and fully functional data intensive parallel astronomical pipelines

NASA Astrophysics Data System (ADS)

Cabral, J. B.; Sánchez, B.; Beroiz, M.; Domínguez, M.; Lares, M.; Gurovich, S.; Granitto, P.

2017-07-01

Data processing pipelines represent an important slice of the astronomical software library that include chains of processes that transform raw data into valuable information via data reduction and analysis. In this work we present Corral, a Python framework for astronomical pipeline generation. Corral features a Model-View-Controller design pattern on top of an SQL Relational Database capable of handling: custom data models; processing stages; and communication alerts, and also provides automatic quality and structural metrics based on unit testing. The Model-View-Controller provides concept separation between the user logic and the data models, delivering at the same time multi-processing and distributed computing capabilities. Corral represents an improvement over commonly found data processing pipelines in astronomysince the design pattern eases the programmer from dealing with processing flow and parallelization issues, allowing them to focus on the specific algorithms needed for the successive data transformations and at the same time provides a broad measure of quality over the created pipeline. Corral and working examples of pipelines that use it are available to the community at https://github.com/toros-astro.

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

NASA Astrophysics Data System (ADS)

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

2014-05-01

The program package escript has been designed for solving mathematical modeling problems using python, see Gross et al. (2013). Its development and maintenance has been funded by the Australian Commonwealth to provide open source software infrastructure for the Australian Earth Science community (recent funding by the Australian Geophysical Observing System EIF (AGOS) and the AuScope Collaborative Research Infrastructure Scheme (CRIS)). The key concepts of escript are based on the terminology of spatial functions and partial differential equations (PDEs) - an approach providing abstraction from the underlying spatial discretization method (i.e. the finite element method (FEM)). This feature presents a programming environment to the user which is easy to use even for complex models. Due to the fact that implementations are independent from data structures simulations are easily portable across desktop computers and scalable compute clusters without modifications to the program code. escript has been successfully applied in a variety of applications including modeling mantel convection, melting processes, volcanic flow, earthquakes, faulting, multi-phase flow, block caving and mineralization (see Poulet et al. 2013). The recent escript release (see Gross et al. (2013)) provides an open framework for solving joint inversion problems for geophysical data sets (potential field, seismic and electro-magnetic). The strategy bases on the idea to formulate the inversion problem as an optimization problem with PDE constraints where the cost function is defined by the data defect and the regularization term for the rock properties, see Gross & Kemp (2013). This approach of first-optimize-then-discretize avoids the assemblage of the - in general- dense sensitivity matrix as used in conventional approaches where discrete programming techniques are applied to the discretized problem (first-discretize-then-optimize). In this paper we will discuss the mathematical framework for inversion and appropriate solution schemes in escript. We will also give a brief introduction into escript's open framework for defining and solving geophysical inversion problems. Finally we will show some benchmark results to demonstrate the computational scalability of the inversion method across a large number of cores and compute nodes in a parallel computing environment. References: - L. Gross et al. (2013): Escript Solving Partial Differential Equations in Python Version 3.4, The University of Queensland, https://launchpad.net/escript-finley - L. Gross and C. Kemp (2013) Large Scale Joint Inversion of Geophysical Data using the Finite Element Method in escript. ASEG Extended Abstracts 2013, http://dx.doi.org/10.1071/ASEG2013ab306 - T. Poulet, L. Gross, D. Georgiev, J. Cleverley (2012): escript-RT: Reactive transport simulation in Python using escript, Computers & Geosciences, Volume 45, 168-176. http://dx.doi.org/10.1016/j.cageo.2011.11.005.

ALOHA: Automatic libraries of helicity amplitudes for Feynman diagram computations

NASA Astrophysics Data System (ADS)

de Aquino, Priscila; Link, William; Maltoni, Fabio; Mattelaer, Olivier; Stelzer, Tim

2012-10-01

We present an application that automatically writes the HELAS (HELicity Amplitude Subroutines) library corresponding to the Feynman rules of any quantum field theory Lagrangian. The code is written in Python and takes the Universal FeynRules Output (UFO) as an input. From this input it produces the complete set of routines, wave-functions and amplitudes, that are needed for the computation of Feynman diagrams at leading as well as at higher orders. The representation is language independent and currently it can output routines in Fortran, C++, and Python. A few sample applications implemented in the MADGRAPH 5 framework are presented. Program summary Program title: ALOHA Catalogue identifier: AEMS_v1_0 Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AEMS_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: http://www.opensource.org/licenses/UoI-NCSA.php No. of lines in distributed program, including test data, etc.: 6094320 No. of bytes in distributed program, including test data, etc.: 7479819 Distribution format: tar.gz Programming language: Python2.6 Computer: 32/64 bit Operating system: Linux/Mac/Windows RAM: 512 Mbytes Classification: 4.4, 11.6 Nature of problem: An effcient numerical evaluation of a squared matrix element can be done with the help of the helicity routines implemented in the HELAS library [1]. This static library contains a limited number of helicity functions and is therefore not always able to provide the needed routine in the presence of an arbitrary interaction. This program provides a way to automatically create the corresponding routines for any given model. Solution method: ALOHA takes the Feynman rules associated to the vertex obtained from the model information (in the UFO format [2]), and multiplies it by the different wavefunctions or propagators. As a result the analytical expression of the helicity routines is obtained. Subsequently, this expression is automatically written in the requested language (Python, Fortran or C++) Restrictions: The allowed fields are currently spin 0, 1/2, 1 and 2, and the propagators of these particles are canonical. Running time: A few seconds for the SM and the MSSM, and up to a few minutes for models with spin 2 particles. References: [1] Murayama, H. and Watanabe, I. and Hagiwara, K., HELAS: HELicity Amplitude Subroutines for Feynman diagram evaluations, KEK-91-11, (1992) http://www-lib.kek.jp/cgi-bin/img_index?199124011 [2] C. Degrande, C. Duhr, B. Fuks, D. Grellscheid, O. Mattelaer, et al., UFO— The Universal FeynRules Output, Comput. Phys. Commun. 183 (2012) 1201-1214. arXiv:1108.2040, doi:10.1016/j.cpc.2012.01.022.

A Python Script to Compute Isochrones for MODFLOW.

PubMed

Feo, Alessandra; Zanini, Andrea; Petrella, Emma; Celico, Fulvio

2018-03-01

MODFLOW constitutes today the most popular modeling tool in the study of water flow in aquifers and in modeling aquifers. To simplify the interface to MODFLOW various GUI have been developed for the creation of model definition files and for the visualization and interpretation of results. Recently Bakker et al. (2016) developed the FloPy interface to MODFLOW that allows to import and use the produced simulation data using Python. This allows to construct model input files, run the models, read and plot simulations results through Python scripts. In this note, we present a Python program (that uses FloPy) interface that allows us to generate time-related capture zones (isochrones) for confined 2D steady-state groundwater flow in unbounded domains, with one or more wells. As an application, we show a validation of the approach and the results of four basic test cases: a homogenous aquifer with one well, a heterogeneous aquifer with one well, an aquifer with four wells located both longitudinal and perpendicular to the flow direction. © 2017, National Ground Water Association.

77 FR 7968 - Semiannual Regulatory Agenda

Federal Register 2010, 2011, 2012, 2013, 2014

2012-02-13

...; Constrictor Species From Python, Boa, and Eunectes Genera. National Park Service--Proposed Rule Stage... Evaluation; Constrictor Species From Python, Boa, and Eunectes Genera Legal Authority: 18 U.S.C. 42 Abstract... wildlife under the Lacey Act: Indian python (including Burmese python), reticulated python, Northern...

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.

LCG/AA build infrastructure

NASA Astrophysics Data System (ADS)

Hodgkins, Alex Liam; Diez, Victor; Hegner, Benedikt

2012-12-01

The Software Process & Infrastructure (SPI) project provides a build infrastructure for regular integration testing and release of the LCG Applications Area software stack. In the past, regular builds have been provided using a system which has been constantly growing to include more features like server-client communication, long-term build history and a summary web interface using present-day web technologies. However, the ad-hoc style of software development resulted in a setup that is hard to monitor, inflexible and difficult to expand. The new version of the infrastructure is based on the Django Python framework, which allows for a structured and modular design, facilitating later additions. Transparency in the workflows and ease of monitoring has been one of the priorities in the design. Formerly missing functionality like on-demand builds or release triggering will support the transition to a more agile development process.

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

PubMed

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

2014-05-15

Physiological cardiac hypertrophy is characterized by reversible enlargement of cardiomyocytes and changes in chamber architecture, which increase stroke volume and via augmented convective oxygen transport. Cardiac hypertrophy is known to occur in response to repeated elevations of O2 demand and/or reduced O2 supply in several species of vertebrate ectotherms, including postprandial Burmese pythons (Python bivittatus). Recent data suggest postprandial cardiac hypertrophy in P. bivittatus is a facultative rather than obligatory response to digestion, though the triggers of this response are unknown. Here, we hypothesized that an O2 supply-demand mismatch stimulates postprandial cardiac enlargement in Burmese pythons. To test this hypothesis, we rendered animals anemic prior to feeding, essentially halving blood oxygen content during the postprandial period. Fed anemic animals had heart rates 126% higher than those of fasted controls, which, coupled with a 71% increase in mean arterial pressure, suggests fed anemic animals were experiencing significantly elevated cardiac work. We found significant cardiac hypertrophy in fed anemic animals, which exhibited ventricles 39% larger than those of fasted controls and 28% larger than in fed controls. These findings support our hypothesis that those animals with a greater magnitude of O2 supply-demand mismatch exhibit the largest hearts. The 'low O2 signal' stimulating postprandial cardiac hypertrophy is likely mediated by elevated ventricular wall stress associated with postprandial hemodynamics. © 2014. Published by The Company of Biologists Ltd.

Open-source Framework for Storing and Manipulation of Plasma Chemical Reaction Data

NASA Astrophysics Data System (ADS)

Jenkins, T. G.; Averkin, S. N.; Cary, J. R.; Kruger, S. E.

2017-10-01

We present a new open-source framework for storage and manipulation of plasma chemical reaction data that has emerged from our in-house project MUNCHKIN. This framework consists of python scripts and C + + programs. It stores data in an SQL data base for fast retrieval and manipulation. For example, it is possible to fit cross-section data into most widely used analytical expressions, calculate reaction rates for Maxwellian distribution functions of colliding particles, and fit them into different analytical expressions. Another important feature of this framework is the ability to calculate transport properties based on the cross-section data and supplied distribution functions. In addition, this framework allows the export of chemical reaction descriptions in LaTeX format for ease of inclusion in scientific papers. With the help of this framework it is possible to generate corresponding VSim (Particle-In-Cell simulation code) and USim (unstructured multi-fluid code) input blocks with appropriate cross-sections.

ProjectQ Software Framework

NASA Astrophysics Data System (ADS)

Steiger, Damian S.; Haener, Thomas; Troyer, Matthias

Quantum computers promise to transform our notions of computation by offering a completely new paradigm. A high level quantum programming language and optimizing compilers are essential components to achieve scalable quantum computation. In order to address this, we introduce the ProjectQ software framework - an open source effort to support both theorists and experimentalists by providing intuitive tools to implement and run quantum algorithms. Here, we present our ProjectQ quantum compiler, which compiles a quantum algorithm from our high-level Python-embedded language down to low-level quantum gates available on the target system. We demonstrate how this compiler can be used to control actual hardware and to run high-performance simulations.

Lipid-converter, a framework for lipid manipulations in molecular dynamics simulations

PubMed Central

Larsson, Per; Kasson, Peter M.

2014-01-01

Construction of lipid membrane and membrane protein systems for molecular dynamics simulations can be a challenging process. In addition, there are few available tools to extend existing studies by repeating simulations using other force fields and lipid compositions. To facilitate this, we introduce lipidconverter, a modular Python framework for exchanging force fields and lipid composition in coordinate files obtained from simulations. Force fields and lipids are specified by simple text files, making it easy to introduce support for additional force fields and lipids. The converter produces simulation input files that can be used for structural relaxation of the new membranes. PMID:25081234

DOE Office of Scientific and Technical Information (OSTI.GOV)

Singleton, Jr., Robert; Israel, Daniel M.; Doebling, Scott William

For code verification, one compares the code output against known exact solutions. There are many standard test problems used in this capacity, such as the Noh and Sedov problems. ExactPack is a utility that integrates many of these exact solution codes into a common API (application program interface), and can be used as a stand-alone code or as a python package. ExactPack consists of python driver scripts that access a library of exact solutions written in Fortran or Python. The spatial profiles of the relevant physical quantities, such as the density, fluid velocity, sound speed, or internal energy, are returnedmore » at a time specified by the user. The solution profiles can be viewed and examined by a command line interface or a graphical user interface, and a number of analysis tools and unit tests are also provided. We have documented the physics of each problem in the solution library, and provided complete documentation on how to extend the library to include additional exact solutions. ExactPack’s code architecture makes it easy to extend the solution-code library to include additional exact solutions in a robust, reliable, and maintainable manner.« less

MSNoise: A framework for Continuous Seismic Noise Analysis

NASA Astrophysics Data System (ADS)

Lecocq, Thomas; Caudron, Corentin; De Plaen, Raphaël; Mordret, Aurélien

2016-04-01

MSNoise is an Open and Free Python package known to be the only complete integrated workflow designed to analyse ambient seismic noise and study relative velocity changes (dv/v) in the crust. It is based on state of the art and well maintained Python modules, among which ObsPy plays an important role. To our knowledge, it is officially used for continuous monitoring at least in three notable places: the Observatory of the Piton de la Fournaise volcano (OVPF, France), the Auckland Volcanic Field (New Zealand) and on the South Napa earthquake (Berkeley, USA). It is also used by many researchers to process archive data to focus e.g. on fault zones, intraplate Europe, geothermal exploitations or Antarctica. We first present the general working of MSNoise, originally written in 2010 to automatically scan data archives and process seismic data in order to produce dv/v time series. We demonstrate that its modularity provides a new potential to easily test new algorithms for each processing step. For example, one could experiment new methods of cross-correlation (done by default in the frequency domain), stacking (default is linear stacking, averaging), or dv/v estimation (default is moving window cross-spectrum "MWCS", so-called "doublet"), etc. We present the last major evolution of MSNoise from a "single workflow: data archive to dv/v" to a framework system that allows plugins and modules to be developed and integrated into the MSNoise ecosystem. Small-scale plugins will be shown as examples, such as "continuous PPSD" (à la McNamarra & Buland) or "Seismic Amplitude Ratio Analysis" (Taisne, Caudron). We will also present the new MSNoise-TOMO package, using MSNoise as a "cross-correlation" toolbox and demystifying surface wave tomography ! Finally, the poster will be a meeting point for all those using or willing to use MSNoise, to meet the developer, exchange ideas and wishes !

Introducing a New Software for Geodetic Analysis

NASA Astrophysics Data System (ADS)

Hjelle, G. A.; Dähnn, M.; Fausk, I.; Kirkvik, A. S.; Mysen, E.

2016-12-01

At the Norwegian Mapping Authority, we are currently developing Where, a newsoftware for geodetic analysis. Where is built on our experiences with theGeosat software, and will be able to analyse and combine data from VLBI, SLR,GNSS and DORIS. The software is mainly written in Python which has proved veryfruitful. The code is quick to write and the architecture is easily extendableand maintainable. The Python community provides a rich eco-system of tools fordoing data-analysis, including effective data storage and powerfulvisualization. Python interfaces well with other languages so that we can easilyreuse existing, well-tested code like the SOFA and IERS libraries. This presentation will show some of the current capabilities of Where,including benchmarks against other software packages. In addition we will reporton some simple investigations we have done using the software, and outline ourplans for further progress.

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 radio-telemetered 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.

Hearing with an atympanic ear: good vibration and poor sound-pressure detection in the royal python, Python regius.

PubMed

Christensen, Christian Bech; Christensen-Dalsgaard, Jakob; Brandt, Christian; Madsen, Peter Teglberg

2012-01-15

Snakes lack both an outer ear and a tympanic middle ear, which in most tetrapods provide impedance matching between the air and inner ear fluids and hence improve pressure hearing in air. Snakes would therefore be expected to have very poor pressure hearing and generally be insensitive to airborne sound, whereas the connection of the middle ear bone to the jaw bones in snakes should confer acute sensitivity to substrate vibrations. Some studies have nevertheless claimed that snakes are quite sensitive to both vibration and sound pressure. Here we test the two hypotheses that: (1) snakes are sensitive to sound pressure and (2) snakes are sensitive to vibrations, but cannot hear the sound pressure per se. Vibration and sound-pressure sensitivities were quantified by measuring brainstem evoked potentials in 11 royal pythons, Python regius. Vibrograms and audiograms showed greatest sensitivity at low frequencies of 80-160 Hz, with sensitivities of -54 dB re. 1 m s(-2) and 78 dB re. 20 μPa, respectively. To investigate whether pythons detect sound pressure or sound-induced head vibrations, we measured the sound-induced head vibrations in three dimensions when snakes were exposed to sound pressure at threshold levels. In general, head vibrations induced by threshold-level sound pressure were equal to or greater than those induced by threshold-level vibrations, and therefore sound-pressure sensitivity can be explained by sound-induced head vibration. From this we conclude that pythons, and possibly all snakes, lost effective pressure hearing with the complete reduction of a functional outer and middle ear, but have an acute vibration sensitivity that may be used for communication and detection of predators and prey.

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

PubMed

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

2014-02-01

Corticosterone (CORT) secretion is influenced by endogenous factors (e.g., physiological status) and environmental stressors (e.g., ambient temperature). Heretofore, the impact of water deprivation on CORT plasma levels has not been thoroughly investigated. However, both baseline CORT and stress-induced CORT are expected to respond to water deprivation not only because of hydric stress per se, but also because CORT is an important mineralocorticoid in vertebrates. We assessed the effects of water deprivation on baseline CORT and stress-induced CORT, in Children's pythons (Antaresia childreni), a species that experiences seasonal droughts in natural conditions. We imposed a 52-day water deprivation on a group of unfed Children's pythons (i.e., water-deprived treatment) and provided water ad libitum to another group (i.e., control treatment). We examined body mass variations throughout the experiment, and baseline CORT and stress-induced CORT at the end of the treatments. Relative body mass loss averaged ~10% in pythons without water, a value 2 to 4 times higher compared to control snakes. Following re-exposition to water, pythons from the water-deprived treatment drank readily and abundantly and attained a body mass similar to pythons from the control treatment. Together, these results suggest a substantial dehydration as a consequence of water deprivation. Interestingly, stress-induced but not baseline CORT level was significantly higher in water-deprived snakes, suggesting that baseline CORT might not respond to this degree of dehydration. Therefore, possible mineralocorticoid role of CORT needs to be clarified in snakes. Because dehydration usually induces adjustments (reduced movements, lowered body temperature) to limit water loss, and decreases locomotor performances, elevated stress-induced CORT in water-deprived snakes might therefore compensate for altered locomotor performances. Future studies should test this hypothesis. Copyright © 2013 Elsevier Inc. All rights reserved.

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.

A multivariate distance-based analytic framework for microbial interdependence association test in longitudinal study.

PubMed

Zhang, Yilong; Han, Sung Won; Cox, Laura M; Li, Huilin

2017-12-01

Human microbiome is the collection of microbes living in and on the various parts of our body. The microbes living on our body in nature do not live alone. They act as integrated microbial community with massive competing and cooperating and contribute to our human health in a very important way. Most current analyses focus on examining microbial differences at a single time point, which do not adequately capture the dynamic nature of the microbiome data. With the advent of high-throughput sequencing and analytical tools, we are able to probe the interdependent relationship among microbial species through longitudinal study. Here, we propose a multivariate distance-based test to evaluate the association between key phenotypic variables and microbial interdependence utilizing the repeatedly measured microbiome data. Extensive simulations were performed to evaluate the validity and efficiency of the proposed method. We also demonstrate the utility of the proposed test using a well-designed longitudinal murine experiment and a longitudinal human study. The proposed methodology has been implemented in the freely distributed open-source R package and Python code. © 2017 WILEY PERIODICALS, INC.

Recent developments in the CCP-EM software suite.

PubMed

Burnley, Tom; Palmer, Colin M; Winn, Martyn

2017-06-01

As part of its remit to provide computational support to the cryo-EM community, the Collaborative Computational Project for Electron cryo-Microscopy (CCP-EM) has produced a software framework which enables easy access to a range of programs and utilities. The resulting software suite incorporates contributions from different collaborators by encapsulating them in Python task wrappers, which are then made accessible via a user-friendly graphical user interface as well as a command-line interface suitable for scripting. The framework includes tools for project and data management. An overview of the design of the framework is given, together with a survey of the functionality at different levels. The current CCP-EM suite has particular strength in the building and refinement of atomic models into cryo-EM reconstructions, which is described in detail.

Recent developments in the CCP-EM software suite

PubMed Central

Burnley, Tom

2017-01-01

As part of its remit to provide computational support to the cryo-EM community, the Collaborative Computational Project for Electron cryo-Microscopy (CCP-EM) has produced a software framework which enables easy access to a range of programs and utilities. The resulting software suite incorporates contributions from different collaborators by encapsulating them in Python task wrappers, which are then made accessible via a user-friendly graphical user interface as well as a command-line interface suitable for scripting. The framework includes tools for project and data management. An overview of the design of the framework is given, together with a survey of the functionality at different levels. The current CCP-EM suite has particular strength in the building and refinement of atomic models into cryo-EM reconstructions, which is described in detail. PMID:28580908

RadVel: General toolkit for modeling Radial Velocities

NASA Astrophysics Data System (ADS)

Fulton, Benjamin J.; Petigura, Erik A.; Blunt, Sarah; Sinukoff, Evan

2018-01-01

RadVel models Keplerian orbits in radial velocity (RV) time series. The code is written in Python with a fast Kepler's equation solver written in C. It provides a framework for fitting RVs using maximum a posteriori optimization and computing robust confidence intervals by sampling the posterior probability density via Markov Chain Monte Carlo (MCMC). RadVel can perform Bayesian model comparison and produces publication quality plots and LaTeX tables.

Mapping, Awareness, and Virtualization Network Administrator Training Tool (MAVNATT) Architecture and Framework

DTIC Science & Technology

2015-06-01

unit may setup and teardown the entire tactical infrastructure multiple times per day. This tactical network administrator training is a critical...language and runs on Linux and Unix based systems. All provisioning is based around the Nagios Core application, a powerful backend solution for network...start up a large number of virtual machines quickly. CORE supports the simulation of fixed and mobile networks. CORE is open-source, written in Python

MYRaf: A new Approach with IRAF for Astronomical Photometric Reduction

NASA Astrophysics Data System (ADS)

Kilic, Y.; Shameoni Niaei, M.; Özeren, F. F.; Yesilyaprak, C.

2016-12-01

In this study, the design and some developments of MYRaf software for astronomical photometric reduction are presented. MYRaf software is an easy to use, reliable, and has a fast IRAF aperture photometry GUI tools. MYRaf software is an important step for the automated software process of robotic telescopes, and uses IRAF, PyRAF, matplotlib, ginga, alipy, and Sextractor with the general-purpose and high-level programming language Python and uses the QT framework.

MEqTrees Telescope and Radio-sky Simulations and CPU Benchmarking

NASA Astrophysics Data System (ADS)

Shanmugha Sundaram, G. A.

2009-09-01

MEqTrees is a Python-based implementation of the classical Measurement Equation, wherein the various 2×2 Jones matrices are parametrized representations in the spatial and sky domains for any generic radio telescope. Customized simulations of radio-source sky models and corrupt Jones terms are demonstrated based on a policy framework, with performance estimates derived for array configurations, ``dirty''-map residuals and processing power requirements for such computations on conventional platforms.

BioInt: an integrative biological object-oriented application framework and interpreter.

PubMed

Desai, Sanket; Burra, Prasad

2015-01-01

BioInt, a biological programming application framework and interpreter, is an attempt to equip the researchers with seamless integration, efficient extraction and effortless analysis of the data from various biological databases and algorithms. Based on the type of biological data, algorithms and related functionalities, a biology-specific framework was developed which has nine modules. The modules are a compilation of numerous reusable BioADTs. This software ecosystem containing more than 450 biological objects underneath the interpreter makes it flexible, integrative and comprehensive. Similar to Python, BioInt eliminates the compilation and linking steps cutting the time significantly. The researcher can write the scripts using available BioADTs (following C++ syntax) and execute them interactively or use as a command line application. It has features that enable automation, extension of the framework with new/external BioADTs/libraries and deployment of complex work flows.

A FEniCS-based programming framework for modeling turbulent flow by the Reynolds-averaged Navier-Stokes equations

NASA Astrophysics Data System (ADS)

Mortensen, Mikael; Langtangen, Hans Petter; Wells, Garth N.

2011-09-01

Finding an appropriate turbulence model for a given flow case usually calls for extensive experimentation with both models and numerical solution methods. This work presents the design and implementation of a flexible, programmable software framework for assisting with numerical experiments in computational turbulence. The framework targets Reynolds-averaged Navier-Stokes models, discretized by finite element methods. The novel implementation makes use of Python and the FEniCS package, the combination of which leads to compact and reusable code, where model- and solver-specific code resemble closely the mathematical formulation of equations and algorithms. The presented ideas and programming techniques are also applicable to other fields that involve systems of nonlinear partial differential equations. We demonstrate the framework in two applications and investigate the impact of various linearizations on the convergence properties of nonlinear solvers for a Reynolds-averaged Navier-Stokes model.

A pipeline for comprehensive and automated processing of electron diffraction data in IPLT.

PubMed

Schenk, Andreas D; Philippsen, Ansgar; Engel, Andreas; Walz, Thomas

2013-05-01

Electron crystallography of two-dimensional crystals allows the structural study of membrane proteins in their native environment, the lipid bilayer. Determining the structure of a membrane protein at near-atomic resolution by electron crystallography remains, however, a very labor-intense and time-consuming task. To simplify and accelerate the data processing aspect of electron crystallography, we implemented a pipeline for the processing of electron diffraction data using the Image Processing Library and Toolbox (IPLT), which provides a modular, flexible, integrated, and extendable cross-platform, open-source framework for image processing. The diffraction data processing pipeline is organized as several independent modules implemented in Python. The modules can be accessed either from a graphical user interface or through a command line interface, thus meeting the needs of both novice and expert users. The low-level image processing algorithms are implemented in C++ to achieve optimal processing performance, and their interface is exported to Python using a wrapper. For enhanced performance, the Python processing modules are complemented with a central data managing facility that provides a caching infrastructure. The validity of our data processing algorithms was verified by processing a set of aquaporin-0 diffraction patterns with the IPLT pipeline and comparing the resulting merged data set with that obtained by processing the same diffraction patterns with the classical set of MRC programs. Copyright © 2013 Elsevier Inc. All rights reserved.

A pipeline for comprehensive and automated processing of electron diffraction data in IPLT

PubMed Central

Schenk, Andreas D.; Philippsen, Ansgar; Engel, Andreas; Walz, Thomas

2013-01-01

Electron crystallography of two-dimensional crystals allows the structural study of membrane proteins in their native environment, the lipid bilayer. Determining the structure of a membrane protein at near-atomic resolution by electron crystallography remains, however, a very labor-intense and time-consuming task. To simplify and accelerate the data processing aspect of electron crystallography, we implemented a pipeline for the processing of electron diffraction data using the Image Processing Library & Toolbox (IPLT), which provides a modular, flexible, integrated, and extendable cross-platform, open-source framework for image processing. The diffraction data processing pipeline is organized as several independent modules implemented in Python. The modules can be accessed either from a graphical user interface or through a command line interface, thus meeting the needs of both novice and expert users. The low-level image processing algorithms are implemented in C++ to achieve optimal processing performance, and their interface is exported to Python using a wrapper. For enhanced performance, the Python processing modules are complemented with a central data managing facility that provides a caching infrastructure. The validity of our data processing algorithms was verified by processing a set of aquaporin-0 diffraction patterns with the IPLT pipeline and comparing the resulting merged data set with that obtained by processing the same diffraction patterns with the classical set of MRC programs. PMID:23500887

Ultrasound imaging of the anterior section of the eye of five different snake species.

PubMed

Lauridsen, Henrik; Da Silva, Mari-Ann O; Hansen, Kasper; Jensen, Heidi M; Warming, Mads; Wang, Tobias; Pedersen, Michael

2014-12-30

Nineteen clinically normal snakes: six ball pythons (Python regius), six Burmese pythons (Python bivittatus), one Children's python (Antaresia childreni), four Amazon tree boas (Corallus hortulanus), and two Malagasy ground boas (Acrantophis madagascariensis) were subjected to ultrasound imaging with 21 MHz (ball python) and 50 MHz (ball python, Burmese python, Children's python, Amazon tree boa, Malagasy ground boa) transducers in order to measure the different structures of the anterior segment in clinically normal snake eyes with the aim to review baseline values for clinically important ophthalmic structures. The ultrasonographic measurements included horizontal spectacle diameter, spectacle thickness, depth of sub-spectacular space and corneal thickness. For comparative purposes, a formalin-fixed head of a Burmese python was subjected to micro computed tomography. In all snakes, the spectacle was thinner than the cornea. There was significant difference in spectacle diameter, and spectacle and corneal thickness between the Amazon tree boa and the Burmese and ball pythons. There was no difference in the depth of the sub-spectacular space. The results obtained in the Burmese python with the 50 MHz transducer were similar to the results obtained with micro computed tomography. Images acquired with the 21 MHz transducer included artifacts which may be misinterpreted as ocular structures. Our measurements of the structures in the anterior segment of the eye can serve as orientative values for snakes examined for ocular diseases. In addition, we demonstrated that using a high frequency transducer minimizes the risk of misinterpreting artifacts as ocular structures.

Evolution of the ATLAS Nightly Build System

NASA Astrophysics Data System (ADS)

Undrus, A.

2012-12-01

The ATLAS Nightly Build System is a major component in the ATLAS collaborative software organization, validation, and code approval scheme. For over 10 years of development it has evolved into a factory for automatic release production and grid distribution. The 50 multi-platform branches of ATLAS releases provide vast opportunities for testing new packages, verification of patches to existing software, and migration to new platforms and compilers for ATLAS code that currently contains 2200 packages with 4 million C++ and 1.4 million python scripting lines written by about 1000 developers. Recent development was focused on the integration of ATLAS Nightly Build and Installation systems. The nightly releases are distributed and validated and some are transformed into stable releases used for data processing worldwide. The ATLAS Nightly System is managed by the NICOS control tool on a computing farm with 50 powerful multiprocessor nodes. NICOS provides the fully automated framework for the release builds, testing, and creation of distribution kits. The ATN testing framework of the Nightly System runs unit and integration tests in parallel suites, fully utilizing the resources of multi-core machines, and provides the first results even before compilations complete. The NICOS error detection system is based on several techniques and classifies the compilation and test errors according to their severity. It is periodically tuned to place greater emphasis on certain software defects by highlighting the problems on NICOS web pages and sending automatic e-mail notifications to responsible developers. These and other recent developments will be presented and future plans will be described.

Ball python nidovirus: a candidate etiologic agent for severe respiratory disease in Python regius.

PubMed

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; DeRisi, Joseph L

2014-09-09

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. Ball pythons are popular pets because of their diverse coloration, generally nonaggressive behavior, and relatively small size. Since the 1990s, veterinarians have been aware of an infectious respiratory disease of unknown cause in ball pythons that can be fatal. We used unbiased shotgun sequencing to discover a novel virus in the order Nidovirales that was present in cases but not controls. While nidoviruses are known to infect a variety of animals, this is the first report of a nidovirus recovered from any reptile. This report will enable diagnostics that will assist in determining the role of this virus in the causation of disease, which would allow control of the disease in zoos and private collections. Given its evolutionary divergence from known nidoviruses and its unique host, the study of reptile nidoviruses may further our understanding of related diseases and the viruses that cause them in humans and other animals. Copyright © 2014 Stenglein et al.

Nidovirus-Associated Proliferative Pneumonia in the Green Tree Python (Morelia viridis)

PubMed Central

Dervas, Eva; Hepojoki, Jussi; Laimbacher, Andrea; Romero-Palomo, Fernando; Jelinek, Christine; Keller, Saskia; Smura, Teemu; Hetzel, Udo

2017-01-01

ABSTRACT In 2014 we observed a noticeable increase in the number of sudden deaths among green tree pythons (Morelia viridis). Pathological examination revealed the accumulation of mucoid material within the airways and lungs in association with enlargement of the entire lung. We performed a full necropsy and histological examination on 12 affected green tree pythons from 7 different breeders to characterize the pathogenesis of this mucinous pneumonia. By histology we could show a marked hyperplasia of the airway epithelium and of faveolar type II pneumocytes. Since routine microbiological tests failed to identify a causative agent, we studied lung tissue samples from a few diseased snakes by next-generation sequencing (NGS). From the NGS data we could assemble a piece of RNA genome whose sequence was <85% identical to that of nidoviruses previously identified in ball pythons and Indian pythons. We then employed reverse transcription-PCR to demonstrate the presence of the novel nidovirus in all diseased snakes. To attempt virus isolation, we established primary cultures of Morelia viridis liver and brain cells, which we inoculated with homogenates of lung tissue from infected individuals. Ultrastructural examination of concentrated cell culture supernatants showed the presence of nidovirus particles, and subsequent NGS analysis yielded the full genome of the novel virus Morelia viridis nidovirus (MVNV). We then generated an antibody against MVNV nucleoprotein, which we used alongside RNA in situ hybridization to demonstrate viral antigen and RNA in the affected lungs. This suggests that in natural infection MVNV damages the respiratory tract epithelium, which then results in epithelial hyperplasia, most likely as an exaggerated regenerative attempt in association with increased epithelial turnover. IMPORTANCE Novel nidoviruses associated with severe respiratory disease were fairly recently identified in ball pythons and Indian pythons. Herein we report on the isolation and identification of a further nidovirus from green tree pythons (Morelia viridis) with fatal pneumonia. We thoroughly characterized the pathological changes in the infected individuals and show that nidovirus infection is associated with marked epithelial proliferation in the respiratory tract. We speculate that this and the associated excess mucus production can lead to the animals' death by inhibiting normal gas exchange in the lungs. The virus was predominantly detected in the respiratory tract, which renders transmission via the respiratory route likely. Nidoviruses cause sudden outbreaks with high rates of mortality in breeding collections, and most affected snakes die without prior clinical signs. These findings, together with those of other groups, indicate that nidoviruses are a likely cause of severe pneumonia in pythons. PMID:28794044

Nidovirus-Associated Proliferative Pneumonia in the Green Tree Python (Morelia viridis).

