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.

Mushu, a free- and open source BCI signal acquisition, written in Python.

PubMed

Venthur, Bastian; Blankertz, Benjamin

2012-01-01

The following paper describes Mushu, a signal acquisition software for retrieval and online streaming of Electroencephalography (EEG) data. It is written, but not limited, to the needs of Brain Computer Interfacing (BCI). It's main goal is to provide a unified interface to EEG data regardless of the amplifiers used. It runs under all major operating systems, like Windows, Mac OS and Linux, is written in Python and is free- and open source software licensed under the terms of the GNU General Public License.

pytc: Open-Source Python Software for Global Analyses of Isothermal Titration Calorimetry Data.

PubMed

Duvvuri, Hiranmayi; Wheeler, Lucas C; Harms, Michael J

2018-05-08

Here we describe pytc, an open-source Python package for global fits of thermodynamic models to multiple isothermal titration calorimetry experiments. Key features include simplicity, the ability to implement new thermodynamic models, a robust maximum likelihood fitter, a fast Bayesian Markov-Chain Monte Carlo sampler, rigorous implementation, extensive documentation, and full cross-platform compatibility. pytc fitting can be done using an application program interface or via a graphical user interface. It is available for download at https://github.com/harmslab/pytc .

A Flexible Method for Producing F.E.M. Analysis of Bone Using Open-Source Software

NASA Technical Reports Server (NTRS)

Boppana, Abhishektha; Sefcik, Ryan; Meyers, Jerry G.; Lewandowski, Beth E.

2016-01-01

This project, performed in support of the NASA GRC Space Academy summer program, sought to develop an open-source workflow methodology that segmented medical image data, created a 3D model from the segmented data, and prepared the model for finite-element analysis. In an initial step, a technological survey evaluated the performance of various existing open-source software that claim to perform these tasks. However, the survey concluded that no single software exhibited the wide array of functionality required for the potential NASA application in the area of bone, muscle and bio fluidic studies. As a result, development of a series of Python scripts provided the bridging mechanism to address the shortcomings of the available open source tools. The implementation of the VTK library provided the most quick and effective means of segmenting regions of interest from the medical images; it allowed for the export of a 3D model by using the marching cubes algorithm to build a surface mesh. To facilitate the development of the model domain from this extracted information required a surface mesh to be processed in the open-source software packages Blender and Gmsh. The Preview program of the FEBio suite proved to be sufficient for volume filling the model with an unstructured mesh and preparing boundaries specifications for finite element analysis. To fully allow FEM modeling, an in house developed Python script allowed assignment of material properties on an element by element basis by performing a weighted interpolation of voxel intensity of the parent medical image correlated to published information of image intensity to material properties, such as ash density. A graphical user interface combined the Python scripts and other software into a user friendly interface. The work using Python scripts provides a potential alternative to expensive commercial software and inadequate, limited open-source freeware programs for the creation of 3D computational models. More work will be needed to validate this approach in creating finite-element models.

Python Open source Waveform ExtractoR (POWER): an open source, Python package to monitor and post-process numerical relativity simulations

NASA Astrophysics Data System (ADS)

Johnson, Daniel; Huerta, E. A.; Haas, Roland

2018-01-01

Numerical simulations of Einstein’s field equations provide unique insights into the physics of compact objects moving at relativistic speeds, and which are driven by strong gravitational interactions. Numerical relativity has played a key role to firmly establish gravitational wave astrophysics as a new field of research, and it is now paving the way to establish whether gravitational wave radiation emitted from compact binary mergers is accompanied by electromagnetic and astro-particle counterparts. As numerical relativity continues to blend in with routine gravitational wave data analyses to validate the discovery of gravitational wave events, it is essential to develop open source tools to streamline these studies. Motivated by our own experience as users and developers of the open source, community software, the Einstein Toolkit, we present an open source, Python package that is ideally suited to monitor and post-process the data products of numerical relativity simulations, and compute the gravitational wave strain at future null infinity in high performance environments. We showcase the application of this new package to post-process a large numerical relativity catalog and extract higher-order waveform modes from numerical relativity simulations of eccentric binary black hole mergers and neutron star mergers. This new software fills a critical void in the arsenal of tools provided by the Einstein Toolkit consortium to the numerical relativity community.

SWMM5 Application Programming Interface and PySWMM: A ...

EPA Pesticide Factsheets

In support of the OpenWaterAnalytics open source initiative, the PySWMM project encompasses the development of a Python interfacing wrapper to SWMM5 with parallel ongoing development of the USEPA Stormwater Management Model (SWMM5) application programming interface (API). ... The purpose of this work is to increase the utility of the SWMM dll by creating a Toolkit API for accessing its functionality. The utility of the Toolkit is further enhanced with a wrapper to allow access from the Python scripting language. This work is being prosecuted as part of an Open Source development strategy and is being performed by volunteer software developers.

scikit-image: image processing in Python.

PubMed

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

2014-01-01

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

ELATE: an open-source online application for analysis and visualization of elastic tensors

NASA Astrophysics Data System (ADS)

Gaillac, Romain; Pullumbi, Pluton; Coudert, François-Xavier

2016-07-01

We report on the implementation of a tool for the analysis of second-order elastic stiffness tensors, provided with both an open-source Python module and a standalone online application allowing the visualization of anisotropic mechanical properties. After describing the software features, how we compute the conventional elastic constants and how we represent them graphically, we explain our technical choices for the implementation. In particular, we focus on why a Python module is used to generate the HTML web page with embedded Javascript for dynamical plots.

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.

Flexible Environmental Modeling with Python and Open - GIS

NASA Astrophysics Data System (ADS)

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

2015-04-01

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

PyPanda: a Python package for gene regulatory network reconstruction

PubMed Central

van IJzendoorn, David G.P.; Glass, Kimberly; Quackenbush, John; Kuijjer, Marieke L.

2016-01-01

Summary: PANDA (Passing Attributes between Networks for Data Assimilation) is a gene regulatory network inference method that uses message-passing to integrate multiple sources of ‘omics data. PANDA was originally coded in C ++. In this application note we describe PyPanda, the Python version of PANDA. PyPanda runs considerably faster than the C ++ version and includes additional features for network analysis. Availability and implementation: The open source PyPanda Python package is freely available at http://github.com/davidvi/pypanda. Contact: mkuijjer@jimmy.harvard.edu or d.g.p.van_ijzendoorn@lumc.nl PMID:27402905

PyPanda: a Python package for gene regulatory network reconstruction.

PubMed

van IJzendoorn, David G P; Glass, Kimberly; Quackenbush, John; Kuijjer, Marieke L

2016-11-01

PANDA (Passing Attributes between Networks for Data Assimilation) is a gene regulatory network inference method that uses message-passing to integrate multiple sources of 'omics data. PANDA was originally coded in C ++. In this application note we describe PyPanda, the Python version of PANDA. PyPanda runs considerably faster than the C ++ version and includes additional features for network analysis. The open source PyPanda Python package is freely available at http://github.com/davidvi/pypanda CONTACT: mkuijjer@jimmy.harvard.edu or d.g.p.van_ijzendoorn@lumc.nl. © The Author 2016. Published by Oxford University Press.

GillesPy: A Python Package for Stochastic Model Building and Simulation.

PubMed

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

2016-09-01

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.