PubMed

Dervas, Eva; Hepojoki, Jussi; Laimbacher, Andrea; Romero-Palomo, Fernando; Jelinek, Christine; Keller, Saskia; Smura, Teemu; Hepojoki, Satu; Kipar, Anja; Hetzel, Udo

2017-08-09

In 2014 we observed a noticeable increase in sudden deaths of green tree pythons ( Morelia viridis ). Pathological examination revealed accumulation of mucoid material within airways and lung, associated with enlargement of the entire lung. We performed full necropsy and histological examination on 12 affected green tree pythons from 7 different breeders to characterise the pathogenesis of this "mucinous" pneumonia. By histology we could show a marked hyperplasia of the airway epithelium and of faveolar type II pneumocytes. Since routine microbiological tests failed to identify a causative agent, we studied lung samples of a few diseased snakes by next-generation sequencing (NGS). From the NGS data we could assemble a piece of RNA genome <85% identical to nidoviruses previously identified in ball pythons and Indian pythons. We then employed RT-PCR to demonstrate the presence of the novel nidovirus in all diseased snakes. To attempt virus isolation, we established primary cell cultures of Morelia viridis liver and brain, which we inoculated with lung homogenates of infected individuals. Ultrastructural examination of concentrated cell culture supernatants showed the presence of nidovirus particles, and subsequent NGS analysis yielded the full genome of the novel virus, Morelia viridis nidovirus (MVNV). We then generated an antibody against MVNV nucleoprotein, which we used alongside RNA in situ hybridisation to demonstrate viral antigen and RNA in the affected lungs. This suggests that in natural infection MVNV damages the respiratory tract epithelium which then results in epithelial hyperplasia, most likely as an exaggerated regenerative attempt in association with increased epithelial turnover. Importance Fairly recently novel nidoviruses associated with severe respiratory disease were identified in ball pythons and Indian pythons. Herein we report isolation and identification of a further nidovirus from green tree pythons ( Morelia viridis ) with fatal pneumonia. We thoroughly characterize the pathological changes in the infected individuals, and show that nidovirus infection is associated with marked epithelial proliferation in the respiratory tract. We speculate that this and the associated excess mucus production can lead to the animals' death, by inhibitingthe normal gas exchange in the lung. The virus was predominantly detected in the respiratory tract, which renders transmission via the respiratory route likely. Nidoviruses cause sudden outbreaks with high mortality in breeding collections, most affected snakes die without prior clinical signs. These findings, together with those of other groups, indicate that nidoviruses are a likely cause of severe pneumonia in pythons. Copyright © 2017 American Society for Microbiology.

Detection and phylogenetic analysis of a new adenoviral polymerase gene in reptiles in Korea.

PubMed

Bak, Eun-Jung; Jho, Yeonsook; Woo, Gye-Hyeong

2018-06-01

Over a period of 7 years (2004-2011), samples from 34 diseased reptiles provided by local governments, zoos, and pet shops were tested for viral infection. Animals were diagnosed based on clinical signs, including loss of appetite, diarrhea, rhinorrhea, and unexpected sudden death. Most of the exotic animals had gastrointestinal problems, such as mucosal redness and ulcers, while the native animals had no clinical symptoms. Viral sequences were found in seven animals. Retroviral genes were amplified from samples from five Burmese pythons (Python molurus bivittatus), an adenovirus was detected in a panther chameleon (Furcifer pardalis), and an adenovirus and a paramyxovirus were detected in a tropical girdled lizard (Cordylus tropidosternum). Phylogenetic analysis of retroviruses and paramyxoviruses showed the highest sequence identity to both a Python molurus endogenous retrovirus and a Python curtus endogenous retrovirus and to a lizard isolate, respectively. Partial sequencing of an adenoviral DNA polymerase gene from the lizard isolate suggested that the corresponding virus was a novel isolate different from the reference strain (accession no. AY576677.1). The virus was not isolated but was detected, using molecular genetic techniques, in a lizard raised in a pet shop. This animal was also coinfected with a paramyxovirus.

Using the Landlab toolkit to evaluate and compare alternative geomorphic and hydrologic model formulations

NASA Astrophysics Data System (ADS)

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

2016-12-01

Developing a better understanding of catchment hydrology and geomorphology ideally involves quantitative hypothesis testing. Often one seeks to identify the simplest mathematical and/or computational model that accounts for the essential dynamics in the system of interest. Development of alternative hypotheses involves testing and comparing alternative formulations, but the process of comparison and evaluation is made challenging by the rigid nature of many computational models, which are often built around a single assumed set of equations. Here we review a software framework for two-dimensional computational modeling that facilitates the creation, testing, and comparison of surface-dynamics models. Landlab is essentially a Python-language software library. Its gridding module allows for easy generation of a structured (raster, hex) or unstructured (Voronoi-Delaunay) mesh, with the capability to attach data arrays to particular types of element. Landlab includes functions that implement common numerical operations, such as gradient calculation and summation of fluxes within grid cells. Landlab also includes a collection of process components, which are encapsulated pieces of software that implement a numerical calculation of a particular process. Examples include downslope flow routing over topography, shallow-water hydrodynamics, stream erosion, and sediment transport on hillslopes. Individual components share a common grid and data arrays, and they can be coupled through the use of a simple Python script. We illustrate Landlab's capabilities with a case study of Holocene landscape development in the northeastern US, in which we seek to identify a collection of model components that can account for the formation of a series of incised canyons that have that developed since the Laurentide ice sheet last retreated. We compare sets of model ingredients related to (1) catchment hydrologic response, (2) hillslope evolution, and (3) stream channel and gully incision. The case-study example demonstrates the value of exploring multiple working hypotheses, in the form of multiple alternative model components.

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.

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

NASA Technical Reports Server (NTRS)

Campbell, Carl E

1951-01-01

Combustion-chamber performance characteristics of a Python turbine-propeller engine were determined from investigation of a complete engine over a range of engine speeds and shaft horsepowers at simulated altitudes. Results indicated the effect of engine operating conditions and altitude on combustion efficiency and combustion-chamber total pressure losses. Performance of this vaporizing type combustion chamber was also compared with several atomizing type combustion chambers. Over the range of test conditions investigated, combustion efficiency varied from approximately 0.95 to 0.99.

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.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bowen, Benjamin; Ruebel, Oliver; Fischer, Curt Fischer R.

BASTet is an advanced software library written in Python. BASTet serves as the analysis and storage library for the OpenMSI project. BASTet is an integrate framework for: i) storage of spectral imaging data, ii) storage of derived analysis data, iii) provenance of analyses, iv) integration and execution of analyses via complex workflows. BASTet implements the API for the HDF5 storage format used by OpenMSI. Analyses that are developed using BASTet benefit from direct integration with storage format, automatic tracking of provenance, and direct integration with command-line and workflow execution tools. BASTet also defines interfaces to enable developers to directly integratemore » their analysis with OpenMSI's web-based viewing infrastruture without having to know OpenMSI. BASTet also provides numerous helper classes and tools to assist with the conversion of data files, ease parallel implementation of analysis algorithms, ease interaction with web-based functions, description methods for data reduction. BASTet also includes detailed developer documentation, user tutorials, iPython notebooks, and other supporting documents.« less

Nestly--a framework for running software with nested parameter choices and aggregating results.

PubMed

McCoy, Connor O; Gallagher, Aaron; Hoffman, Noah G; Matsen, Frederick A

2013-02-01

The execution of a software application or pipeline using various combinations of parameters and inputs is a common task in bioinformatics. In the absence of a specialized tool to organize, streamline and formalize this process, scientists must write frequently complex scripts to perform these tasks. We present nestly, a Python package to facilitate running tools with nested combinations of parameters and inputs. nestly provides three components. First, a module to build nested directory structures corresponding to choices of parameters. Second, the nestrun script to run a given command using each set of parameter choices. Third, the nestagg script to aggregate results of the individual runs into a CSV file, as well as support for more complex aggregation. We also include a module for easily specifying nested dependencies for the SCons build tool, enabling incremental builds. Source, documentation and tutorial examples are available at http://github.com/fhcrc/nestly. nestly can be installed from the Python Package Index via pip; it is open source (MIT license).

AESOP: A Python Library for Investigating Electrostatics in Protein Interactions.

PubMed

Harrison, Reed E S; Mohan, Rohith R; Gorham, Ronald D; Kieslich, Chris A; Morikis, Dimitrios

2017-05-09

Electric fields often play a role in guiding the association of protein complexes. Such interactions can be further engineered to accelerate complex association, resulting in protein systems with increased productivity. This is especially true for enzymes where reaction rates are typically diffusion limited. To facilitate quantitative comparisons of electrostatics in protein families and to describe electrostatic contributions of individual amino acids, we previously developed a computational framework called AESOP. We now implement this computational tool in Python with increased usability and the capability of performing calculations in parallel. AESOP utilizes PDB2PQR and Adaptive Poisson-Boltzmann Solver to generate grid-based electrostatic potential files for protein structures provided by the end user. There are methods within AESOP for quantitatively comparing sets of grid-based electrostatic potentials in terms of similarity or generating ensembles of electrostatic potential files for a library of mutants to quantify the effects of perturbations in protein structure and protein-protein association. Copyright © 2017 Biophysical Society. Published by Elsevier Inc. All rights reserved.

A statistical framework for neuroimaging data analysis based on mutual information estimated via a gaussian copula

PubMed Central

Giordano, Bruno L.; Kayser, Christoph; Rousselet, Guillaume A.; Gross, Joachim; Schyns, Philippe G.

2016-01-01

Abstract We begin by reviewing the statistical framework of information theory as applicable to neuroimaging data analysis. A major factor hindering wider adoption of this framework in neuroimaging is the difficulty of estimating information theoretic quantities in practice. We present a novel estimation technique that combines the statistical theory of copulas with the closed form solution for the entropy of Gaussian variables. This results in a general, computationally efficient, flexible, and robust multivariate statistical framework that provides effect sizes on a common meaningful scale, allows for unified treatment of discrete, continuous, unidimensional and multidimensional variables, and enables direct comparisons of representations from behavioral and brain responses across any recording modality. We validate the use of this estimate as a statistical test within a neuroimaging context, considering both discrete stimulus classes and continuous stimulus features. We also present examples of analyses facilitated by these developments, including application of multivariate analyses to MEG planar magnetic field gradients, and pairwise temporal interactions in evoked EEG responses. We show the benefit of considering the instantaneous temporal derivative together with the raw values of M/EEG signals as a multivariate response, how we can separately quantify modulations of amplitude and direction for vector quantities, and how we can measure the emergence of novel information over time in evoked responses. Open‐source Matlab and Python code implementing the new methods accompanies this article. Hum Brain Mapp 38:1541–1573, 2017. © 2016 Wiley Periodicals, Inc. PMID:27860095

A Case Study in Web 2.0 Application Development

NASA Astrophysics Data System (ADS)

Marganian, P.; Clark, M.; Shelton, A.; McCarty, M.; Sessoms, E.

2010-12-01

Recent web technologies focusing on languages, frameworks, and tools are discussed, using the Robert C. Byrd Green Bank Telescopes (GBT) new Dynamic Scheduling System as the primary example. Within that example, we use a popular Python web framework, Django, to build the extensive web services for our users. We also use a second complimentary server, written in Haskell, to incorporate the core scheduling algorithms. We provide a desktop-quality experience across all the popular browsers for our users with the Google Web Toolkit and judicious use of JQuery in Django templates. Single sign-on and authentication throughout all NRAO web services is accomplished via the Central Authentication Service protocol, or CAS.

Cross-platform validation and analysis environment for particle physics

NASA Astrophysics Data System (ADS)

Chekanov, S. V.; Pogrebnyak, I.; Wilbern, D.

2017-11-01

A multi-platform validation and analysis framework for public Monte Carlo simulation for high-energy particle collisions is discussed. The front-end of this framework uses the Python programming language, while the back-end is written in Java, which provides a multi-platform environment that can be run from a web browser and can easily be deployed at the grid sites. The analysis package includes all major software tools used in high-energy physics, such as Lorentz vectors, jet algorithms, histogram packages, graphic canvases, and tools for providing data access. This multi-platform software suite, designed to minimize OS-specific maintenance and deployment time, is used for online validation of Monte Carlo event samples through a web interface.

Instrument Control (iC) – An Open-Source Software to Automate Test Equipment

PubMed Central

Pernstich, K. P.

2012-01-01

It has become common practice to automate data acquisition from programmable instrumentation, and a range of different software solutions fulfill this task. Many routine measurements require sequential processing of certain tasks, for instance to adjust the temperature of a sample stage, take a measurement, and repeat that cycle for other temperatures. This paper introduces an open-source Java program that processes a series of text-based commands that define the measurement sequence. These commands are in an intuitive format which provides great flexibility and allows quick and easy adaptation to various measurement needs. For each of these commands, the iC-framework calls a corresponding Java method that addresses the specified instrument to perform the desired task. The functionality of iC can be extended with minimal programming effort in Java or Python, and new measurement equipment can be addressed by defining new commands in a text file without any programming. PMID:26900522

A precise goniometer/tensiometer using a low cost single-board computer

NASA Astrophysics Data System (ADS)

Favier, Benoit; Chamakos, Nikolaos T.; Papathanasiou, Athanasios G.

2017-12-01

Measuring the surface tension and the Young contact angle of a droplet is extremely important for many industrial applications. Here, considering the booming interest for small and cheap but precise experimental instruments, we have constructed a low-cost contact angle goniometer/tensiometer, based on a single-board computer (Raspberry Pi). The device runs an axisymmetric drop shape analysis (ADSA) algorithm written in Python. The code, here named DropToolKit, was developed in-house. We initially present the mathematical framework of our algorithm and then we validate our software tool against other well-established ADSA packages, including the commercial ramé-hart DROPimage Advanced as well as the DropAnalysis plugin in ImageJ. After successfully testing for various combinations of liquids and solid surfaces, we concluded that our prototype device would be highly beneficial for industrial applications as well as for scientific research in wetting phenomena compared to the commercial solutions.

Instrument Control (iC) - An Open-Source Software to Automate Test Equipment.

PubMed

Pernstich, K P

2012-01-01

It has become common practice to automate data acquisition from programmable instrumentation, and a range of different software solutions fulfill this task. Many routine measurements require sequential processing of certain tasks, for instance to adjust the temperature of a sample stage, take a measurement, and repeat that cycle for other temperatures. This paper introduces an open-source Java program that processes a series of text-based commands that define the measurement sequence. These commands are in an intuitive format which provides great flexibility and allows quick and easy adaptation to various measurement needs. For each of these commands, the iC-framework calls a corresponding Java method that addresses the specified instrument to perform the desired task. The functionality of iC can be extended with minimal programming effort in Java or Python, and new measurement equipment can be addressed by defining new commands in a text file without any programming.

Precision Cosmology: The First Half Million Years

NASA Astrophysics Data System (ADS)

Jones, Bernard J. T.

2017-06-01

Cosmology seeks to characterise our Universe in terms of models based on well-understood and tested physics. Today we know our Universe with a precision that once would have been unthinkable. This book develops the entire mathematical, physical and statistical framework within which this has been achieved. It tells the story of how we arrive at our profound conclusions, starting from the early twentieth century and following developments up to the latest data analysis of big astronomical datasets. It provides an enlightening description of the mathematical, physical and statistical basis for understanding and interpreting the results of key space- and ground-based data. Subjects covered include general relativity, cosmological models, the inhomogeneous Universe, physics of the cosmic background radiation, and methods and results of data analysis. Extensive online supplementary notes, exercises, teaching materials, and exercises in Python make this the perfect companion for researchers, teachers and students in physics, mathematics, and astrophysics.

DOE Office of Scientific and Technical Information (OSTI.GOV)

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.

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

A resource oriented webs service for environmental modeling

NASA Astrophysics Data System (ADS)

Ferencik, Ioan

2013-04-01

Environmental modeling is a largely adopted practice in the study of natural phenomena. Environmental models can be difficult to build and use and thus sharing them within the community is an important aspect. The most common approach to share a model is to expose it as a web service. In practice the interaction with this web service is cumbersome due to lack of standardized contract and the complexity of the model being exposed. In this work we investigate the use of a resource oriented approach in exposing environmental models as web services. We view a model as a layered resource build atop the object concept from Object Oriented Programming, augmented with persistence capabilities provided by an embedded object database to keep track of its state and implementing the four basic principles of resource oriented architectures: addressability, statelessness, representation and uniform interface. For implementation we use exclusively open source software: Django framework, dyBase object oriented database and Python programming language. We developed a generic framework of resources structured into a hierarchy of types and consequently extended this typology with recurses specific to the domain of environmental modeling. To test our web service we used cURL, a robust command-line based web client.

Development of a Distributed Parallel Computing Framework to Facilitate Regional/Global Gridded Crop Modeling with Various Scenarios

NASA Astrophysics Data System (ADS)

Jang, W.; Engda, T. A.; Neff, J. C.; Herrick, J.

2017-12-01

Many crop models are increasingly used to evaluate crop yields at regional and global scales. However, implementation of these models across large areas using fine-scale grids is limited by computational time requirements. In order to facilitate global gridded crop modeling with various scenarios (i.e., different crop, management schedule, fertilizer, and irrigation) using the Environmental Policy Integrated Climate (EPIC) model, we developed a distributed parallel computing framework in Python. Our local desktop with 14 cores (28 threads) was used to test the distributed parallel computing framework in Iringa, Tanzania which has 406,839 grid cells. High-resolution soil data, SoilGrids (250 x 250 m), and climate data, AgMERRA (0.25 x 0.25 deg) were also used as input data for the gridded EPIC model. The framework includes a master file for parallel computing, input database, input data formatters, EPIC model execution, and output analyzers. Through the master file for parallel computing, the user-defined number of threads of CPU divides the EPIC simulation into jobs. Then, Using EPIC input data formatters, the raw database is formatted for EPIC input data and the formatted data moves into EPIC simulation jobs. Then, 28 EPIC jobs run simultaneously and only interesting results files are parsed and moved into output analyzers. We applied various scenarios with seven different slopes and twenty-four fertilizer ranges. Parallelized input generators create different scenarios as a list for distributed parallel computing. After all simulations are completed, parallelized output analyzers are used to analyze all outputs according to the different scenarios. This saves significant computing time and resources, making it possible to conduct gridded modeling at regional to global scales with high-resolution data. For example, serial processing for the Iringa test case would require 113 hours, while using the framework developed in this study requires only approximately 6 hours, a nearly 95% reduction in computing time.

Towards a cyber-physical era: soft computing framework based multi-sensor array for water quality monitoring

NASA Astrophysics Data System (ADS)

Bhardwaj, Jyotirmoy; Gupta, Karunesh K.; Gupta, Rajiv

2018-02-01

New concepts and techniques are replacing traditional methods of water quality parameter measurement systems. This paper introduces a cyber-physical system (CPS) approach for water quality assessment in a distribution network. Cyber-physical systems with embedded sensors, processors and actuators can be designed to sense and interact with the water environment. The proposed CPS is comprised of sensing framework integrated with five different water quality parameter sensor nodes and soft computing framework for computational modelling. Soft computing framework utilizes the applications of Python for user interface and fuzzy sciences for decision making. Introduction of multiple sensors in a water distribution network generates a huge number of data matrices, which are sometimes highly complex, difficult to understand and convoluted for effective decision making. Therefore, the proposed system framework also intends to simplify the complexity of obtained sensor data matrices and to support decision making for water engineers through a soft computing framework. The target of this proposed research is to provide a simple and efficient method to identify and detect presence of contamination in a water distribution network using applications of CPS.

A software framework for real-time multi-modal detection of microsleeps.

PubMed

Knopp, Simon J; Bones, Philip J; Weddell, Stephen J; Jones, Richard D

2017-09-01

A software framework is described which was designed to process EEG, video of one eye, and head movement in real time, towards achieving early detection of microsleeps for prevention of fatal accidents, particularly in transport sectors. The framework is based around a pipeline structure with user-replaceable signal processing modules. This structure can encapsulate a wide variety of feature extraction and classification techniques and can be applied to detecting a variety of aspects of cognitive state. Users of the framework can implement signal processing plugins in C++ or Python. The framework also provides a graphical user interface and the ability to save and load data to and from arbitrary file formats. Two small studies are reported which demonstrate the capabilities of the framework in typical applications: monitoring eye closure and detecting simulated microsleeps. While specifically designed for microsleep detection/prediction, the software framework can be just as appropriately applied to (i) other measures of cognitive state and (ii) development of biomedical instruments for multi-modal real-time physiological monitoring and event detection in intensive care, anaesthesiology, cardiology, neurosurgery, etc. The software framework has been made freely available for researchers to use and modify under an open source licence.

Towards a cross-platform software framework to support end-to-end hydrometeorological sensor network deployment

NASA Astrophysics Data System (ADS)

Celicourt, P.; Sam, R.; Piasecki, M.

2016-12-01

Global phenomena such as climate change and large scale environmental degradation require the collection of accurate environmental data at detailed spatial and temporal scales from which knowledge and actionable insights can be derived using data science methods. Despite significant advances in sensor network technologies, sensors and sensor network deployment remains a labor-intensive, time consuming, cumbersome and expensive task. These factors demonstrate why environmental data collection remains a challenge especially in developing countries where technical infrastructure, expertise and pecuniary resources are scarce. In addition, they also demonstrate the reason why dense and long-term environmental data collection has been historically quite difficult. Moreover, hydrometeorological data collection efforts usually overlook the (critically important) inclusion of a standards-based system for storing, managing, organizing, indexing, documenting and sharing sensor data. We are developing a cross-platform software framework using the Python programming language that will allow us to develop a low cost end-to-end (from sensor to publication) system for hydrometeorological conditions monitoring. The software framework contains provision for sensor, sensor platforms, calibration and network protocols description, sensor programming, data storage, data publication and visualization and more importantly data retrieval in a desired unit system. It is being tested on the Raspberry Pi microcomputer as end node and a laptop PC as the base station in a wireless setting.

Isca, v1.0: a framework for the global modelling of the atmospheres of Earth and other planets at varying levels of complexity

NASA Astrophysics Data System (ADS)

Vallis, Geoffrey K.; Colyer, Greg; Geen, Ruth; Gerber, Edwin; Jucker, Martin; Maher, Penelope; Paterson, Alexander; Pietschnig, Marianne; Penn, James; Thomson, Stephen I.

2018-03-01

Isca is a framework for the idealized modelling of the global circulation of planetary atmospheres at varying levels of complexity and realism. The framework is an outgrowth of models from the Geophysical Fluid Dynamics Laboratory in Princeton, USA, designed for Earth's atmosphere, but it may readily be extended into other planetary regimes. Various forcing and radiation options are available, from dry, time invariant, Newtonian thermal relaxation to moist dynamics with radiative transfer. Options are available in the dry thermal relaxation scheme to account for the effects of obliquity and eccentricity (and so seasonality), different atmospheric optical depths and a surface mixed layer. An idealized grey radiation scheme, a two-band scheme, and a multiband scheme are also available, all with simple moist effects and astronomically based solar forcing. At the complex end of the spectrum the framework provides a direct connection to comprehensive atmospheric general circulation models. For Earth modelling, options include an aquaplanet and configurable continental outlines and topography. Continents may be defined by changing albedo, heat capacity, and evaporative parameters and/or by using a simple bucket hydrology model. Oceanic Q fluxes may be added to reproduce specified sea surface temperatures, with arbitrary continental distributions. Planetary atmospheres may be configured by changing planetary size and mass, solar forcing, atmospheric mass, radiation, and other parameters. Examples are given of various Earth configurations as well as a giant planet simulation, a slowly rotating terrestrial planet simulation, and tidally locked and other orbitally resonant exoplanet simulations. The underlying model is written in Fortran and may largely be configured with Python scripts. Python scripts are also used to run the model on different architectures, to archive the output, and for diagnostics, graphics, and post-processing. All of these features are publicly available in a Git-based repository.

Transcriptome analysis of the response of Burmese python to digestion

PubMed Central

Sanggaard, Kristian Wejse; Schauser, Leif; Lauridsen, Sanne Enok; Enghild, Jan J.

2017-01-01

Abstract Exceptional and extreme feeding behaviour makes the Burmese python (Python bivittatus) an interesting model to study physiological remodelling and metabolic adaptation in response to refeeding after prolonged starvation. In this study, we used transcriptome sequencing of 5 visceral organs during fasting as well as 24 hours and 48 hours after ingestion of a large meal to unravel the postprandial changes in Burmese pythons. We first used the pooled data to perform a de novo assembly of the transcriptome and supplemented this with a proteomic survey of enzymes in the plasma and gastric fluid. We constructed a high-quality transcriptome with 34 423 transcripts, of which 19 713 (57%) were annotated. Among highly expressed genes (fragments per kilo base per million sequenced reads > 100 in 1 tissue), we found that the transition from fasting to digestion was associated with differential expression of 43 genes in the heart, 206 genes in the liver, 114 genes in the stomach, 89 genes in the pancreas, and 158 genes in the intestine. We interrogated the function of these genes to test previous hypotheses on the response to feeding. We also used the transcriptome to identify 314 secreted proteins in the gastric fluid of the python. Digestion was associated with an upregulation of genes related to metabolic processes, and translational changes therefore appear to support the postprandial rise in metabolism. We identify stomach-related proteins from a digesting individual and demonstrate that the sensitivity of modern liquid chromatography/tandem mass spectrometry equipment allows the identification of gastric juice proteins that are present during digestion. PMID:28873961

Transcriptome analysis of the response of Burmese python to digestion.

PubMed

Duan, Jinjie; Sanggaard, Kristian Wejse; Schauser, Leif; Lauridsen, Sanne Enok; Enghild, Jan J; Schierup, Mikkel Heide; Wang, Tobias

2017-08-01

Exceptional and extreme feeding behaviour makes the Burmese python (Python bivittatus) an interesting model to study physiological remodelling and metabolic adaptation in response to refeeding after prolonged starvation. In this study, we used transcriptome sequencing of 5 visceral organs during fasting as well as 24 hours and 48 hours after ingestion of a large meal to unravel the postprandial changes in Burmese pythons. We first used the pooled data to perform a de novo assembly of the transcriptome and supplemented this with a proteomic survey of enzymes in the plasma and gastric fluid. We constructed a high-quality transcriptome with 34 423 transcripts, of which 19 713 (57%) were annotated. Among highly expressed genes (fragments per kilo base per million sequenced reads > 100 in 1 tissue), we found that the transition from fasting to digestion was associated with differential expression of 43 genes in the heart, 206 genes in the liver, 114 genes in the stomach, 89 genes in the pancreas, and 158 genes in the intestine. We interrogated the function of these genes to test previous hypotheses on the response to feeding. We also used the transcriptome to identify 314 secreted proteins in the gastric fluid of the python. Digestion was associated with an upregulation of genes related to metabolic processes, and translational changes therefore appear to support the postprandial rise in metabolism. We identify stomach-related proteins from a digesting individual and demonstrate that the sensitivity of modern liquid chromatography/tandem mass spectrometry equipment allows the identification of gastric juice proteins that are present during digestion. © The Authors 2017. Published by Oxford University Press.

ExoData: A Python package to handle large exoplanet catalogue data

NASA Astrophysics Data System (ADS)

Varley, Ryan

2016-10-01

Exoplanet science often involves using the system parameters of real exoplanets for tasks such as simulations, fitting routines, and target selection for proposals. Several exoplanet catalogues are already well established but often lack a version history and code friendly interfaces. Software that bridges the barrier between the catalogues and code enables users to improve the specific repeatability of results by facilitating the retrieval of exact system parameters used in articles results along with unifying the equations and software used. As exoplanet science moves towards large data, gone are the days where researchers can recall the current population from memory. An interface able to query the population now becomes invaluable for target selection and population analysis. ExoData is a Python interface and exploratory analysis tool for the Open Exoplanet Catalogue. It allows the loading of exoplanet systems into Python as objects (Planet, Star, Binary, etc.) from which common orbital and system equations can be calculated and measured parameters retrieved. This allows researchers to use tested code of the common equations they require (with units) and provides a large science input catalogue of planets for easy plotting and use in research. Advanced querying of targets is possible using the database and Python programming language. ExoData is also able to parse spectral types and fill in missing parameters according to programmable specifications and equations. Examples of use cases are integration of equations into data reduction pipelines, selecting planets for observing proposals and as an input catalogue to large scale simulation and analysis of planets. ExoData is a Python package available freely on GitHub.

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

NASA Astrophysics Data System (ADS)

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

2014-03-01

Recent developments in path integral methodology have significantly reduced the computational expense of including quantum mechanical effects in the nuclear motion in ab initio molecular dynamics simulations. However, the implementation of these developments requires a considerable programming effort, which has hindered their adoption. Here we describe i-PI, an interface written in Python that has been designed to minimise the effort required to bring state-of-the-art path integral techniques to an electronic structure program. While it is best suited to first principles calculations and path integral molecular dynamics, i-PI can also be used to perform classical molecular dynamics simulations, and can just as easily be interfaced with an empirical forcefield code. To give just one example of the many potential applications of the interface, we use it in conjunction with the CP2K electronic structure package to showcase the importance of nuclear quantum effects in high-pressure water. Catalogue identifier: AERN_v1_0 Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AERN_v1_0.html Program obtainable from: CPC Program Library, Queen’s University, Belfast, N. Ireland Licensing provisions: GNU General Public License, version 3 No. of lines in distributed program, including test data, etc.: 138626 No. of bytes in distributed program, including test data, etc.: 3128618 Distribution format: tar.gz Programming language: Python. Computer: Multiple architectures. Operating system: Linux, Mac OSX, Windows. RAM: Less than 256 Mb Classification: 7.7. External routines: NumPy Nature of problem: Bringing the latest developments in the modelling of nuclear quantum effects with path integral molecular dynamics to ab initio electronic structure programs with minimal implementational effort. Solution method: State-of-the-art path integral molecular dynamics techniques are implemented in a Python interface. Any electronic structure code can be patched to receive the atomic coordinates from the Python interface, and to return the forces and energy that are used to integrate the equations of motion. Restrictions: This code only deals with distinguishable particles. It does not include fermonic or bosonic exchanges between equivalent nuclei, which can become important at very low temperatures. Running time: Depends dramatically on the nature of the simulation being performed. A few minutes for short tests with empirical force fields, up to several weeks for production calculations with ab initio forces. The examples provided with the code run in less than an hour.

PyR@TE. Renormalization group equations for general gauge theories

NASA Astrophysics Data System (ADS)

Lyonnet, F.; Schienbein, I.; Staub, F.; Wingerter, A.

2014-03-01

Although the two-loop renormalization group equations for a general gauge field theory have been known for quite some time, deriving them for specific models has often been difficult in practice. This is mainly due to the fact that, albeit straightforward, the involved calculations are quite long, tedious and prone to error. The present work is an attempt to facilitate the practical use of the renormalization group equations in model building. To that end, we have developed two completely independent sets of programs written in Python and Mathematica, respectively. The Mathematica scripts will be part of an upcoming release of SARAH 4. The present article describes the collection of Python routines that we dubbed PyR@TE which is an acronym for “Python Renormalization group equations At Two-loop for Everyone”. In PyR@TE, once the user specifies the gauge group and the particle content of the model, the routines automatically generate the full two-loop renormalization group equations for all (dimensionless and dimensionful) parameters. The results can optionally be exported to LaTeX and Mathematica, or stored in a Python data structure for further processing by other programs. For ease of use, we have implemented an interactive mode for PyR@TE in form of an IPython Notebook. As a first application, we have generated with PyR@TE the renormalization group equations for several non-supersymmetric extensions of the Standard Model and found some discrepancies with the existing literature. Catalogue identifier: AERV_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AERV_v1_0.html Program obtainable from: CPC Program Library, Queen’s University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 924959 No. of bytes in distributed program, including test data, etc.: 495197 Distribution format: tar.gz Programming language: Python. Computer: Personal computer. Operating system: Tested on Fedora 15, MacOS 10 and 11, Ubuntu 12. Classification: 11.1. External routines: SymPy, PyYAML, NumPy, IPython, SciPy Nature of problem: Deriving the renormalization group equations for a general quantum field theory. Solution method: Group theory, tensor algebra Running time: Tens of seconds per model (one-loop), tens of minutes (two-loop)

MYRaf: An Easy Aperture Photometry GUI for IRAF

NASA Astrophysics Data System (ADS)

Niaei, M. S.; KiliÇ, Y.; Özeren, F. F.

2015-07-01

We describe the design and development of MYRaf, a GUI (Graphical User Interface) that aims to be completely open-source under General Public License (GPL). MYRaf is an easy to use, reliable, and a fast IRAF aperture photometry GUI tool for those who are conversant with text-based software and command-line procedures in GNU/Linux OSs. MYRaf uses IRAF, PyRAF, matplotlib, ginga, alipy, and SExtractor with the general-purpose and high-level programming language Python, and uses the Qt framework.

GIAnT - Generic InSAR Analysis Toolbox

NASA Astrophysics Data System (ADS)

Agram, P.; Jolivet, R.; Riel, B. V.; Simons, M.; Doin, M.; Lasserre, C.; Hetland, E. A.

2012-12-01

We present a computing framework for studying the spatio-temporal evolution of ground deformation from interferometric synthetic aperture radar (InSAR) data. Several open-source tools including Repeat Orbit Interferometry PACkage (ROI-PAC) and InSAR Scientific Computing Environment (ISCE) from NASA-JPL, and Delft Object-oriented Repeat Interferometric Software (DORIS), have enabled scientists to generate individual interferograms from raw radar data with relative ease. Numerous computational techniques and algorithms that reduce phase information from multiple interferograms to a deformation time-series have been developed and verified over the past decade. However, the sharing and direct comparison of products from multiple processing approaches has been hindered by - 1) absence of simple standards for sharing of estimated time-series products, 2) use of proprietary software tools with license restrictions and 3) the closed source nature of the exact implementation of many of these algorithms. We have developed this computing framework to address all of the above issues. We attempt to take the first steps towards creating a community software repository for InSAR time-series analysis. To date, we have implemented the short baseline subset algorithm (SBAS), NSBAS and multi-scale interferometric time-series (MInTS) in this framework and the associated source code is included in the GIAnT distribution. A number of the associated routines have been optimized for performance and scalability with large data sets. Some of the new features in our processing framework are - 1) the use of daily solutions from continuous GPS stations to correct for orbit errors, 2) the use of meteorological data sets to estimate the tropospheric delay screen and 3) a data-driven bootstrapping approach to estimate the uncertainties associated with estimated time-series products. We are currently working on incorporating tidal load corrections for individual interferograms and propagation of noise covariance models through the processing chain for robust estimation of uncertainties in the deformation estimates. We will demonstrate the ease of use of our framework with results ranging from regional scale analysis around Long Valley, CA and Parkfield, CA to continental scale analysis in Western South America. We will also present preliminary results from a new time-series approach that simultaneously estimates deformation over the complete spatial domain at all time epochs on a distributed computing platform. GIAnT has been developed entirely using open source tools and uses Python as the underlying platform. We build on the extensive numerical (NumPy) and scientific (SciPy) computing Python libraries to develop an object-oriented, flexible and modular framework for time-series InSAR applications. The toolbox is currently configured to work with outputs from ROI-PAC, ISCE and DORIS, but can easily be extended to support products from other SAR/InSAR processors. The toolbox libraries include support for hierarchical data format (HDF5) memory mapped files, parallel processing with Python's multi-processing module and support for many convex optimization solvers like CSDP, CVXOPT etc. An extensive set of routines to deal with ASCII and XML files has also been included for controlling the processing parameters.

Giving pandas ROOT to chew on: experiences with the XENON1T Dark Matter experiment

NASA Astrophysics Data System (ADS)

Remenska, D.; Tunnell, C.; Aalbers, J.; Verhoeven, S.; Maassen, J.; Templon, J.

2017-10-01

In preparation for the XENON1T Dark Matter data acquisition, we have prototyped and implemented a new computing model. The XENON signal and data processing software is developed fully in Python 3, and makes extensive use of generic scientific data analysis libraries, such as the SciPy stack. A certain tension between modern “Big Data” solutions and existing HEP frameworks is typically experienced in smaller particle physics experiments. ROOT is still the “standard” data format in our field, defined by large experiments (ATLAS, CMS). To ease the transition, our computing model caters to both analysis paradigms, leaving the choice of using ROOT-specific C++ libraries, or alternatively, Python and its data analytics tools, as a front-end choice of developing physics algorithms. We present our path on harmonizing these two ecosystems, which allowed us to use off-the-shelf software libraries (e.g., NumPy, SciPy, scikit-learn, matplotlib) and lower the cost of development and maintenance. To analyse the data, our software allows researchers to easily create “mini-trees” small, tabular ROOT structures for Python analysis, which can be read directly into pandas DataFrame structures. One of our goals was making ROOT available as a cross-platform binary for an easy installation from the Anaconda Cloud (without going through the “dependency hell”). In addition to helping us discover dark matter interactions, lowering this barrier helps shift the particle physics toward non-domain-specific code.

MEvoLib v1.0: the first molecular evolution library for Python.

PubMed

Álvarez-Jarreta, Jorge; Ruiz-Pesini, Eduardo

2016-10-28

Molecular evolution studies involve many different hard computational problems solved, in most cases, with heuristic algorithms that provide a nearly optimal solution. Hence, diverse software tools exist for the different stages involved in a molecular evolution workflow. We present MEvoLib, the first molecular evolution library for Python, providing a framework to work with different tools and methods involved in the common tasks of molecular evolution workflows. In contrast with already existing bioinformatics libraries, MEvoLib is focused on the stages involved in molecular evolution studies, enclosing the set of tools with a common purpose in a single high-level interface with fast access to their frequent parameterizations. The gene clustering from partial or complete sequences has been improved with a new method that integrates accessible external information (e.g. GenBank's features data). Moreover, MEvoLib adjusts the fetching process from NCBI databases to optimize the download bandwidth usage. In addition, it has been implemented using parallelization techniques to cope with even large-case scenarios. MEvoLib is the first library for Python designed to facilitate molecular evolution researches both for expert and novel users. Its unique interface for each common task comprises several tools with their most used parameterizations. It has also included a method to take advantage of biological knowledge to improve the gene partition of sequence datasets. Additionally, its implementation incorporates parallelization techniques to enhance computational costs when handling very large input datasets.

One-dimensional statistical parametric mapping in Python.

PubMed

Pataky, Todd C

2012-01-01

Statistical parametric mapping (SPM) is a topological methodology for detecting field changes in smooth n-dimensional continua. Many classes of biomechanical data are smooth and contained within discrete bounds and as such are well suited to SPM analyses. The current paper accompanies release of 'SPM1D', a free and open-source Python package for conducting SPM analyses on a set of registered 1D curves. Three example applications are presented: (i) kinematics, (ii) ground reaction forces and (iii) contact pressure distribution in probabilistic finite element modelling. In addition to offering a high-level interface to a variety of common statistical tests like t tests, regression and ANOVA, SPM1D also emphasises fundamental concepts of SPM theory through stand-alone example scripts. Source code and documentation are available at: www.tpataky.net/spm1d/.

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

PubMed

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

2012-05-15

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

htsint: a Python library for sequencing pipelines that combines data through gene set generation.

PubMed

Richards, Adam J; Herrel, Anthony; Bonneaud, Camille

2015-09-24

Sequencing technologies provide a wealth of details in terms of genes, expression, splice variants, polymorphisms, and other features. A standard for sequencing analysis pipelines is to put genomic or transcriptomic features into a context of known functional information, but the relationships between ontology terms are often ignored. For RNA-Seq, considering genes and their genetic variants at the group level enables a convenient way to both integrate annotation data and detect small coordinated changes between experimental conditions, a known caveat of gene level analyses. We introduce the high throughput data integration tool, htsint, as an extension to the commonly used gene set enrichment frameworks. The central aim of htsint is to compile annotation information from one or more taxa in order to calculate functional distances among all genes in a specified gene space. Spectral clustering is then used to partition the genes, thereby generating functional modules. The gene space can range from a targeted list of genes, like a specific pathway, all the way to an ensemble of genomes. Given a collection of gene sets and a count matrix of transcriptomic features (e.g. expression, polymorphisms), the gene sets produced by htsint can be tested for 'enrichment' or conditional differences using one of a number of commonly available packages. The database and bundled tools to generate functional modules were designed with sequencing pipelines in mind, but the toolkit nature of htsint allows it to also be used in other areas of genomics. The software is freely available as a Python library through GitHub at https://github.com/ajrichards/htsint.

Parcels v0.9: prototyping a Lagrangian ocean analysis framework for the petascale age

NASA Astrophysics Data System (ADS)

Lange, Michael; van Sebille, Erik

2017-11-01

As ocean general circulation models (OGCMs) move into the petascale age, where the output of single simulations exceeds petabytes of storage space, tools to analyse the output of these models will need to scale up too. Lagrangian ocean analysis, where virtual particles are tracked through hydrodynamic fields, is an increasingly popular way to analyse OGCM output, by mapping pathways and connectivity of biotic and abiotic particulates. However, the current software stack of Lagrangian ocean analysis codes is not dynamic enough to cope with the increasing complexity, scale and need for customization of use-cases. Furthermore, most community codes are developed for stand-alone use, making it a nontrivial task to integrate virtual particles at runtime of the OGCM. Here, we introduce the new Parcels code, which was designed from the ground up to be sufficiently scalable to cope with petascale computing. We highlight its API design that combines flexibility and customization with the ability to optimize for HPC workflows, following the paradigm of domain-specific languages. Parcels is primarily written in Python, utilizing the wide range of tools available in the scientific Python ecosystem, while generating low-level C code and using just-in-time compilation for performance-critical computation. We show a worked-out example of its API, and validate the accuracy of the code against seven idealized test cases. This version 0.9 of Parcels is focused on laying out the API, with future work concentrating on support for curvilinear grids, optimization, efficiency and at-runtime coupling with OGCMs.

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

PubMed

Gautier, Laurent

2010-12-21

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. 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. 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/.

Prospects and limitations of citizen science in invasive species management: A case study with Burmese pythons in Everglades National Park

USGS Publications Warehouse

Falk, Bryan; Snow, Raymond W.; Reed, Robert

2016-01-01

Citizen-science programs have the potential to contribute to the management of invasive species, including Python molurus bivittatus (Burmese Python) in Florida. We characterized citizen-science–generated Burmese Python information from Everglades National Park (ENP) to explore how citizen science may be useful in this effort. As an initial step, we compiled and summarized records of Burmese Python observations and removals collected by both professional and citizen scientists in ENP during 2000–2014 and found many patterns of possible significance, including changes in annual observations and in demographic composition after a cold event. These patterns are difficult to confidently interpret because the records lack search-effort information, however, and differences among years may result from differences in search effort. We began collecting search-effort information in 2014 by leveraging an ongoing citizen-science program in ENP. Program participation was generally low, with most authorized participants in 2014 not searching for the snakes at all. We discuss the possible explanations for low participation, especially how the low likelihood of observing pythons weakens incentives to search. The monthly rate of Burmese Python observations for 2014 averaged ~1 observation for every 8 h of searching, but during several months, the rate was 1 python per >40 h of searching. These low observation-rates are a natural outcome of the snakes’ low detectability—few Burmese Pythons are likely to be observed even if many are present. The general inaccessibility of the southern Florida landscape also severely limits the effectiveness of using visual searches to find and remove pythons for the purposes of population control. Instead, and despite the difficulties in incentivizing voluntary participation, the value of citizen-science efforts in the management of the Burmese Python population is in collecting search-effort information.

A modern Python interface for the Generic Mapping Tools

NASA Astrophysics Data System (ADS)

Uieda, L.; Wessel, P.

2017-12-01

Figures generated by The Generic Mapping Tools (GMT) are present in countless publications across the Earth sciences. The command-line interface of GMT lends the tool its flexibility but also creates a barrier to entry for begginers. Meanwhile, adoption of the Python programming language has grown across the scientific community. This growth is largely due to the simplicity and low barrier to entry of the language and its ecosystem of tools. Thus, it is not surprising that there have been at least three attempts to create Python interfaces for GMT: gmtpy (github.com/emolch/gmtpy), pygmt (github.com/ian-r-rose/pygmt), and PyGMT (github.com/glimmer-cism/PyGMT). None of these projects are currently active and, with the exception of pygmt, they do not use the GMT Application Programming Interface (API) introduced in GMT 5. The two main Python libraries for plotting data on maps are the matplotlib Basemap toolkit (matplotlib.org/basemap) and Cartopy (scitools.org.uk/cartopy), both of which rely on matplotlib (matplotlib.org) as the backend for generating the figures. Basemap is known to have limitations and is being discontinued. Cartopy is an improvement over Basemap but is still bound by the speed and memory constraints of matplotlib. We present a new Python interface for GMT (GMT/Python) that makes use of the GMT API and of new features being developed for the upcoming GMT 6 release. The GMT/Python library is designed according to the norms and styles of the Python community. The library integrates with the scientific Python ecosystem by using the "virtual files" from the GMT API to implement input and output of Python data types (numpy "ndarray" for tabular data and xarray "Dataset" for grids). Other features include an object-oriented interface for creating figures, the ability to display figures in the Jupyter notebook, and descriptive aliases for GMT arguments (e.g., "region" instead of "R" and "projection" instead of "J"). GMT/Python can also serve as a backend for developing new high-level interfaces, which can help make GMT more accessible to beginners and more intuitive for Python users. GMT/Python is an open-source project hosted on Github (github.com/GenericMappingTools/gmt-python) and is in early stages of development. A first release will accompany the release of GMT 6, which is expected for early 2018.

The Unlock Project: a Python-based framework for practical brain-computer interface communication "app" development.

PubMed

Brumberg, Jonathan S; Lorenz, Sean D; Galbraith, Byron V; Guenther, Frank H

2012-01-01

In this paper we present a framework for reducing the development time needed for creating applications for use in non-invasive brain-computer interfaces (BCI). Our framework is primarily focused on facilitating rapid software "app" development akin to current efforts in consumer portable computing (e.g. smart phones and tablets). This is accomplished by handling intermodule communication without direct user or developer implementation, instead relying on a core subsystem for communication of standard, internal data formats. We also provide a library of hardware interfaces for common mobile EEG platforms for immediate use in BCI applications. A use-case example is described in which a user with amyotrophic lateral sclerosis participated in an electroencephalography-based BCI protocol developed using the proposed framework. We show that our software environment is capable of running in real-time with updates occurring 50-60 times per second with limited computational overhead (5 ms system lag) while providing accurate data acquisition and signal analysis.

PyMidas: Interface from Python to Midas

NASA Astrophysics Data System (ADS)

Maisala, Sami; Oittinen, Tero

2014-01-01

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

Endocardial fibrosarcoma in a reticulated python (Python reticularis).

PubMed

Gumber, Sanjeev; Nevarez, Javier G; Cho, Doo-Youn

2010-11-01

A female, reticulated python (Python reticularis) of unknown age was presented with a history of lethargy, weakness, and distended coelom. Physical examination revealed severe dystocia and stomatitis. The reticulated python was euthanized due to a poor clinical prognosis. Postmortem examination revealed marked distention of the reproductive tract with 26 eggs (10-12 cm in diameter), pericardial effusion, and a slightly firm, pale tan mass (3-4 cm in diameter) adhered to the endocardium at the base of aorta. Based on histopathologic and transmission electron microscopic findings, the diagnosis of endocardial fibrosarcoma was made.

Leveraging Python Interoperability Tools to Improve Sapphire's Usability

DOE Office of Scientific and Technical Information (OSTI.GOV)

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.

The zoonotic implications of pentastomiasis in the royal python (python regius).

PubMed

Ayinmode, Ab; Adedokun, Ao; Aina, A; Taiwo, V

2010-09-01

Pentastomes are worm-like endoparasites of the phylum Pentastomida found principally in the respiratory tract of reptiles, birds, and mammals. They cause a zoonotic disease known as pentastomiasis in humans and other mammals. The autopsy of a Nigerian royal python (Python regius) revealed two yellowish-white parasites in the lungs, tissue necrosis and inflammatory lesions. The parasite was confirmed to be Armillifer spp (Pentastomid); this is the first recorded case of pentastomiasis in the royal python (Python regius) in Nigeria. This report may be an alert of the possibility of on-going zoonotic transmission of pentastomiasis from snake to man, especially in the sub-urban/rural areas of Nigeria and other West African countries where people consume snake meat.

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.

Cross-platform validation and analysis environment for particle physics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chekanov, S. V.; Pogrebnyak, I.; Wilbern, D.

A multi-platform validation and analysis framework for public Monte Carlo simulation for high-energy particle collisions is discussed. The front-end of this framework uses the Python programming language, while the back-end is written in Java, which provides a multi-platform environment that can be run from a web browser and can easily be deployed at the grid sites. The analysis package includes all major software tools used in high-energy physics, such as Lorentz vectors, jet algorithms, histogram packages, graphic canvases, and tools for providing data access. This multi-platform software suite, designed to minimize OS-specific maintenance and deployment time, is used for onlinemore » validation of Monte Carlo event samples through a web interface.« less

OpenElectrophy: An Electrophysiological Data- and Analysis-Sharing Framework

PubMed Central

Garcia, Samuel; Fourcaud-Trocmé, Nicolas

2008-01-01

Progress in experimental tools and design is allowing the acquisition of increasingly large datasets. Storage, manipulation and efficient analyses of such large amounts of data is now a primary issue. We present OpenElectrophy, an electrophysiological data- and analysis-sharing framework developed to fill this niche. It stores all experiment data and meta-data in a single central MySQL database, and provides a graphic user interface to visualize and explore the data, and a library of functions for user analysis scripting in Python. It implements multiple spike-sorting methods, and oscillation detection based on the ridge extraction methods due to Roux et al. (2007). OpenElectrophy is open source and is freely available for download at http://neuralensemble.org/trac/OpenElectrophy. PMID:19521545

pyNS: an open-source framework for 0D haemodynamic modelling.

PubMed

Manini, Simone; Antiga, Luca; Botti, Lorenzo; Remuzzi, Andrea

2015-06-01

A number of computational approaches have been proposed for the simulation of haemodynamics and vascular wall dynamics in complex vascular networks. Among them, 0D pulse wave propagation methods allow to efficiently model flow and pressure distributions and wall displacements throughout vascular networks at low computational costs. Although several techniques are documented in literature, the availability of open-source computational tools is still limited. We here present python Network Solver, a modular solver framework for 0D problems released under a BSD license as part of the archToolkit ( http://archtk.github.com ). As an application, we describe patient-specific models of the systemic circulation and detailed upper extremity for use in the prediction of maturation after surgical creation of vascular access for haemodialysis.

Damage Based Analysis (DBA) - Theory, Derivation and Practical Application Using Both an Acceleration and Pseudo Velocity Approach

NASA Technical Reports Server (NTRS)

Grillo, Vince

2017-01-01

The objective of this presentation is to give a brief overview of the theory behind the (DBA) method, an overview of the derivation and a practical application of the theory using the Python computer language. The Theory and Derivation will use both Acceleration and Pseudo Velocity methods to derive a series of equations for processing by Python. We will take the results and compare both Acceleration and Pseudo Velocity methods and discuss implementation of the Python functions. Also, we will discuss the efficiency of the methods and the amount of computer time required for the solution. In conclusion, (DBA) offers a powerful method to evaluate the amount of energy imparted into a system in the form of both Amplitude and Duration during qualification testing and flight environments. Many forms of steady state and transient vibratory motion can be characterized using this technique. (DBA) provides a more robust alternative to traditional methods such Power Spectral Density (PSD) using a maximax approach.

Damage Based Analysis (DBA): Theory, Derivation and Practical Application - Using Both an Acceleration and Pseudo-Velocity Approach

NASA Technical Reports Server (NTRS)

Grillo, Vince

2016-01-01

The objective of this presentation is to give a brief overview of the theory behind the (DBA) method, an overview of the derivation and a practical application of the theory using the Python computer language. The Theory and Derivation will use both Acceleration and Pseudo Velocity methods to derive a series of equations for processing by Python. We will take the results and compare both Acceleration and Pseudo Velocity methods and discuss implementation of the Python functions. Also, we will discuss the efficiency of the methods and the amount of computer time required for the solution. In conclusion, (DBA) offers a powerful method to evaluate the amount of energy imparted into a system in the form of both Amplitude and Duration during qualification testing and flight environments. Many forms of steady state and transient vibratory motion can be characterized using this technique. (DBA) provides a more robust alternative to traditional methods such Power Spectral Density (PSD) using a Maximax approach.

Component Framework for Loosely Coupled High Performance Integrated Plasma Simulations

NASA Astrophysics Data System (ADS)

Elwasif, W. R.; Bernholdt, D. E.; Shet, A. G.; Batchelor, D. B.; Foley, S.

2010-11-01

We present the design and implementation of a component-based simulation framework for the execution of coupled time-dependent plasma modeling codes. The Integrated Plasma Simulator (IPS) provides a flexible lightweight component model that streamlines the integration of stand alone codes into coupled simulations. Standalone codes are adapted to the IPS component interface specification using a thin wrapping layer implemented in the Python programming language. The framework provides services for inter-component method invocation, configuration, task, and data management, asynchronous event management, simulation monitoring, and checkpoint/restart capabilities. Services are invoked, as needed, by the computational components to coordinate the execution of different aspects of coupled simulations on Massive parallel Processing (MPP) machines. A common plasma state layer serves as the foundation for inter-component, file-based data exchange. The IPS design principles, implementation details, and execution model will be presented, along with an overview of several use cases.

Modeling Geomagnetic Variations using a Machine Learning Framework

NASA Astrophysics Data System (ADS)

Cheung, C. M. M.; Handmer, C.; Kosar, B.; Gerules, G.; Poduval, B.; Mackintosh, G.; Munoz-Jaramillo, A.; Bobra, M.; Hernandez, T.; McGranaghan, R. M.

2017-12-01

We present a framework for data-driven modeling of Heliophysics time series data. The Solar Terrestrial Interaction Neural net Generator (STING) is an open source python module built on top of state-of-the-art statistical learning frameworks (traditional machine learning methods as well as deep learning). To showcase the capability of STING, we deploy it for the problem of predicting the temporal variation of geomagnetic fields. The data used includes solar wind measurements from the OMNI database and geomagnetic field data taken by magnetometers at US Geological Survey observatories. We examine the predictive capability of different machine learning techniques (recurrent neural networks, support vector machines) for a range of forecasting times (minutes to 12 hours). STING is designed to be extensible to other types of data. We show how STING can be used on large sets of data from different sensors/observatories and adapted to tackle other problems in Heliophysics.

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

NASA Astrophysics Data System (ADS)

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

2008-03-01

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

Combining Open-Source Packages for Planetary Exploration

NASA Astrophysics Data System (ADS)

Schmidt, Albrecht; Grieger, Björn; Völk, Stefan

2015-04-01

The science planning of the ESA Rosetta mission has presented challenges which were addressed with combining various open-source software packages, such as the SPICE toolkit, the Python language and the Web graphics library three.js. The challenge was to compute certain parameters from a pool of trajectories and (possible) attitudes to describe the behaviour of the spacecraft. To be able to do this declaratively and efficiently, a C library was implemented that allows to interface the SPICE toolkit for geometrical computations from the Python language and process as much data as possible during one subroutine call. To minimise the lines of code one has to write special care was taken to ensure that the bindings were idiomatic and thus integrate well into the Python language and ecosystem. When done well, this very much simplifies the structure of the code and facilitates the testing for correctness by automatic test suites and visual inspections. For rapid visualisation and confirmation of correctness of results, the geometries were visualised with the three.js library, a popular Javascript library for displaying three-dimensional graphics in a Web browser. Programmatically, this was achieved by generating data files from SPICE sources that were included into templated HTML and displayed by a browser, thus made easily accessible to interested parties at large. As feedback came and new ideas were to be explored, the authors benefited greatly from the design of the Python-to-SPICE library which allowed the expression of algorithms to be concise and easier to communicate. In summary, by combining several well-established open-source tools, we were able to put together a flexible computation and visualisation environment that helped communicate and build confidence in planning ideas.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Python script for querying a list of web sites and their details from Splunk and dynamically creating tests for monitoring uptime. The data generated from this script is then sent back to Splunk for creating reports and alerts.

Identification of snake arenaviruses in live boas and pythons in a zoo in Germany.

PubMed

Aqrawi, T; Stöhr, A C; Knauf-Witzens, T; Krengel, A; Heckers, K O; Marschang, R E

2015-01-01

Recent studies have described the detection and characterisation of new, snake specific arenaviruses in boas and pythons with inclusion body disease (IBD). The objective of this study was to detect arenaviral RNA in live snakes and to determine if these were associated with IBD in all cases. Samples for arenavirus detection in live animals were compared. Detected viruses were compared in order to understand their genetic variability. Esophageal swabs and whole blood was collected from a total of 28 boas and pythons. Samples were tested for arenaviral RNA by RT-PCR. Blood smears from all animals were examined for the presence of inclusion bodies. Internal tissues from animals that died or were euthanized during the study were examined for inclusions and via RT-PCR for arenaviral RNA. All PCR products were sequenced and the genomic sequences phylogenetically analysed. Nine live animals were found to be arenavirus-positive. Two additional snakes tested positive following necropsy. Five new arenaviruses were detected and identified. The detected viruses were named "Boa Arenavirus Deutschland (Boa Av DE) numbers 1-4" and one virus detected in a python (Morelia viridis) was named "Python Av DE1". Results from sequence analyses revealed considerable similarities to a portion of the glycoprotein genes of recently identified boid snake arenaviruses. Both oral swabs and whole blood can be used for the detection of arenaviruses in snakes. In most cases, but not in all, the presence of arenaviral RNA correlated with the presence of inclusions in the tissues of infected animals. There was evidence that some animals may be able to clear arenavirus infection without development of IBD. This is the first detection of arenaviruses in live snakes. The detection of arenaviruses in live snakes is of importance for both disease detection and prevention and for use in quarantine situations. The findings in this study support the theory that arenaviruses are the cause of IBD, but indicate that in some cases it may be possible for animals to clear arenavirus infections without developing IBD.

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

ng: What next-generation languages can teach us about HENP frameworks in the manycore era

NASA Astrophysics Data System (ADS)

Binet, Sébastien

2011-12-01

Current High Energy and Nuclear Physics (HENP) frameworks were written before multicore systems became widely deployed. A 'single-thread' execution model naturally emerged from that environment, however, this no longer fits into the processing model on the dawn of the manycore era. Although previous work focused on minimizing the changes to be applied to the LHC frameworks (because of the data taking phase) while still trying to reap the benefits of the parallel-enhanced CPU architectures, this paper explores what new languages could bring to the design of the next-generation frameworks. Parallel programming is still in an intensive phase of R&D and no silver bullet exists despite the 30+ years of literature on the subject. Yet, several parallel programming styles have emerged: actors, message passing, communicating sequential processes, task-based programming, data flow programming, ... to name a few. We present the work of the prototyping of a next-generation framework in new and expressive languages (python and Go) to investigate how code clarity and robustness are affected and what are the downsides of using languages younger than FORTRAN/C/C++.

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.

Acariasis on pet Burmese python, Python molurus bivittatus in Malaysia.

PubMed

Mariana, A; Vellayan, S; Halimaton, I; Ho, T M

2011-03-01

To identify the acari present on pet Burmese pythons in Malaysia and to determine whether there is any potential public health risk related to handling of the snakes. Two sub-adult Burmese pythons kept as pets for a period of about 6 to 7 months by different owners, were brought to an exotic animal practice for treatment. On a complete medical examination, some ticks and mites (acari) were detected beneath the dorsal and ventral scales along body length of the snakes. Ticks were directly identified and mites were mounted prior to identification. A total of 12 ticks represented by 3 males, 2 females and 7 nymphal stages of Rhipicephalus sanguineus (R. sanguineus) were extracted from the first python while the other one was with 25 female Ophionyssus natricis (O. natricis) mesostigmatid mites. Only adult female mites were found. These mites are common ectoparasites of Burmese pythons. Both the acarine species found on the Burmese pythons are known vectors of pathogens. This is the first record that R. sanguineus has been reported from a pet Burmese python in Malaysia. Copyright © 2011 Hainan Medical College. Published by Elsevier B.V. All rights reserved.

The Discovery of XY Sex Chromosomes in a Boa and Python.

PubMed

Gamble, Tony; Castoe, Todd A; Nielsen, Stuart V; Banks, Jaison L; Card, Daren C; Schield, Drew R; Schuett, Gordon W; Booth, Warren

2017-07-24

For over 50 years, biologists have accepted that all extant snakes share the same ZW sex chromosomes derived from a common ancestor [1-3], with different species exhibiting sex chromosomes at varying stages of differentiation. Accordingly, snakes have been a well-studied model for sex chromosome evolution in animals [1, 4]. A review of the literature, however, reveals no compelling support that boas and pythons possess ZW sex chromosomes [2, 5]. Furthermore, phylogenetic patterns of facultative parthenogenesis in snakes and a sex-linked color mutation in the ball python (Python regius) are best explained by boas and pythons possessing an XY sex chromosome system [6, 7]. Here we demonstrate that a boa (Boa imperator) and python (Python bivittatus) indeed possess XY sex chromosomes, based on the discovery of male-specific genetic markers in both species. We use these markers, along with transcriptomic and genomic data, to identify distinct sex chromosomes in boas and pythons, demonstrating that XY systems evolved independently in each lineage. This discovery highlights the dynamic evolution of vertebrate sex chromosomes and further enhances the value of snakes as a model for studying sex chromosome evolution. Copyright © 2017 Elsevier Ltd. All rights reserved.

Python in the NERSC Exascale Science Applications Program for Data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ronaghi, Zahra; Thomas, Rollin; Deslippe, Jack

We describe a new effort at the National Energy Re- search Scientific Computing Center (NERSC) in performance analysis and optimization of scientific Python applications targeting the Intel Xeon Phi (Knights Landing, KNL) many- core architecture. The Python-centered work outlined here is part of a larger effort called the NERSC Exascale Science Applications Program (NESAP) for Data. NESAP for Data focuses on applications that process and analyze high-volume, high-velocity data sets from experimental/observational science (EOS) facilities supported by the US Department of Energy Office of Science. We present three case study applications from NESAP for Data that use Python. These codesmore » vary in terms of “Python purity” from applications developed in pure Python to ones that use Python mainly as a convenience layer for scientists without expertise in lower level programming lan- guages like C, C++ or Fortran. The science case, requirements, constraints, algorithms, and initial performance optimizations for each code are discussed. Our goal with this paper is to contribute to the larger conversation around the role of Python in high-performance computing today and tomorrow, highlighting areas for future work and emerging best practices« less

Responses of python gastrointestinal regulatory peptides to feeding

PubMed Central

Secor, Stephen M.; Fehsenfeld, Drew; Diamond, Jared; Adrian, Thomas E.

2001-01-01

In the Burmese python (Python molurus), the rapid up-regulation of gastrointestinal (GI) function and morphology after feeding, and subsequent down-regulation on completing digestion, are expected to be mediated by GI hormones and neuropeptides. Hence, we examined postfeeding changes in plasma and tissue concentrations of 11 GI hormones and neuropeptides in the python. Circulating levels of cholecystokinin (CCK), glucose-dependent insulinotropic peptide (GIP), glucagon, and neurotensin increase by respective factors of 25-, 6-, 6-, and 3.3-fold within 24 h after feeding. In digesting pythons, the regulatory peptides neurotensin, somatostatin, motilin, and vasoactive intestinal peptide occur largely in the stomach, GIP and glucagon in the pancreas, and CCK and substance P in the small intestine. Tissue concentrations of CCK, GIP, and neurotensin decline with feeding. Tissue distributions and molecular forms (as determined by gel-permeation chromatography) of many python GI peptides are similar or identical to those of their mammalian counterparts. The postfeeding release of GI peptides from tissues, and their concurrent rise in plasma concentrations, suggests that they play a role in regulating python-digestive responses. These large postfeeding responses, and similarities of peptide structure with mammals, make pythons an attractive model for studying GI peptides. PMID:11707600

A Python Implementation of an Intermediate-Level Tropical Circulation Model and Implications for How Modeling Science is Done

NASA Astrophysics Data System (ADS)

Lin, J. W. B.

2015-12-01

Historically, climate models have been developed incrementally and in compiled languages like Fortran. While the use of legacy compiledlanguages results in fast, time-tested code, the resulting model is limited in its modularity and cannot take advantage of functionalityavailable with modern computer languages. Here we describe an effort at using the open-source, object-oriented language Pythonto create more flexible climate models: the package qtcm, a Python implementation of the intermediate-level Neelin-Zeng Quasi-Equilibrium Tropical Circulation model (QTCM1) of the atmosphere. The qtcm package retains the core numerics of QTCM1, written in Fortran, to optimize model performance but uses Python structures and utilities to wrap the QTCM1 Fortran routines and manage model execution. The resulting "mixed language" modeling package allows order and choice of subroutine execution to be altered at run time, and model analysis and visualization to be integrated in interactively with model execution at run time. This flexibility facilitates more complex scientific analysis using less complex code than would be possible using traditional languages alone and provides tools to transform the traditional "formulate hypothesis → write and test code → run model → analyze results" sequence into a feedback loop that can be executed automatically by the computer.

TriBITS (Tribal Build, Integrate, and Test System)

DOE Office of Scientific and Technical Information (OSTI.GOV)

2013-05-16

TriBITS is a configuration, build, test, and reporting system that uses the Kitware open-source CMake/CTest/CDash system. TriBITS contains a number of custom CMake/CTest scripts and python scripts that extend the functionality of the out-of-the-box CMake/CTest/CDash system.

Sailfish: A flexible multi-GPU implementation of the lattice Boltzmann method

NASA Astrophysics Data System (ADS)

Januszewski, M.; Kostur, M.

2014-09-01

We present Sailfish, an open source fluid simulation package implementing the lattice Boltzmann method (LBM) on modern Graphics Processing Units (GPUs) using CUDA/OpenCL. We take a novel approach to GPU code implementation and use run-time code generation techniques and a high level programming language (Python) to achieve state of the art performance, while allowing easy experimentation with different LBM models and tuning for various types of hardware. We discuss the general design principles of the code, scaling to multiple GPUs in a distributed environment, as well as the GPU implementation and optimization of many different LBM models, both single component (BGK, MRT, ELBM) and multicomponent (Shan-Chen, free energy). The paper also presents results of performance benchmarks spanning the last three NVIDIA GPU generations (Tesla, Fermi, Kepler), which we hope will be useful for researchers working with this type of hardware and similar codes. Catalogue identifier: AETA_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AETA_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: GNU Lesser General Public License, version 3 No. of lines in distributed program, including test data, etc.: 225864 No. of bytes in distributed program, including test data, etc.: 46861049 Distribution format: tar.gz Programming language: Python, CUDA C, OpenCL. Computer: Any with an OpenCL or CUDA-compliant GPU. Operating system: No limits (tested on Linux and Mac OS X). RAM: Hundreds of megabytes to tens of gigabytes for typical cases. Classification: 12, 6.5. External routines: PyCUDA/PyOpenCL, Numpy, Mako, ZeroMQ (for multi-GPU simulations), scipy, sympy Nature of problem: GPU-accelerated simulation of single- and multi-component fluid flows. Solution method: A wide range of relaxation models (LBGK, MRT, regularized LB, ELBM, Shan-Chen, free energy, free surface) and boundary conditions within the lattice Boltzmann method framework. Simulations can be run in single or double precision using one or more GPUs. Restrictions: The lattice Boltzmann method works for low Mach number flows only. Unusual features: The actual numerical calculations run exclusively on GPUs. The numerical code is built dynamically at run-time in CUDA C or OpenCL, using templates and symbolic formulas. The high-level control of the simulation is maintained by a Python process. Additional comments: !!!!! The distribution file for this program is over 45 Mbytes and therefore is not delivered directly when Download or Email is requested. Instead a html file giving details of how the program can be obtained is sent. !!!!! Running time: Problem-dependent, typically minutes (for small cases or short simulations) to hours (large cases or long simulations).

Fatty acids identified in the Burmese python promote beneficial cardiac growth.

PubMed

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

2011-10-28

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

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

geoKepler Workflow Module for Computationally Scalable and Reproducible Geoprocessing and Modeling

NASA Astrophysics Data System (ADS)

Cowart, C.; Block, J.; Crawl, D.; Graham, J.; Gupta, A.; Nguyen, M.; de Callafon, R.; Smarr, L.; Altintas, I.

2015-12-01

The NSF-funded WIFIRE project has developed an open-source, online geospatial workflow platform for unifying geoprocessing tools and models for for fire and other geospatially dependent modeling applications. It is a product of WIFIRE's objective to build an end-to-end cyberinfrastructure for real-time and data-driven simulation, prediction and visualization of wildfire behavior. geoKepler includes a set of reusable GIS components, or actors, for the Kepler Scientific Workflow System (https://kepler-project.org). Actors exist for reading and writing GIS data in formats such as Shapefile, GeoJSON, KML, and using OGC web services such as WFS. The actors also allow for calling geoprocessing tools in other packages such as GDAL and GRASS. Kepler integrates functions from multiple platforms and file formats into one framework, thus enabling optimal GIS interoperability, model coupling, and scalability. Products of the GIS actors can be fed directly to models such as FARSITE and WRF. Kepler's ability to schedule and scale processes using Hadoop and Spark also makes geoprocessing ultimately extensible and computationally scalable. The reusable workflows in geoKepler can be made to run automatically when alerted by real-time environmental conditions. Here, we show breakthroughs in the speed of creating complex data for hazard assessments with this platform. We also demonstrate geoKepler workflows that use Data Assimilation to ingest real-time weather data into wildfire simulations, and for data mining techniques to gain insight into environmental conditions affecting fire behavior. Existing machine learning tools and libraries such as R and MLlib are being leveraged for this purpose in Kepler, as well as Kepler's Distributed Data Parallel (DDP) capability to provide a framework for scalable processing. geoKepler workflows can be executed via an iPython notebook as a part of a Jupyter hub at UC San Diego for sharing and reporting of the scientific analysis and results from various runs of geoKepler workflows. The communication between iPython and Kepler workflow executions is established through an iPython magic function for Kepler that we have implemented. In summary, geoKepler is an ecosystem that makes geospatial processing and analysis of any kind programmable, reusable, scalable and sharable.

A statistical framework for neuroimaging data analysis based on mutual information estimated via a gaussian copula.