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

scikit-image: image processing in Python

PubMed Central

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

2014-01-01

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

Ibmdbpy-spatial : An Open-source implementation of in-database geospatial analytics in Python

NASA Astrophysics Data System (ADS)

Roy, Avipsa; Fouché, Edouard; Rodriguez Morales, Rafael; Moehler, Gregor

2017-04-01

As the amount of spatial data acquired from several geodetic sources has grown over the years and as data infrastructure has become more powerful, the need for adoption of in-database analytic technology within geosciences has grown rapidly. In-database analytics on spatial data stored in a traditional enterprise data warehouse enables much faster retrieval and analysis for making better predictions about risks and opportunities, identifying trends and spot anomalies. Although there are a number of open-source spatial analysis libraries like geopandas and shapely available today, most of them have been restricted to manipulation and analysis of geometric objects with a dependency on GEOS and similar libraries. We present an open-source software package, written in Python, to fill the gap between spatial analysis and in-database analytics. Ibmdbpy-spatial provides a geospatial extension to the ibmdbpy package, implemented in 2015. It provides an interface for spatial data manipulation and access to in-database algorithms in IBM dashDB, a data warehouse platform with a spatial extender that runs as a service on IBM's cloud platform called Bluemix. Working in-database reduces the network overload, as the complete data need not be replicated into the user's local system altogether and only a subset of the entire dataset can be fetched into memory in a single instance. Ibmdbpy-spatial accelerates Python analytics by seamlessly pushing operations written in Python into the underlying database for execution using the dashDB spatial extender, thereby benefiting from in-database performance-enhancing features, such as columnar storage and parallel processing. The package is currently supported on Python versions from 2.7 up to 3.4. The basic architecture of the package consists of three main components - 1) a connection to the dashDB represented by the instance IdaDataBase, which uses a middleware API namely - pypyodbc or jaydebeapi to establish the database connection via ODBC or JDBC respectively, 2) an instance to represent the spatial data stored in the database as a dataframe in Python, called the IdaGeoDataFrame, with a specific geometry attribute which recognises a planar geometry column in dashDB and 3) Python wrappers for spatial functions like within, distance, area, buffer} and more which dashDB currently supports to make the querying process from Python much simpler for the users. The spatial functions translate well-known geopandas-like syntax into SQL queries utilising the database connection to perform spatial operations in-database and can operate on single geometries as well two different geometries from different IdaGeoDataFrames. The in-database queries strictly follow the standards of OpenGIS Implementation Specification for Geographic information - Simple feature access for SQL. The results of the operations obtained can thereby be accessed dynamically via interactive Jupyter notebooks from any system which supports Python, without any additional dependencies and can also be combined with other open source libraries such as matplotlib and folium in-built within Jupyter notebooks for visualization purposes. We built a use case to analyse crime hotspots in New York city to validate our implementation and visualized the results as a choropleth map for each borough.

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.

Open source clustering software.

PubMed

de Hoon, M J L; Imoto, S; Nolan, J; Miyano, S

2004-06-12

We have implemented k-means clustering, hierarchical clustering and self-organizing maps in a single multipurpose open-source library of C routines, callable from other C and C++ programs. Using this library, we have created an improved version of Michael Eisen's well-known Cluster program for Windows, Mac OS X and Linux/Unix. In addition, we generated a Python and a Perl interface to the C Clustering Library, thereby combining the flexibility of a scripting language with the speed of C. The C Clustering Library and the corresponding Python C extension module Pycluster were released under the Python License, while the Perl module Algorithm::Cluster was released under the Artistic License. The GUI code Cluster 3.0 for Windows, Macintosh and Linux/Unix, as well as the corresponding command-line program, were released under the same license as the original Cluster code. The complete source code is available at http://bonsai.ims.u-tokyo.ac.jp/mdehoon/software/cluster. Alternatively, Algorithm::Cluster can be downloaded from CPAN, while Pycluster is also available as part of the Biopython distribution.

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.

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

PubMed

Helmus, Jonathan J; Jaroniec, Christopher P

2013-04-01

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

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

PubMed Central

Helmus, Jonathan J.; Jaroniec, Christopher P.

2013-01-01

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

PlasmaPy: beginning a community developed Python package for plasma physics

NASA Astrophysics Data System (ADS)

Murphy, Nicholas A.; Huang, Yi-Min; PlasmaPy Collaboration

2016-10-01

In recent years, researchers in several disciplines have collaborated on community-developed open source Python packages such as Astropy, SunPy, and SpacePy. These packages provide core functionality, common frameworks for data analysis and visualization, and educational tools. We propose that our community begins the development of PlasmaPy: a new open source core Python package for plasma physics. PlasmaPy could include commonly used functions in plasma physics, easy-to-use plasma simulation codes, Grad-Shafranov solvers, eigenmode solvers, and tools to analyze both simulations and experiments. The development will include modern programming practices such as version control, embedding documentation in the code, unit tests, and avoiding premature optimization. We will describe early code development on PlasmaPy, and discuss plans moving forward. The success of PlasmaPy depends on active community involvement and a welcoming and inclusive environment, so anyone interested in joining this collaboration should contact the authors.

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

PubMed

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

2015-02-01

In modern laboratories, software can drive the full experimental process from data acquisition to storage, processing, and analysis. The automation of laboratory data acquisition is an important consideration for every laboratory. When implementing a laboratory automation scheme, important parameters include its reliability, time to implement, adaptability, and compatibility with software used at other stages of experimentation. In this article, we present an open-source, flexible, and extensible Python package for Laboratory Automation, Control, and Experimentation (PLACE). The package uses modular organization and clear design principles; therefore, it can be easily customized or expanded to meet the needs of diverse laboratories. We discuss the organization of PLACE, data-handling considerations, and then present an example using PLACE for laser-ultrasound experiments. Finally, we demonstrate the seamless transition to post-processing and analysis with Python through the development of an analysis module for data produced by PLACE automation. © 2014 Society for Laboratory Automation and Screening.

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

Meurer, Aaron; Smith, Christopher P.; Paprocki, Mateusz

Here, SymPy is a full featured computer algebra system (CAS) written in the Python programming language. It is open source, being licensed under the extremely permissive 3-clause BSD license. SymPy was started by Ondrej Certik in 2005, and it has since grown into a large open source project, with over 500 contributors.

ImagePy: an open-source, Python-based and platform-independent software package for boimage analysis.

PubMed

Wang, Anliang; Yan, Xiaolong; Wei, Zhijun

2018-04-27

This note presents the design of a scalable software package named ImagePy for analysing biological images. Our contribution is concentrated on facilitating extensibility and interoperability of the software through decoupling the data model from the user interface. Especially with assistance from the Python ecosystem, this software framework makes modern computer algorithms easier to be applied in bioimage analysis. ImagePy is free and open source software, with documentation and code available at https://github.com/Image-Py/imagepy under the BSD license. It has been tested on the Windows, Mac and Linux operating systems. wzjdlut@dlut.edu.cn or yxdragon@imagepy.org.

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)

Eddylicious: A Python package for turbulent inflow generation

NASA Astrophysics Data System (ADS)

Mukha, Timofey; Liefvendahl, Mattias

2018-01-01

A Python package for generating inflow for scale-resolving computer simulations of turbulent flow is presented. The purpose of the package is to unite existing inflow generation methods in a single code-base and make them accessible to users of various Computational Fluid Dynamics (CFD) solvers. The currently existing functionality consists of an accurate inflow generation method suitable for flows with a turbulent boundary layer inflow and input/output routines for coupling with the open-source CFD solver OpenFOAM.

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.

Guided Tour of Pythonian Museum

NASA Technical Reports Server (NTRS)

Lee, H. Joe

2017-01-01

At http:hdfeos.orgzoo, we have a large collection of Python examples of dealing with NASA HDF (Hierarchical Data Format) products. During this hands-on Python tutorial session, we'll present a few common hacks to access and visualize local NASA HDF data. We'll also cover how to access remote data served by OPeNDAP (Open-source Project for a Network Data Access Protocol). As a glue language, we will demonstrate how you can use Python for your data workflow - from searching data to analyzing data with machine learning.

OpenSesame: an open-source, graphical experiment builder for the social sciences.

PubMed

Mathôt, Sebastiaan; Schreij, Daniel; Theeuwes, Jan

2012-06-01

In the present article, we introduce OpenSesame, a graphical experiment builder for the social sciences. OpenSesame is free, open-source, and cross-platform. It features a comprehensive and intuitive graphical user interface and supports Python scripting for complex tasks. Additional functionality, such as support for eyetrackers, input devices, and video playback, is available through plug-ins. OpenSesame can be used in combination with existing software for creating experiments.

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.

MGtoolkit: A python package for implementing metagraphs

NASA Astrophysics Data System (ADS)

Ranathunga, D.; Nguyen, H.; Roughan, M.

In this paper we present MGtoolkit: an open-source Python package for implementing metagraphs - a first of its kind. Metagraphs are commonly used to specify and analyse business and computer-network policies alike. MGtoolkit can help verify such policies and promotes learning and experimentation with metagraphs. The package currently provides purely textual output for visualising metagraphs and their analysis results.

RdTools: An Open Source Python Library for PV Degradation Analysis

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

Deceglie, Michael G; Jordan, Dirk; Nag, Ambarish

RdTools is a set of Python tools for analysis of photovoltaic data. In particular, PV production data is evaluated over several years to obtain rates of performance degradation over time. Rdtools can handle both high frequency (hourly or better) or low frequency (daily, weekly, etc.) datasets. Best results are obtained with higher frequency data.

astroplan: An Open Source Observation Planning Package in Python

NASA Astrophysics Data System (ADS)

Morris, Brett M.; Tollerud, Erik; Sipőcz, Brigitta; Deil, Christoph; Douglas, Stephanie T.; Berlanga Medina, Jazmin; Vyhmeister, Karl; Smith, Toby R.; Littlefair, Stuart; Price-Whelan, Adrian M.; Gee, Wilfred T.; Jeschke, Eric

2018-03-01

We present astroplan—an open source, open development, Astropy affiliated package for ground-based observation planning and scheduling in Python. astroplan is designed to provide efficient access to common observational quantities such as celestial rise, set, and meridian transit times and simple transformations from sky coordinates to altitude-azimuth coordinates without requiring a detailed understanding of astropy’s implementation of coordinate systems. astroplan provides convenience functions to generate common observational plots such as airmass and parallactic angle as a function of time, along with basic sky (finder) charts. Users can determine whether or not a target is observable given a variety of observing constraints, such as airmass limits, time ranges, Moon illumination/separation ranges, and more. A selection of observation schedulers are included that divide observing time among a list of targets, given observing constraints on those targets. Contributions to the source code from the community are welcome.

An Open-Source Automated Peptide Synthesizer Based on Arduino and Python.

PubMed

Gali, Hariprasad

2017-10-01

The development of the first open-source automated peptide synthesizer, PepSy, using Arduino UNO and readily available components is reported. PepSy was primarily designed to synthesize small peptides in a relatively small scale (<100 µmol). Scripts to operate PepSy in a fully automatic or manual mode were written in Python. Fully automatic script includes functions to carry out resin swelling, resin washing, single coupling, double coupling, Fmoc deprotection, ivDde deprotection, on-resin oxidation, end capping, and amino acid/reagent line cleaning. Several small peptides and peptide conjugates were successfully synthesized on PepSy with reasonably good yields and purity depending on the complexity of the peptide.

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.

OpenStereo: Open Source, Cross-Platform Software for Structural Geology Analysis

NASA Astrophysics Data System (ADS)

Grohmann, C. H.; Campanha, G. A.

2010-12-01

Free and open source software (FOSS) are increasingly seen as synonyms of innovation and progress. Freedom to run, copy, distribute, study, change and improve the software (through access to the source code) assure a high level of positive feedback between users and developers, which results in stable, secure and constantly updated systems. Several software packages for structural geology analysis are available to the user, with commercial licenses or that can be downloaded at no cost from the Internet. Some provide basic tools of stereographic projections such as plotting poles, great circles, density contouring, eigenvector analysis, data rotation etc, while others perform more specific tasks, such as paleostress or geotechnical/rock stability analysis. This variety also means a wide range of data formating for input, Graphical User Interface (GUI) design and graphic export format. The majority of packages is built for MS-Windows and even though there are packages for the UNIX-based MacOS, there aren't native packages for *nix (UNIX, Linux, BSD etc) Operating Systems (OS), forcing the users to run these programs with emulators or virtual machines. Those limitations lead us to develop OpenStereo, an open source, cross-platform software for stereographic projections and structural geology. The software is written in Python, a high-level, cross-platform programming language and the GUI is designed with wxPython, which provide a consistent look regardless the OS. Numeric operations (like matrix and linear algebra) are performed with the Numpy module and all graphic capabilities are provided by the Matplolib library, including on-screen plotting and graphic exporting to common desktop formats (emf, eps, ps, pdf, png, svg). Data input is done with simple ASCII text files, with values of dip direction and dip/plunge separated by spaces, tabs or commas. The user can open multiple file at the same time (or the same file more than once), and overlay different elements of each dataset (poles, great circles etc). The GUI shows the opened files in a tree structure, similar to “layers” of many illustration software, where the vertical order of the files in the tree reflects the drawing order of the selected elements. At this stage, the software performs plotting operations of poles to planes, lineations, great circles, density contours and rose diagrams. A set of statistics is calculated for each file and its eigenvalues and eigenvectors are used to suggest if the data is clustered about a mean value or distributed along a girdle. Modified Flinn, Triangular and histograms plots are also available. Next step of development will focus on tools as merging and rotation of datasets, possibility to save 'projects' and paleostress analysis. In its current state, OpenStereo requires Python, wxPython, Numpy and Matplotlib installed in the system. We recommend installing PythonXY or the Enthought Python Distribution on MS-Windows and MacOS machines, since all dependencies are provided. Most Linux distributions provide an easy way to install all dependencies through software repositories. OpenStereo is released under the GNU General Public License. Programmers willing to contribute are encouraged to contact the authors directly. FAPESP Grant #09/17675-5

Simulation with Python on transverse modes of the symmetric confocal resonator

NASA Astrophysics Data System (ADS)

Wang, Qing Hua; Qi, Jing; Ji, Yun Jing; Song, Yang; Li, Zhenhua

2017-08-01

Python is a popular open-source programming language that can be used to simulate various optical phenomena. We have developed a suite of programs to help teach the course of laser principle. The complicated transverse modes of the symmetric confocal resonator can be visualized in personal computers, which is significant to help the students understand the pattern distribution of laser resonator.

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.

GRACKLE: a chemistry and cooling library for astrophysics

NASA Astrophysics Data System (ADS)

Smith, Britton D.; Bryan, Greg L.; Glover, Simon C. O.; Goldbaum, Nathan J.; Turk, Matthew J.; Regan, John; Wise, John H.; Schive, Hsi-Yu; Abel, Tom; Emerick, Andrew; O'Shea, Brian W.; Anninos, Peter; Hummels, Cameron B.; Khochfar, Sadegh

2017-04-01

We present the GRACKLE chemistry and cooling library for astrophysical simulations and models. GRACKLE provides a treatment of non-equilibrium primordial chemistry and cooling for H, D and He species, including H2 formation on dust grains; tabulated primordial and metal cooling; multiple ultraviolet background models; and support for radiation transfer and arbitrary heat sources. The library has an easily implementable interface for simulation codes written in C, C++ and FORTRAN as well as a PYTHON interface with added convenience functions for semi-analytical models. As an open-source project, GRACKLE provides a community resource for accessing and disseminating astrochemical data and numerical methods. We present the full details of the core functionality, the simulation and PYTHON interfaces, testing infrastructure, performance and range of applicability. GRACKLE is a fully open-source project and new contributions are welcome.

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

PubMed

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

2009-06-01

The Biopython project is a mature open source international collaboration of volunteer developers, providing Python libraries for a wide range of bioinformatics problems. Biopython includes modules for reading and writing different sequence file formats and multiple sequence alignments, dealing with 3D macro molecular structures, interacting with common tools such as BLAST, ClustalW and EMBOSS, accessing key online databases, as well as providing numerical methods for statistical learning. Biopython is freely available, with documentation and source code at (www.biopython.org) under the Biopython license.

Online characterization of planetary surfaces: PlanetServer, an open-source analysis and visualization tool

NASA Astrophysics Data System (ADS)

Marco Figuera, R.; Pham Huu, B.; Rossi, A. P.; Minin, M.; Flahaut, J.; Halder, A.

2018-01-01

The lack of open-source tools for hyperspectral data visualization and analysis creates a demand for new tools. In this paper we present the new PlanetServer, a set of tools comprising a web Geographic Information System (GIS) and a recently developed Python Application Programming Interface (API) capable of visualizing and analyzing a wide variety of hyperspectral data from different planetary bodies. Current WebGIS open-source tools are evaluated in order to give an overview and contextualize how PlanetServer can help in this matters. The web client is thoroughly described as well as the datasets available in PlanetServer. Also, the Python API is described and exposed the reason of its development. Two different examples of mineral characterization of different hydrosilicates such as chlorites, prehnites and kaolinites in the Nili Fossae area on Mars are presented. As the obtained results show positive outcome in hyperspectral analysis and visualization compared to previous literature, we suggest using the PlanetServer approach for such investigations.

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.

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

Pulseq-Graphical Programming Interface: Open source visual environment for prototyping pulse sequences and integrated magnetic resonance imaging algorithm development.