PubMed

Ince, Robin A A; Giordano, Bruno L; Kayser, Christoph; Rousselet, Guillaume A; Gross, Joachim; Schyns, Philippe G

2017-03-01

We begin by reviewing the statistical framework of information theory as applicable to neuroimaging data analysis. A major factor hindering wider adoption of this framework in neuroimaging is the difficulty of estimating information theoretic quantities in practice. We present a novel estimation technique that combines the statistical theory of copulas with the closed form solution for the entropy of Gaussian variables. This results in a general, computationally efficient, flexible, and robust multivariate statistical framework that provides effect sizes on a common meaningful scale, allows for unified treatment of discrete, continuous, unidimensional and multidimensional variables, and enables direct comparisons of representations from behavioral and brain responses across any recording modality. We validate the use of this estimate as a statistical test within a neuroimaging context, considering both discrete stimulus classes and continuous stimulus features. We also present examples of analyses facilitated by these developments, including application of multivariate analyses to MEG planar magnetic field gradients, and pairwise temporal interactions in evoked EEG responses. We show the benefit of considering the instantaneous temporal derivative together with the raw values of M/EEG signals as a multivariate response, how we can separately quantify modulations of amplitude and direction for vector quantities, and how we can measure the emergence of novel information over time in evoked responses. Open-source Matlab and Python code implementing the new methods accompanies this article. Hum Brain Mapp 38:1541-1573, 2017. © 2016 Wiley Periodicals, Inc. 2016 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

CIF2Cell: Generating geometries for electronic structure programs

NASA Astrophysics Data System (ADS)

Björkman, Torbjörn

2011-05-01

The CIF2Cell program generates the geometrical setup for a number of electronic structure programs based on the crystallographic information in a Crystallographic Information Framework (CIF) file. The program will retrieve the space group number, Wyckoff positions and crystallographic parameters, make a sensible choice for Bravais lattice vectors (primitive or principal cell) and generate all atomic positions. Supercells can be generated and alloys are handled gracefully. The code currently has output interfaces to the electronic structure programs ABINIT, CASTEP, CPMD, Crystal, Elk, Exciting, EMTO, Fleur, RSPt, Siesta and VASP. Program summaryProgram title: CIF2Cell Catalogue identifier: AEIM_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEIM_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: GNU GPL version 3 No. of lines in distributed program, including test data, etc.: 12 691 No. of bytes in distributed program, including test data, etc.: 74 933 Distribution format: tar.gz Programming language: Python (versions 2.4-2.7) Computer: Any computer that can run Python (versions 2.4-2.7) Operating system: Any operating system that can run Python (versions 2.4-2.7) Classification: 7.3, 7.8, 8 External routines: PyCIFRW [1] Nature of problem: Generate the geometrical setup of a crystallographic cell for a variety of electronic structure programs from data contained in a CIF file. Solution method: The CIF file is parsed using routines contained in the library PyCIFRW [1], and crystallographic as well as bibliographic information is extracted. The program then generates the principal cell from symmetry information, crystal parameters, space group number and Wyckoff sites. Reduction to a primitive cell is then performed, and the resulting cell is output to suitably named files along with documentation of the information source generated from any bibliographic information contained in the CIF file. If the space group symmetries is not present in the CIF file the program will fall back on internal tables, so only the minimal input of space group, crystal parameters and Wyckoff positions are required. Additional key features are handling of alloys and supercell generation. Additional comments: Currently implements support for the following general purpose electronic structure programs: ABINIT [2,3], CASTEP [4], CPMD [5], Crystal [6], Elk [7], exciting [8], EMTO [9], Fleur [10], RSPt [11], Siesta [12] and VASP [13-16]. Running time: The examples provided in the distribution take only seconds to run.

Factors affecting hematology and plasma biochemistry in the southwest carpet python (Morelia spilota imbricata).

PubMed

Bryant, Gillian L; Fleming, Patricia A; Twomey, Leanne; Warren, Kristin A

2012-04-01

Despite increased worldwide popularity of keeping reptiles as pets, we know little about hematologic and biochemical parameters of most reptile species, or how these measures may be influenced by intrinsic and extrinsic factors. Blood samples from 43 wild-caught pythons (Morelia spilota imbricata) were collected at various stages of a 3-yr ecological study in Western Australia. Reference intervals are reported for 35 individuals sampled at the commencement of the study. As pythons were radiotracked for varying lengths of time (radiotransmitters were surgically implanted), repeated sampling was undertaken from some individuals. However, because of our ad hoc sampling design we cannot be definitive about temporal factors that were most important or that exclusively influenced blood parameters. There was no significant effect of sex or the presence of a hemogregarine parasite on blood parameters. Erythrocyte measures were highest for pythons captured in the jarrah forest and at the stage of radiotransmitter implantation, which was also linked with shorter time in captivity. Basophil count, the only leukocyte influenced by the factors tested, was highest when the python was anesthetized, as was globulin concentration. Albumin and the albumin:globulin ratio were more concentrated in summer (as was phosphorous) and at the initial stage of radiotransmitter placement (as was calcium). No intrinsic or extrinsic factors influenced creatinine kinase, aspartate aminotransferase, uric acid, or total protein. This study demonstrates that factors including season, location, surgical radiotransmitter placement, and anesthetic state can influence blood parameters of M. s. imbricata. For accurate diagnosis, veterinarians should be aware that the current reference intervals used to identify the health status of individuals for this species are outdated and the interpretation and an understanding of the influence of intrinsic and extrinsic factors are limited.

First record of invasive Burmese Python oviposition and brooding inside an anthropogenic structure

USGS Publications Warehouse

Hanslowe, Emma; Falk, Bryan; Collier, Michelle A. M.; Josimovich, Jillian; Rahill, Thomas; Reed, Robert

2016-01-01

We discovered an adult female Python bivittatus (Burmese Python) coiled around a clutch of 25 eggs in a cement culvert in Flamingo, FL, in Everglades National Park. To our knowledge, this is the first record of an invasive Burmese Python laying eggs and brooding inside an anthropogenic structure in Florida. A 92% hatch-success rate suggests that the cement culvert provided suitable conditions for oviposition, embryonic development, and hatching. Given the plenitude of such anthropogenic structures across the landscape, available sites for oviposition and brooding may not be limiting for the invasive Burmese Python population.

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.

MACARON: A python framework to identify and re-annotate multi-base affected codons in whole genome/exome sequence data.

PubMed

Khan, Waqasuddin; Saripella, Ganapathi Varma-; Ludwig, Thomas; Cuppens, Tania; Thibord, Florian; Génin, Emmanuelle; Deleuze, Jean-Francois; Trégouët, David-Alexandre

2018-05-03

Predicted deleteriousness of coding variants is a frequently used criterion to filter out variants detected in next-generation sequencing projects and to select candidates impacting on the risk of human diseases. Most available dedicated tools implement a base-to-base annotation approach that could be biased in presence of several variants in the same genetic codon. We here proposed the MACARON program that, from a standard VCF file, identifies, re-annotates and predicts the amino acid change resulting from multiple single nucleotide variants (SNVs) within the same genetic codon. Applied to the whole exome dataset of 573 individuals, MACARON identifies 114 situations where multiple SNVs within a genetic codon induce an amino acid change that is different from those predicted by standard single SNV annotation tool. Such events are not uncommon and deserve to be studied in sequencing projects with inconclusive findings. MACARON is written in python with codes available on the GENMED website (www.genmed.fr). david-alexandre.tregouet@inserm.fr. Supplementary data are available at Bioinformatics online.

QmeQ 1.0: An open-source Python package for calculations of transport through quantum dot devices

NASA Astrophysics Data System (ADS)

Kiršanskas, Gediminas; Pedersen, Jonas Nyvold; Karlström, Olov; Leijnse, Martin; Wacker, Andreas

2017-12-01

QmeQ is an open-source Python package for numerical modeling of transport through quantum dot devices with strong electron-electron interactions using various approximate master equation approaches. The package provides a framework for calculating stationary particle or energy currents driven by differences in chemical potentials or temperatures between the leads which are tunnel coupled to the quantum dots. The electronic structures of the quantum dots are described by their single-particle states and the Coulomb matrix elements between the states. When transport is treated perturbatively to lowest order in the tunneling couplings, the possible approaches are Pauli (classical), first-order Redfield, and first-order von Neumann master equations, and a particular form of the Lindblad equation. When all processes involving two-particle excitations in the leads are of interest, the second-order von Neumann approach can be applied. All these approaches are implemented in QmeQ. We here give an overview of the basic structure of the package, give examples of transport calculations, and outline the range of applicability of the different approximate approaches.

Efficient generation of connectivity in neuronal networks from simulator-independent descriptions

PubMed Central

Djurfeldt, Mikael; Davison, Andrew P.; Eppler, Jochen M.

2014-01-01

Simulator-independent descriptions of connectivity in neuronal networks promise greater ease of model sharing, improved reproducibility of simulation results, and reduced programming effort for computational neuroscientists. However, until now, enabling the use of such descriptions in a given simulator in a computationally efficient way has entailed considerable work for simulator developers, which must be repeated for each new connectivity-generating library that is developed. We have developed a generic connection generator interface that provides a standard way to connect a connectivity-generating library to a simulator, such that one library can easily be replaced by another, according to the modeler's needs. We have used the connection generator interface to connect C++ and Python implementations of the previously described connection-set algebra to the NEST simulator. We also demonstrate how the simulator-independent modeling framework PyNN can transparently take advantage of this, passing a connection description through to the simulator layer for rapid processing in C++ where a simulator supports the connection generator interface and falling-back to slower iteration in Python otherwise. A set of benchmarks demonstrates the good performance of the interface. PMID:24795620

The Landlab v1.0 OverlandFlow component: a Python tool for computing shallow-water flow across watersheds

NASA Astrophysics Data System (ADS)

Adams, Jordan M.; Gasparini, Nicole M.; Hobley, Daniel E. J.; Tucker, Gregory E.; Hutton, Eric W. H.; Nudurupati, Sai S.; Istanbulluoglu, Erkan

2017-04-01

Representation of flowing water in landscape evolution models (LEMs) is often simplified compared to hydrodynamic models, as LEMs make assumptions reducing physical complexity in favor of computational efficiency. The Landlab modeling framework can be used to bridge the divide between complex runoff models and more traditional LEMs, creating a new type of framework not commonly used in the geomorphology or hydrology communities. Landlab is a Python-language library that includes tools and process components that can be used to create models of Earth-surface dynamics over a range of temporal and spatial scales. The Landlab OverlandFlow component is based on a simplified inertial approximation of the shallow water equations, following the solution of de Almeida et al.(2012). This explicit two-dimensional hydrodynamic algorithm simulates a flood wave across a model domain, where water discharge and flow depth are calculated at all locations within a structured (raster) grid. Here, we illustrate how the OverlandFlow component contained within Landlab can be applied as a simplified event-based runoff model and how to couple the runoff model with an incision model operating on decadal timescales. Examples of flow routing on both real and synthetic landscapes are shown. Hydrographs from a single storm at multiple locations in the Spring Creek watershed, Colorado, USA, are illustrated, along with a map of shear stress applied on the land surface by flowing water. The OverlandFlow component can also be coupled with the Landlab DetachmentLtdErosion component to illustrate how the non-steady flow routing regime impacts incision across a watershed. The hydrograph and incision results are compared to simulations driven by steady-state runoff. Results from the coupled runoff and incision model indicate that runoff dynamics can impact landscape relief and channel concavity, suggesting that, on landscape evolution timescales, the OverlandFlow model may lead to differences in simulated topography in comparison with traditional methods. The exploratory test cases described within demonstrate how the OverlandFlow component can be used in both hydrologic and geomorphic applications.

Immunohistochemical Detection of a Unique Protein within Cells of Snakes Having Inclusion Body Disease, a World-Wide Disease Seen in Members of the Families Boidae and Pythonidae

PubMed Central

Chang, Li-Wen; Fu, Ann; Wozniak, Edward; Chow, Marjorie; Duke, Diane G.; Green, Linda; Kelley, Karen; Hernandez, Jorge A.; Jacobson, Elliott R.

2013-01-01

Inclusion body disease (IBD) is a worldwide disease in captive boa constrictors (boa constrictor) and occasionally in other snakes of the families Boidae and Pythonidae. The exact causative agent(s) and pathogenesis are not yet fully understood. Currently, diagnosis of IBD is based on the light microscopic identification of eosinophilic intracytoplasmic inclusion bodies in hematoxylin and eosin stained tissues or blood smears. An antigenically unique 68 KDa protein was identified within the IBD inclusion bodies, called IBD protein. A validated immuno-based ante-mortem diagnostic test is needed for screening snakes that are at risk of having IBD. In this study, despite difficulties in solubilizing semi-purified inclusion bodies, utilizing hybridoma technology a mouse anti-IBD protein monoclonal antibody (MAB) was produced. The antigenic specificity of the antibody was confirmed and validated by western blots, enzyme-linked immunosorbent assay, immuno-transmission electron microscopy, and immunohistochemical staining. Paraffin embedded tissues of IBD positive and negative boa constrictors (n=94) collected from 1990 to 2011 were tested with immunohistochemical staining. In boa constrictors, the anti-IBDP MAB had a sensitivity of 83% and specificity of 100% in detecting IBD. The antibody also cross-reacted with IBD inclusion bodies in carpet pythons (Morelia spilota) and a ball python (python regius). This validated antibody can serve as a tool for the development of ante-mortem immunodiagnostic tests for IBD. PMID:24340066

Immunohistochemical detection of a unique protein within cells of snakes having inclusion body disease, a world-wide disease seen in members of the families Boidae and Pythonidae.

PubMed

Chang, Li-Wen; Fu, Ann; Wozniak, Edward; Chow, Marjorie; Duke, Diane G; Green, Linda; Kelley, Karen; Hernandez, Jorge A; Jacobson, Elliott R

2013-01-01

Inclusion body disease (IBD) is a worldwide disease in captive boa constrictors (boa constrictor) and occasionally in other snakes of the families Boidae and Pythonidae. The exact causative agent(s) and pathogenesis are not yet fully understood. Currently, diagnosis of IBD is based on the light microscopic identification of eosinophilic intracytoplasmic inclusion bodies in hematoxylin and eosin stained tissues or blood smears. An antigenically unique 68 KDa protein was identified within the IBD inclusion bodies, called IBD protein. A validated immuno-based ante-mortem diagnostic test is needed for screening snakes that are at risk of having IBD. In this study, despite difficulties in solubilizing semi-purified inclusion bodies, utilizing hybridoma technology a mouse anti-IBD protein monoclonal antibody (MAB) was produced. The antigenic specificity of the antibody was confirmed and validated by western blots, enzyme-linked immunosorbent assay, immuno-transmission electron microscopy, and immunohistochemical staining. Paraffin embedded tissues of IBD positive and negative boa constrictors (n=94) collected from 1990 to 2011 were tested with immunohistochemical staining. In boa constrictors, the anti-IBDP MAB had a sensitivity of 83% and specificity of 100% in detecting IBD. The antibody also cross-reacted with IBD inclusion bodies in carpet pythons (Morelia spilota) and a ball python (python regius). This validated antibody can serve as a tool for the development of ante-mortem immunodiagnostic tests for IBD.

Simulating pesticide transport in urbanized catchments: a new spatially-distributed dynamic pesticide runoff model

NASA Astrophysics Data System (ADS)

Tang, Ting; Seuntjens, Piet; van Griensven, Ann; Bronders, Jan

2016-04-01

Urban areas can significantly contribute to pesticide contamination in surface water. However, pesticide behaviours in urban areas, particularly on hard surfaces, are far less studied than those in agricultural areas. Pesticide application on hard surfaces (e.g. roadsides and walkways) is of particular concern due to the high imperviousness and therefore high pesticide runoff potential. Experimental studies have shown that pesticide behaviours on and interactions with hard surfaces are important factors controlling the pesticide runoff potential, and therefore the magnitude and timing of peak concentrations in surface water. We conceptualized pesticide behaviours on hard surfaces and incorporated the conceptualization into a new pesticide runoff model. The pesticide runoff model was implemented in a catchment hydrological model WetSpa-Python (Water and Energy Transfer between Soil, Plants and Atmosphere, Python version). The conceptualization for pesticide processes on hard surfaces accounts for the differences in pesticide behaviour on different hard surfaces. Four parameters are used to describe the partitioning and wash-off of each pesticide on hard surfaces. We tested the conceptualization using experimental dataset for five pesticides on two types of hard surfaces, namely concrete and asphalt. The conceptualization gave good performance in accounting for the wash-off pattern for the modelled pesticides and surfaces, according to quantitative evaluations using the Nash-Sutcliffe efficiency and percent bias. The resulting pesticide runoff model WetSpa-PST (WetSpa for PeSTicides) can simulate pesticides and their metabolites at the catchment scale. Overall, it includes four groups of pesticide processes, namely pesticide application, pesticide interception by plant foliage, pesticide processes on land surfaces (including partitioning, degradation and wash-off on hard surface; partitioning, dissipation, infiltration and runoff in soil) and pesticide processes in depression storage (including degradation, infiltration and runoff). Processes on hard surfaces employs the conceptualization described in the paragraph above. The WetSpa-PST model can account for various spatial details of the urban features in a catchment, such as asphalt, concrete and roof areas. The distributed feature also allows users to input detailed pesticide application data of both non-point and point origins. Thanks to the Python modelling framework prototype used in the WetSpa-Python model, processes in the WetSpa-PST model can be simulated at different time steps depending on data availability and the characteristic temporal scale of each process. This helps to increase the computational accuracy during heavy rainfall events, especially for the associated fast transport of pesticides into surface water. Overall, the WetSpa-PST model has good potential in predicting effects of management options on pesticide releases from heavily urbanized catchments.

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.

Pycellerator: an arrow-based reaction-like modelling language for biological simulations.

PubMed

Shapiro, Bruce E; Mjolsness, Eric

2016-02-15

We introduce Pycellerator, a Python library for reading Cellerator arrow notation from standard text files, conversion to differential equations, generating stand-alone Python solvers, and optionally running and plotting the solutions. All of the original Cellerator arrows, which represent reactions ranging from mass action, Michales-Menten-Henri (MMH) and Gene-Regulation (GRN) to Monod-Wyman-Changeaux (MWC), user defined reactions and enzymatic expansions (KMech), were previously represented with the Mathematica extended character set. These are now typed as reaction-like commands in ASCII text files that are read by Pycellerator, which includes a Python command line interface (CLI), a Python application programming interface (API) and an iPython notebook interface. Cellerator reaction arrows are now input in text files. The arrows are parsed by Pycellerator and translated into differential equations in Python, and Python code is automatically generated to solve the system. Time courses are produced by executing the auto-generated Python code. Users have full freedom to modify the solver and utilize the complete set of standard Python tools. The new libraries are completely independent of the old Cellerator software and do not require Mathematica. All software is available (GPL) from the github repository at https://github.com/biomathman/pycellerator/releases. Details, including installation instructions and a glossary of acronyms and terms, are given in the Supplementary information. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

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

ERDC MSRC Resource. High Performance Computing for the Warfighter. Fall 2006

DTIC Science & Technology

2006-01-01

to as Aggregated Combat Modeling, putting us at the campaign level).” Incorporating UIT within DAC The DAC system is written in Python and uses...API calls with two Python classes, UITConnectionFactory and UITConnection. UITConnectionFactory supports Kerberos authentication and establishes a...API calls within these Python classes, we insulated the DAC code from the Python SOAP interface requirements and details of the ERDC MSRC Resource

Stata Hybrids: Updates and Ideas

NASA Technical Reports Server (NTRS)

Fieldler, James

2014-01-01

At last year's Stata conference I presented two projects for using Python with Stata: a plugin that embeds the Python programming language within Stata and code for using Stata data sets in Python. In this talk I will describe some small improvements being made to these projects, and I will present other ideas for combining tools with Stata. Some of these ideas use Python, some use JavaScript and a web browser.

Supersize me: Remains of three white-tailed deer (Odocoileus virginianus) in an invasive Burmese python (Python molurus bivittatus) in Florida

USGS Publications Warehouse

Boback, Scott M.; Snow, Ray W.; Hsu, Teresa; Peurach, Suzanne C.; Dove, Carla J.; Reed, Robert N.

2016-01-01

Snakes have become successful invaders in a wide variety of ecosystems worldwide. In southern Florida, USA, the Burmese python (Python molurus bivittatus) has become established across thousands of square kilometers including all of Everglades National Park (ENP). Both experimental and correlative data have supported a relationship between Burmese python predation and declines or extirpations of mid- to large-sized mammals in ENP. In June 2013 a large python (4.32 m snout-vent length, 48.3 kg) was captured and removed from the park. Subsequent necropsy revealed a massive amount of fecal matter (79 cm in length, 6.5 kg) within the snake’s large intestine. A comparative examination of bone, teeth, and hooves extracted from the fecal contents revealed that this snake consumed three white-tailed deer (Odocoileus virginianus). This is the first report of an invasive Burmese python containing the remains of multiple white-tailed deer in its gut. Because the largest snakes native to southern Florida are not capable of consuming even mid-sized mammals, pythons likely represent a novel predatory threat to white-tailed deer in these habitats. This work highlights the potential impact of this large-bodied invasive snake and supports the need for more work on invasive predator-native prey relationships.

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.

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

Hemodynamic consequences of cardiac malformations in two juvenile ball pythons (Python regius).

PubMed

Jensen, Bjarke; Wang, Tobias

2009-12-01

Two cases of bifid ventricles and cardiac malformations in juvenile ball python (Python regius) were investigated by blood pressure measurements and macro- and microscopic sectioning. A study of a normal ball python was included for reference. In both cases, all cardiac chambers were enlarged and abnormally shaped. Internal assessment of the ventricles revealed a pronounced defect of the muscular ridge, which normally is responsible for separating the systemic and pulmonary circuits. Consistent with the small muscular ridge, systolic pressures were identical in the pulmonary and systemic arteries, but, the snakes, nevertheless, lived to reach body weights severalfold of their hatchling weight.

Practical Approach for Hyperspectral Image Processing in Python

NASA Astrophysics Data System (ADS)

Annala, L.; Eskelinen, M. A.; Hämäläinen, J.; Riihinen, A.; Pölönen, I.

2018-04-01

Python is a very popular programming language among data scientists around the world. Python can also be used in hyperspectral data analysis. There are some toolboxes designed for spectral imaging, such as Spectral Python and HyperSpy, but there is a need for analysis pipeline, which is easy to use and agile for different solutions. We propose a Python pipeline which is built on packages xarray, Holoviews and scikit-learn. We have developed some of own tools, MaskAccessor, VisualisorAccessor and a spectral index library. They also fulfill our goal of easy and agile data processing. In this paper we will present our processing pipeline and demonstrate it in practice.

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.

Pizza.py Toolkit

DOE Office of Scientific and Technical Information (OSTI.GOV)

Plimpton, Steve; Jones, Matt; Crozier, Paul

2006-01-01

Pizza.py is a loosely integrated collection of tools, many of which provide support for the LAMMPS molecular dynamics and ChemCell cell modeling packages. There are tools to create input files. convert between file formats, process log and dump files, create plots, and visualize and animate simulation snapshots. Software packages that are wrapped by Pizza.py. so they can invoked from within Python, include GnuPlot, MatLab, Raster3d. and RasMol. Pizza.py is written in Python and runs on any platform that supports Python. Pizza.py enhances the standard Python interpreter in a few simple ways. Its tools are Python modules which can be invokedmore » interactively, from scripts, or from GUIs when appropriate. Some of the tools require additional Python packages to be installed as part of the users Python. Others are wrappers on software packages (as listed above) which must be available on the users system. It is easy to modify or extend Pizza.py with new functionality or new tools, which need not have anything to do with LAMMPS or ChemCell.« less

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hart, William Eugene

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:more » 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.« less

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

NASA Astrophysics Data System (ADS)

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

2013-12-01

Since 2001, the IRIS Data Management Center (DMC) WILBER II system has provided a convenient web-based interface for locating seismic data related to a particular event, and requesting a subset of that data for download. Since its launch, both the scale of available data and the technology of web-based applications have developed significantly. Wilber 3 is a ground-up redesign that leverages a number of public and open-source projects to provide an event-oriented data request interface with a high level of interactivity and scalability for multiple data types. Wilber 3 uses the IRIS/Federation of Digital Seismic Networks (FDSN) web services for event data, metadata, and time-series data. Combining a carefully optimized Google Map with the highly scalable SlickGrid data API, the Wilber 3 client-side interface can load tens of thousands of events or networks/stations in a single request, and provide instantly responsive browsing, sorting, and filtering of event and meta data in the web browser, without further reliance on the data service. The server-side of Wilber 3 is a Python-Django application, one of over a dozen developed in the last year at IRIS, whose common framework, components, and administrative overhead represent a massive savings in developer resources. Requests for assembled datasets, which may include thousands of data channels and gigabytes of data, are queued and executed using the Celery distributed Python task scheduler, giving Wilber 3 the ability to operate in parallel across a large number of nodes.

pySAPC, a python package for sparse affinity propagation clustering: Application to odontogenesis whole genome time series gene-expression data.

PubMed

Cao, Huojun; Amendt, Brad A

2016-11-01

Developmental dental anomalies are common forms of congenital defects. The molecular mechanisms of dental anomalies are poorly understood. Systematic approaches such as clustering genes based on similar expression patterns could identify novel genes involved in dental anomalies and provide a framework for understanding molecular regulatory mechanisms of these genes during tooth development (odontogenesis). A python package (pySAPC) of sparse affinity propagation clustering algorithm for large datasets was developed. Whole genome pair-wise similarity was calculated based on expression pattern similarity based on 45 microarrays of several stages during odontogenesis. pySAPC identified 743 gene clusters based on expression pattern similarity during mouse tooth development. Three clusters are significantly enriched for genes associated with dental anomalies (with FDR <0.1). The three clusters of genes have distinct expression patterns during odontogenesis. Clustering genes based on similar expression profiles recovered several known regulatory relationships for genes involved in odontogenesis, as well as many novel genes that may be involved with the same genetic pathways as genes that have already been shown to contribute to dental defects. By using sparse similarity matrix, pySAPC use much less memory and CPU time compared with the original affinity propagation program that uses a full similarity matrix. This python package will be useful for many applications where dataset(s) are too large to use full similarity matrix. This article is part of a Special Issue entitled "System Genetics" Guest Editor: Dr. Yudong Cai and Dr. Tao Huang. Copyright © 2016. Published by Elsevier B.V.

The Virtual Brain: a simulator of primate brain network dynamics.

PubMed

Sanz Leon, Paula; Knock, Stuart A; Woodman, M Marmaduke; Domide, Lia; Mersmann, Jochen; McIntosh, Anthony R; Jirsa, Viktor

2013-01-01

We present The Virtual Brain (TVB), a neuroinformatics platform for full brain network simulations using biologically realistic connectivity. This simulation environment enables the model-based inference of neurophysiological mechanisms across different brain scales that underlie the generation of macroscopic neuroimaging signals including functional MRI (fMRI), EEG and MEG. Researchers from different backgrounds can benefit from an integrative software platform including a supporting framework for data management (generation, organization, storage, integration and sharing) and a simulation core written in Python. TVB allows the reproduction and evaluation of personalized configurations of the brain by using individual subject data. This personalization facilitates an exploration of the consequences of pathological changes in the system, permitting to investigate potential ways to counteract such unfavorable processes. The architecture of TVB supports interaction with MATLAB packages, for example, the well known Brain Connectivity Toolbox. TVB can be used in a client-server configuration, such that it can be remotely accessed through the Internet thanks to its web-based HTML5, JS, and WebGL graphical user interface. TVB is also accessible as a standalone cross-platform Python library and application, and users can interact with the scientific core through the scripting interface IDLE, enabling easy modeling, development and debugging of the scientific kernel. This second interface makes TVB extensible by combining it with other libraries and modules developed by the Python scientific community. In this article, we describe the theoretical background and foundations that led to the development of TVB, the architecture and features of its major software components as well as potential neuroscience applications.

The Virtual Brain: a simulator of primate brain network dynamics

PubMed Central

Sanz Leon, Paula; Knock, Stuart A.; Woodman, M. Marmaduke; Domide, Lia; Mersmann, Jochen; McIntosh, Anthony R.; Jirsa, Viktor

2013-01-01

We present The Virtual Brain (TVB), a neuroinformatics platform for full brain network simulations using biologically realistic connectivity. This simulation environment enables the model-based inference of neurophysiological mechanisms across different brain scales that underlie the generation of macroscopic neuroimaging signals including functional MRI (fMRI), EEG and MEG. Researchers from different backgrounds can benefit from an integrative software platform including a supporting framework for data management (generation, organization, storage, integration and sharing) and a simulation core written in Python. TVB allows the reproduction and evaluation of personalized configurations of the brain by using individual subject data. This personalization facilitates an exploration of the consequences of pathological changes in the system, permitting to investigate potential ways to counteract such unfavorable processes. The architecture of TVB supports interaction with MATLAB packages, for example, the well known Brain Connectivity Toolbox. TVB can be used in a client-server configuration, such that it can be remotely accessed through the Internet thanks to its web-based HTML5, JS, and WebGL graphical user interface. TVB is also accessible as a standalone cross-platform Python library and application, and users can interact with the scientific core through the scripting interface IDLE, enabling easy modeling, development and debugging of the scientific kernel. This second interface makes TVB extensible by combining it with other libraries and modules developed by the Python scientific community. In this article, we describe the theoretical background and foundations that led to the development of TVB, the architecture and features of its major software components as well as potential neuroscience applications. PMID:23781198

A fast and efficient python library for interfacing with the Biological Magnetic Resonance Data Bank.

PubMed

Smelter, Andrey; Astra, Morgan; Moseley, Hunter N B

2017-03-17

The Biological Magnetic Resonance Data Bank (BMRB) is a public repository of Nuclear Magnetic Resonance (NMR) spectroscopic data of biological macromolecules. It is an important resource for many researchers using NMR to study structural, biophysical, and biochemical properties of biological macromolecules. It is primarily maintained and accessed in a flat file ASCII format known as NMR-STAR. While the format is human readable, the size of most BMRB entries makes computer readability and explicit representation a practical requirement for almost any rigorous systematic analysis. To aid in the use of this public resource, we have developed a package called nmrstarlib in the popular open-source programming language Python. The nmrstarlib's implementation is very efficient, both in design and execution. The library has facilities for reading and writing both NMR-STAR version 2.1 and 3.1 formatted files, parsing them into usable Python dictionary- and list-based data structures, making access and manipulation of the experimental data very natural within Python programs (i.e. "saveframe" and "loop" records represented as individual Python dictionary data structures). Another major advantage of this design is that data stored in original NMR-STAR can be easily converted into its equivalent JavaScript Object Notation (JSON) format, a lightweight data interchange format, facilitating data access and manipulation using Python and any other programming language that implements a JSON parser/generator (i.e., all popular programming languages). We have also developed tools to visualize assigned chemical shift values and to convert between NMR-STAR and JSONized NMR-STAR formatted files. Full API Reference Documentation, User Guide and Tutorial with code examples are also available. We have tested this new library on all current BMRB entries: 100% of all entries are parsed without any errors for both NMR-STAR version 2.1 and version 3.1 formatted files. We also compared our software to three currently available Python libraries for parsing NMR-STAR formatted files: PyStarLib, NMRPyStar, and PyNMRSTAR. The nmrstarlib package is a simple, fast, and efficient library for accessing data from the BMRB. The library provides an intuitive dictionary-based interface with which Python programs can read, edit, and write NMR-STAR formatted files and their equivalent JSONized NMR-STAR files. The nmrstarlib package can be used as a library for accessing and manipulating data stored in NMR-STAR files and as a command-line tool to convert from NMR-STAR file format into its equivalent JSON file format and vice versa, and to visualize chemical shift values. Furthermore, the nmrstarlib implementation provides a guide for effectively JSONizing other older scientific formats, improving the FAIRness of data in these formats.

Database of extended radiation maps and its access system

NASA Astrophysics Data System (ADS)

Verkhodanov, O. V.; Naiden, Ya. V.; Chernenkov, V. N.; Verkhodanova, N. V.

2014-01-01

We describe the architecture of the developed computing web server http://cmb.sao.ru allowing to synthesize the maps of extended radiation on the full sphere from the spherical harmonics in the GLESP pixelization grid, smooth them with the power beam pattern with various angular resolutions in the multipole space, and identify regions of the sky with given coordinates. We describe the server access and administration systems as well as the technique constructing the sky region maps, organized in Python in the Django web-application development framework.

Polyglot Programming in Applications Used for Genetic Data Analysis

PubMed Central

Nowak, Robert M.

2014-01-01

Applications used for the analysis of genetic data process large volumes of data with complex algorithms. High performance, flexibility, and a user interface with a web browser are required by these solutions, which can be achieved by using multiple programming languages. In this study, I developed a freely available framework for building software to analyze genetic data, which uses C++, Python, JavaScript, and several libraries. This system was used to build a number of genetic data processing applications and it reduced the time and costs of development. PMID:25197633

Polyglot programming in applications used for genetic data analysis.

PubMed

Nowak, Robert M

2014-01-01

Applications used for the analysis of genetic data process large volumes of data with complex algorithms. High performance, flexibility, and a user interface with a web browser are required by these solutions, which can be achieved by using multiple programming languages. In this study, I developed a freely available framework for building software to analyze genetic data, which uses C++, Python, JavaScript, and several libraries. This system was used to build a number of genetic data processing applications and it reduced the time and costs of development.

LMC: Logarithmantic Monte Carlo

NASA Astrophysics Data System (ADS)

Mantz, Adam B.

2017-06-01

LMC is a Markov Chain Monte Carlo engine in Python that implements adaptive Metropolis-Hastings and slice sampling, as well as the affine-invariant method of Goodman & Weare, in a flexible framework. It can be used for simple problems, but the main use case is problems where expensive likelihood evaluations are provided by less flexible third-party software, which benefit from parallelization across many nodes at the sampling level. The parallel/adaptive methods use communication through MPI, or alternatively by writing/reading files, and mostly follow the approaches pioneered by CosmoMC (ascl:1106.025).

Renaissance of the Web

NASA Astrophysics Data System (ADS)

McCarty, M.

2009-09-01

The renaissance of the web has driven development of many new technologies that have forever changed the way we write software. The resulting tools have been applied to both solve problems and creat new ones in a wide range of domains ranging from monitor and control user interfaces to information distribution. This discussion covers which of and how these technologies are being used in the astronomical computing community. Topics include JavaScript, Cascading Style Sheets, HTML, XML, JSON, RSS, iCalendar, Java, PHP, Python, Ruby on Rails, database technologies, and web frameworks/design patterns.

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)....

Automating Disk Forensic Processing with SleuthKit, XML and Python

DTIC Science & Technology

2009-05-01

1 Automating Disk Forensic Processing with SleuthKit, XML and Python Simson L. Garfinkel Abstract We have developed a program called fiwalk which...files themselves. We show how it is relatively simple to create automated disk forensic applications using a Python module we have written that reads...software that the portable device may contain. Keywords: Computer Forensics; XML; Sleuth Kit; Python I. INTRODUCTION In recent years we have found many

Pybel: a Python wrapper for the OpenBabel cheminformatics toolkit

PubMed Central

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

2008-01-01

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

Pybel: a Python wrapper for the OpenBabel cheminformatics toolkit.

PubMed

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

2008-03-09

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

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.

Cross-species 3D virtual reality toolbox for visual and cognitive experiments.

PubMed

Doucet, Guillaume; Gulli, Roberto A; Martinez-Trujillo, Julio C

2016-06-15

Although simplified visual stimuli, such as dots or gratings presented on homogeneous backgrounds, provide strict control over the stimulus parameters during visual experiments, they fail to approximate visual stimulation in natural conditions. Adoption of virtual reality (VR) in neuroscience research has been proposed to circumvent this problem, by combining strict control of experimental variables and behavioral monitoring within complex and realistic environments. We have created a VR toolbox that maximizes experimental flexibility while minimizing implementation costs. A free VR engine (Unreal 3) has been customized to interface with any control software via text commands, allowing seamless introduction into pre-existing laboratory data acquisition frameworks. Furthermore, control functions are provided for the two most common programming languages used in visual neuroscience: Matlab and Python. The toolbox offers milliseconds time resolution necessary for electrophysiological recordings and is flexible enough to support cross-species usage across a wide range of paradigms. Unlike previously proposed VR solutions whose implementation is complex and time-consuming, our toolbox requires minimal customization or technical expertise to interface with pre-existing data acquisition frameworks as it relies on already familiar programming environments. Moreover, as it is compatible with a variety of display and input devices, identical VR testing paradigms can be used across species, from rodents to humans. This toolbox facilitates the addition of VR capabilities to any laboratory without perturbing pre-existing data acquisition frameworks, or requiring any major hardware changes. Copyright © 2016 Z. All rights reserved.

Evaluating GPS biologging technology for studying spatial ecology of large constricting snakes

USGS Publications Warehouse

Smith, Brian; Hart, Kristen M.; Mazzotti, Frank J.; Basille, Mathieu; Romagosa, Christina M.