PubMed

Ravi, Keerthi Sravan; Potdar, Sneha; Poojar, Pavan; Reddy, Ashok Kumar; Kroboth, Stefan; Nielsen, Jon-Fredrik; Zaitsev, Maxim; Venkatesan, Ramesh; Geethanath, Sairam

2018-03-11

To provide a single open-source platform for comprehensive MR algorithm development inclusive of simulations, pulse sequence design and deployment, reconstruction, and image analysis. We integrated the "Pulseq" platform for vendor-independent pulse programming with Graphical Programming Interface (GPI), a scientific development environment based on Python. Our integrated platform, Pulseq-GPI, permits sequences to be defined visually and exported to the Pulseq file format for execution on an MR scanner. For comparison, Pulseq files using either MATLAB only ("MATLAB-Pulseq") or Python only ("Python-Pulseq") were generated. We demonstrated three fundamental sequences on a 1.5 T scanner. Execution times of the three variants of implementation were compared on two operating systems. In vitro phantom images indicate equivalence with the vendor supplied implementations and MATLAB-Pulseq. The examples demonstrated in this work illustrate the unifying capability of Pulseq-GPI. The execution times of all the three implementations were fast (a few seconds). The software is capable of user-interface based development and/or command line programming. The tool demonstrated here, Pulseq-GPI, integrates the open-source simulation, reconstruction and analysis capabilities of GPI Lab with the pulse sequence design and deployment features of Pulseq. Current and future work includes providing an ISMRMRD interface and incorporating Specific Absorption Ratio and Peripheral Nerve Stimulation computations. Copyright © 2018 Elsevier Inc. All rights reserved.

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.

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.

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

Comparison of cyclic correlation algorithm implemented in matlab and python

NASA Astrophysics Data System (ADS)

Carr, Richard; Whitney, James

Simulation is a necessary step for all engineering projects. Simulation gives the engineers an approximation of how their devices will perform under different circumstances, without hav-ing to build, or before building a physical prototype. This is especially true for space bound devices, i.e., space communication systems, where the impact of system malfunction or failure is several orders of magnitude over that of terrestrial applications. Therefore having a reliable simulation tool is key in developing these devices and systems. Math Works Matrix Laboratory (MATLAB) is a matrix based software used by scientists and engineers to solve problems and perform complex simulations. MATLAB has a number of applications in a wide variety of fields which include communications, signal processing, image processing, mathematics, eco-nomics and physics. Because of its many uses MATLAB has become the preferred software for many engineers; it is also very expensive, especially for students and startups. One alternative to MATLAB is Python. The Python is a powerful, easy to use, open source programming environment that can be used to perform many of the same functions as MATLAB. Python programming environment has been steadily gaining popularity in niche programming circles. While there are not as many function included in the software as MATLAB, there are many open source functions that have been developed that are available to be downloaded for free. This paper illustrates how Python can implement the cyclic correlation algorithm and com-pares the results to the cyclic correlation algorithm implemented in the MATLAB environment. Some of the characteristics to be compared are the accuracy and precision of the results, and the length of the programs. The paper will demonstrate that Python is capable of performing simulations of complex algorithms such cyclic correlation.

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.

Mocking the weak lensing universe: The LensTools Python computing package

NASA Astrophysics Data System (ADS)

Petri, A.

2016-10-01

We present a newly developed software package which implements a wide range of routines frequently used in Weak Gravitational Lensing (WL). With the continuously increasing size of the WL scientific community we feel that easy to use Application Program Interfaces (APIs) for common calculations are a necessity to ensure efficiency and coordination across different working groups. Coupled with existing open source codes, such as CAMB (Lewis et al., 2000) and Gadget2 (Springel, 2005), LensTools brings together a cosmic shear simulation pipeline which, complemented with a variety of WL feature measurement tools and parameter sampling routines, provides easy access to the numerics for theoretical studies of WL as well as for experiment forecasts. Being implemented in PYTHON (Rossum, 1995), LensTools takes full advantage of a range of state-of-the art techniques developed by the large and growing open-source software community (Jones et al., 2001; McKinney, 2010; Astrophy Collaboration, 2013; Pedregosa et al., 2011; Foreman-Mackey et al., 2013). We made the LensTools code available on the Python Package Index and published its documentation on http://lenstools.readthedocs.io.

Py4Syn: Python for synchrotrons.

PubMed

Slepicka, H H; Canova, H F; Beniz, D B; Piton, J R

2015-09-01

In this report, Py4Syn, an open-source Python-based library for data acquisition, device manipulation, scan routines and other helper functions, is presented. Driven by easy-to-use and scalability ideals, Py4Syn offers control system agnostic solution and high customization level for scans and data output, covering distinct techniques and facilities. Here, most of the library functionalities are described, examples of use are shown and ideas for future implementations are presented.

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

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.

ConKit: a python interface to contact predictions.

PubMed

Simkovic, Felix; Thomas, Jens M H; Rigden, Daniel J

2017-07-15

Recent advances in protein residue contact prediction algorithms have led to the emergence of many new methods and a variety of file formats. We present ConKit , an open source, modular and extensible Python interface which allows facile conversion between formats and provides an interface to analyses of sequence alignments and sets of contact predictions. ConKit is available via the Python Package Index. The documentation can be found at http://www.conkit.org . ConKit is licensed under the BSD 3-Clause. hlfsimko@liverpool.ac.uk or drigden@liverpool.ac.uk. Supplementary data are available at Bioinformatics online. © The Author(s) 2017. Published by Oxford University Press.

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

PubMed

Yesylevskyy, Semen O

2015-07-15

Pteros is the high-performance open-source library for molecular modeling and analysis of molecular dynamics trajectories. Starting from version 2.0 Pteros is available for C++ and Python programming languages with very similar interfaces. This makes it suitable for writing complex reusable programs in C++ and simple interactive scripts in Python alike. New version improves the facilities for asynchronous trajectory reading and parallel execution of analysis tasks by introducing analysis plugins which could be written in either C++ or Python in completely uniform way. The high level of abstraction provided by analysis plugins greatly simplifies prototyping and implementation of complex analysis algorithms. Pteros is available for free under Artistic License from http://sourceforge.net/projects/pteros/. © 2015 Wiley Periodicals, Inc.

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

NASA Astrophysics Data System (ADS)

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

2015-06-01

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

PyGaze: an open-source, cross-platform toolbox for minimal-effort programming of eyetracking experiments.

PubMed

Dalmaijer, Edwin S; Mathôt, Sebastiaan; Van der Stigchel, Stefan

2014-12-01

The PyGaze toolbox is an open-source software package for Python, a high-level programming language. It is designed for creating eyetracking experiments in Python syntax with the least possible effort, and it offers programming ease and script readability without constraining functionality and flexibility. PyGaze can be used for visual and auditory stimulus presentation; for response collection via keyboard, mouse, joystick, and other external hardware; and for the online detection of eye movements using a custom algorithm. A wide range of eyetrackers of different brands (EyeLink, SMI, and Tobii systems) are supported. The novelty of PyGaze lies in providing an easy-to-use layer on top of the many different software libraries that are required for implementing eyetracking experiments. Essentially, PyGaze is a software bridge for eyetracking research.

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

Lehe, Remi

Many simulation software produce data in the form of a set of field values or of a set of particle positions. (one such example is that of particle-in-cell codes, which produce data on the electromagnetic fields that they simulate.) However, each particular software uses its own particular format and layout, for the output data. This makes it difficult to compare the results of different simulation software, or to have a common visualization tool for these results. However, a standardized layout for fields and particles has recently been developed: the openPMD format ( HYPERLINK "http://www.openpmd.org/"www.openpmd.org) This format is open- source, andmore » specifies a standard way in which field data and particle data should be written. The openPMD format is already implemented in the particle-in-cell code Warp (developed at LBL) and in PIConGPU (developed at HZDR, Germany). In this context, the proposed software (openPMD-viewer) is a Python package, which allows to access and visualize any data which has been formatted according to the openPMD standard. This package contains two main components: - a Python API, which allows to read and extract the data from a openPMD file, so as to be able to work with it within the Python environment. (e.g. plot the data and reprocess it with particular Python functions) - a graphical interface, which works with the ipython notebook, and allows to quickly visualize the data and browse through a set of openPMD files. The proposed software will be typically used when analyzing the results of numerical simulations. It will be useful to quickly extract scientific meaning from a set of numerical data.« less

Python for large-scale electrophysiology.

PubMed

Spacek, Martin; Blanche, Tim; Swindale, Nicholas

2008-01-01

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

SimulatorToFMU v0.1

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

Nouidui, Thierry; Wetter, Michael

SimulatorToFMU is a software package written in Python which allows users to export a memoryless Python-driven simulation program or script as a Functional Mock-up Unit (FMU) for model exchange or co-simulation.In CyDER (Cyber Physical Co-simulation Platform for Distributed Energy Resources in Smart Grids), SimulatorToFMU will allow exporting OPAL-RT as an FMU. This will enable OPAL-RT to be linked to CYMDIST and GridDyn FMUs through a standardized open source interface.

The fast azimuthal integration Python library: pyFAI.

PubMed

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

2015-04-01

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

Dem Generation from Close-Range Photogrammetry Using Extended Python Photogrammetry Toolbox

NASA Astrophysics Data System (ADS)

Belmonte, A. A.; Biong, M. M. P.; Macatulad, E. G.

2017-10-01

Digital elevation models (DEMs) are widely used raster data for different applications concerning terrain, such as for flood modelling, viewshed analysis, mining, land development, engineering design projects, to name a few. DEMs can be obtained through various methods, including topographic survey, LiDAR or photogrammetry, and internet sources. Terrestrial close-range photogrammetry is one of the alternative methods to produce DEMs through the processing of images using photogrammetry software. There are already powerful photogrammetry software that are commercially-available and can produce high-accuracy DEMs. However, this entails corresponding cost. Although, some of these software have free or demo trials, these trials have limits in their usable features and usage time. One alternative is the use of free and open-source software (FOSS), such as the Python Photogrammetry Toolbox (PPT), which provides an interface for performing photogrammetric processes implemented through python script. For relatively small areas such as in mining or construction excavation, a relatively inexpensive, fast and accurate method would be advantageous. In this study, PPT was used to generate 3D point cloud data from images of an open pit excavation. The PPT was extended to add an algorithm converting the generated point cloud data into a usable DEM.

Optics simulations: a Python workshop

NASA Astrophysics Data System (ADS)

Ghalila, H.; Ammar, A.; Varadharajan, S.; Majdi, Y.; Zghal, M.; Lahmar, S.; Lakshminarayanan, V.

2017-08-01

Numerical simulations allow teachers and students to indirectly perform sophisticated experiments that cannot be realizable otherwise due to cost and other constraints. During the past few decades there has been an explosion in the development of numerical tools concurrently with open source environments such as Python software. This availability of open source software offers an incredible opportunity for advancing teaching methodologies as well as in research. More specifically it is possible to correlate theoretical knowledge with experimental measurements using "virtual" experiments. We have been working on the development of numerical simulation tools using the Python program package and we have concentrated on geometric and physical optics simulations. The advantage of doing hands-on numerical experiments is that it allows the student learner to be an active participant in the pedagogical/learning process rather than playing a passive role as in the traditional lecture format. Even in laboratory classes because of constraints of space, lack of equipment and often-large numbers of students, many students play a passive role since they work in groups of 3 or more students. Furthermore these new tools help students get a handle on numerical methods as well simulations and impart a "feel" for the physics under investigation.

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

NASA Astrophysics Data System (ADS)

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

2012-08-01

We present an object-oriented open-source framework for solving the dynamics of open quantum systems written in Python. Arbitrary Hamiltonians, including time-dependent systems, may be built up from operators and states defined by a quantum object class, and then passed on to a choice of master equation or Monte Carlo solvers. We give an overview of the basic structure for the framework before detailing the numerical simulation of open system dynamics. Several examples are given to illustrate the build up to a complete calculation. Finally, we measure the performance of our library against that of current implementations. The framework described here is particularly well suited to the fields of quantum optics, superconducting circuit devices, nanomechanics, and trapped ions, while also being ideal for use in classroom instruction. Catalogue identifier: AEMB_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEMB_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: GNU General Public License, version 3 No. of lines in distributed program, including test data, etc.: 16 482 No. of bytes in distributed program, including test data, etc.: 213 438 Distribution format: tar.gz Programming language: Python Computer: i386, x86-64 Operating system: Linux, Mac OSX, Windows RAM: 2+ Gigabytes Classification: 7 External routines: NumPy (http://numpy.scipy.org/), SciPy (http://www.scipy.org/), Matplotlib (http://matplotlib.sourceforge.net/) Nature of problem: Dynamics of open quantum systems. Solution method: Numerical solutions to Lindblad master equation or Monte Carlo wave function method. Restrictions: Problems must meet the criteria for using the master equation in Lindblad form. Running time: A few seconds up to several tens of minutes, depending on size of underlying Hilbert space.

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.

Post hoc support vector machine learning for impedimetric biosensors based on weak protein-ligand interactions.

PubMed

Rong, Y; Padron, A V; Hagerty, K J; Nelson, N; Chi, S; Keyhani, N O; Katz, J; Datta, S P A; Gomes, C; McLamore, E S

2018-04-30

Impedimetric biosensors for measuring small molecules based on weak/transient interactions between bioreceptors and target analytes are a challenge for detection electronics, particularly in field studies or in the analysis of complex matrices. Protein-ligand binding sensors have enormous potential for biosensing, but achieving accuracy in complex solutions is a major challenge. There is a need for simple post hoc analytical tools that are not computationally expensive, yet provide near real time feedback on data derived from impedance spectra. Here, we show the use of a simple, open source support vector machine learning algorithm for analyzing impedimetric data in lieu of using equivalent circuit analysis. We demonstrate two different protein-based biosensors to show that the tool can be used for various applications. We conclude with a mobile phone-based demonstration focused on the measurement of acetone, an important biomarker related to the onset of diabetic ketoacidosis. In all conditions tested, the open source classifier was capable of performing as well as, or better, than the equivalent circuit analysis for characterizing weak/transient interactions between a model ligand (acetone) and a small chemosensory protein derived from the tsetse fly. In addition, the tool has a low computational requirement, facilitating use for mobile acquisition systems such as mobile phones. The protocol is deployed through Jupyter notebook (an open source computing environment available for mobile phone, tablet or computer use) and the code was written in Python. For each of the applications, we provide step-by-step instructions in English, Spanish, Mandarin and Portuguese to facilitate widespread use. All codes were based on scikit-learn, an open source software machine learning library in the Python language, and were processed in Jupyter notebook, an open-source web application for Python. The tool can easily be integrated with the mobile biosensor equipment for rapid detection, facilitating use by a broad range of impedimetric biosensor users. This post hoc analysis tool can serve as a launchpad for the convergence of nanobiosensors in planetary health monitoring applications based on mobile phone hardware.

Teaching Introductory GIS Programming to Geographers Using an Open Source Python Approach

ERIC Educational Resources Information Center

Etherington, Thomas R.

2016-01-01

Computer programming is not commonly taught to geographers as a part of geographic information system (GIS) courses, but the advent of NeoGeography, big data and open GIS means that programming skills are becoming more important. To encourage the teaching of programming to geographers, this paper outlines a course based around a series of…

Generic Space Science Visualization in 2D/3D using SDDAS

NASA Astrophysics Data System (ADS)

Mukherjee, J.; Murphy, Z. B.; Gonzalez, C. A.; Muller, M.; Ybarra, S.

2017-12-01

The Southwest Data Display and Analysis System (SDDAS) is a flexible multi-mission / multi-instrument software system intended to support space physics data analysis, and has been in active development for over 20 years. For the Magnetospheric Multi-Scale (MMS), Juno, Cluster, and Mars Express missions, we have modified these generic tools for visualizing data in two and three dimensions. The SDDAS software is open source and makes use of various other open source packages, including VTK and Qwt. The software offers interactive plotting as well as a Python and Lua module to modify the data before plotting. In theory, by writing a Lua or Python module to read the data, any data could be used. Currently, the software can natively read data in IDFS, CEF, CDF, FITS, SEG-Y, ASCII, and XLS formats. We have integrated the software with other Python packages such as SPICE and SpacePy. Included with the visualization software is a database application and other utilities for managing data that can retrieve data from the Cluster Active Archive and Space Physics Data Facility at Goddard, as well as other local archives. Line plots, spectrograms, geographic, volume plots, strip charts, etc. are just some of the types of plots one can generate with SDDAS. Furthermore, due to the design, output is not limited to strictly visualization as SDDAS can also be used to generate stand-alone IDL or Python visualization code.. Lastly, SDDAS has been successfully used as a backend for several web based analysis systems as well.