2018-01-01

Background: GPS telemetry has revolutionized the study of animal spatial ecology in the last two decades. Until recently, it has mainly been deployed on large mammals and birds, but the technology is rapidly becoming miniaturized, and applications in diverse taxa are becoming possible. Large constricting snakes are top predators in their ecosystems, and accordingly they are often a management priority, whether their populations are threatened or invasive. Fine-scale GPS tracking datasets could greatly improve our ability to understand and manage these snakes, but the ability of this new technology to deliver high-quality data in this system is unproven. In order to evaluate GPS technology in large constrictors, we GPS-tagged 13 Burmese pythons (Python bivittatus) in Everglades National Park and deployed an additional 7 GPS tags on stationary platforms to evaluate habitat-driven biases in GPS locations. Both python and test platform GPS tags were programmed to attempt a GPS fix every 90 min.Results: While overall fix rates for the tagged pythons were low (18.1%), we were still able to obtain an average of 14.5 locations/animal/week, a large improvement over once-weekly VHF tracking. We found overall accuracy and precision to be very good (mean accuracy = 7.3 m, mean precision = 12.9 m), but a very few imprecise locations were still recorded (0.2% of locations with precision > 1.0 km). We found that dense vegetation did decrease fix rate, but we concluded that the low observed fix rate was also due to python microhabitat selection underground or underwater. Half of our recovered pythons were either missing their tag or the tag had malfunctioned, resulting in no data being recovered.Conclusions: GPS biologging technology is a promising tool for obtaining frequent, accurate, and precise locations of large constricting snakes. We recommend future studies couple GPS telemetry with frequent VHF locations in order to reduce bias and limit the impact of catastrophic failures on data collection, and we recommend improvements to GPS tag design to lessen the frequency of these failures.

PyPWA: A partial-wave/amplitude analysis software framework

NASA Astrophysics Data System (ADS)

Salgado, Carlos

2016-05-01

The PyPWA project aims to develop a software framework for Partial Wave and Amplitude Analysis of data; providing the user with software tools to identify resonances from multi-particle final states in photoproduction. Most of the code is written in Python. The software is divided into two main branches: one general-shell where amplitude's parameters (or any parametric model) are to be estimated from the data. This branch also includes software to produce simulated data-sets using the fitted amplitudes. A second branch contains a specific realization of the isobar model (with room to include Deck-type and other isobar model extensions) to perform PWA with an interface into the computer resources at Jefferson Lab. We are currently implementing parallelism and vectorization using the Intel's Xeon Phi family of coprocessors.

PyEEG: an open source Python module for EEG/MEG feature extraction.

PubMed

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

2011-01-01

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

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

PubMed Central

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

2011-01-01

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

A Browser-Based Multi-User Working Environment for Physicists

NASA Astrophysics Data System (ADS)

Erdmann, M.; Fischer, R.; Glaser, C.; Klingebiel, D.; Komm, M.; Müller, G.; Rieger, M.; Steggemann, J.; Urban, M.; Winchen, T.

2014-06-01

Many programs in experimental particle physics do not yet have a graphical interface, or demand strong platform and software requirements. With the most recent development of the VISPA project, we provide graphical interfaces to existing software programs and access to multiple computing clusters through standard web browsers. The scalable clientserver system allows analyses to be performed in sizable teams, and disburdens the individual physicist from installing and maintaining a software environment. The VISPA graphical interfaces are implemented in HTML, JavaScript and extensions to the Python webserver. The webserver uses SSH and RPC to access user data, code and processes on remote sites. As example applications we present graphical interfaces for steering the reconstruction framework OFFLINE of the Pierre-Auger experiment, and the analysis development toolkit PXL. The browser based VISPA system was field-tested in biweekly homework of a third year physics course by more than 100 students. We discuss the system deployment and the evaluation by the students.

Bayesian ISOLA: new tool for automated centroid moment tensor inversion

NASA Astrophysics Data System (ADS)

Vackář, Jiří; Burjánek, Jan; Gallovič, František; Zahradník, Jiří; Clinton, John

2017-08-01

We have developed a new, fully automated tool for the centroid moment tensor (CMT) inversion in a Bayesian framework. It includes automated data retrieval, data selection where station components with various instrumental disturbances are rejected and full-waveform inversion in a space-time grid around a provided hypocentre. A data covariance matrix calculated from pre-event noise yields an automated weighting of the station recordings according to their noise levels and also serves as an automated frequency filter suppressing noisy frequency ranges. The method is tested on synthetic and observed data. It is applied on a data set from the Swiss seismic network and the results are compared with the existing high-quality MT catalogue. The software package programmed in Python is designed to be as versatile as possible in order to be applicable in various networks ranging from local to regional. The method can be applied either to the everyday network data flow, or to process large pre-existing earthquake catalogues and data sets.

SeisFlows-Flexible waveform inversion software

NASA Astrophysics Data System (ADS)

Modrak, Ryan T.; Borisov, Dmitry; Lefebvre, Matthieu; Tromp, Jeroen

2018-06-01

SeisFlows is an open source Python package that provides a customizable waveform inversion workflow and framework for research in oil and gas exploration, earthquake tomography, medical imaging, and other areas. New methods can be rapidly prototyped in SeisFlows by inheriting from default inversion or migration classes, and code can be tested on 2D examples before application to more expensive 3D problems. Wave simulations must be performed using an external software package such as SPECFEM3D. The ability to interface with external solvers lends flexibility, and the choice of SPECFEM3D as a default option provides optional GPU acceleration and other useful capabilities. Through support for massively parallel solvers and interfaces for high-performance computing (HPC) systems, inversions with thousands of seismic traces and billions of model parameters can be performed. So far, SeisFlows has run on clusters managed by the Department of Defense, Chevron Corp., Total S.A., Princeton University, and the University of Alaska, Fairbanks.

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.

A flexible, interactive software tool for fitting the parameters of neuronal models.

PubMed

Friedrich, Péter; Vella, Michael; Gulyás, Attila I; Freund, Tamás F; Káli, Szabolcs

2014-01-01

The construction of biologically relevant neuronal models as well as model-based analysis of experimental data often requires the simultaneous fitting of multiple model parameters, so that the behavior of the model in a certain paradigm matches (as closely as possible) the corresponding output of a real neuron according to some predefined criterion. Although the task of model optimization is often computationally hard, and the quality of the results depends heavily on technical issues such as the appropriate choice (and implementation) of cost functions and optimization algorithms, no existing program provides access to the best available methods while also guiding the user through the process effectively. Our software, called Optimizer, implements a modular and extensible framework for the optimization of neuronal models, and also features a graphical interface which makes it easy for even non-expert users to handle many commonly occurring scenarios. Meanwhile, educated users can extend the capabilities of the program and customize it according to their needs with relatively little effort. Optimizer has been developed in Python, takes advantage of open-source Python modules for nonlinear optimization, and interfaces directly with the NEURON simulator to run the models. Other simulators are supported through an external interface. We have tested the program on several different types of problems of varying complexity, using different model classes. As targets, we used simulated traces from the same or a more complex model class, as well as experimental data. We successfully used Optimizer to determine passive parameters and conductance densities in compartmental models, and to fit simple (adaptive exponential integrate-and-fire) neuronal models to complex biological data. Our detailed comparisons show that Optimizer can handle a wider range of problems, and delivers equally good or better performance than any other existing neuronal model fitting tool.

A flexible, interactive software tool for fitting the parameters of neuronal models

PubMed Central

Friedrich, Péter; Vella, Michael; Gulyás, Attila I.; Freund, Tamás F.; Káli, Szabolcs

2014-01-01

The construction of biologically relevant neuronal models as well as model-based analysis of experimental data often requires the simultaneous fitting of multiple model parameters, so that the behavior of the model in a certain paradigm matches (as closely as possible) the corresponding output of a real neuron according to some predefined criterion. Although the task of model optimization is often computationally hard, and the quality of the results depends heavily on technical issues such as the appropriate choice (and implementation) of cost functions and optimization algorithms, no existing program provides access to the best available methods while also guiding the user through the process effectively. Our software, called Optimizer, implements a modular and extensible framework for the optimization of neuronal models, and also features a graphical interface which makes it easy for even non-expert users to handle many commonly occurring scenarios. Meanwhile, educated users can extend the capabilities of the program and customize it according to their needs with relatively little effort. Optimizer has been developed in Python, takes advantage of open-source Python modules for nonlinear optimization, and interfaces directly with the NEURON simulator to run the models. Other simulators are supported through an external interface. We have tested the program on several different types of problems of varying complexity, using different model classes. As targets, we used simulated traces from the same or a more complex model class, as well as experimental data. We successfully used Optimizer to determine passive parameters and conductance densities in compartmental models, and to fit simple (adaptive exponential integrate-and-fire) neuronal models to complex biological data. Our detailed comparisons show that Optimizer can handle a wider range of problems, and delivers equally good or better performance than any other existing neuronal model fitting tool. PMID:25071540

PlasmaPy: initial development of a Python package for plasma physics

NASA Astrophysics Data System (ADS)

Murphy, Nicholas; Leonard, Andrew J.; Stańczak, Dominik; Haggerty, Colby C.; Parashar, Tulasi N.; Huang, Yu-Min; PlasmaPy Community

2017-10-01

We report on initial development of PlasmaPy: an open source community-driven Python package for plasma physics. PlasmaPy seeks to provide core functionality that is needed for the formation of a fully open source Python ecosystem for plasma physics. PlasmaPy prioritizes code readability, consistency, and maintainability while using best practices for scientific computing such as version control, continuous integration testing, embedding documentation in code, and code review. We discuss our current and planned capabilities, including features presently under development. The development roadmap includes features such as fluid and particle simulation capabilities, a Grad-Shafranov solver, a dispersion relation solver, atomic data retrieval methods, and tools to analyze simulations and experiments. We describe several ways to contribute to PlasmaPy. PlasmaPy has a code of conduct and is being developed under a BSD license, with a version 0.1 release planned for 2018. The success of PlasmaPy depends on active community involvement, so anyone interested in contributing to this project should contact the authors. This work was partially supported by the U.S. Department of Energy.

Programming biological models in Python using PySB.

PubMed

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.

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

Pyviko: an automated Python tool to design gene knockouts in complex viruses with overlapping genes.

PubMed

Taylor, Louis J; Strebel, Klaus

2017-01-07

Gene knockouts are a common tool used to study gene function in various organisms. However, designing gene knockouts is complicated in viruses, which frequently contain sequences that code for multiple overlapping genes. Designing mutants that can be traced by the creation of new or elimination of existing restriction sites further compounds the difficulty in experimental design of knockouts of overlapping genes. While software is available to rapidly identify restriction sites in a given nucleotide sequence, no existing software addresses experimental design of mutations involving multiple overlapping amino acid sequences in generating gene knockouts. Pyviko performed well on a test set of over 240,000 gene pairs collected from viral genomes deposited in the National Center for Biotechnology Information Nucleotide database, identifying a point mutation which added a premature stop codon within the first 20 codons of the target gene in 93.2% of all tested gene-overprinted gene pairs. This shows that Pyviko can be used successfully in a wide variety of contexts to facilitate the molecular cloning and study of viral overprinted genes. Pyviko is an extensible and intuitive Python tool for designing knockouts of overlapping genes. Freely available as both a Python package and a web-based interface ( http://louiejtaylor.github.io/pyViKO/ ), Pyviko simplifies the experimental design of gene knockouts in complex viruses with overlapping genes.

pyres: a Python wrapper for electrical resistivity modeling with R2

NASA Astrophysics Data System (ADS)

Befus, Kevin M.

2018-04-01

A Python package, pyres, was written to handle common as well as specialized input and output tasks for the R2 electrical resistivity (ER) modeling program. Input steps including handling field data, creating quadrilateral or triangular meshes, and data filtering allow repeatable and flexible ER modeling within a programming environment. pyres includes non-trivial routines and functions for locating and constraining specific known or separately-parameterized regions in both quadrilateral and triangular meshes. Three basic examples of how to run forward and inverse models with pyres are provided. The importance of testing mesh convergence and model sensitivity are also addressed with higher-level examples that show how pyres can facilitate future research-grade ER analyses.

Urbanization may limit impacts of an invasive predator on native mammal diversity

USGS Publications Warehouse

Reichert, Brian E.; Sovie, Adia R.; Udell, Brad J.; Hart, Kristen M.; Borkhataria, Rena R.; Bonneau, Mathieu; Reed, Robert; McCleery, Robert A.

2017-01-01

AimOur understanding of the effects of invasive species on faunal diversity is limited in part because invasions often occur in modified landscapes where other drivers of community diversity can exacerbate or reduce the net impacts of an invader. Furthermore, rigorous assessments of the effects of invasive species on native communities that account for variation in sampling, species-specific detection and occurrence of rare species are lacking. Invasive Burmese pythons (Python molurus bivittatus) may be causing declines in medium- to large-sized mammals throughout the Greater Everglades Ecosystem (GEE); however, other factors such as urbanization, habitat changes and drastic alteration in water flow may also be influential in structuring mammal communities. The aim of this study was to gain an understanding of how mammal communities simultaneously facing invasive predators and intensively human-altered landscapes are influenced by these drivers and their interactions.LocationFlorida, USA.MethodsWe used data from trail cameras and scat searches with a hierarchical community model that accounts for undetected species to determine the relative influence of introduced Burmese pythons, urbanization, local hydrology, habitat types and interactive effects between pythons and urbanization on mammal species occurrence, site-level species richness, and turnover.ResultsPython density had significant negative effects on all species except coyotes. Despite these negative effects, occurrence of some generalist species increased significantly near urban areas. At the community level, pythons had the greatest impact on species richness, while turnover was greatest along the urbanization gradient where communities were increasingly similar as distance to urbanization decreased.Main conclusionsWe found evidence for an antagonistic interaction between pythons and urbanization where the impacts of pythons were reduced near urban development. Python-induced changes to mammal communities may be mediated near urban development, but elsewhere in the GEE, pythons are likely causing a fundamental restructuring of the food web, declines in ecosystem function, and creating complex and unpredictable cascading effects.

Pythons metabolize prey to fuel the response to feeding.

PubMed Central

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

2004-01-01

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

Pynamic: the Python Dynamic Benchmark

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lee, G L; Ahn, D H; de Supinksi, B R

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, wemore » 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.« less

Accelerating wave propagation modeling in the frequency domain using Python

NASA Astrophysics Data System (ADS)

Jo, Sang Hoon; Park, Min Jun; Ha, Wan Soo

2017-04-01

Python is a dynamic programming language adopted in many science and engineering areas. We used Python to simulate wave propagation in the frequency domain. We used the Pardiso matrix solver to solve the impedance matrix of the wave equation. Numerical examples shows that Python with numpy consumes longer time to construct the impedance matrix using the finite element method when compared with Fortran; however we could reduce the time significantly to be comparable to that of Fortran using a simple Numba decorator.

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.

CyNEST: a maintainable Cython-based interface for the NEST simulator

PubMed Central

Zaytsev, Yury V.; Morrison, Abigail

2014-01-01

NEST is a simulator for large-scale networks of spiking point neuron models (Gewaltig and Diesmann, 2007). Originally, simulations were controlled via the Simulation Language Interpreter (SLI), a built-in scripting facility implementing a language derived from PostScript (Adobe Systems, Inc., 1999). The introduction of PyNEST (Eppler et al., 2008), the Python interface for NEST, enabled users to control simulations using Python. As the majority of NEST users found PyNEST easier to use and to combine with other applications, it immediately displaced SLI as the default NEST interface. However, developing and maintaining PyNEST has become increasingly difficult over time. This is partly because adding new features requires writing low-level C++ code intermixed with calls to the Python/C API, which is unrewarding. Moreover, the Python/C API evolves with each new version of Python, which results in a proliferation of version-dependent code branches. In this contribution we present the re-implementation of PyNEST in the Cython language, a superset of Python that additionally supports the declaration of C/C++ types for variables and class attributes, and provides a convenient foreign function interface (FFI) for invoking C/C++ routines (Behnel et al., 2011). Code generation via Cython allows the production of smaller and more maintainable bindings, including increased compatibility with all supported Python releases without additional burden for NEST developers. Furthermore, this novel approach opens up the possibility to support alternative implementations of the Python language at no cost given a functional Cython back-end for the corresponding implementation, and also enables cross-compilation of Python bindings for embedded systems and supercomputers alike. PMID:24672470

Differential Disease Susceptibilities in Experimentally Reptarenavirus-Infected Boa Constrictors and Ball Pythons

PubMed Central

Sanchez-Migallon Guzman, David; Garcia, Valentina E.; Layton, Marylee L.; Hoon-Hanks, Laura L.; Boback, Scott M.; Keel, M. Kevin; Drazenovich, Tracy

2017-01-01

ABSTRACT Inclusion body disease (IBD) is an infectious disease originally described in captive snakes. It has traditionally been diagnosed by the presence of large eosinophilic cytoplasmic inclusions and is associated with neurological, gastrointestinal, and lymphoproliferative disorders. Previously, we identified and established a culture system for a novel lineage of arenaviruses isolated from boa constrictors diagnosed with IBD. Although ample circumstantial evidence suggested that these viruses, now known as reptarenaviruses, cause IBD, there has been no formal demonstration of disease causality since their discovery. We therefore conducted a long-term challenge experiment to test the hypothesis that reptarenaviruses cause IBD. We infected boa constrictors and ball pythons by cardiac injection of purified virus. We monitored the progression of viral growth in tissues, blood, and environmental samples. Infection produced dramatically different disease outcomes in snakes of the two species. Ball pythons infected with Golden Gate virus (GoGV) and with another reptarenavirus displayed severe neurological signs within 2 months, and viral replication was detected only in central nervous system tissues. In contrast, GoGV-infected boa constrictors remained free of clinical signs for 2 years, despite high viral loads and the accumulation of large intracellular inclusions in multiple tissues, including the brain. Inflammation was associated with infection in ball pythons but not in boa constrictors. Thus, reptarenavirus infection produces inclusions and inclusion body disease, although inclusions per se are neither necessarily associated with nor required for disease. Although the natural distribution of reptarenaviruses has yet to be described, the different outcomes of infection may reflect differences in geographical origin. IMPORTANCE New DNA sequencing technologies have made it easier than ever to identify the sequences of microorganisms in diseased tissues, i.e., to identify organisms that appear to cause disease, but to be certain that a candidate pathogen actually causes disease, it is necessary to provide additional evidence of causality. We have done this to demonstrate that reptarenaviruses cause inclusion body disease (IBD), a serious transmissible disease of snakes. We infected boa constrictors and ball pythons with purified reptarenavirus. Ball pythons fell ill within 2 months of infection and displayed signs of neurological disease typical of IBD. In contrast, boa constrictors remained healthy over 2 years, despite high levels of virus throughout their bodies. This difference matches previous reports that pythons are more susceptible to IBD than boas and could reflect the possibility that boas are natural hosts of these viruses in the wild. PMID:28515291

The CMIP5 Model Documentation Questionnaire: Development of a Metadata Retrieval System for the METAFOR Common Information Model

NASA Astrophysics Data System (ADS)

Pascoe, Charlotte; Lawrence, Bryan; Moine, Marie-Pierre; Ford, Rupert; Devine, Gerry

2010-05-01

The EU METAFOR Project (http://metaforclimate.eu) has created a web-based model documentation questionnaire to collect metadata from the modelling groups that are running simulations in support of the Coupled Model Intercomparison Project - 5 (CMIP5). The CMIP5 model documentation questionnaire will retrieve information about the details of the models used, how the simulations were carried out, how the simulations conformed to the CMIP5 experiment requirements and details of the hardware used to perform the simulations. The metadata collected by the CMIP5 questionnaire will allow CMIP5 data to be compared in a scientifically meaningful way. This paper describes the life-cycle of the CMIP5 questionnaire development which starts with relatively unstructured input from domain specialists and ends with formal XML documents that comply with the METAFOR Common Information Model (CIM). Each development step is associated with a specific tool. (1) Mind maps are used to capture information requirements from domain experts and build a controlled vocabulary, (2) a python parser processes the XML files generated by the mind maps, (3) Django (python) is used to generate the dynamic structure and content of the web based questionnaire from processed xml and the METAFOR CIM, (4) Python parsers ensure that information entered into the CMIP5 questionnaire is output as CIM compliant xml, (5) CIM compliant output allows automatic information capture tools to harvest questionnaire content into databases such as the Earth System Grid (ESG) metadata catalogue. This paper will focus on how Django (python) and XML input files are used to generate the structure and content of the CMIP5 questionnaire. It will also address how the choice of development tools listed above provided a framework that enabled working scientists (who we would never ordinarily get to interact with UML and XML) to be part the iterative development process and ensure that the CMIP5 model documentation questionnaire reflects what scientists want to know about the models. Keywords: metadata, CMIP5, automatic information capture, tool development

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

Morley, Steven K; Welling, Daniel T; Koller, Josef

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

Towards a Software Framework to Support Deployment of Low Cost End-to-End Hydroclimatological Sensor Network

NASA Astrophysics Data System (ADS)

Celicourt, P.; Piasecki, M.

2015-12-01

Deployment of environmental sensors assemblies based on cheap platforms such as Raspberry Pi and Arduino have gained much attention over the past few years. While they are more attractive due to their ability to be controlled with a few programming language choices, the configuration task can become quite complex due to the need of having to learn several different proprietary data formats and protocols which constitute a bottleneck for the expansion of sensor network. In response to this rising complexity the Institute of Electrical and Electronics Engineers (IEEE) has sponsored the development of the IEEE 1451 standard in an attempt to introduce a common standard. The most innovative concept of the standard is the Transducer Electronic Data Sheet (TEDS) which enables transducers to self-identify, self-describe, self-calibrate, to exhibit plug-and-play functionality, etc. We used Python to develop an IEEE 1451.0 platform-independent graphical user interface to generate and provide sufficient information about almost ANY sensor and sensor platforms for sensor programming purposes, automatic calibration of sensors data, incorporation of back-end demands on data management in TEDS for automatic standard-based data storage, search and discovery purposes. These features are paramount to make data management much less onerous in large scale sensor network. Along with the TEDS Creator, we developed a tool namely HydroUnits for three specific purposes: encoding of physical units in the TEDS, dimensional analysis, and on-the-fly conversion of time series allowing users to retrieve data in a desired equivalent unit while accommodating unforeseen and user-defined units. In addition, our back-end data management comprises the Python/Django equivalent of the CUAHSI Observations Data Model (ODM) namely DjangODM that will be hosted by a MongoDB Database Server which offers more convenience for our application. We are also developing a data which will be paired with the data autoloading capability of Django and a TEDS processing script to populate the database with the incoming data. The Python WaterOneFlow Web Services developed by the Texas Water Development Board will be used to publish the data. The software suite is being tested on the Raspberry Pi as end node and a laptop PC as the base station in a wireless setting.

GIS methodology for geothermal play fairway analysis: Example from the Snake River Plain volcanic province

USGS Publications Warehouse

DeAngelo, Jacob; Shervais, John W.; Glen, Jonathan; Nielson, Dennis L.; Garg, Sabodh; Dobson, Patrick; Gasperikova, Erika; Sonnenthal, Eric; Visser, Charles; Liberty, Lee M.; Siler, Drew; Evans, James P.; Santellanes, Sean

2016-01-01

Play fairway analysis in geothermal exploration derives from a systematic methodology originally developed within the petroleum industry and is based on a geologic and hydrologic framework of identified geothermal systems. We are tailoring this methodology to study the geothermal resource potential of the Snake River Plain and surrounding region. This project has contributed to the success of this approach by cataloging the critical elements controlling exploitable hydrothermal systems, establishing risk matrices that evaluate these elements in terms of both probability of success and level of knowledge, and building automated tools to process results. ArcGIS was used to compile a range of different data types, which we refer to as ‘elements’ (e.g., faults, vents, heatflow…), with distinct characteristics and confidence values. Raw data for each element were transformed into data layers with a common format. Because different data types have different uncertainties, each evidence layer had an accompanying confidence layer, which reflects spatial variations in these uncertainties. Risk maps represent the product of evidence and confidence layers, and are the basic building blocks used to construct Common Risk Segment (CRS) maps for heat, permeability, and seal. CRS maps quantify the variable risk associated with each of these critical components. In a final step, the three CRS maps were combined into a Composite Common Risk Segment (CCRS) map for analysis that reveals favorable areas for geothermal exploration. Python scripts were developed to automate data processing and to enhance the flexibility of the data analysis. Python scripting provided the structure that makes a custom workflow possible. Nearly every tool available in the ArcGIS ArcToolbox can be executed using commands in the Python programming language. This enabled the construction of a group of tools that could automate most of the processing for the project. Currently, our tools are repeatable, scalable, modifiable, and transferrable, allowing us to automate the task of data analysis and the production of CRS and CCRS maps. Our ultimate goal is to produce a toolkit that can be imported into ArcGIS and applied to any geothermal play type, with fully tunable parameters that will allow for the production of multiple versions of the CRS and CCRS maps in order to better test for sensitivity and to validate results.

Tachycardia in response to remote capsaicin injection as a model for nociception in the ball python (Python regius).

PubMed

Williams, Catherine J A; James, Lauren E; Bertelsen, Mads F; Wang, Tobias

2016-07-01

To quantify the effect of subcutaneous (SC) capsaicin injection on heart rate (HR) in ball pythons (Python regius) and to assess the efficacy of two opioids (morphine and butorphanol) in modifying this response. Prospective, randomized, unmatched study. Eleven mixed-sex, captive-bred ball pythons. Snakes were randomly assigned to three groups (n = 6) by intramuscular premedication: 1) control: saline (0.9 mL); 2) morphine (10 mg kg(-1) ); and 3) butorphanol (10 mg kg(-1) ). Three snakes were tested twice and another two were tested three times in different treatments administered 1 month apart. Under isoflurane anaesthesia, snakes were instrumented with SC electrocardiogram (ECG) electrodes and an SC catheter for remote stimulus delivery. After recovery from anaesthesia, all snakes, in visual and audial isolation from the experimenter, received a sham stimulus of saline (0.4 mL) via the SC catheter. A nociceptive stimulus of SC capsaicin (3 mg in 0.2 mL saline with 7% Tween 80) was then applied by catheter at 7 hours after premedication. In a subset (n = 3), two sham injections (saline 0.2 mL) preceded the capsaicin treatment. HR was recorded via ECG, and changes in HR (ΔHR) from baseline were calculated for all stimulations. Capsaicin injection was associated with a significant increase in HR [peak ΔHR: saline group: 8.8 ± 7.1 beats minute(-1) ; capsaicin group: 21.1 ± 5.8 beats minute(-1) (p = 0.0055)] and integrated ΔHR as a function of time. The administration of morphine or butorphanol 7 hours prior to nociception failed to significantly reduce the peak and integrated ΔHR. Butorphanol caused marked, long-lasting sedation as assessed by muscle tone. The HR response to an SC capsaicin injection can serve as a nociceptive model in P. regius. Morphine and butorphanol administration did not reduce HR response to capsaicin stimulation but produced significantly different effects on pre-stimulation HR and sedation. © 2015 Association of Veterinary Anaesthetists and the American College of Veterinary Anesthesia and Analgesia.

The Python pit organ: imaging and immunocytochemical analysis of an extremely sensitive natural infrared detector.

PubMed

Grace, M S; Church, D R; Kelly, C T; Lynn, W F; Cooper, T M

1999-01-01

The Python infrared-sensitive pit organ is a natural infrared imager that combines high sensitivity, ambient temperature function, microscopic dimensions, and self-repair. We are investigating the spectral sensitivity and signal transduction process in snake infrared-sensitive neurons, neither of which is understood. For example, it is unknown whether infrared receptor neurons function on a thermal or a photic mechanism. We imaged pit organs in living Python molurus and Python regius using infrared-sensitive digital video cameras. Pit organs were significantly more absorptive and/or emissive than surrounding tissues in both 3-5 microns and 8-12 microns wavelength ranges. Pit organs exhibited greater absorption/emissivity in the 8-12 microns range than in the 3-5 microns range. To directly test the relationship between photoreceptors and pit organ infrared-sensitive neurons, we performed immunocytochemistry using antisera directed against retinal photoreceptor opsins. Retinal photoreceptors were labeled with antisera specific for retinal opsins, but these antisera failed to label terminals of infrared-sensitive neurons in the pit organ. Infrared-receptive neurons were also distinguished from retinal photoreceptors on the basis of their calcium-binding protein content. These results indicate that the pit organ absorbs infrared radiation in two major atmospheric transmission windows, one of which (8-12 microns) matches emission of targeted prey, and that infrared receptors are biochemically distinct from retinal photoreceptors. These results also provide the first identification of prospective biochemical components of infrared signal transduction in pit organ receptor neurons.

Light-weight Parallel Python Tools for Earth System Modeling Workflows

NASA Astrophysics Data System (ADS)

Mickelson, S. A.; Paul, K.; Xu, H.; Dennis, J.; Brown, D. I.

2015-12-01

With the growth in computing power over the last 30 years, earth system modeling codes have become increasingly data-intensive. As an example, it is expected that the data required for the next Intergovernmental Panel on Climate Change (IPCC) Assessment Report (AR6) will increase by more than 10x to an expected 25PB per climate model. Faced with this daunting challenge, developers of the Community Earth System Model (CESM) have chosen to change the format of their data for long-term storage from time-slice to time-series, in order to reduce the required download bandwidth needed for later analysis and post-processing by climate scientists. Hence, efficient tools are required to (1) perform the transformation of the data from time-slice to time-series format and to (2) compute climatology statistics, needed for many diagnostic computations, on the resulting time-series data. To address the first of these two challenges, we have developed a parallel Python tool for converting time-slice model output to time-series format. To address the second of these challenges, we have developed a parallel Python tool to perform fast time-averaging of time-series data. These tools are designed to be light-weight, be easy to install, have very few dependencies, and can be easily inserted into the Earth system modeling workflow with negligible disruption. In this work, we present the motivation, approach, and testing results of these two light-weight parallel Python tools, as well as our plans for future research and development.

A Coupled Simulation Architecture for Agent-Based/Geohydrological Modelling

NASA Astrophysics Data System (ADS)

Jaxa-Rozen, M.

2016-12-01

The quantitative modelling of social-ecological systems can provide useful insights into the interplay between social and environmental processes, and their impact on emergent system dynamics. However, such models should acknowledge the complexity and uncertainty of both of the underlying subsystems. For instance, the agent-based models which are increasingly popular for groundwater management studies can be made more useful by directly accounting for the hydrological processes which drive environmental outcomes. Conversely, conventional environmental models can benefit from an agent-based depiction of the feedbacks and heuristics which influence the decisions of groundwater users. From this perspective, this work describes a Python-based software architecture which couples the popular NetLogo agent-based platform with the MODFLOW/SEAWAT geohydrological modelling environment. This approach enables users to implement agent-based models in NetLogo's user-friendly platform, while benefiting from the full capabilities of MODFLOW/SEAWAT packages or reusing existing geohydrological models. The software architecture is based on the pyNetLogo connector, which provides an interface between the NetLogo agent-based modelling software and the Python programming language. This functionality is then extended and combined with Python's object-oriented features, to design a simulation architecture which couples NetLogo with MODFLOW/SEAWAT through the FloPy library (Bakker et al., 2016). The Python programming language also provides access to a range of external packages which can be used for testing and analysing the coupled models, which is illustrated for an application of Aquifer Thermal Energy Storage (ATES).

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.

PyCPR - a python-based implementation of the Conjugate Peak Refinement (CPR) algorithm for finding transition state structures.

PubMed

Gisdon, Florian J; Culka, Martin; Ullmann, G Matthias

2016-10-01

Conjugate peak refinement (CPR) is a powerful and robust method to search transition states on a molecular potential energy surface. Nevertheless, the method was to the best of our knowledge so far only implemented in CHARMM. In this paper, we present PyCPR, a new Python-based implementation of the CPR algorithm within the pDynamo framework. We provide a detailed description of the theory underlying our implementation and discuss the different parts of the implementation. The method is applied to two different problems. First, we illustrate the method by analyzing the gauche to anti-periplanar transition of butane using a semiempirical QM method. Second, we reanalyze the mechanism of a glycyl-radical enzyme, namely of 4-hydroxyphenylacetate decarboxylase (HPD) using QM/MM calculations. In the end, we suggest a strategy how to use our implementation of the CPR algorithm. The integration of PyCPR into the framework pDynamo allows the combination of CPR with the large variety of methods implemented in pDynamo. PyCPR can be used in combination with quantum mechanical and molecular mechanical methods (and hybrid methods) implemented directly in pDynamo, but also in combination with external programs such as ORCA using pDynamo as interface. PyCPR is distributed as free, open source software and can be downloaded from http://www.bisb.uni-bayreuth.de/index.php?page=downloads . Graphical Abstract PyCPR is a search tool for finding saddle points on the potential energy landscape of a molecular system.

Status of the calibration and alignment framework at the Belle II experiment

NASA Astrophysics Data System (ADS)

Dossett, D.; Sevior, M.; Ritter, M.; Kuhr, T.; Bilka, T.; Yaschenko, S.; Belle Software Group, II

2017-10-01

The Belle II detector at the Super KEKB e+e-collider plans to take first collision data in 2018. The monetary and CPU time costs associated with storing and processing the data mean that it is crucial for the detector components at Belle II to be calibrated quickly and accurately. A fast and accurate calibration system would allow the high level trigger to increase the efficiency of event selection, and can give users analysis-quality reconstruction promptly. A flexible framework to automate the fast production of calibration constants is being developed in the Belle II Analysis Software Framework (basf2). Detector experts only need to create two components from C++ base classes in order to use the automation system. The first collects data from Belle II event data files and outputs much smaller files to pass to the second component. This runs the main calibration algorithm to produce calibration constants ready for upload into the conditions database. A Python framework coordinates the input files, order of processing, and submission of jobs. Splitting the operation into collection and algorithm processing stages allows the framework to optionally parallelize the collection stage on a batch system.

78 FR 44275 - Semiannual Regulatory Agenda

Federal Register 2010, 2011, 2012, 2013, 2014

2013-07-23

... 1018-AV68 Evaluation; Constrictor Species From Python, Boa, and Eunectes Genera. National Park Service... Species From Python, Boa, and Eunectes Genera Legal Authority: 18 U.S.C. 42 Abstract: We are making a... Lacey Act: Reticulated python, DeSchauensee's anaconda, green anaconda, and Beni anaconda. The boa...

A software framework for assessing the resilience of drinking water systems to disasters with an example earthquake case study

DOE PAGES

Klise, Katherine A.; Bynum, Michael; Moriarty, Dylan; ...

2017-07-07

Water utilities are vulnerable to a wide variety of human-caused and natural disasters. The Water Network Tool for Resilience (WNTR) is a new open source PythonTM package designed to help water utilities investigate resilience of water distribution systems to hazards and evaluate resilience-enhancing actions. In this paper, the WNTR modeling framework is presented and a case study is described that uses WNTR to simulate the effects of an earthquake on a water distribution system. The case study illustrates that the severity of damage is not only a function of system integrity and earthquake magnitude, but also of the available resourcesmore » and repair strategies used to return the system to normal operating conditions. While earthquakes are particularly concerning since buried water distribution pipelines are highly susceptible to damage, the software framework can be applied to other types of hazards, including power outages and contamination incidents.« less

A software framework for assessing the resilience of drinking water systems to disasters with an example earthquake case study

DOE Office of Scientific and Technical Information (OSTI.GOV)

Klise, Katherine A.; Bynum, Michael; Moriarty, Dylan

Water utilities are vulnerable to a wide variety of human-caused and natural disasters. The Water Network Tool for Resilience (WNTR) is a new open source PythonTM package designed to help water utilities investigate resilience of water distribution systems to hazards and evaluate resilience-enhancing actions. In this paper, the WNTR modeling framework is presented and a case study is described that uses WNTR to simulate the effects of an earthquake on a water distribution system. The case study illustrates that the severity of damage is not only a function of system integrity and earthquake magnitude, but also of the available resourcesmore » and repair strategies used to return the system to normal operating conditions. While earthquakes are particularly concerning since buried water distribution pipelines are highly susceptible to damage, the software framework can be applied to other types of hazards, including power outages and contamination incidents.« less

The Diamond Beamline Controls and Data Acquisition Software Architecture

NASA Astrophysics Data System (ADS)

Rees, N.

2010-06-01

The software for the Diamond Light Source beamlines[1] is based on two complementary software frameworks: low level control is provided by the Experimental Physics and Industrial Control System (EPICS) framework[2][3] and the high level user interface is provided by the Java based Generic Data Acquisition or GDA[4][5]. EPICS provides a widely used, robust, generic interface across a wide range of hardware where the user interfaces are focused on serving the needs of engineers and beamline scientists to obtain detailed low level views of all aspects of the beamline control systems. The GDA system provides a high-level system that combines an understanding of scientific concepts, such as reciprocal lattice coordinates, a flexible python syntax scripting interface for the scientific user to control their data acquisition, and graphical user interfaces where necessary. This paper describes the beamline software architecture in more detail, highlighting how these complementary frameworks provide a flexible system that can accommodate a wide range of requirements.

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

PubMed

Castoe, Todd A; de Koning, Jason A P; Hall, Kathryn T; Yokoyama, Ken D; Gu, Wanjun; Smith, Eric N; Feschotte, Cédric; Uetz, Peter; Ray, David A; Dobry, Jason; Bogden, Robert; Mackessy, Stephen P; Bronikowski, Anne M; Warren, Wesley C; Secor, Stephen M; Pollock, David D

2011-07-28

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.

SpiceyPy, a Python Wrapper for SPICE

NASA Astrophysics Data System (ADS)

Annex, A.

2017-06-01

SpiceyPy is an open source Python wrapper for the NAIF SPICE toolkit. It is available for macOS, Linux, and Windows platforms and for Python versions 2.7.x and 3.x as well as Anaconda. SpiceyPy can be installed by running: “pip install spiceypy.”

Double valvular insufficiency in a Burmese python (Python molurus bivittatus, Linnaeus, 1758) suffering from concomitant bacterial pneumonia.

PubMed

Schilliger, Lionel; Tréhiou-Sechi, Emilie; Petit, Amandine M P; Misbach, Charlotte; Chetboul, Valérie

2010-12-01

Ultrasonography, and, to a lesser extent, echocardiography are now well-established, noninvasive, and painless diagnostic tools in herpetologic medicine. Various cardiac lesions have been previously described in reptiles, but valvulopathy is rarely documented in these animals and, consequently, is poorly understood. In this report, sinoatrial and atrioventricular insufficiencies were diagnosed in a 5-yr-old captive dyspneic Burmese python (Python molurus bivittatus) on the basis of echocardiographic and Doppler examination. This case report is the first to document Doppler assessment of valvular regurgitations in a reptile.

Report on the ''ESO Python Boot Camp — Pilot Version''

NASA Astrophysics Data System (ADS)

Dias, B.; Milli, J.

2017-03-01

The Python programming language is becoming very popular within the astronomical community. Python is a high-level language with multiple applications including database management, handling FITS images and tables, statistical analysis, and more advanced topics. Python is a very powerful tool both for astronomical publications and for observatory operations. Since the best way to learn a new programming language is through practice, we therefore organised a two-day hands-on workshop to share expertise among ESO colleagues. We report here the outcome and feedback from this pilot event.

NEURON and Python.

PubMed

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

2009-01-01

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

NEURON and Python

PubMed Central

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

2008-01-01

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

GenomeDiagram: a python package for the visualization of large-scale genomic data.

PubMed

Pritchard, Leighton; White, Jennifer A; Birch, Paul R J; Toth, Ian K

2006-03-01

We present GenomeDiagram, a flexible, open-source Python module for the visualization of large-scale genomic, comparative genomic and other data with reference to a single chromosome or other biological sequence. GenomeDiagram may be used to generate publication-quality vector graphics, rastered images and in-line streamed graphics for webpages. The package integrates with datatypes from the BioPython project, and is available for Windows, Linux and Mac OS X systems. GenomeDiagram is freely available as source code (under GNU Public License) at http://bioinf.scri.ac.uk/lp/programs.html, and requires Python 2.3 or higher, and recent versions of the ReportLab and BioPython packages. A user manual, example code and images are available at http://bioinf.scri.ac.uk/lp/programs.html.

In Silico Analysis of Gene Expression Network Components Underlying Pigmentation Phenotypes in the Python Identified Evolutionarily Conserved Clusters of Transcription Factor Binding Sites

PubMed Central

2016-01-01

Color variation provides the opportunity to investigate the genetic basis of evolution and selection. Reptiles are less studied than mammals. Comparative genomics approaches allow for knowledge gained in one species to be leveraged for use in another species. We describe a comparative vertebrate analysis of conserved regulatory modules in pythons aimed at assessing bioinformatics evidence that transcription factors important in mammalian pigmentation phenotypes may also be important in python pigmentation phenotypes. We identified 23 python orthologs of mammalian genes associated with variation in coat color phenotypes for which we assessed the extent of pairwise protein sequence identity between pythons and mouse, dog, horse, cow, chicken, anole lizard, and garter snake. We next identified a set of melanocyte/pigment associated transcription factors (CREB, FOXD3, LEF-1, MITF, POU3F2, and USF-1) that exhibit relatively conserved sequence similarity within their DNA binding regions across species based on orthologous alignments across multiple species. Finally, we identified 27 evolutionarily conserved clusters of transcription factor binding sites within ~200-nucleotide intervals of the 1500-nucleotide upstream regions of AIM1, DCT, MC1R, MITF, MLANA, OA1, PMEL, RAB27A, and TYR from Python bivittatus. Our results provide insight into pigment phenotypes in pythons. PMID:27698666

In Silico Analysis of Gene Expression Network Components Underlying Pigmentation Phenotypes in the Python Identified Evolutionarily Conserved Clusters of Transcription Factor Binding Sites.

PubMed

Irizarry, Kristopher J L; Bryden, Randall L

2016-01-01

Color variation provides the opportunity to investigate the genetic basis of evolution and selection. Reptiles are less studied than mammals. Comparative genomics approaches allow for knowledge gained in one species to be leveraged for use in another species. We describe a comparative vertebrate analysis of conserved regulatory modules in pythons aimed at assessing bioinformatics evidence that transcription factors important in mammalian pigmentation phenotypes may also be important in python pigmentation phenotypes. We identified 23 python orthologs of mammalian genes associated with variation in coat color phenotypes for which we assessed the extent of pairwise protein sequence identity between pythons and mouse, dog, horse, cow, chicken, anole lizard, and garter snake. We next identified a set of melanocyte/pigment associated transcription factors (CREB, FOXD3, LEF-1, MITF, POU3F2, and USF-1) that exhibit relatively conserved sequence similarity within their DNA binding regions across species based on orthologous alignments across multiple species. Finally, we identified 27 evolutionarily conserved clusters of transcription factor binding sites within ~200-nucleotide intervals of the 1500-nucleotide upstream regions of AIM1, DCT, MC1R, MITF, MLANA, OA1, PMEL, RAB27A, and TYR from Python bivittatus . Our results provide insight into pigment phenotypes in pythons.

Methods for transition toward computer assisted cognitive examination.

PubMed

Jurica, P; Valenzi, S; Struzik, Z R; Cichocki, A

2015-01-01

We present a software framework which enables the extension of current methods for the assessment of cognitive fitness using recent technological advances. Screening for cognitive impairment is becoming more important as the world's population grows older. Current methods could be enhanced by use of computers. Introduction of new methods to clinics requires basic tools for collection and communication of collected data. To develop tools that, with minimal interference, offer new opportunities for the enhancement of the current interview based cognitive examinations. We suggest methods and discuss process by which established cognitive tests can be adapted for data collection through digitization by pen enabled tablets. We discuss a number of methods for evaluation of collected data, which promise to increase the resolution and objectivity of the common scoring strategy based on visual inspection. By involving computers in the roles of both instructing and scoring, we aim to increase the precision and reproducibility of cognitive examination. The tools provided in Python framework CogExTools available at http://bsp. brain.riken.jp/cogextools/ enable the design, application and evaluation of screening tests for assessment of cognitive impairment. The toolbox is a research platform; it represents a foundation for further collaborative development by the wider research community and enthusiasts. It is free to download and use, and open-source. We introduce a set of open-source tools that facilitate the design and development of new cognitive tests for modern technology. We provide these tools in order to enable the adaptation of technology for cognitive examination in clinical settings. The tools provide the first step in a possible transition toward standardized mental state examination using computers.

Modeling a Packed Bed Reactor Utilizing the Sabatier Process

NASA Technical Reports Server (NTRS)

Shah, Malay G.; Meier, Anne J.; Hintze, Paul E.

2017-01-01

A numerical model is being developed using Python which characterizes the conversion and temperature profiles of a packed bed reactor (PBR) that utilizes the Sabatier process; the reaction produces methane and water from carbon dioxide and hydrogen. While the specific kinetics of the Sabatier reaction on the RuAl2O3 catalyst pellets are unknown, an empirical reaction rate equation1 is used for the overall reaction. As this reaction is highly exothermic, proper thermal control is of the utmost importance to ensure maximum conversion and to avoid reactor runaway. It is therefore necessary to determine what wall temperature profile will ensure safe and efficient operation of the reactor. This wall temperature will be maintained by active thermal controls on the outer surface of the reactor. Two cylindrical PBRs are currently being tested experimentally and will be used for validation of the Python model. They are similar in design except one of them is larger and incorporates a preheat loop by feeding the reactant gas through a pipe along the center of the catalyst bed. The further complexity of adding a preheat pipe to the model to mimic the larger reactor is yet to be implemented and validated; preliminary validation is done using the smaller PBR with no reactant preheating. When mapping experimental values of the wall temperature from the smaller PBR into the Python model, a good approximation of the total conversion and temperature profile has been achieved. A separate CFD model incorporates more complex three-dimensional effects by including the solid catalyst pellets within the domain. The goal is to improve the Python model to the point where the results of other reactor geometry can be reasonably predicted relatively quickly when compared to the much more computationally expensive CFD approach. Once a reactor size is narrowed down using the Python approach, CFD will be used to generate a more thorough prediction of the reactors performance.

Morphological Pulmonary Diffusion Capacity for Oxygen of Burmese Pythons (Python molurus): a Comparison of Animals in Healthy Condition and with Different Pulmonary Infections.

PubMed

Starck, J M; Weimer, I; Aupperle, H; Müller, K; Marschang, R E; Kiefer, I; Pees, M

2015-11-01

A qualitative and quantitative morphological study of the pulmonary exchange capacity of healthy and diseased Burmese pythons (Python molurus) was carried out in order to test the hypothesis that the high morphological excess capacity for oxygen exchange in the lungs of these snakes is one of the reasons why pathological processes extend throughout the lung parenchyma and impair major parts of the lungs before clinical signs of respiratory disease become apparent. Twenty-four Burmese pythons (12 healthy and 12 diseased) were included in the study. A stereology-based approach was used to quantify the lung parenchyma using computed tomography. Light microscopy was used to quantify tissue compartments and the respiratory exchange surface, and transmission electron microscopy was used to measure the thickness of the diffusion barrier. The morphological diffusion capacity for oxygen of the lungs and the anatomical diffusion factor were calculated. The calculated anatomical diffusion capacity was compared with published values for oxygen consumption of healthy snakes, and the degree to which the exchange capacity can be obstructed before normal physiological function is impaired was estimated. Heterogeneous pulmonary infections result in graded morphological transformations of pulmonary parenchyma involving lymphocyte migration into the connective tissue and thickening of the septal connective tissue, increasing thickness of the diffusion barrier and increasing transformation of the pulmonary epithelium into a columnar pseudostratified or stratified epithelium. The transformed epithelium developed by hyperplasia of ciliated cells arising from the tip of the faveolar septa and by hyperplasia of type II pneumocytes. These results support the idea that the lungs have a remarkable overcapacity for oxygen consumption and that the development of pulmonary disease continuously reduces the capacity for oxygen consumption. However, due to the overcapacity of the lungs, this reduction does not result in clinical signs and disease can progress unrecognized for an extended period. Copyright © 2015 Elsevier Ltd. All rights reserved.

Graph Mining Meets the Semantic Web

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lee, Sangkeun; Sukumar, Sreenivas R; Lim, Seung-Hwan

The Resource Description Framework (RDF) and SPARQL Protocol and RDF Query Language (SPARQL) were introduced about a decade ago to enable flexible schema-free data interchange on the Semantic Web. Today, data scientists use the framework as a scalable graph representation for integrating, querying, exploring and analyzing data sets hosted at different sources. With increasing adoption, the need for graph mining capabilities for the Semantic Web has emerged. We address that need through implementation of three popular iterative Graph Mining algorithms (Triangle count, Connected component analysis, and PageRank). We implement these algorithms as SPARQL queries, wrapped within Python scripts. We evaluatemore » the performance of our implementation on 6 real world data sets and show graph mining algorithms (that have a linear-algebra formulation) can indeed be unleashed on data represented as RDF graphs using the SPARQL query interface.« less

PySeqLab: an open source Python package for sequence labeling and segmentation.

PubMed

Allam, Ahmed; Krauthammer, Michael

2017-11-01

Text and genomic data are composed of sequential tokens, such as words and nucleotides that give rise to higher order syntactic constructs. In this work, we aim at providing a comprehensive Python library implementing conditional random fields (CRFs), a class of probabilistic graphical models, for robust prediction of these constructs from sequential data. Python Sequence Labeling (PySeqLab) is an open source package for performing supervised learning in structured prediction tasks. It implements CRFs models, that is discriminative models from (i) first-order to higher-order linear-chain CRFs, and from (ii) first-order to higher-order semi-Markov CRFs (semi-CRFs). Moreover, it provides multiple learning algorithms for estimating model parameters such as (i) stochastic gradient descent (SGD) and its multiple variations, (ii) structured perceptron with multiple averaging schemes supporting exact and inexact search using 'violation-fixing' framework, (iii) search-based probabilistic online learning algorithm (SAPO) and (iv) an interface for Broyden-Fletcher-Goldfarb-Shanno (BFGS) and the limited-memory BFGS algorithms. Viterbi and Viterbi A* are used for inference and decoding of sequences. Using PySeqLab, we built models (classifiers) and evaluated their performance in three different domains: (i) biomedical Natural language processing (NLP), (ii) predictive DNA sequence analysis and (iii) Human activity recognition (HAR). State-of-the-art performance comparable to machine-learning based systems was achieved in the three domains without feature engineering or the use of knowledge sources. PySeqLab is available through https://bitbucket.org/A_2/pyseqlab with tutorials and documentation. ahmed.allam@yale.edu or michael.krauthammer@yale.edu. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

MacSyFinder: A Program to Mine Genomes for Molecular Systems with an Application to CRISPR-Cas Systems

PubMed Central

Abby, Sophie S.; Néron, Bertrand; Ménager, Hervé; Touchon, Marie; Rocha, Eduardo P. C.

2014-01-01

Motivation Biologists often wish to use their knowledge on a few experimental models of a given molecular system to identify homologs in genomic data. We developed a generic tool for this purpose. Results Macromolecular System Finder (MacSyFinder) provides a flexible framework to model the properties of molecular systems (cellular machinery or pathway) including their components, evolutionary associations with other systems and genetic architecture. Modelled features also include functional analogs, and the multiple uses of a same component by different systems. Models are used to search for molecular systems in complete genomes or in unstructured data like metagenomes. The components of the systems are searched by sequence similarity using Hidden Markov model (HMM) protein profiles. The assignment of hits to a given system is decided based on compliance with the content and organization of the system model. A graphical interface, MacSyView, facilitates the analysis of the results by showing overviews of component content and genomic context. To exemplify the use of MacSyFinder we built models to detect and class CRISPR-Cas systems following a previously established classification. We show that MacSyFinder allows to easily define an accurate “Cas-finder” using publicly available protein profiles. Availability and Implementation MacSyFinder is a standalone application implemented in Python. It requires Python 2.7, Hmmer and makeblastdb (version 2.2.28 or higher). It is freely available with its source code under a GPLv3 license at https://github.com/gem-pasteur/macsyfinder. It is compatible with all platforms supporting Python and Hmmer/makeblastdb. The “Cas-finder” (models and HMM profiles) is distributed as a compressed tarball archive as Supporting Information. PMID:25330359

The connectome viewer toolkit: an open source framework to manage, analyze, and visualize connectomes.

PubMed

Gerhard, Stephan; Daducci, Alessandro; Lemkaddem, Alia; Meuli, Reto; Thiran, Jean-Philippe; Hagmann, Patric

2011-01-01

Advanced neuroinformatics tools are required for methods of connectome mapping, analysis, and visualization. The inherent multi-modality of connectome datasets poses new challenges for data organization, integration, and sharing. We have designed and implemented the Connectome Viewer Toolkit - a set of free and extensible open source neuroimaging tools written in Python. The key components of the toolkit are as follows: (1) The Connectome File Format is an XML-based container format to standardize multi-modal data integration and structured metadata annotation. (2) The Connectome File Format Library enables management and sharing of connectome files. (3) The Connectome Viewer is an integrated research and development environment for visualization and analysis of multi-modal connectome data. The Connectome Viewer's plugin architecture supports extensions with network analysis packages and an interactive scripting shell, to enable easy development and community contributions. Integration with tools from the scientific Python community allows the leveraging of numerous existing libraries for powerful connectome data mining, exploration, and comparison. We demonstrate the applicability of the Connectome Viewer Toolkit using Diffusion MRI datasets processed by the Connectome Mapper. The Connectome Viewer Toolkit is available from http://www.cmtk.org/

Enhancing reproducibility in scientific computing: Metrics and registry for Singularity containers.

PubMed

Sochat, Vanessa V; Prybol, Cameron J; Kurtzer, Gregory M

2017-01-01

Here we present Singularity Hub, a framework to build and deploy Singularity containers for mobility of compute, and the singularity-python software with novel metrics for assessing reproducibility of such containers. Singularity containers make it possible for scientists and developers to package reproducible software, and Singularity Hub adds automation to this workflow by building, capturing metadata for, visualizing, and serving containers programmatically. Our novel metrics, based on custom filters of content hashes of container contents, allow for comparison of an entire container, including operating system, custom software, and metadata. First we will review Singularity Hub's primary use cases and how the infrastructure has been designed to support modern, common workflows. Next, we conduct three analyses to demonstrate build consistency, reproducibility metric and performance and interpretability, and potential for discovery. This is the first effort to demonstrate a rigorous assessment of measurable similarity between containers and operating systems. We provide these capabilities within Singularity Hub, as well as the source software singularity-python that provides the underlying functionality. Singularity Hub is available at https://singularity-hub.org, and we are excited to provide it as an openly available platform for building, and deploying scientific containers.

Enhancing reproducibility in scientific computing: Metrics and registry for Singularity containers

PubMed Central

Prybol, Cameron J.; Kurtzer, Gregory M.

2017-01-01

Here we present Singularity Hub, a framework to build and deploy Singularity containers for mobility of compute, and the singularity-python software with novel metrics for assessing reproducibility of such containers. Singularity containers make it possible for scientists and developers to package reproducible software, and Singularity Hub adds automation to this workflow by building, capturing metadata for, visualizing, and serving containers programmatically. Our novel metrics, based on custom filters of content hashes of container contents, allow for comparison of an entire container, including operating system, custom software, and metadata. First we will review Singularity Hub’s primary use cases and how the infrastructure has been designed to support modern, common workflows. Next, we conduct three analyses to demonstrate build consistency, reproducibility metric and performance and interpretability, and potential for discovery. This is the first effort to demonstrate a rigorous assessment of measurable similarity between containers and operating systems. We provide these capabilities within Singularity Hub, as well as the source software singularity-python that provides the underlying functionality. Singularity Hub is available at https://singularity-hub.org, and we are excited to provide it as an openly available platform for building, and deploying scientific containers. PMID:29186161

The Connectome Viewer Toolkit: An Open Source Framework to Manage, Analyze, and Visualize Connectomes

PubMed Central

Gerhard, Stephan; Daducci, Alessandro; Lemkaddem, Alia; Meuli, Reto; Thiran, Jean-Philippe; Hagmann, Patric

2011-01-01

Advanced neuroinformatics tools are required for methods of connectome mapping, analysis, and visualization. The inherent multi-modality of connectome datasets poses new challenges for data organization, integration, and sharing. We have designed and implemented the Connectome Viewer Toolkit – a set of free and extensible open source neuroimaging tools written in Python. The key components of the toolkit are as follows: (1) The Connectome File Format is an XML-based container format to standardize multi-modal data integration and structured metadata annotation. (2) The Connectome File Format Library enables management and sharing of connectome files. (3) The Connectome Viewer is an integrated research and development environment for visualization and analysis of multi-modal connectome data. The Connectome Viewer's plugin architecture supports extensions with network analysis packages and an interactive scripting shell, to enable easy development and community contributions. Integration with tools from the scientific Python community allows the leveraging of numerous existing libraries for powerful connectome data mining, exploration, and comparison. We demonstrate the applicability of the Connectome Viewer Toolkit using Diffusion MRI datasets processed by the Connectome Mapper. The Connectome Viewer Toolkit is available from http://www.cmtk.org/ PMID:21713110

PyPele Rewritten To Use MPI

NASA Technical Reports Server (NTRS)

Hockney, George; Lee, Seungwon

2008-01-01

A computer program known as PyPele, originally written as a Pythonlanguage extension module of a C++ language program, has been rewritten in pure Python language. The original version of PyPele dispatches and coordinates parallel-processing tasks on cluster computers and provides a conceptual framework for spacecraft-mission- design and -analysis software tools to run in an embarrassingly parallel mode. The original version of PyPele uses SSH (Secure Shell a set of standards and an associated network protocol for establishing a secure channel between a local and a remote computer) to coordinate parallel processing. Instead of SSH, the present Python version of PyPele uses Message Passing Interface (MPI) [an unofficial de-facto standard language-independent application programming interface for message- passing on a parallel computer] while keeping the same user interface. The use of MPI instead of SSH and the preservation of the original PyPele user interface make it possible for parallel application programs written previously for the original version of PyPele to run on MPI-based cluster computers. As a result, engineers using the previously written application programs can take advantage of embarrassing parallelism without need to rewrite those programs.

Photodermatitis and photokeratoconjunctivitis in a ball python (Python regius) and a blue-tongue skink (Tiliqua spp.).

PubMed

Gardiner, David W; Baines, Frances M; Pandher, Karamjeet

2009-12-01

A male ball python (Python regius) and a female blue tongue skink (Tiliqua spp.) of unknown age were evaluated for anorexia, lethargy, excessive shedding, corneal opacity (python), and weight loss (skink) of approximately three weeks' duration. These animals represented the worst affected animals from a private herpetarium where many animals exhibited similar signs. At necropsy, the python had bilateral corneal opacity and scattered moderate dysecdysis. The skink had mild dysecdysis, poor body condition, moderate intestinal nematodiasis, and mild liver atrophy. Microscopic evaluation revealed epidermal erosion and ulceration, with severe epidermal basal cell degeneration and necrosis, and superficial dermatitis (python and skink). Severe bilateral ulcerative keratoconjunctivitis with bacterial colonization was noted in the ball python. Microscopic findings within the skin and eyes were suggestive of ultraviolet (UV) radiation damage or of photodermatitis and photokeratoconjunctivitis. Removal of the recently installed new lamps from the terrariums of the surviving reptiles resulted in resolution of clinical signs. Evaluation of a sample lamp of the type associated with these cases revealed an extremely high UV output, including very-short-wavelength UVB, neither found in natural sunlight nor emitted by several other UVB lamps unassociated with photokeratoconjunctivitis. Exposure to high-intensity and/or inappropriate wavelengths of UV radiation may be associated with significant morbidity, and even mortality, in reptiles. Veterinarians who are presented with reptiles with ocular and/or cutaneous disease of unapparent cause should fully evaluate the specifics of the vivarium light sources. Further research is needed to determine the characteristics of appropriate and of toxic UV light for reptiles kept in captivity.

Ultrasonographic anatomy of the coelomic organs of boid snakes (Boa constrictor imperator, Python regius, Python molurus molurus, and Python curtus).

PubMed

Banzato, Tommaso; Russo, Elisa; Finotti, Luca; Milan, Maria C; Gianesella, Matteo; Zotti, Alessandro

2012-05-01

To determine the ultrasonographic features of the coelomic organs of healthy snakes belonging to the Boidae and Pythonidae families. 16 ball pythons (Python regius; 7 males, 8 females, and 1 sexually immature), 10 Indian rock pythons (Python molurus molurus; 5 males, 4 females, and 1 sexually immature), 12 Python curtus (5 males and 7 females), and 8 boa constrictors (Boa constrictor imperator; 4 males and 4 females). All snakes underwent complete ultrasonographic evaluation of the coelomic cavity; chemical restraint was not necessary. A dorsolateral approach to probe placement was chosen to increase image quality and to avoid injury to the snakes and operators. Qualitative and quantitative observations were recorded. The liver, stomach, gallbladder, pancreas, small and large intestines, kidneys, cloaca, and scent glands were identified in all snakes. The hemipenes were identified in 10 of the 21 (48%) male snakes. The spleen was identified in 5 of the 46 (11%) snakes, and ureters were identified in 6 (13%). In 2 sexually immature snakes, the gonads were not visible. One (2%) snake was gravid, and 7 (15%) had small amounts of free fluid in the coelomic cavity. A significant positive correlation was identified between several measurements (diameter and thickness of scent glands, gastric and pyloric walls, and colonic wall) and body length (snout to vent) and body weight. The study findings can be used as an atlas of the ultrasonographic anatomy of the coelomic cavity in healthy boid snakes. Ultrasonography was reasonably fast to perform and was well tolerated in conscious snakes.

The rhinoceros among Serpents: Comparative anatomy and experimental biophysics of Calabar burrowing python (Calabaria reinhardtii) skin.

PubMed

Han, Dawei; Young, Bruce A

2018-01-01

The Calabar burrowing python (Calabaria reinhardtii) has a unique combination of marked thickness of the integumentary layers, a highly organized lamellate arrangement of the dermal collagen bundles, and a reduction in the size of the interscale hinge region of the integument. Biomechanical testing demonstrates that the skin of C. reinhardtii is more resistant to penetration than the skin of other snakes. The laminar arrangement of the collagen bundles provides for penetrative resistance, even while maintaining the flexibility characteristic of snake skin. Considering the life history of this species, it is hypothesized that the specialized integument of C. reinhardtii is a passive defensive mechanism against penetrative bites from maternal rodents and predators. © 2017 Wiley Periodicals, Inc.

Scraping EDGAR with Python

ERIC Educational Resources Information Center

Ashraf, Rasha

2017-01-01

This article presents Python codes that can be used to extract data from Securities and Exchange Commission (SEC) filings. The Python program web crawls to obtain URL paths for company filings of required reports, such as Form 10-K. The program then performs a textual analysis and counts the number of occurrences of words in the filing that…

On Parallel Software Engineering Education Using Python

ERIC Educational Resources Information Center

Marowka, Ami

2018-01-01

Python is gaining popularity in academia as the preferred language to teach novices serial programming. The syntax of Python is clean, easy, and simple to understand. At the same time, it is a high-level programming language that supports multi programming paradigms such as imperative, functional, and object-oriented. Therefore, by default, it is…

Satiety and eating patterns in two species of constricting snakes.

PubMed

Nielsen, Torben P; Jacobsen, Magnus W; Wang, Tobias

2011-01-10

Satiety has been studied extensively in mammals, birds and fish but very little information exists on reptiles. Here we investigate time-dependent satiation in two species of constricting snakes, ball pythons (Python regius) and yellow anacondas (Eunectes notaeus). Satiation was shown to depend on both fasting time and prey size. In the ball pythons fed with mice of a relative prey mass RPM (mass of the prey/mass of the snake×100) of 15%, we observed a satiety response that developed between 6 and 12h after feeding, but after 24h pythons regained their appetite. With an RPM of 10% the pythons kept eating throughout the experiment. The anacondas showed a non-significant tendency for satiety to develop between 6 and 12h after ingesting a prey of 20% RPM. Unlike pythons, anacondas remained satiated after 24h. Handling time (from strike until prey swallowed) increased with RPM. We also found a significant decrease in handling time between the first and the second prey and a positive correlation between handling time and the mass of the snake. 2010 Elsevier Inc. All rights reserved.

Scripting MODFLOW Model Development Using Python and FloPy.

PubMed

Bakker, M; Post, V; Langevin, C D; Hughes, J D; White, J T; Starn, J J; Fienen, M N

2016-09-01

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. © 2016, National Ground Water Association.

Geospatial Data Stream Processing in Python Using FOSS4G Components

NASA Astrophysics Data System (ADS)

McFerren, G.; van Zyl, T.

2016-06-01

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

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

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.

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.

Selected regulation of gastrointestinal acid-base secretion and tissue metabolism for the diamondback water snake and Burmese python.

PubMed

Secor, Stephen M; Taylor, Josi R; Grosell, Martin

2012-01-01

Snakes exhibit an apparent dichotomy in the regulation of gastrointestinal (GI) performance with feeding and fasting; frequently feeding species modestly regulate intestinal function whereas infrequently feeding species rapidly upregulate and downregulate intestinal function with the start and completion of each meal, respectively. The downregulatory response with fasting for infrequently feeding snakes is hypothesized to be a selective attribute that reduces energy expenditure between meals. To ascertain the links between feeding habit, whole-animal metabolism, and GI function and metabolism, we measured preprandial and postprandial metabolic rates and gastric and intestinal acid-base secretion, epithelial conductance and oxygen consumption for the frequently feeding diamondback water snake (Nerodia rhombifer) and the infrequently feeding Burmese python (Python molurus). Independent of body mass, Burmese pythons possess a significantly lower standard metabolic rate and respond to feeding with a much larger metabolic response compared with water snakes. While fasting, pythons cease gastric acid and intestinal base secretion, both of which are stimulated with feeding. In contrast, fasted water snakes secreted gastric acid and intestinal base at rates similar to those of digesting snakes. We observed no difference between fasted and fed individuals for either species in gastric or intestinal transepithelial potential and conductance, with the exception of a significantly greater gastric transepithelial potential for fed pythons at the start of titration. Water snakes experienced no significant change in gastric or intestinal metabolism with feeding. Fed pythons, in contrast, experienced a near-doubling of gastric metabolism and a tripling of intestinal metabolic rate. For fasted individuals, the metabolic rate of the stomach and small intestine was significantly lower for pythons than for water snakes. The fasting downregulation of digestive function for pythons is manifested in a depressed gastric and intestinal metabolism, which selectively serves to reduce basal metabolism and hence promote survival between infrequent meals. By maintaining elevated GI performance between meals, fasted water snakes incur the additional cost of tissue activity, which is expressed in a higher standard metabolic rate.

Services for domain specific developments in the Cloud

NASA Astrophysics Data System (ADS)

Schwichtenberg, Horst; Gemuend, André

2015-04-01

We will discuss and demonstrate the possibilities of new Cloud Services where the complete development of code is in the Cloud. We will discuss the possibilities of such services where the complete development cycle from programing to testing is in the cloud. This can be also combined with dedicated research domain specific services and hide the burden of accessing available infrastructures. As an example, we will show a service that is intended to complement the services of the VERCE projects infrastructure, a service that utilizes Cloud resources to offer simplified execution of data pre- and post-processing scripts. It offers users access to the ObsPy seismological toolbox for processing data with the Python programming language, executed on virtual Cloud resources in a secured sandbox. The solution encompasses a frontend with a modern graphical user interface, a messaging infrastructure as well as Python worker nodes for background processing. All components are deployable in the Cloud and have been tested on different environments based on OpenStack and OpenNebula. Deployments on commercial, public Clouds will be tested in the future.

pymzML--Python module for high-throughput bioinformatics on mass spectrometry data.

PubMed

Bald, Till; Barth, Johannes; Niehues, Anna; Specht, Michael; Hippler, Michael; Fufezan, Christian

2012-04-01

pymzML is an extension to Python that offers (i) an easy access to mass spectrometry (MS) data that allows the rapid development of tools, (ii) a very fast parser for mzML data, the standard data format in MS and (iii) a set of functions to compare or handle spectra. pymzML requires Python2.6.5+ and is fully compatible with Python3. The module is freely available on http://pymzml.github.com or pypi, is published under LGPL license and requires no additional modules to be installed. christian@fufezan.net.

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

PubMed

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

2013-01-01

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

Effects of meal size, clutch, and metabolism on the energy efficiencies of juvenile Burmese pythons, Python molurus.

PubMed

Cox, Christian L; Secor, Stephen M

2007-12-01

We explored meal size and clutch (i.e., genetic) effects on the relative proportion of ingested energy that is absorbed by the gut (apparent digestive efficiency), becomes available for metabolism and growth (apparent assimilation efficiency), and is used for growth (production efficiency) for juvenile Burmese pythons (Python molurus). Sibling pythons were fed rodent meals equaling 15%, 25%, and 35% of their body mass and individuals from five different clutches were fed rodent meals equaling 25% of their body mass. For each of 11-12 consecutive feeding trials, python body mass was recorded and feces and urate of each snake was collected, dried, and weighed. Energy contents of meals (mice and rats), feces, urate, and pythons were determined using bomb calorimetry. For siblings fed three different meal sizes, growth rate increased with larger meals, but there was no significant variation among the meal sizes for any of the calculated energy efficiencies. Among the three meal sizes, apparent digestive efficiency, apparent assimilation efficiency, and production efficiency averaged 91.0%, 84.7%, and 40.7%, respectively. In contrast, each of these energy efficiencies varied significantly among the five different clutches. Among these clutches production efficiency was negatively correlated with standard metabolic rate (SMR). Clutches containing individuals with low SMR were therefore able to allocate more of ingested energy into growth.

Clinical and histologic effects of intracardiac administration of propofol for induction of anesthesia in ball pythons (Python regius).

PubMed

McFadden, Michael S; Bennett, R Avery; Reavill, Drury R; Ragetly, Guillaume R; Clark-Price, Stuart C

2011-09-15

To assess the clinical differences between induction of anesthesia in ball pythons with intracardiac administration of propofol and induction with isoflurane in oxygen and to assess the histologic findings over time in hearts following intracardiac administration of propofol. Prospective randomized study. 30 hatchling ball pythons (Python regius). Anesthesia was induced with intracardiac administration of propofol (10 mg/kg [4.5 mg/lb]) in 18 ball pythons and with 5% isoflurane in oxygen in 12 ball pythons. Induction time, time of anesthesia, and recovery time were recorded. Hearts from snakes receiving intracardiac administration of propofol were evaluated histologically 3, 7, 14, 30, and 60 days following propofol administration. Induction time with intracardiac administration of propofol was significantly shorter than induction time with 5% isoflurane in oxygen. No significant differences were found in total anesthesia time. Recovery following intracardiac administration of propofol was significantly longer than recovery following induction of anesthesia with isoflurane in oxygen. Heart tissue evaluated histologically at 3, 7, and 14 days following intracardiac administration of propofol had mild inflammatory changes, and no histopathologic lesions were seen 30 and 60 days following propofol administration. Intracardiac injection of propofol in snakes is safe and provides a rapid induction of anesthesia but leads to prolonged recovery, compared with that following induction with isoflurane. Histopathologic lesions in heart tissues following intracardiac injection of propofol were mild and resolved after 14 days.