Pythran: enabling static optimization of scientific Python programs

NASA Astrophysics Data System (ADS)

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

2015-01-01

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

Python for Large-Scale Electrophysiology

PubMed Central

Spacek, Martin; Blanche, Tim; Swindale, Nicholas

2008-01-01

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

A Python-based interface to examine motions in time series of solar images

NASA Astrophysics Data System (ADS)

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

2017-10-01

Python is considered to be a mature programming language, besides of being widely accepted as an engaging option for scientific analysis in multiple areas, as will be presented in this work for the particular case of solar physics research. SunPy is an open-source library based on Python that has been recently developed to furnish software tools to solar data analysis and visualization. In this work we present a graphical user interface (GUI) based on Python and Qt to effectively compute proper motions for the analysis of time series of solar data. This user-friendly computing interface, that is intended to be incorporated to the Sunpy library, uses a local correlation tracking technique and some extra tools that allows the selection of different parameters to calculate, vizualize and analyze vector velocity fields of solar data, i.e. time series of solar filtergrams and magnetograms.

PYTHON for Variable Star Astronomy (Abstract)

NASA Astrophysics Data System (ADS)

Craig, M.

2018-06-01

(Abstract only) Open source PYTHON packages that are useful for data reduction, photometry, and other tasks relevant to variable star astronomy have been developed over the last three to four years as part of the Astropy project. Using this software, it is relatively straightforward to reduce images, automatically detect sources, and match them to catalogs. Over the last year browser-based tools for performing some of those tasks have been developed that minimize or eliminate the need to write any of your own code. After providing an overview of the current state of the software, an application that calculates transformation coefficients on a frame-by-frame basis by matching stars in an image to the APASS catalog will be described.

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

PubMed Central

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

2009-01-01

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

pyBSM: A Python package for modeling imaging systems

NASA Astrophysics Data System (ADS)

LeMaster, Daniel A.; Eismann, Michael T.

2017-05-01

There are components that are common to all electro-optical and infrared imaging system performance models. The purpose of the Python Based Sensor Model (pyBSM) is to provide open source access to these functions for other researchers to build upon. Specifically, pyBSM implements much of the capability found in the ERIM Image Based Sensor Model (IBSM) V2.0 along with some improvements. The paper also includes two use-case examples. First, performance of an airborne imaging system is modeled using the General Image Quality Equation (GIQE). The results are then decomposed into factors affecting noise and resolution. Second, pyBSM is paired with openCV to evaluate performance of an algorithm used to detect objects in an image.

Python Winding Itself Around Datacubes: How to Access Massive Multi-Dimensional Arrays in a Pythonic Way

NASA Astrophysics Data System (ADS)

Merticariu, Vlad; Misev, Dimitar; Baumann, Peter

2017-04-01

While python has developed into the lingua franca in Data Science there is often a paradigm break when accessing specialized tools. In particular for one of the core data categories in science and engineering, massive multi-dimensional arrays, out-of-memory solutions typically employ their own, different models. We discuss this situation on the example of the scalable open-source array engine, rasdaman ("raster data manager") which offers access to and processing of Petascale multi-dimensional arrays through an SQL-style array query language, rasql. Such queries are executed in the server on a storage engine utilizing adaptive array partitioning and based on a processing engine implementing a "tile streaming" paradigm to allow processing of arrays massively larger than server RAM. The rasdaman QL has acted as blueprint for forthcoming ISO Array SQL and the Open Geospatial Consortium (OGC) geo analytics language, Web Coverage Processing Service, adopted in 2008. Not surprisingly, rasdaman is OGC and INSPIRE Reference Implementation for their "Big Earth Data" standards suite. Recently, rasdaman has been augmented with a python interface which allows to transparently interact with the database (credits go to Siddharth Shukla's Master Thesis at Jacobs University). Programmers do not need to know the rasdaman query language, as the operators are silently transformed, through lazy evaluation, into queries. Arrays delivered are likewise automatically transformed into their python representation. In the talk, the rasdaman concept will be illustrated with the help of large-scale real-life examples of operational satellite image and weather data services, and sample python code.

An object oriented implementation of the Yeadon human inertia model

PubMed Central

Dembia, Christopher; Moore, Jason K.; Hubbard, Mont

2015-01-01

We present an open source software implementation of a popular mathematical method developed by M.R. Yeadon for calculating the body and segment inertia parameters of a human body. The software is written in a high level open source language and provides three interfaces for manipulating the data and the model: a Python API, a command-line user interface, and a graphical user interface. Thus the software can fit into various data processing pipelines and requires only simple geometrical measures as input. PMID:25717365

An object oriented implementation of the Yeadon human inertia model.

PubMed

Dembia, Christopher; Moore, Jason K; Hubbard, Mont

2014-01-01

We present an open source software implementation of a popular mathematical method developed by M.R. Yeadon for calculating the body and segment inertia parameters of a human body. The software is written in a high level open source language and provides three interfaces for manipulating the data and the model: a Python API, a command-line user interface, and a graphical user interface. Thus the software can fit into various data processing pipelines and requires only simple geometrical measures as input.

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.

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

Using WNTR to Model Water Distribution System Resilience ...

EPA Pesticide Factsheets

The Water Network Tool for Resilience (WNTR) is a new open source Python package developed by the U.S. Environmental Protection Agency and Sandia National Laboratories to model and evaluate resilience of water distribution systems. WNTR can be used to simulate a wide range of disruptive events, including earthquakes, contamination incidents, floods, climate change, and fires. The software includes the EPANET solver as well as a WNTR solver with the ability to model pressure-driven demand hydraulics, pipe breaks, component degradation and failure, changes to supply and demand, and cascading failure. Damage to individual components in the network (i.e. pipes, tanks) can be selected probabilistically using fragility curves. WNTR can also simulate different types of resilience-enhancing actions, including scheduled pipe repair or replacement, water conservation efforts, addition of back-up power, and use of contamination warning systems. The software can be used to estimate potential damage in a network, evaluate preparedness, prioritize repair strategies, and identify worse case scenarios. As a Python package, WNTR takes advantage of many existing python capabilities, including parallel processing of scenarios and graphics capabilities. This presentation will outline the modeling components in WNTR, demonstrate their use, give the audience information on how to get started using the code, and invite others to participate in this open source project. This pres

Source Code Stylometry Improvements in Python

DTIC Science & Technology

2017-12-14

person can be identified via their handwriting or an author identified by their style or prose, programmers can be identified by their code...to say , picking 1 author out of a known complete set. However, expanded open-world classification and multiauthor classification have also been

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.

PyPDB: a Python API for the Protein Data Bank.

PubMed

Gilpin, William

2016-01-01

We have created a Python programming interface for the RCSB Protein Data Bank (PDB) that allows search and data retrieval for a wide range of result types, including BLAST and sequence motif queries. The API relies on the existing XML-based API and operates by creating custom XML requests from native Python types, allowing extensibility and straightforward modification. The package has the ability to perform many types of advanced search of the PDB that are otherwise only available through the PDB website. PyPDB is implemented exclusively in Python 3 using standard libraries for maximal compatibility. The most up-to-date version, including iPython notebooks containing usage tutorials, is available free-of-charge under an open-source MIT license via GitHub at https://github.com/williamgilpin/pypdb, and the full API reference is at http://williamgilpin.github.io/pypdb_docs/html/. The latest stable release is also available on PyPI. wgilpin@stanford.edu. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

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.

Interactive, process-oriented climate modeling with CLIMLAB

NASA Astrophysics Data System (ADS)

Rose, B. E. J.

2016-12-01

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

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

PubMed

Spielman, Stephanie J; Wilke, Claus O

2015-01-01

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

Analyzing microtomography data with Python and the scikit-image library.

PubMed

Gouillart, Emmanuelle; Nunez-Iglesias, Juan; van der Walt, Stéfan

2017-01-01

The exploration and processing of images is a vital aspect of the scientific workflows of many X-ray imaging modalities. Users require tools that combine interactivity, versatility, and performance. scikit-image is an open-source image processing toolkit for the Python language that supports a large variety of file formats and is compatible with 2D and 3D images. The toolkit exposes a simple programming interface, with thematic modules grouping functions according to their purpose, such as image restoration, segmentation, and measurements. scikit-image users benefit from a rich scientific Python ecosystem that contains many powerful libraries for tasks such as visualization or machine learning. scikit-image combines a gentle learning curve, versatile image processing capabilities, and the scalable performance required for the high-throughput analysis of X-ray imaging data.

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.