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.

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.; 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.

pyPaSWAS: Python-based multi-core CPU and GPU sequence alignment.

PubMed

Warris, Sven; Timal, N Roshan N; Kempenaar, Marcel; Poortinga, Arne M; van de Geest, Henri; Varbanescu, Ana L; Nap, Jan-Peter

2018-01-01

Our previously published CUDA-only application PaSWAS for Smith-Waterman (SW) sequence alignment of any type of sequence on NVIDIA-based GPUs is platform-specific and therefore adopted less than could be. The OpenCL language is supported more widely and allows use on a variety of hardware platforms. Moreover, there is a need to promote the adoption of parallel computing in bioinformatics by making its use and extension more simple through more and better application of high-level languages commonly used in bioinformatics, such as Python. The novel application pyPaSWAS presents the parallel SW sequence alignment code fully packed in Python. It is a generic SW implementation running on several hardware platforms with multi-core systems and/or GPUs that provides accurate sequence alignments that also can be inspected for alignment details. Additionally, pyPaSWAS support the affine gap penalty. Python libraries are used for automated system configuration, I/O and logging. This way, the Python environment will stimulate further extension and use of pyPaSWAS. pyPaSWAS presents an easy Python-based environment for accurate and retrievable parallel SW sequence alignments on GPUs and multi-core systems. The strategy of integrating Python with high-performance parallel compute languages to create a developer- and user-friendly environment should be considered for other computationally intensive bioinformatics algorithms.

GRASS GIS: The first Open Source Temporal GIS

NASA Astrophysics Data System (ADS)

Gebbert, Sören; Leppelt, Thomas

2015-04-01

GRASS GIS is a full featured, general purpose Open Source geographic information system (GIS) with raster, 3D raster and vector processing support[1]. Recently, time was introduced as a new dimension that transformed GRASS GIS into the first Open Source temporal GIS with comprehensive spatio-temporal analysis, processing and visualization capabilities[2]. New spatio-temporal data types were introduced in GRASS GIS version 7, to manage raster, 3D raster and vector time series. These new data types are called space time datasets. They are designed to efficiently handle hundreds of thousands of time stamped raster, 3D raster and vector map layers of any size. Time stamps can be defined as time intervals or time instances in Gregorian calendar time or relative time. Space time datasets are simplifying the processing and analysis of large time series in GRASS GIS, since these new data types are used as input and output parameter in temporal modules. The handling of space time datasets is therefore equal to the handling of raster, 3D raster and vector map layers in GRASS GIS. A new dedicated Python library, the GRASS GIS Temporal Framework, was designed to implement the spatio-temporal data types and their management. The framework provides the functionality to efficiently handle hundreds of thousands of time stamped map layers and their spatio-temporal topological relations. The framework supports reasoning based on the temporal granularity of space time datasets as well as their temporal topology. It was designed in conjunction with the PyGRASS [3] library to support parallel processing of large datasets, that has a long tradition in GRASS GIS [4,5]. We will present a subset of more than 40 temporal modules that were implemented based on the GRASS GIS Temporal Framework, PyGRASS and the GRASS GIS Python scripting library. These modules provide a comprehensive temporal GIS tool set. The functionality range from space time dataset and time stamped map layer management over temporal aggregation, temporal accumulation, spatio-temporal statistics, spatio-temporal sampling, temporal algebra, temporal topology analysis, time series animation and temporal topology visualization to time series import and export capabilities with support for NetCDF and VTK data formats. We will present several temporal modules that support parallel processing of raster and 3D raster time series. [1] GRASS GIS Open Source Approaches in Spatial Data Handling In Open Source Approaches in Spatial Data Handling, Vol. 2 (2008), pp. 171-199, doi:10.1007/978-3-540-74831-19 by M. Neteler, D. Beaudette, P. Cavallini, L. Lami, J. Cepicky edited by G. Brent Hall, Michael G. Leahy [2] Gebbert, S., Pebesma, E., 2014. A temporal GIS for field based environmental modeling. Environ. Model. Softw. 53, 1-12. [3] Zambelli, P., Gebbert, S., Ciolli, M., 2013. Pygrass: An Object Oriented Python Application Programming Interface (API) for Geographic Resources Analysis Support System (GRASS) Geographic Information System (GIS). ISPRS Intl Journal of Geo-Information 2, 201-219. [4] Löwe, P., Klump, J., Thaler, J. (2012): The FOSS GIS Workbench on the GFZ Load Sharing Facility compute cluster, (Geophysical Research Abstracts Vol. 14, EGU2012-4491, 2012), General Assembly European Geosciences Union (Vienna, Austria 2012). [5] Akhter, S., Aida, K., Chemin, Y., 2010. "GRASS GIS on High Performance Computing with MPI, OpenMP and Ninf-G Programming Framework". ISPRS Conference, Kyoto, 9-12 August 2010

ARCHANGEL: Galaxy Photometry System

NASA Astrophysics Data System (ADS)

Schombert, James

2011-07-01

ARCHANGEL is a Unix-based package for the surface photometry of galaxies. While oriented for large angular size systems (i.e. many pixels), its tools can be applied to any imaging data of any size. The package core contains routines to perform the following critical galaxy photometry functions: sky determination; frame cleaning; ellipse fitting; profile fitting; and total and isophotal magnitudes. The goal of the package is to provide an automated, assembly-line type of reduction system for galaxy photometry of space-based or ground-based imaging data. The procedures outlined in the documentation are flux independent, thus, these routines can be used for non-optical data as well as typical imaging datasets. ARCHANGEL has been tested on several current OS's (RedHat Linux, Ubuntu Linux, Solaris, Mac OS X). A tarball for installation is available at the download page. The main routines are Python and FORTRAN based, therefore, a current installation of Python and a FORTRAN compiler are required. The ARCHANGEL package also contains Python hooks to the PGPLOT package, an XML processor and network tools which automatically link to data archives (i.e. NED, HST, 2MASS, etc) to download images in a non-interactive manner.

PyFLOWGO: An open-source platform for simulation of channelized lava thermo-rheological properties

NASA Astrophysics Data System (ADS)

Chevrel, Magdalena Oryaëlle; Labroquère, Jérémie; Harris, Andrew J. L.; Rowland, Scott K.

2018-02-01

Lava flow advance can be modeled through tracking the evolution of the thermo-rheological properties of a control volume of lava as it cools and crystallizes. An example of such a model was conceived by Harris and Rowland (2001) who developed a 1-D model, FLOWGO, in which the velocity of a control volume flowing down a channel depends on rheological properties computed following the thermal path estimated via a heat balance box model. We provide here an updated version of FLOWGO written in Python that is an open-source, modern and flexible language. Our software, named PyFLOWGO, allows selection of heat fluxes and rheological models of the user's choice to simulate the thermo-rheological evolution of the lava control volume. We describe its architecture which offers more flexibility while reducing the risk of making error when changing models in comparison to the previous FLOWGO version. Three cases are tested using actual data from channel-fed lava flow systems and results are discussed in terms of model validation and convergence. PyFLOWGO is open-source and packaged in a Python library to be imported and reused in any Python program (https://github.com/pyflowgo/pyflowgo)

Image Classification Workflow Using Machine Learning Methods

NASA Astrophysics Data System (ADS)

Christoffersen, M. S.; Roser, M.; Valadez-Vergara, R.; Fernández-Vega, J. A.; Pierce, S. A.; Arora, R.

2016-12-01

Recent increases in the availability and quality of remote sensing datasets have fueled an increasing number of scientifically significant discoveries based on land use classification and land use change analysis. However, much of the software made to work with remote sensing data products, specifically multispectral images, is commercial and often prohibitively expensive. The free to use solutions that are currently available come bundled up as small parts of much larger programs that are very susceptible to bugs and difficult to install and configure. What is needed is a compact, easy to use set of tools to perform land use analysis on multispectral images. To address this need, we have developed software using the Python programming language with the sole function of land use classification and land use change analysis. We chose Python to develop our software because it is relatively readable, has a large body of relevant third party libraries such as GDAL and Spectral Python, and is free to install and use on Windows, Linux, and Macintosh operating systems. In order to test our classification software, we performed a K-means unsupervised classification, Gaussian Maximum Likelihood supervised classification, and a Mahalanobis Distance based supervised classification. The images used for testing were three Landsat rasters of Austin, Texas with a spatial resolution of 60 meters for the years of 1984 and 1999, and 30 meters for the year 2015. The testing dataset was easily downloaded using the Earth Explorer application produced by the USGS. The software should be able to perform classification based on any set of multispectral rasters with little to no modification. Our software makes the ease of land use classification using commercial software available without an expensive license.

EggLib: processing, analysis and simulation tools for population genetics and genomics

PubMed Central

2012-01-01

Background With the considerable growth of available nucleotide sequence data over the last decade, integrated and flexible analytical tools have become a necessity. In particular, in the field of population genetics, there is a strong need for automated and reliable procedures to conduct repeatable and rapid polymorphism analyses, coalescent simulations, data manipulation and estimation of demographic parameters under a variety of scenarios. Results In this context, we present EggLib (Evolutionary Genetics and Genomics Library), a flexible and powerful C++/Python software package providing efficient and easy to use computational tools for sequence data management and extensive population genetic analyses on nucleotide sequence data. EggLib is a multifaceted project involving several integrated modules: an underlying computationally efficient C++ library (which can be used independently in pure C++ applications); two C++ programs; a Python package providing, among other features, a high level Python interface to the C++ library; and the egglib script which provides direct access to pre-programmed Python applications. Conclusions EggLib has been designed aiming to be both efficient and easy to use. A wide array of methods are implemented, including file format conversion, sequence alignment edition, coalescent simulations, neutrality tests and estimation of demographic parameters by Approximate Bayesian Computation (ABC). Classes implementing different demographic scenarios for ABC analyses can easily be developed by the user and included to the package. EggLib source code is distributed freely under the GNU General Public License (GPL) from its website http://egglib.sourceforge.net/ where a full documentation and a manual can also be found and downloaded. PMID:22494792

Mammal decline, linked to invasive Burmese python, shifts host use of vector mosquito towards reservoir hosts of a zoonotic disease.

PubMed

Hoyer, Isaiah J; Blosser, Erik M; Acevedo, Carolina; Thompson, Anna Carels; Reeves, Lawrence E; Burkett-Cadena, Nathan D

2017-10-01

Invasive apex predators have profound impacts on natural communities, yet the consequences of these impacts on the transmission of zoonotic pathogens are unexplored. Collapse of large- and medium-sized mammal populations in the Florida Everglades has been linked to the invasive Burmese python, Python bivittatus Kuhl. We used historic and current data to investigate potential impacts of these community effects on contact between the reservoir hosts (certain rodents) and vectors of Everglades virus, a zoonotic mosquito-borne pathogen that circulates in southern Florida. The percentage of blood meals taken from the primary reservoir host, the hispid cotton rat, Sigmodon hispidus Say and Ord, increased dramatically (422.2%) from 1979 (14.7%) to 2016 (76.8%), while blood meals from deer, raccoons and opossums decreased by 98.2%, reflecting precipitous declines in relative abundance of these larger mammals, attributed to python predation. Overall species diversity of hosts detected in Culex cedecei blood meals from the Everglades declined by 40.2% over the same period ( H (1979) = 1.68, H (2016) = 1.01). Predictions based upon the dilution effect theory suggest that increased relative feedings upon reservoir hosts translate into increased abundance of infectious vectors, and a corresponding upsurge of Everglades virus occurrence and risk of human exposure, although this was not tested in the current study. This work constitutes the first indication that an invasive predator can increase contact between vectors and reservoirs of a human pathogen and highlights unrecognized indirect impacts of invasive predators. © 2017 The Author(s).

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

NASA Astrophysics Data System (ADS)

Sainio, J.

2012-04-01

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

EggLib: processing, analysis and simulation tools for population genetics and genomics.

PubMed

De Mita, Stéphane; Siol, Mathieu

2012-04-11

With the considerable growth of available nucleotide sequence data over the last decade, integrated and flexible analytical tools have become a necessity. In particular, in the field of population genetics, there is a strong need for automated and reliable procedures to conduct repeatable and rapid polymorphism analyses, coalescent simulations, data manipulation and estimation of demographic parameters under a variety of scenarios. In this context, we present EggLib (Evolutionary Genetics and Genomics Library), a flexible and powerful C++/Python software package providing efficient and easy to use computational tools for sequence data management and extensive population genetic analyses on nucleotide sequence data. EggLib is a multifaceted project involving several integrated modules: an underlying computationally efficient C++ library (which can be used independently in pure C++ applications); two C++ programs; a Python package providing, among other features, a high level Python interface to the C++ library; and the egglib script which provides direct access to pre-programmed Python applications. EggLib has been designed aiming to be both efficient and easy to use. A wide array of methods are implemented, including file format conversion, sequence alignment edition, coalescent simulations, neutrality tests and estimation of demographic parameters by Approximate Bayesian Computation (ABC). Classes implementing different demographic scenarios for ABC analyses can easily be developed by the user and included to the package. EggLib source code is distributed freely under the GNU General Public License (GPL) from its website http://egglib.sourceforge.net/ where a full documentation and a manual can also be found and downloaded.

pyMOOGi - python wrapper for MOOG

NASA Astrophysics Data System (ADS)

Adamow, Monika M.

2017-06-01

pyMOOGi is a python wrapper for MOOG. It allows to use MOOG in a classical, interactive way, but with all graphics handled by python libraries. Some MOOG features have been redesigned, like plotting with abfind driver. Also, new funtions have been added, like automatic rescaling of stellar spectrum for synth driver. pyMOOGi is an open source project.

Short telomeres in hatchling snakes: erythrocyte telomere dynamics and longevity in tropical pythons.

PubMed

Ujvari, Beata; Madsen, Thomas

2009-10-16

Telomere length (TL) has been found to be associated with life span in birds and humans. However, other studies have demonstrated that TL does not affect survival among old humans. Furthermore, replicative senescence has been shown to be induced by changes in the protected status of the telomeres rather than the loss of TL. In the present study we explore whether age- and sex-specific telomere dynamics affect life span in a long-lived snake, the water python (Liasis fuscus). Erythrocyte TL was measured using the Telo TAGGG TL Assay Kit (Roche). In contrast to other vertebrates, TL of hatchling pythons was significantly shorter than that of older snakes. However, during their first year of life hatchling TL increased substantially. While TL of older snakes decreased with age, we did not observe any correlation between TL and age in cross-sectional sampling. In older snakes, female TL was longer than that of males. When using recapture as a proxy for survival, our results do not support that longer telomeres resulted in an increased water python survival/longevity. In fish high telomerase activity has been observed in somatic cells exhibiting high proliferation rates. Hatchling pythons show similar high somatic cell proliferation rates. Thus, the increase in TL of this group may have been caused by increased telomerase activity. In older humans female TL is longer than that of males. This has been suggested to be caused by high estrogen levels that stimulate increased telomerase activity. Thus, high estrogen levels may also have caused the longer telomeres in female pythons. The lack of correlation between TL and age among old snakes and the fact that longer telomeres did not appear to affect python survival do not support that erythrocyte telomere dynamics has a major impact on water python longevity.

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

PubMed

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

2015-01-01

Snake venom gene evolution has been studied intensively over the past several decades, yet most previous studies have lacked the context of complete snake genomes and the full context of gene expression across diverse snake tissues. We took a novel approach to studying snake venom evolution by leveraging the complete genome of the Burmese python, including information from tissue-specific patterns of gene expression. We identified the orthologs of snake venom genes in the python genome, and conducted detailed analysis of gene expression of these venom homologs to identify patterns that differ between snake venom gene families and all other genes. We found that venom gene homologs in the python are expressed in many different tissues outside of oral glands, which illustrates the pitfalls of using transcriptomic data alone to define "venom toxins." We hypothesize that the python may represent an ancestral state prior to major venom development, which is supported by our finding that the expansion of venom gene families is largely restricted to highly venomous caenophidian snakes. Therefore, the python provides insight into biases in which genes were recruited for snake venom systems. Python venom homologs are generally expressed at lower levels, have higher variance among tissues, and are expressed in fewer organs compared with all other python genes. We propose a model for the evolution of snake venoms in which venom genes are recruited preferentially from genes with particular expression profile characteristics, which facilitate a nearly neutral transition toward specialized venom system expression. © The Author 2014. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Unified EDGE

DOE Office of Scientific and Technical Information (OSTI.GOV)

2007-06-18

UEDGE is an interactive suite of physics packages using the Python or BASIS scripting systems. The plasma is described by time-dependent 2D plasma fluid equations that include equations for density, velocity, ion temperature, electron temperature, electrostatic potential, and gas density in the edge region of a magnetic fusion energy confinement device. Slab, cylindrical, and toroidal geometries are allowed, and closed and open magnetic field-line regions are included. Classical transport is assumed along magnetic field lines, and anomalous transport is assumed across field lines. Multi-charge state impurities can be included with the corresponding line-radiation energy loss. Although UEDGE is written inmore » Fortran, for efficient execution and analysis of results, it utilizes either Python or BASIS scripting shells. Python is easily available for many platforms (http://www.Python.org/). The features and availability of BASIS are described in "Basis Manual Set" by P.F. Dubois, Z.C. Motteler, et al., Lawrence Livermore National Laboratory report UCRL-MA-1 18541, June, 2002 and http://basis.llnl.gov. BASIS has been reviewed and released by LLNL for unlimited distribution. The Python version utilizes PYBASIS scripts developed by D.P. Grote, LLNL. The Python version also uses MPPL code and MAC Perl script, available from the public-domain BASIS source above. The Forthon version of UEDGE uses the same source files, but utilizes Forthon to produce a Python-compatible source. Forthon has been developed by D.P. Grote at LBL (see http://hifweb.lbl.gov/Forthon/ and Grote et al. in the references below), and it is freely available. The graphics can be performed by any package importable to Python, such as PYGIST.« less

Identification of a novel nidovirus in an outbreak of fatal respiratory disease in ball pythons (Python regius).

PubMed

Uccellini, Lorenzo; Ossiboff, Robert J; de Matos, Ricardo E C; Morrisey, James K; Petrosov, Alexandra; Navarrete-Macias, Isamara; Jain, Komal; Hicks, Allison L; Buckles, Elizabeth L; Tokarz, Rafal; McAloose, Denise; Lipkin, Walter Ian

2014-08-08

Respiratory infections are important causes of morbidity and mortality in reptiles; however, the causative agents are only infrequently identified. Pneumonia, tracheitis and esophagitis were reported in a collection of ball pythons (Python regius). Eight of 12 snakes had evidence of bacterial pneumonia. High-throughput sequencing of total extracted nucleic acids from lung, esophagus and spleen revealed a novel nidovirus. PCR indicated the presence of viral RNA in lung, trachea, esophagus, liver, and spleen. In situ hybridization confirmed the presence of intracellular, intracytoplasmic viral nucleic acids in the lungs of infected snakes. Phylogenetic analysis based on a 1,136 amino acid segment of the polyprotein suggests that this virus may represent a new species in the subfamily Torovirinae. This report of a novel nidovirus in ball pythons may provide insight into the pathogenesis of respiratory disease in this species and enhances our knowledge of the diversity of nidoviruses.

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.

Dtest Testing Software

NASA Technical Reports Server (NTRS)

Jain, Abhinandan; Cameron, Jonathan M.; Myint, Steven

2013-01-01

This software runs a suite of arbitrary software tests spanning various software languages and types of tests (unit level, system level, or file comparison tests). The dtest utility can be set to automate periodic testing of large suites of software, as well as running individual tests. It supports distributing multiple tests over multiple CPU cores, if available. The dtest tool is a utility program (written in Python) that scans through a directory (and its subdirectories) and finds all directories that match a certain pattern and then executes any tests in that directory as described in simple configuration files.

Python based high-level synthesis compiler

NASA Astrophysics Data System (ADS)

Cieszewski, Radosław; Pozniak, Krzysztof; Romaniuk, Ryszard

2014-11-01

This paper presents a python based High-Level synthesis (HLS) compiler. The compiler interprets an algorithmic description of a desired behavior written in Python and map it to VHDL. FPGA combines many benefits of both software and ASIC implementations. Like software, the mapped circuit is flexible, and can be reconfigured over the lifetime of the system. FPGAs therefore have the potential to achieve far greater performance than software as a result of bypassing the fetch-decode-execute operations of traditional processors, and possibly exploiting a greater level of parallelism. Creating parallel programs implemented in FPGAs is not trivial. This article describes design, implementation and first results of created Python based compiler.

Implementation of quantum game theory simulations using Python

NASA Astrophysics Data System (ADS)

Madrid S., A.

2013-05-01

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

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. Copyright © 2014 Elsevier Ltd. All rights reserved.

Amebiasis in four ball pythons, Python reginus.

PubMed

Kojimoto, A; Uchida, K; Horii, Y; Okumura, S; Yamaguch, R; Tateyama, S

2001-12-01

Between September 13th and November 18th in 1999, four ball pythons, Python reginus kept in the same display, showed anorexia and died one after another. At necropsy, all four snakes had severe hemorrhagic colitis. Microscopically, all snakes had severe necrotizing hemorrhagic colitis, in association with ameba-like protozoa. Some of the protozoa had macrophage-like morphology and others formed protozoal cysts with thickened walls. These protozoa were distributed throughout the wall in the large intestine. Based on the pathological findings, these snakes were infested with a member of Entamoeba sp., presumably with infection by Entamoeba invadens, the most prevalent type of reptilian amoebae.

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

PubMed

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

2011-01-01

Current neuroimaging software offer users an incredible opportunity to analyze their data in different ways, with different underlying assumptions. Several sophisticated software packages (e.g., AFNI, BrainVoyager, FSL, FreeSurfer, Nipy, R, SPM) are used to process and analyze large and often diverse (highly multi-dimensional) data. However, this heterogeneous collection of specialized applications creates several issues that hinder replicable, efficient, and optimal use of neuroimaging analysis approaches: (1) No uniform access to neuroimaging analysis software and usage information; (2) No framework for comparative algorithm development and dissemination; (3) Personnel turnover in laboratories often limits methodological continuity and training new personnel takes time; (4) Neuroimaging software packages do not address computational efficiency; and (5) Methods sections in journal articles are inadequate for reproducing results. To address these issues, we present Nipype (Neuroimaging in Python: Pipelines and Interfaces; http://nipy.org/nipype), an open-source, community-developed, software package, and scriptable library. Nipype solves the issues by providing Interfaces to existing neuroimaging software with uniform usage semantics and by facilitating interaction between these packages using Workflows. Nipype provides an environment that encourages interactive exploration of algorithms, eases the design of Workflows within and between packages, allows rapid comparative development of algorithms and reduces the learning curve necessary to use different packages. Nipype supports both local and remote execution on multi-core machines and clusters, without additional scripting. Nipype is Berkeley Software Distribution licensed, allowing anyone unrestricted usage. An open, community-driven development philosophy allows the software to quickly adapt and address the varied needs of the evolving neuroimaging community, especially in the context of increasing demand for reproducible research.

A lightweight, flow-based toolkit for parallel and distributed bioinformatics pipelines

PubMed Central

2011-01-01

Background Bioinformatic analyses typically proceed as chains of data-processing tasks. A pipeline, or 'workflow', is a well-defined protocol, with a specific structure defined by the topology of data-flow interdependencies, and a particular functionality arising from the data transformations applied at each step. In computer science, the dataflow programming (DFP) paradigm defines software systems constructed in this manner, as networks of message-passing components. Thus, bioinformatic workflows can be naturally mapped onto DFP concepts. Results To enable the flexible creation and execution of bioinformatics dataflows, we have written a modular framework for parallel pipelines in Python ('PaPy'). A PaPy workflow is created from re-usable components connected by data-pipes into a directed acyclic graph, which together define nested higher-order map functions. The successive functional transformations of input data are evaluated on flexibly pooled compute resources, either local or remote. Input items are processed in batches of adjustable size, all flowing one to tune the trade-off between parallelism and lazy-evaluation (memory consumption). An add-on module ('NuBio') facilitates the creation of bioinformatics workflows by providing domain specific data-containers (e.g., for biomolecular sequences, alignments, structures) and functionality (e.g., to parse/write standard file formats). Conclusions PaPy offers a modular framework for the creation and deployment of parallel and distributed data-processing workflows. Pipelines derive their functionality from user-written, data-coupled components, so PaPy also can be viewed as a lightweight toolkit for extensible, flow-based bioinformatics data-processing. The simplicity and flexibility of distributed PaPy pipelines may help users bridge the gap between traditional desktop/workstation and grid computing. PaPy is freely distributed as open-source Python code at http://muralab.org/PaPy, and includes extensive documentation and annotated usage examples. PMID:21352538

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

PubMed Central

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

2011-01-01

Current neuroimaging software offer users an incredible opportunity to analyze their data in different ways, with different underlying assumptions. Several sophisticated software packages (e.g., AFNI, BrainVoyager, FSL, FreeSurfer, Nipy, R, SPM) are used to process and analyze large and often diverse (highly multi-dimensional) data. However, this heterogeneous collection of specialized applications creates several issues that hinder replicable, efficient, and optimal use of neuroimaging analysis approaches: (1) No uniform access to neuroimaging analysis software and usage information; (2) No framework for comparative algorithm development and dissemination; (3) Personnel turnover in laboratories often limits methodological continuity and training new personnel takes time; (4) Neuroimaging software packages do not address computational efficiency; and (5) Methods sections in journal articles are inadequate for reproducing results. To address these issues, we present Nipype (Neuroimaging in Python: Pipelines and Interfaces; http://nipy.org/nipype), an open-source, community-developed, software package, and scriptable library. Nipype solves the issues by providing Interfaces to existing neuroimaging software with uniform usage semantics and by facilitating interaction between these packages using Workflows. Nipype provides an environment that encourages interactive exploration of algorithms, eases the design of Workflows within and between packages, allows rapid comparative development of algorithms and reduces the learning curve necessary to use different packages. Nipype supports both local and remote execution on multi-core machines and clusters, without additional scripting. Nipype is Berkeley Software Distribution licensed, allowing anyone unrestricted usage. An open, community-driven development philosophy allows the software to quickly adapt and address the varied needs of the evolving neuroimaging community, especially in the context of increasing demand for reproducible research. PMID:21897815

A lightweight, flow-based toolkit for parallel and distributed bioinformatics pipelines.

PubMed

Cieślik, Marcin; Mura, Cameron

2011-02-25

Bioinformatic analyses typically proceed as chains of data-processing tasks. A pipeline, or 'workflow', is a well-defined protocol, with a specific structure defined by the topology of data-flow interdependencies, and a particular functionality arising from the data transformations applied at each step. In computer science, the dataflow programming (DFP) paradigm defines software systems constructed in this manner, as networks of message-passing components. Thus, bioinformatic workflows can be naturally mapped onto DFP concepts. To enable the flexible creation and execution of bioinformatics dataflows, we have written a modular framework for parallel pipelines in Python ('PaPy'). A PaPy workflow is created from re-usable components connected by data-pipes into a directed acyclic graph, which together define nested higher-order map functions. The successive functional transformations of input data are evaluated on flexibly pooled compute resources, either local or remote. Input items are processed in batches of adjustable size, all flowing one to tune the trade-off between parallelism and lazy-evaluation (memory consumption). An add-on module ('NuBio') facilitates the creation of bioinformatics workflows by providing domain specific data-containers (e.g., for biomolecular sequences, alignments, structures) and functionality (e.g., to parse/write standard file formats). PaPy offers a modular framework for the creation and deployment of parallel and distributed data-processing workflows. Pipelines derive their functionality from user-written, data-coupled components, so PaPy also can be viewed as a lightweight toolkit for extensible, flow-based bioinformatics data-processing. The simplicity and flexibility of distributed PaPy pipelines may help users bridge the gap between traditional desktop/workstation and grid computing. PaPy is freely distributed as open-source Python code at http://muralab.org/PaPy, and includes extensive documentation and annotated usage examples.

EAGLE: 'EAGLE'Is an' Algorithmic Graph Library for Exploration

DOE Office of Scientific and Technical Information (OSTI.GOV)

2015-01-16

The Resource Description Framework (RDF) and SPARQL Protocol and RDF Query Language (SPARQL) were introduced about a decade ago to enable flexible schema-free data interchange on the Semantic Web. Today data scientists use the framework as a scalable graph representation for integrating, querying, exploring and analyzing data sets hosted at different sources. With increasing adoption, the need for graph mining capabilities for the Semantic Web has emerged. Today there is no tools to conduct "graph mining" on RDF standard data sets. We address that need through implementation of popular iterative Graph Mining algorithms (Triangle count, Connected component analysis, degree distribution,more » diversity degree, PageRank, etc.). We implement these algorithms as SPARQL queries, wrapped within Python scripts and call our software tool as EAGLE. In RDF style, EAGLE stands for "EAGLE 'Is an' algorithmic graph library for exploration. EAGLE is like 'MATLAB' for 'Linked Data.'« less

Bitshuffle: Filter for improving compression of typed binary data

NASA Astrophysics Data System (ADS)

Masui, Kiyoshi

2017-12-01

Bitshuffle rearranges typed, binary data for improving compression; the algorithm is implemented in a python/C package within the Numpy framework. The library can be used alongside HDF5 to compress and decompress datasets and is integrated through the dynamically loaded filters framework. Algorithmically, Bitshuffle is closely related to HDF5's Shuffle filter except it operates at the bit level instead of the byte level. Arranging a typed data array in to a matrix with the elements as the rows and the bits within the elements as the columns, Bitshuffle "transposes" the matrix, such that all the least-significant-bits are in a row, etc. This transposition is performed within blocks of data roughly 8kB long; this does not in itself compress data, but rearranges it for more efficient compression. A compression library is necessary to perform the actual compression. This scheme has been used for compression of radio data in high performance computing.

IceProd 2: A Next Generation Data Analysis Framework for the IceCube Neutrino Observatory

NASA Astrophysics Data System (ADS)

Schultz, D.

2015-12-01

We describe the overall structure and new features of the second generation of IceProd, a data processing and management framework. IceProd was developed by the IceCube Neutrino Observatory for processing of Monte Carlo simulations, detector data, and analysis levels. It runs as a separate layer on top of grid and batch systems. This is accomplished by a set of daemons which process job workflow, maintaining configuration and status information on the job before, during, and after processing. IceProd can also manage complex workflow DAGs across distributed computing grids in order to optimize usage of resources. IceProd is designed to be very light-weight; it runs as a python application fully in user space and can be set up easily. For the initial completion of this second version of IceProd, improvements have been made to increase security, reliability, scalability, and ease of use.

AMP: a science-driven web-based application for the TeraGrid

NASA Astrophysics Data System (ADS)

Woitaszek, M.; Metcalfe, T.; Shorrock, I.

The Asteroseismic Modeling Portal (AMP) provides a web-based interface for astronomers to run and view simulations that derive the properties of Sun-like stars from observations of their pulsation frequencies. In this paper, we describe the architecture and implementation of AMP, highlighting the lightweight design principles and tools used to produce a functional fully-custom web-based science application in less than a year. Targeted as a TeraGrid science gateway, AMP's architecture and implementation are intended to simplify its orchestration of TeraGrid computational resources. AMP's web-based interface was developed as a traditional standalone database-backed web application using the Python-based Django web development framework, allowing us to leverage the Django framework's capabilities while cleanly separating the user interface development from the grid interface development. We have found this combination of tools flexible and effective for rapid gateway development and deployment.

Ecological correlates of invasion impact for Burmese pythons in Florida.

PubMed

Reed, Robert N; Willson, John D; Rodda, Gordon H; Dorcas, Michael E

2012-09-01

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. © 2012 Wiley Publishing Asia Pty Ltd, ISZS and IOZ/CAS.

Pharmacokinetics of a long-acting ceftiofur formulation (ceftiofur crystalline free acid) in the ball python (Python regius).

PubMed

Adkesson, Michael J; Fernandez-Varon, Emilio; Cox, Sherry; Martín-Jiménez, Tomás

2011-09-01

The objective of this study was to determine the pharmacokinetics of a long-acting formulation of ceftiofur crystalline-free acid (CCFA) following intramuscular injection in ball pythons (Python regius). Six adult ball pythons received an injection of CCFA (15 mg/kg) in the epaxial muscles. Blood samples were collected by cardiocentesis immediately prior to and at 0.5, 1, 2, 4, 8, 12, 18, 24, 48, 72, 96, 144, 192, 240, 288, 384, 480, 576, 720, and 864 hr after CCFA administration. Plasma ceftiofur concentrations were determined by high-performance liquid chromatography. A noncompartmental pharmacokinetic analysis was applied to the data. Maximum plasma concentration (Cmax) was 7.096 +/- 1.95 microg/ml and occurred at (Tmax) 2.17 +/- 0.98 hr. The area under the curve (0 to infinity) for ceftiofur was 74.59 +/- 13.05 microg x h/ml and the elimination half-life associated with the terminal slope of the concentration-time curve was 64.31 +/- 14.2 hr. Mean residence time (0 to infinity) was 46.85 +/- 13.53 hr. CCFA at 15 mg/kg was well tolerated in all the pythons. Minimum inhibitory concentration (MIC) data for bacterial isolates from snakes are not well established. For MIC values of < or =0.1 microg/ml, a single dose of CCFA (15 mg/kg) provides adequate plasma concentrations for at least 5 days in the ball python. For MICs > or =0.5 microg/ml, more frequent dosing or a higher dosage may be required.

Development of a technique for contrast radiographic examination of the gastrointestinal tract in ball pythons (Python regius).

PubMed

Banzato, Tommaso; Russo, Elisa; Finotti, Luca; Zotti, Alessandro

2012-07-01

To develop a technique for radiographic evaluation of the gastrointestinal tract in ball pythons (Python regius). 10 ball python cadavers (5 males and 5 females) and 18 healthy adult ball pythons (10 males and 8 females). Live snakes were allocated to 3 groups (A, B, and C). A dose (25 mL/kg) of barium sulfate suspension at 3 concentrations (25%, 35%, and 45% [wt/vol]) was administered through an esophageal probe to snakes in groups A, B, and C, respectively. Each evaluation ended when all the contrast medium had reached the large intestine. Transit times through the esophagus, stomach, and small intestine were recorded. Imaging quality was evaluated by 3 investigators who assigned a grading score on the basis of predetermined criteria. Statistical analysis was conducted to evaluate differences in quality among the study groups. The esophagus and stomach had a consistent distribution pattern of contrast medium, whereas 3 distribution patterns of contrast medium were identified in the small intestine, regardless of barium concentration. Significant differences in imaging quality were detected among the 3 groups. Radiographic procedures were tolerated well by all snakes. The 35% concentration of contrast medium yielded the best imaging quality. Use of contrast medium for evaluation of the cranial portion of the gastrointestinal tract could be a reliable technique for the diagnosis of gastrointestinal diseases in ball pythons. However, results of this study may not translate to other snake species because of variables identified in this group of snakes.

Anatomy of the python heart.

PubMed

Jensen, Bjarke; Nyengaard, Jens R; Pedersen, Michael; Wang, Tobias

2010-12-01

The hearts of all snakes and lizards consist of two atria and a single incompletely divided ventricle. In general, the squamate ventricle is subdivided into three chambers: cavum arteriosum (left), cavum venosum (medial) and cavum pulmonale (right). Although a similar division also applies to the heart of pythons, this family of snakes is unique amongst snakes in having intracardiac pressure separation. Here we provide a detailed anatomical description of the cardiac structures that confer this functional division. We measured the masses and volumes of the ventricular chambers, and we describe the gross morphology based on dissections of the heart from 13 ball pythons (Python regius) and one Burmese python (P. molurus). The cavum venosum is much reduced in pythons and constitutes approximately 10% of the cavum arteriosum. We suggest that shunts will always be less than 20%, while other studies conclude up to 50%. The high-pressure cavum arteriosum accounted for approximately 75% of the total ventricular mass, and was twice as dense as the low-pressure cavum pulmonale. The reptile ventricle has a core of spongious myocardium, but the three ventricular septa that separate the pulmonary and systemic chambers--the muscular ridge, the bulbuslamelle and the vertical septum--all had layers of compact myocardium. Pythons, however, have unique pads of connective tissue on the site of pressure separation. Because the hearts of varanid lizards, which also are endowed with pressure separation, share many of these morphological specializations, we propose that intraventricular compact myocardium is an indicator of high-pressure systems and possibly pressure separation.

Large reptiles and cold temperatures: Do extreme cold spells set distributional limits for tropical reptiles in Florida?

USGS Publications Warehouse

Mazzotti, Frank J.; Cherkiss, Michael S.; Parry, Mark; Beauchamp, Jeff; Rochford, Mike; Smith, Brian J.; Hart, Kristen M.; Brandt, Laura A.

2016-01-01

Distributional limits of many tropical species in Florida are ultimately determined by tolerance to low temperature. An unprecedented cold spell during 2–11 January 2010, in South Florida provided an opportunity to compare the responses of tropical American crocodiles with warm-temperate American alligators and to compare the responses of nonnative Burmese pythons with native warm-temperate snakes exposed to prolonged cold temperatures. After the January 2010 cold spell, a record number of American crocodiles (n = 151) and Burmese pythons (n = 36) were found dead. In contrast, no American alligators and no native snakes were found dead. American alligators and American crocodiles behaved differently during the cold spell. American alligators stopped basking and retreated to warmer water. American crocodiles apparently continued to bask during extreme cold temperatures resulting in lethal body temperatures. The mortality of Burmese pythons compared to the absence of mortality for native snakes suggests that the current population of Burmese pythons in the Everglades is less tolerant of cold temperatures than native snakes. Burmese pythons introduced from other parts of their native range may be more tolerant of cold temperatures. We documented the direct effects of cold temperatures on crocodiles and pythons; however, evidence of long-term effects of cold temperature on their populations within their established ranges remains lacking. Mortality of crocodiles and pythons outside of their current established range may be more important in setting distributional limits.

pyomo.dae: a modeling and automatic discretization framework for optimization with differential and algebraic equations

DOE PAGES

Nicholson, Bethany; Siirola, John D.; Watson, Jean-Paul; ...

2017-12-20

We describe pyomo.dae, an open source Python-based modeling framework that enables high-level abstract specification of optimization problems with differential and algebraic equations. The pyomo.dae framework is integrated with the Pyomo open source algebraic modeling language, and is available at http://www.pyomo.org. One key feature of pyomo.dae is that it does not restrict users to standard, predefined forms of differential equations, providing a high degree of modeling flexibility and the ability to express constraints that cannot be easily specified in other modeling frameworks. Other key features of pyomo.dae are the ability to specify optimization problems with high-order differential equations and partial differentialmore » equations, defined on restricted domain types, and the ability to automatically transform high-level abstract models into finite-dimensional algebraic problems that can be solved with off-the-shelf solvers. Moreover, pyomo.dae users can leverage existing capabilities of Pyomo to embed differential equation models within stochastic and integer programming models and mathematical programs with equilibrium constraint formulations. Collectively, these features enable the exploration of new modeling concepts, discretization schemes, and the benchmarking of state-of-the-art optimization solvers.« less

pyBadlands: A framework to simulate sediment transport, landscape dynamics and basin stratigraphic evolution through space and time

PubMed Central

2018-01-01

Understanding Earth surface responses in terms of sediment dynamics to climatic variability and tectonics forcing is hindered by limited ability of current models to simulate long-term evolution of sediment transfer and associated morphological changes. This paper presents pyBadlands, an open-source python-based framework which computes over geological time (1) sediment transport from landmasses to coasts, (2) reworking of marine sediments by longshore currents and (3) development of coral reef systems. pyBadlands is cross-platform, distributed under the GPLv3 license and available on GitHub (http://github.com/badlands-model). Here, we describe the underlying physical assumptions behind the simulated processes and the main options already available in the numerical framework. Along with the source code, a list of hands-on examples is provided that illustrates the model capabilities. In addition, pre and post-processing classes have been built and are accessible as a companion toolbox which comprises a series of workflows to efficiently build, quantify and explore simulation input and output files. While the framework has been primarily designed for research, its simplicity of use and portability makes it a great tool for teaching purposes. PMID:29649301

pyomo.dae: a modeling and automatic discretization framework for optimization with differential and algebraic equations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nicholson, Bethany; Siirola, John D.; Watson, Jean-Paul

We describe pyomo.dae, an open source Python-based modeling framework that enables high-level abstract specification of optimization problems with differential and algebraic equations. The pyomo.dae framework is integrated with the Pyomo open source algebraic modeling language, and is available at http://www.pyomo.org. One key feature of pyomo.dae is that it does not restrict users to standard, predefined forms of differential equations, providing a high degree of modeling flexibility and the ability to express constraints that cannot be easily specified in other modeling frameworks. Other key features of pyomo.dae are the ability to specify optimization problems with high-order differential equations and partial differentialmore » equations, defined on restricted domain types, and the ability to automatically transform high-level abstract models into finite-dimensional algebraic problems that can be solved with off-the-shelf solvers. Moreover, pyomo.dae users can leverage existing capabilities of Pyomo to embed differential equation models within stochastic and integer programming models and mathematical programs with equilibrium constraint formulations. Collectively, these features enable the exploration of new modeling concepts, discretization schemes, and the benchmarking of state-of-the-art optimization solvers.« less

A-Track: A New Approach for Detection of Moving Objects in FITS Images

NASA Astrophysics Data System (ADS)

Kılıç, Yücel; Karapınar, Nurdan; Atay, Tolga; Kaplan, Murat

2016-07-01

Small planet and asteroid observations are important for understanding the origin and evolution of the Solar System. In this work, we have developed a fast and robust pipeline, called A-Track, for detecting asteroids and comets in sequential telescope images. The moving objects are detected using a modified line detection algorithm, called ILDA. We have coded the pipeline in Python 3, where we have made use of various scientific modules in Python to process the FITS images. We tested the code on photometrical data taken by an SI-1100 CCD with a 1-meter telescope at TUBITAK National Observatory, Antalya. The pipeline can be used to analyze large data archives or daily sequential data. The code is hosted on GitHub under the GNU GPL v3 license.

PyEPL: a cross-platform experiment-programming library.

PubMed

Geller, Aaron S; Schlefer, Ian K; Sederberg, Per B; Jacobs, Joshua; Kahana, Michael J

2007-11-01

PyEPL (the Python Experiment-Programming Library) is a Python library which allows cross-platform and object-oriented coding of behavioral experiments. It provides functions for displaying text and images onscreen, as well as playing and recording sound, and is capable of rendering 3-D virtual environments forspatial-navigation tasks. It is currently tested for Mac OS X and Linux. It interfaces with Activewire USB cards (on Mac OS X) and the parallel port (on Linux) for synchronization of experimental events with physiological recordings. In this article, we first present two sample programs which illustrate core PyEPL features. The examples demonstrate visual stimulus presentation, keyboard input, and simulation and exploration of a simple 3-D environment. We then describe the components and strategies used in implementing PyEPL.

PyEPL: A cross-platform experiment-programming library

PubMed Central

Geller, Aaron S.; Schleifer, Ian K.; Sederberg, Per B.; Jacobs, Joshua; Kahana, Michael J.

2009-01-01

PyEPL (the Python Experiment-Programming Library) is a Python library which allows cross-platform and object-oriented coding of behavioral experiments. It provides functions for displaying text and images onscreen, as well as playing and recording sound, and is capable of rendering 3-D virtual environments for spatial-navigation tasks. It is currently tested for Mac OS X and Linux. It interfaces with Activewire USB cards (on Mac OS X) and the parallel port (on Linux) for synchronization of experimental events with physiological recordings. In this article, we first present two sample programs which illustrate core PyEPL features. The examples demonstrate visual stimulus presentation, keyboard input, and simulation and exploration of a simple 3-D environment. We then describe the components and strategies used in implementing PyEPL. PMID:18183912

Developing a Conceptual Architecture for a Generalized Agent-based Modeling Environment (GAME)

DTIC Science & Technology

2008-03-01

4. REPAST (Java, Python , C#, Open Source) ........28 5. MASON: Multi-Agent Modeling Language (Swarm Extension... Python , C#, Open Source) Repast (Recursive Porous Agent Simulation Toolkit) was designed for building agent-based models and simulations in the...Repast makes it easy for inexperienced users to build models by including a built-in simple model and provide interfaces through which menus and Python

Robust, open-source removal of systematics in Kepler data

NASA Astrophysics Data System (ADS)

Aigrain, S.; Parviainen, H.; Roberts, S.; Reece, S.; Evans, T.

2017-10-01

We present ARC2 (Astrophysically Robust Correction 2), an open-source python-based systematics-correction pipeline, to correct for the Kepler prime mission long-cadence light curves. The ARC2 pipeline identifies and corrects any isolated discontinuities in the light curves and then removes trends common to many light curves. These trends are modelled using the publicly available co-trending basis vectors, within an (approximate) Bayesian framework with 'shrinkage' priors to minimize the risk of overfitting and the injection of any additional noise into the corrected light curves, while keeping any astrophysical signals intact. We show that the ARC2 pipeline's performance matches that of the standard Kepler PDC-MAP data products using standard noise metrics, and demonstrate its ability to preserve astrophysical signals using injection tests with simulated stellar rotation and planetary transit signals. Although it is not identical, the ARC2 pipeline can thus be used as an open-source alternative to PDC-MAP, whenever the ability to model the impact of the systematics removal process on other kinds of signal is important.

CaveMan Enterprise version 1.0 Software Validation and Verification.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hart, David

The U.S. Department of Energy Strategic Petroleum Reserve stores crude oil in caverns solution-mined in salt domes along the Gulf Coast of Louisiana and Texas. The CaveMan software program has been used since the late 1990s as one tool to analyze pressure mea- surements monitored at each cavern. The purpose of this monitoring is to catch potential cavern integrity issues as soon as possible. The CaveMan software was written in Microsoft Visual Basic, and embedded in a Microsoft Excel workbook; this method of running the CaveMan software is no longer sustainable. As such, a new version called CaveMan Enter- prisemore » has been developed. CaveMan Enterprise version 1.0 does not have any changes to the CaveMan numerical models. CaveMan Enterprise represents, instead, a change from desktop-managed work- books to an enterprise framework, moving data management into coordinated databases and porting the numerical modeling codes into the Python programming language. This document provides a report of the code validation and verification testing.« less

Patterns of Interspecific Variation in the Heart Rates of Embryonic Reptiles

PubMed Central

Du, Wei-Guo; Ye, Hua; Zhao, Bo; Pizzatto, Ligia; Ji, Xiang; Shine, Richard

2011-01-01

New non-invasive technologies allow direct measurement of heart rates (and thus, developmental rates) of embryos. We applied these methods to a diverse array of oviparous reptiles (24 species of lizards, 18 snakes, 11 turtles, 1 crocodilian), to identify general influences on cardiac rates during embryogenesis. Heart rates increased with ambient temperature in all lineages, but (at the same temperature) were faster in lizards and turtles than in snakes and crocodilians. We analysed these data within a phylogenetic framework. Embryonic heart rates were faster in species with smaller adult sizes, smaller egg sizes, and shorter incubation periods. Phylogenetic changes in heart rates were negatively correlated with concurrent changes in adult body mass and residual incubation period among the lizards, snakes (especially within pythons) and crocodilians. The total number of embryonic heart beats between oviposition and hatching was lower in squamates than in turtles or the crocodilian. Within squamates, embryonic iguanians and gekkonids required more heartbeats to complete development than did embryos of the other squamate families that we tested. These differences plausibly reflect phylogenetic divergence in the proportion of embryogenesis completed before versus after laying. PMID:22174948

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.

GNU Data Language (GDL) - a free and open-source implementation of IDL

NASA Astrophysics Data System (ADS)

Arabas, Sylwester; Schellens, Marc; Coulais, Alain; Gales, Joel; Messmer, Peter

2010-05-01

GNU Data Language (GDL) is developed with the aim of providing an open-source drop-in replacement for the ITTVIS's Interactive Data Language (IDL). It is free software developed by an international team of volunteers led by Marc Schellens - the project's founder (a list of contributors is available on the project's website). The development is hosted on SourceForge where GDL continuously ranks in the 99th percentile of most active projects. GDL with its library routines is designed as a tool for numerical data analysis and visualisation. As its proprietary counterparts (IDL and PV-WAVE), GDL is used particularly in geosciences and astronomy. GDL is dynamically-typed, vectorized and has object-oriented programming capabilities. The library routines handle numerical calculations, data visualisation, signal/image processing, interaction with host OS and data input/output. GDL supports several data formats such as netCDF, HDF4, HDF5, GRIB, PNG, TIFF, DICOM, etc. Graphical output is handled by X11, PostScript, SVG or z-buffer terminals, the last one allowing output to be saved in a variety of raster graphics formats. GDL is an incremental compiler with integrated debugging facilities. It is written in C++ using the ANTLR language-recognition framework. Most of the library routines are implemented as interfaces to open-source packages such as GNU Scientific Library, PLPlot, FFTW, ImageMagick, and others. GDL features a Python bridge (Python code can be called from GDL; GDL can be compiled as a Python module). Extensions to GDL can be written in C++, GDL, and Python. A number of open software libraries written in IDL, such as the NASA Astronomy Library, MPFIT, CMSVLIB and TeXtoIDL are fully or partially functional under GDL. Packaged versions of GDL are available for several Linux distributions and Mac OS X. The source code compiles on some other UNIX systems, including BSD and OpenSolaris. The presentation will cover the current status of the project, the key accomplishments, and the weaknesses - areas where contributions and users' feedback are welcome! While still being in beta-stage of development, GDL proved to be a useful tool for classroom work on data analysis. Its usage for teaching meteorological-data processing at the University of Warsaw will serve as an example.

ExEP yield modeling tool and validation test results

NASA Astrophysics Data System (ADS)

Morgan, Rhonda; Turmon, Michael; Delacroix, Christian; Savransky, Dmitry; Garrett, Daniel; Lowrance, Patrick; Liu, Xiang Cate; Nunez, Paul

2017-09-01

EXOSIMS is an open-source simulation tool for parametric modeling of the detection yield and characterization of exoplanets. EXOSIMS has been adopted by the Exoplanet Exploration Programs Standards Definition and Evaluation Team (ExSDET) as a common mechanism for comparison of exoplanet mission concept studies. To ensure trustworthiness of the tool, we developed a validation test plan that leverages the Python-language unit-test framework, utilizes integration tests for selected module interactions, and performs end-to-end crossvalidation with other yield tools. This paper presents the test methods and results, with the physics-based tests such as photometry and integration time calculation treated in detail and the functional tests treated summarily. The test case utilized a 4m unobscured telescope with an idealized coronagraph and an exoplanet population from the IPAC radial velocity (RV) exoplanet catalog. The known RV planets were set at quadrature to allow deterministic validation of the calculation of physical parameters, such as working angle, photon counts and integration time. The observing keepout region was tested by generating plots and movies of the targets and the keepout zone over a year. Although the keepout integration test required the interpretation of a user, the test revealed problems in the L2 halo orbit and the parameterization of keepout applied to some solar system bodies, which the development team was able to address. The validation testing of EXOSIMS was performed iteratively with the developers of EXOSIMS and resulted in a more robust, stable, and trustworthy tool that the exoplanet community can use to simulate exoplanet direct-detection missions from probe class, to WFIRST, up to large mission concepts such as HabEx and LUVOIR.

PYCHEM: a multivariate analysis package for python.

PubMed

Jarvis, Roger M; Broadhurst, David; Johnson, Helen; O'Boyle, Noel M; Goodacre, Royston

2006-10-15

We have implemented a multivariate statistical analysis toolbox, with an optional standalone graphical user interface (GUI), using the Python scripting language. This is a free and open source project that addresses the need for a multivariate analysis toolbox in Python. Although the functionality provided does not cover the full range of multivariate tools that are available, it has a broad complement of methods that are widely used in the biological sciences. In contrast to tools like MATLAB, PyChem 2.0.0 is easily accessible and free, allows for rapid extension using a range of Python modules and is part of the growing amount of complementary and interoperable scientific software in Python based upon SciPy. One of the attractions of PyChem is that it is an open source project and so there is an opportunity, through collaboration, to increase the scope of the software and to continually evolve a user-friendly platform that has applicability across a wide range of analytical and post-genomic disciplines. http://sourceforge.net/projects/pychem

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.

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.

A web-server of cell type discrimination system.

PubMed

Wang, Anyou; Zhong, Yan; Wang, Yanhua; He, Qianchuan

2014-01-01

Discriminating cell types is a daily request for stem cell biologists. However, there is not a user-friendly system available to date for public users to discriminate the common cell types, embryonic stem cells (ESCs), induced pluripotent stem cells (iPSCs), and somatic cells (SCs). Here, we develop WCTDS, a web-server of cell type discrimination system, to discriminate the three cell types and their subtypes like fetal versus adult SCs. WCTDS is developed as a top layer application of our recent publication regarding cell type discriminations, which employs DNA-methylation as biomarkers and machine learning models to discriminate cell types. Implemented by Django, Python, R, and Linux shell programming, run under Linux-Apache web server, and communicated through MySQL, WCTDS provides a friendly framework to efficiently receive the user input and to run mathematical models for analyzing data and then to present results to users. This framework is flexible and easy to be expended for other applications. Therefore, WCTDS works as a user-friendly framework to discriminate cell types and subtypes and it can also be expended to detect other cell types like cancer cells.

A Web-Server of Cell Type Discrimination System

PubMed Central

Zhong, Yan

2014-01-01

Discriminating cell types is a daily request for stem cell biologists. However, there is not a user-friendly system available to date for public users to discriminate the common cell types, embryonic stem cells (ESCs), induced pluripotent stem cells (iPSCs), and somatic cells (SCs). Here, we develop WCTDS, a web-server of cell type discrimination system, to discriminate the three cell types and their subtypes like fetal versus adult SCs. WCTDS is developed as a top layer application of our recent publication regarding cell type discriminations, which employs DNA-methylation as biomarkers and machine learning models to discriminate cell types. Implemented by Django, Python, R, and Linux shell programming, run under Linux-Apache web server, and communicated through MySQL, WCTDS provides a friendly framework to efficiently receive the user input and to run mathematical models for analyzing data and then to present results to users. This framework is flexible and easy to be expended for other applications. Therefore, WCTDS works as a user-friendly framework to discriminate cell types and subtypes and it can also be expended to detect other cell types like cancer cells. PMID:24578634

BEATBOX v1.0: Background Error Analysis Testbed with Box Models

NASA Astrophysics Data System (ADS)

Knote, Christoph; Barré, Jérôme; Eckl, Max

2018-02-01

The Background Error Analysis Testbed (BEATBOX) is a new data assimilation framework for box models. Based on the BOX Model eXtension (BOXMOX) to the Kinetic Pre-Processor (KPP), this framework allows users to conduct performance evaluations of data assimilation experiments, sensitivity analyses, and detailed chemical scheme diagnostics from an observation simulation system experiment (OSSE) point of view. The BEATBOX framework incorporates an observation simulator and a data assimilation system with the possibility of choosing ensemble, adjoint, or combined sensitivities. A user-friendly, Python-based interface allows for the tuning of many parameters for atmospheric chemistry and data assimilation research as well as for educational purposes, for example observation error, model covariances, ensemble size, perturbation distribution in the initial conditions, and so on. In this work, the testbed is described and two case studies are presented to illustrate the design of a typical OSSE experiment, data assimilation experiments, a sensitivity analysis, and a method for diagnosing model errors. BEATBOX is released as an open source tool for the atmospheric chemistry and data assimilation communities.

Network Traffic Generator for Low-rate Small Network Equipment Software

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lanzisera, Steven

2013-05-28

Application that uses the Python low-level socket interface to pass network traffic between devices on the local side of a NAT router and the WAN side of the NAT router. This application is designed to generate traffic that complies with the Energy Star Small Network Equipment Test Method.

DataViewer3D: An Open-Source, Cross-Platform Multi-Modal Neuroimaging Data Visualization Tool

PubMed Central

Gouws, André; Woods, Will; Millman, Rebecca; Morland, Antony; Green, Gary

2008-01-01

Integration and display of results from multiple neuroimaging modalities [e.g. magnetic resonance imaging (MRI), magnetoencephalography, EEG] relies on display of a diverse range of data within a common, defined coordinate frame. DataViewer3D (DV3D) is a multi-modal imaging data visualization tool offering a cross-platform, open-source solution to simultaneous data overlay visualization requirements of imaging studies. While DV3D is primarily a visualization tool, the package allows an analysis approach where results from one imaging modality can guide comparative analysis of another modality in a single coordinate space. DV3D is built on Python, a dynamic object-oriented programming language with support for integration of modular toolkits, and development of cross-platform software for neuroimaging. DV3D harnesses the power of the Visualization Toolkit (VTK) for two-dimensional (2D) and 3D rendering, calling VTK's low level C++ functions from Python. Users interact with data via an intuitive interface that uses Python to bind wxWidgets, which in turn calls the user's operating system dialogs and graphical user interface tools. DV3D currently supports NIfTI-1, ANALYZE™ and DICOM formats for MRI data display (including statistical data overlay). Formats for other data types are supported. The modularity of DV3D and ease of use of Python allows rapid integration of additional format support and user development. DV3D has been tested on Mac OSX, RedHat Linux and Microsoft Windows XP. DV3D is offered for free download with an extensive set of tutorial resources and example data. PMID:19352444

Optimal Repair And Replacement Policy For A System With Multiple Components

DTIC Science & Technology

2016-06-17

Numerical Demonstration To implement the linear program, we use the Python Programming Language (PSF 2016) with the Pyomo optimization modeling language...opre.1040.0133. Hart, W.E., C. Laird, J. Watson, D.L. Woodruff. 2012. Pyomo–optimization modeling in python , vol. 67. Springer Science & Business...Media. Hart, W.E., J. Watson, D.L. Woodruff. 2011. Pyomo: modeling and solving mathematical programs in python . Mathematical Programming Computation 3(3

Methods to Secure Databases Against Vulnerabilities

DTIC Science & Technology

2015-12-01

for several languages such as C, C++, PHP, Java and Python [16]. MySQL will work well with very large databases. The documentation references...using Eclipse and connected to each database management system using Python and Java drivers provided by MySQL , MongoDB, and Datastax (for Cassandra...tiers in Python and Java . Problem MySQL MongoDB Cassandra 1. Injection a. Tautologies Vulnerable Vulnerable Not Vulnerable b. Illegal query

Realistic Simulations of Coronagraphic Observations with WFIRST

NASA Astrophysics Data System (ADS)

Rizzo, Maxime; Zimmerman, Neil; Roberge, Aki; Lincowski, Andrew; Arney, Giada; Stark, Chris; Jansen, Tiffany; Turnbull, Margaret; WFIRST Science Investigation Team (Turnbull)

2018-01-01

We present a framework to simulate observing scenarios with the WFIRST Coronagraphic Instrument (CGI). The Coronagraph and Rapid Imaging Spectrograph in Python (crispy) is an open-source package that can be used to create CGI data products for analysis and development of post-processing routines. The software convolves time-varying coronagraphic PSFs with realistic astrophysical scenes which contain a planetary architecture, a consistent dust structure, and a background field composed of stars and galaxies. The focal plane can be read out by a WFIRST electron-multiplying CCD model directly, or passed through a WFIRST integral field spectrograph model first. Several elementary post-processing routines are provided as part of the package.

Pathway Inspector: a pathway based web application for RNAseq analysis of model and non-model organisms.

PubMed

Bianco, Luca; Riccadonna, Samantha; Lavezzo, Enrico; Falda, Marco; Formentin, Elide; Cavalieri, Duccio; Toppo, Stefano; Fontana, Paolo

2017-02-01

Pathway Inspector is an easy-to-use web application helping researchers to find patterns of expression in complex RNAseq experiments. The tool combines two standard approaches for RNAseq analysis: the identification of differentially expressed genes and a topology-based analysis of enriched pathways. Pathway Inspector is equipped with ad hoc interactive graphical interfaces simplifying the discovery of modulated pathways and the integration of the differentially expressed genes in the corresponding pathway topology. Pathway Inspector is available at the website http://admiral.fmach.it/PI and has been developed in Python, making use of the Django Web Framework. Contact:paolo.fontana@fmach.it

Prevalence of Amblyomma gervaisi ticks on captive snakes in Tamil Nadu.

PubMed

Catherine, B R; Jayathangaraj, M G; Soundararajan, C; Bala Guru, C; Yogaraj, D

2017-12-01

Ticks are the important ectoparasites that occur on snakes and transmit rickettsiosis, anaplasmosis and ehrlichiosis. A total of 62 snakes (Reticulated python, Indian Rock Python, Rat snakes and Spectacled cobra) were examined for tick infestation at Chennai Snake Park Trust (Guindy), Arignar Anna Zoological Park (Vandalur) and Rescue centre (Velachery) in Tamil Nadu from September, 2015 to June, 2016. Ticks from infested snakes were collected and were identified as Amblyomma gervaisi (previously known as Aponomma gervaisi ). Overall occurrence of tick infestation on snakes was 66.13%. Highest prevalence of tick infestation was observed more on Reticulated Python ( Python reticulatus , 90.91%) followed by Indian Rock Python ( Python molurus , 88.89%), Spectacled cobra ( Naja naja, 33.33%) and Rat snake ( Ptyas mucosa, 21.05%). Highest prevalence of ticks were observed on snakes reared at Chennai Snake Park Trust, Guindy (83.33%), followed by Arignar Anna Zoological Park, Vandalur (60.00%) and low level prevalence of 37.50% on snakes at Rescue centre, Velachery. Among the system of management, the prevalence of ticks were more on captive snakes (70.37%) than the free ranging snakes (37.5%). The presences of ticks were more on the first quarter when compared to other three quarters and were highly significant ( P  ≤ 0.01).

Gist: A scientific graphics package for Python

DOE Office of Scientific and Technical Information (OSTI.GOV)

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 ismore » 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.« less

Chained nuclei and python pattern in skeletal muscle cells as histological markers for electrical injury.

PubMed

Tanaka, Hiroki; Okuda, Katsuhiro; Ohtani, Seiji; Asari, Masaru; Horioka, Kie; Isozaki, Shotaro; Hayakawa, Akira; Ogawa, Katsuhiro; Hiroshi, Shiono; Shimizu, Keiko

2018-05-01

Electrical injury is damage caused by an electrical current passing through the body. We have previously reported that irregular stripes crossing skeletal muscle fibers (python pattern) and multiple small nuclei arranged in the longitudinal direction of the muscle fibers (chained nuclear change) are uniquely observed by histopathological analysis in the skeletal muscle tissues of patients with electrical injury. However, it remains unclear whether these phenomena are caused by the electrical current itself or by the joule heat generated by the electric current passing through the body. To clarify the causes underlying these changes, we applied electric and heat injury to the exteriorized rat soleus muscle in situ. Although both the python pattern and chained nuclear change were induced by electric injury, only the python pattern was induced by heat injury. Furthermore, a chained nuclear change was induced in the soleus muscle cells by electric current flow in physiological saline at 40 °C ex vivo, but a python pattern was not observed. When the skeletal muscle was exposed to electrical injury in cardiac-arrested rats, a python pattern was induced within 5 h after cardiac arrest, but no chained nuclear change was observed. Therefore, a chained nuclear change is induced by an electrical current alone in tissues in vital condition, whereas a python pattern is caused by joule heat, which may occur shortly after death. The degree and distribution of these skeletal muscle changes may be useful histological markers for analyzing cases of electrical injury in forensic medicine. Copyright © 2017 Elsevier B.V. All rights reserved.

SunPy: Python for Solar Physics

NASA Astrophysics Data System (ADS)

Bobra, M.; Inglis, A. R.; Mumford, S.; Christe, S.; Freij, N.; Hewett, R.; Ireland, J.; Martinez Oliveros, J. C.; Reardon, K.; Savage, S. L.; Shih, A. Y.; Pérez-Suárez, D.

2017-12-01

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. SunPy aims to provide the software for obtaining and analyzing solar and heliospheric data. This poster introduces a new major release, SunPy version 0.8. The first major new feature introduced is Fido, the new primary interface to download data. It provides a consistent and powerful search interface to all major data providers including the VSO and the JSOC, as well as individual data sources such as GOES XRS time series. It is also easy to add new data sources as they become available, i.e. DKIST. The second major new feature is the SunPy coordinate framework. This provides a powerful way of representing coordinates, allowing simple and intuitive conversion between coordinate systems and viewpoints of different instruments (i.e., Solar Orbiter and the Parker Solar Probe), including transformation to astrophysical frames like ICRS. Other new features including new timeseries capabilities with better support for concatenation and metadata, updated documentation and example gallery. SunPy is distributed through pip and conda and all of its code is publicly available (sunpy.org).

The ATLAS PanDA Monitoring System and its Evolution

NASA Astrophysics Data System (ADS)

Klimentov, A.; Nevski, P.; Potekhin, M.; Wenaus, T.

2011-12-01

The PanDA (Production and Distributed Analysis) Workload Management System is used for ATLAS distributed production and analysis worldwide. The needs of ATLAS global computing imposed challenging requirements on the design of PanDA in areas such as scalability, robustness, automation, diagnostics, and usability for both production shifters and analysis users. Through a system-wide job database, the PanDA monitor provides a comprehensive and coherent view of the system and job execution, from high level summaries to detailed drill-down job diagnostics. It is (like the rest of PanDA) an Apache-based Python application backed by Oracle. The presentation layer is HTML code generated on the fly in the Python application which is also responsible for managing database queries. However, this approach is lacking in user interface flexibility, simplicity of communication with external systems, and ease of maintenance. A decision was therefore made to migrate the PanDA monitor server to Django Web Application Framework and apply JSON/AJAX technology in the browser front end. This allows us to greatly reduce the amount of application code, separate data preparation from presentation, leverage open source for tools such as authentication and authorization mechanisms, and provide a richer and more dynamic user experience. We describe our approach, design and initial experience with the migration process.

PcapDB: Search Optimized Packet Capture, Version 0.1.0.0

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ferrell, Paul; Steinfadt, Shannon

PcapDB is a packet capture system designed to optimize the captured data for fast search in the typical (network incident response) use case. The technology involved in this software has been submitted via the IDEAS system and has been filed as a provisional patent. It includes the following primary components: capture: The capture component utilizes existing capture libraries to retrieve packets from network interfaces. Once retrieved the packets are passed to additional threads for sorting into flows and indexing. The sorted flows and indexes are passed to other threads so that they can be written to disk. These components aremore » written in the C programming language. search: The search components provide a means to find relevant flows and the associated packets. A search query is parsed and represented as a search tree. Various search commands, written in C, are then used resolve this tree into a set of search results. The tree generation and search execution management components are written in python. interface: The PcapDB web interface is written in Python on the Django framework. It provides a series of pages, API's, and asynchronous tasks that allow the user to manage the capture system, perform searches, and retrieve results. Web page components are written in HTML,CSS and Javascript.« less

Web application for detailed real-time database transaction monitoring for CMS condition data

NASA Astrophysics Data System (ADS)

de Gruttola, Michele; Di Guida, Salvatore; Innocente, Vincenzo; Pierro, Antonio

2012-12-01

In the upcoming LHC era, database have become an essential part for the experiments collecting data from LHC, in order to safely store, and consistently retrieve, a wide amount of data, which are produced by different sources. In the CMS experiment at CERN, all this information is stored in ORACLE databases, allocated in several servers, both inside and outside the CERN network. In this scenario, the task of monitoring different databases is a crucial database administration issue, since different information may be required depending on different users' tasks such as data transfer, inspection, planning and security issues. We present here a web application based on Python web framework and Python modules for data mining purposes. To customize the GUI we record traces of user interactions that are used to build use case models. In addition the application detects errors in database transactions (for example identify any mistake made by user, application failure, unexpected network shutdown or Structured Query Language (SQL) statement error) and provides warning messages from the different users' perspectives. Finally, in order to fullfill the requirements of the CMS experiment community, and to meet the new development in many Web client tools, our application was further developed, and new features were deployed.

NIFTY - Numerical Information Field Theory. A versatile PYTHON library for signal inference

NASA Astrophysics Data System (ADS)

Selig, M.; Bell, M. R.; Junklewitz, H.; Oppermann, N.; Reinecke, M.; Greiner, M.; Pachajoa, C.; Enßlin, T. A.

2013-06-01

NIFTy (Numerical Information Field Theory) is a software package designed to enable the development of signal inference algorithms that operate regardless of the underlying spatial grid and its resolution. Its object-oriented framework is written in Python, although it accesses libraries written in Cython, C++, and C for efficiency. NIFTy offers a toolkit that abstracts discretized representations of continuous spaces, fields in these spaces, and operators acting on fields into classes. Thereby, the correct normalization of operations on fields is taken care of automatically without concerning the user. This allows for an abstract formulation and programming of inference algorithms, including those derived within information field theory. Thus, NIFTy permits its user to rapidly prototype algorithms in 1D, and then apply the developed code in higher-dimensional settings of real world problems. The set of spaces on which NIFTy operates comprises point sets, n-dimensional regular grids, spherical spaces, their harmonic counterparts, and product spaces constructed as combinations of those. The functionality and diversity of the package is demonstrated by a Wiener filter code example that successfully runs without modification regardless of the space on which the inference problem is defined. NIFTy homepage http://www.mpa-garching.mpg.de/ift/nifty/; Excerpts of this paper are part of the NIFTy source code and documentation.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sauter, Nicholas K., E-mail: nksauter@lbl.gov; Hattne, Johan; Grosse-Kunstleve, Ralf W.

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 realmore » 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.« less

Gsolve, a Python computer program with a graphical user interface to transform relative gravity survey measurements to absolute gravity values and gravity anomalies

NASA Astrophysics Data System (ADS)

McCubbine, Jack; Tontini, Fabio Caratori; Stagpoole, Vaughan; Smith, Euan; O'Brien, Grant

2018-01-01

A Python program (Gsolve) with a graphical user interface has been developed to assist with routine data processing of relative gravity measurements. Gsolve calculates the gravity at each measurement site of a relative gravity survey, which is referenced to at least one known gravity value. The tidal effects of the sun and moon, gravimeter drift and tares in the data are all accounted for during the processing of the survey measurements. The calculation is based on a least squares formulation where the difference between the absolute gravity at each surveyed location and parameters relating to the dynamics of the gravimeter are minimized with respect to the relative gravity observations, and some supplied gravity reference site values. The program additionally allows the user to compute free air gravity anomalies, with respect to the GRS80 and GRS67 reference ellipsoids, from the determined gravity values and calculate terrain corrections at each of the surveyed sites using a prism formula and a user supplied digital elevation model. This paper reviews the mathematical framework used to reduce relative gravimeter survey observations to gravity values. It then goes on to detail how the processing steps can be implemented using the software.