Xarray: multi-dimensional data analysis in Python

NASA Astrophysics Data System (ADS)

Hoyer, Stephan; Hamman, Joe; Maussion, Fabien

2017-04-01

xarray (http://xarray.pydata.org) is an open source project and Python package that provides a toolkit and data structures for N-dimensional labeled arrays, which are the bread and butter of modern geoscientific data analysis. Key features of the package include label-based indexing and arithmetic, interoperability with the core scientific Python packages (e.g., pandas, NumPy, Matplotlib, Cartopy), out-of-core computation on datasets that don't fit into memory, a wide range of input/output options, and advanced multi-dimensional data manipulation tools such as group-by and resampling. In this contribution we will present the key features of the library and demonstrate its great potential for a wide range of applications, from (big-)data processing on super computers to data exploration in front of a classroom.

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.

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

NASA Astrophysics Data System (ADS)

Rose, B. E. J.

2015-12-01

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

GLACiAR, an Open-Source Python Tool for Simulations of Source Recovery and Completeness in Galaxy Surveys

NASA Astrophysics Data System (ADS)

Carrasco, D.; Trenti, M.; Mutch, S.; Oesch, P. A.

2018-06-01

The luminosity function is a fundamental observable for characterising how galaxies form and evolve throughout the cosmic history. One key ingredient to derive this measurement from the number counts in a survey is the characterisation of the completeness and redshift selection functions for the observations. In this paper, we present GLACiAR, an open python tool available on GitHub to estimate the completeness and selection functions in galaxy surveys. The code is tailored for multiband imaging surveys aimed at searching for high-redshift galaxies through the Lyman-break technique, but it can be applied broadly. The code generates artificial galaxies that follow Sérsic profiles with different indexes and with customisable size, redshift, and spectral energy distribution properties, adds them to input images, and measures the recovery rate. To illustrate this new software tool, we apply it to quantify the completeness and redshift selection functions for J-dropouts sources (redshift z 10 galaxies) in the Hubble Space Telescope Brightest of Reionizing Galaxies Survey. Our comparison with a previous completeness analysis on the same dataset shows overall agreement, but also highlights how different modelling assumptions for the artificial sources can impact completeness estimates.

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

PubMed

Armano, Giuliano; Manconi, Andrea

2009-10-02

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

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.

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.

ChemoPy: freely available python package for computational biology and chemoinformatics.

PubMed

Cao, Dong-Sheng; Xu, Qing-Song; Hu, Qian-Nan; Liang, Yi-Zeng

2013-04-15

Molecular representation for small molecules has been routinely used in QSAR/SAR, virtual screening, database search, ranking, drug ADME/T prediction and other drug discovery processes. To facilitate extensive studies of drug molecules, we developed a freely available, open-source python package called chemoinformatics in python (ChemoPy) for calculating the commonly used structural and physicochemical features. It computes 16 drug feature groups composed of 19 descriptors that include 1135 descriptor values. In addition, it provides seven types of molecular fingerprint systems for drug molecules, including topological fingerprints, electro-topological state (E-state) fingerprints, MACCS keys, FP4 keys, atom pairs fingerprints, topological torsion fingerprints and Morgan/circular fingerprints. By applying a semi-empirical quantum chemistry program MOPAC, ChemoPy can also compute a large number of 3D molecular descriptors conveniently. The python package, ChemoPy, is freely available via http://code.google.com/p/pychem/downloads/list, and it runs on Linux and MS-Windows. Supplementary data are available at Bioinformatics online.

PyMICE: APython library for analysis of IntelliCage data.

PubMed

Dzik, Jakub M; Puścian, Alicja; Mijakowska, Zofia; Radwanska, Kasia; Łęski, Szymon

2018-04-01

IntelliCage is an automated system for recording the behavior of a group of mice housed together. It produces rich, detailed behavioral data calling for new methods and software for their analysis. Here we present PyMICE, a free and open-source library for analysis of IntelliCage data in the Python programming language. We describe the design and demonstrate the use of the library through a series of examples. PyMICE provides easy and intuitive access to IntelliCage data, and thus facilitates the possibility of using numerous other Python scientific libraries to form a complete data analysis workflow.

Interfacing of high temperature Z-meter setup using python

NASA Astrophysics Data System (ADS)

Patel, Ashutosh; Sisodia, Shashank; Pandey, Sudhir K.

2017-05-01

In this work, we interface high temperature Z-meter setup to automize the whole measurement process. A program is built on open source programming language `Python' which convert the manual measurement process into fully automated process without any cost addition. Using this program, simultaneous measurement of Seebeck coefficient (α), thermal conductivity (κ) and electrical resistivity (ρ), are performed and using all three, figure-of-merit (ZT) is calculated. Developed program is verified by performing measurement over p-type Bi0.36Sb1.45Te3 sample and the data obtained are found to be in good agreement with the reported data.

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.

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.

Using WNTR to Model Water Distribution System Resilience

EPA Science Inventory

The Water Network Tool for Resilience (WNTR) is a new open source Python package developed by the U.S. Environmental Protection Agency and Sandia National Laboratories to model and evaluate resilience of water distribution systems. WNTR can be used to simulate a wide range of di...

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

PubMed

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

2014-01-01

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

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

Open-source Software for Exoplanet Atmospheric Modeling

NASA Astrophysics Data System (ADS)

Cubillos, Patricio; Blecic, Jasmina; Harrington, Joseph

2018-01-01

I will present a suite of self-standing open-source tools to model and retrieve exoplanet spectra implemented for Python. These include: (1) a Bayesian-statistical package to run Levenberg-Marquardt optimization and Markov-chain Monte Carlo posterior sampling, (2) a package to compress line-transition data from HITRAN or Exomol without loss of information, (3) a package to compute partition functions for HITRAN molecules, (4) a package to compute collision-induced absorption, and (5) a package to produce radiative-transfer spectra of transit and eclipse exoplanet observations and atmospheric retrievals.

mmpdb: An Open-Source Matched Molecular Pair Platform for Large Multiproperty Data Sets.

PubMed

Dalke, Andrew; Hert, Jérôme; Kramer, Christian

2018-05-29

Matched molecular pair analysis (MMPA) enables the automated and systematic compilation of medicinal chemistry rules from compound/property data sets. Here we present mmpdb, an open-source matched molecular pair (MMP) platform to create, compile, store, retrieve, and use MMP rules. mmpdb is suitable for the large data sets typically found in pharmaceutical and agrochemical companies and provides new algorithms for fragment canonicalization and stereochemistry handling. The platform is written in Python and based on the RDKit toolkit. It is freely available from https://github.com/rdkit/mmpdb .

Turning a remotely controllable observatory into a fully autonomous system

NASA Astrophysics Data System (ADS)

Swindell, Scott; Johnson, Chris; Gabor, Paul; Zareba, Grzegorz; Kubánek, Petr; Prouza, Michael

2014-08-01

We describe a complex process needed to turn an existing, old, operational observatory - The Steward Observatory's 61" Kuiper Telescope - into a fully autonomous system, which observers without an observer. For this purpose, we employed RTS2,1 an open sourced, Linux based observatory control system, together with other open sourced programs and tools (GNU compilers, Python language for scripting, JQuery UI for Web user interface). This presentation provides a guide with time estimates needed for a newcomers to the field to handle such challenging tasks, as fully autonomous observatory operations.

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.

SCoT: a Python toolbox for EEG source connectivity.

PubMed

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

2014-01-01

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

SCoT: a Python toolbox for EEG source connectivity

PubMed Central

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

2014-01-01

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

Low cost and open source multi-fluorescence imaging system for teaching and research in biology and bioengineering.

PubMed

Nuñez, Isaac; Matute, Tamara; Herrera, Roberto; Keymer, Juan; Marzullo, Timothy; Rudge, Timothy; Federici, Fernán

2017-01-01

The advent of easy-to-use open source microcontrollers, off-the-shelf electronics and customizable manufacturing technologies has facilitated the development of inexpensive scientific devices and laboratory equipment. In this study, we describe an imaging system that integrates low-cost and open-source hardware, software and genetic resources. The multi-fluorescence imaging system consists of readily available 470 nm LEDs, a Raspberry Pi camera and a set of filters made with low cost acrylics. This device allows imaging in scales ranging from single colonies to entire plates. We developed a set of genetic components (e.g. promoters, coding sequences, terminators) and vectors following the standard framework of Golden Gate, which allowed the fabrication of genetic constructs in a combinatorial, low cost and robust manner. In order to provide simultaneous imaging of multiple wavelength signals, we screened a series of long stokes shift fluorescent proteins that could be combined with cyan/green fluorescent proteins. We found CyOFP1, mBeRFP and sfGFP to be the most compatible set for 3-channel fluorescent imaging. We developed open source Python code to operate the hardware to run time-lapse experiments with automated control of illumination and camera and a Python module to analyze data and extract meaningful biological information. To demonstrate the potential application of this integral system, we tested its performance on a diverse range of imaging assays often used in disciplines such as microbial ecology, microbiology and synthetic biology. We also assessed its potential use in a high school environment to teach biology, hardware design, optics, and programming. Together, these results demonstrate the successful integration of open source hardware, software, genetic resources and customizable manufacturing to obtain a powerful, low cost and robust system for education, scientific research and bioengineering. All the resources developed here are available under open source licenses.

Low cost and open source multi-fluorescence imaging system for teaching and research in biology and bioengineering

PubMed Central

Herrera, Roberto; Keymer, Juan; Marzullo, Timothy; Rudge, Timothy

2017-01-01

The advent of easy-to-use open source microcontrollers, off-the-shelf electronics and customizable manufacturing technologies has facilitated the development of inexpensive scientific devices and laboratory equipment. In this study, we describe an imaging system that integrates low-cost and open-source hardware, software and genetic resources. The multi-fluorescence imaging system consists of readily available 470 nm LEDs, a Raspberry Pi camera and a set of filters made with low cost acrylics. This device allows imaging in scales ranging from single colonies to entire plates. We developed a set of genetic components (e.g. promoters, coding sequences, terminators) and vectors following the standard framework of Golden Gate, which allowed the fabrication of genetic constructs in a combinatorial, low cost and robust manner. In order to provide simultaneous imaging of multiple wavelength signals, we screened a series of long stokes shift fluorescent proteins that could be combined with cyan/green fluorescent proteins. We found CyOFP1, mBeRFP and sfGFP to be the most compatible set for 3-channel fluorescent imaging. We developed open source Python code to operate the hardware to run time-lapse experiments with automated control of illumination and camera and a Python module to analyze data and extract meaningful biological information. To demonstrate the potential application of this integral system, we tested its performance on a diverse range of imaging assays often used in disciplines such as microbial ecology, microbiology and synthetic biology. We also assessed its potential use in a high school environment to teach biology, hardware design, optics, and programming. Together, these results demonstrate the successful integration of open source hardware, software, genetic resources and customizable manufacturing to obtain a powerful, low cost and robust system for education, scientific research and bioengineering. All the resources developed here are available under open source licenses. PMID:29140977

GIS-Based Noise Simulation Open Source Software: N-GNOIS

NASA Astrophysics Data System (ADS)

Vijay, Ritesh; Sharma, A.; Kumar, M.; Shende, V.; Chakrabarti, T.; Gupta, Rajesh

2015-12-01

Geographical information system (GIS)-based noise simulation software (N-GNOIS) has been developed to simulate the noise scenario due to point and mobile sources considering the impact of geographical features and meteorological parameters. These have been addressed in the software through attenuation modules of atmosphere, vegetation and barrier. N-GNOIS is a user friendly, platform-independent and open geospatial consortia (OGC) compliant software. It has been developed using open source technology (QGIS) and open source language (Python). N-GNOIS has unique features like cumulative impact of point and mobile sources, building structure and honking due to traffic. Honking is the most common phenomenon in developing countries and is frequently observed on any type of roads. N-GNOIS also helps in designing physical barrier and vegetation cover to check the propagation of noise and acts as a decision making tool for planning and management of noise component in environmental impact assessment (EIA) studies.

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.

Sleep: An Open-Source Python Software for Visualization, Analysis, and Staging of Sleep Data

PubMed Central

Combrisson, Etienne; Vallat, Raphael; Eichenlaub, Jean-Baptiste; O'Reilly, Christian; Lajnef, Tarek; Guillot, Aymeric; Ruby, Perrine M.; Jerbi, Karim

2017-01-01

We introduce Sleep, a new Python open-source graphical user interface (GUI) dedicated to visualization, scoring and analyses of sleep data. Among its most prominent features are: (1) Dynamic display of polysomnographic data, spectrogram, hypnogram and topographic maps with several customizable parameters, (2) Implementation of several automatic detection of sleep features such as spindles, K-complexes, slow waves, and rapid eye movements (REM), (3) Implementation of practical signal processing tools such as re-referencing or filtering, and (4) Display of main descriptive statistics including publication-ready tables and figures. The software package supports loading and reading raw EEG data from standard file formats such as European Data Format, in addition to a range of commercial data formats. Most importantly, Sleep is built on top of the VisPy library, which provides GPU-based fast and high-level visualization. As a result, it is capable of efficiently handling and displaying large sleep datasets. Sleep is freely available (http://visbrain.org/sleep) and comes with sample datasets and an extensive documentation. Novel functionalities will continue to be added and open-science community efforts are expected to enhance the capacities of this module. PMID:28983246

Sleep: An Open-Source Python Software for Visualization, Analysis, and Staging of Sleep Data.

PubMed

Combrisson, Etienne; Vallat, Raphael; Eichenlaub, Jean-Baptiste; O'Reilly, Christian; Lajnef, Tarek; Guillot, Aymeric; Ruby, Perrine M; Jerbi, Karim

2017-01-01

We introduce Sleep, a new Python open-source graphical user interface (GUI) dedicated to visualization, scoring and analyses of sleep data. Among its most prominent features are: (1) Dynamic display of polysomnographic data, spectrogram, hypnogram and topographic maps with several customizable parameters, (2) Implementation of several automatic detection of sleep features such as spindles, K-complexes, slow waves, and rapid eye movements (REM), (3) Implementation of practical signal processing tools such as re-referencing or filtering, and (4) Display of main descriptive statistics including publication-ready tables and figures. The software package supports loading and reading raw EEG data from standard file formats such as European Data Format, in addition to a range of commercial data formats. Most importantly, Sleep is built on top of the VisPy library, which provides GPU-based fast and high-level visualization. As a result, it is capable of efficiently handling and displaying large sleep datasets. Sleep is freely available (http://visbrain.org/sleep) and comes with sample datasets and an extensive documentation. Novel functionalities will continue to be added and open-science community efforts are expected to enhance the capacities of this module.

A Multidisciplinary Tool for Systems Analysis of Planetary Entry, Descent, and Landing (SAPE)

NASA Technical Reports Server (NTRS)

Samareh, Jamshid A.

2009-01-01

SAPE is a Python-based multidisciplinary analysis tool for systems analysis of planetary entry, descent, and landing (EDL) for Venus, Earth, Mars, Jupiter, Saturn, Uranus, Neptune, and Titan. The purpose of SAPE is to provide a variable-fidelity capability for conceptual and preliminary analysis within the same framework. SAPE includes the following analysis modules: geometry, trajectory, aerodynamics, aerothermal, thermal protection system, and structural sizing. SAPE uses the Python language-a platform-independent open-source software for integration and for the user interface. The development has relied heavily on the object-oriented programming capabilities that are available in Python. Modules are provided to interface with commercial and government off-the-shelf software components (e.g., thermal protection systems and finite-element analysis). SAPE runs on Microsoft Windows and Apple Mac OS X and has been partially tested on Linux.

Extension Scripts to caffe for Running COWC Data

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

Mundhenk, T. Nathan; Konjevod, Goran; Sakla, Wesam A.

2016-07-14

These are scripts In python whlch extend the functlonallty of the open source software Caffe and allow 1t to scan an overhead scene of Images and detect or count cars from that scene. tt returns the number of cars or the location of cars as a marked scene lmase.

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

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/.

Forward Field Computation with OpenMEEG

PubMed Central

Gramfort, Alexandre; Papadopoulo, Théodore; Olivi, Emmanuel; Clerc, Maureen

2011-01-01

To recover the sources giving rise to electro- and magnetoencephalography in individual measurements, realistic physiological modeling is required, and accurate numerical solutions must be computed. We present OpenMEEG, which solves the electromagnetic forward problem in the quasistatic regime, for head models with piecewise constant conductivity. The core of OpenMEEG consists of the symmetric Boundary Element Method, which is based on an extended Green Representation theorem. OpenMEEG is able to provide lead fields for four different electromagnetic forward problems: Electroencephalography (EEG), Magnetoencephalography (MEG), Electrical Impedance Tomography (EIT), and intracranial electric potentials (IPs). OpenMEEG is open source and multiplatform. It can be used from Python and Matlab in conjunction with toolboxes that solve the inverse problem; its integration within FieldTrip is operational since release 2.0. PMID:21437231

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.

Embracing Open Software Development in Solar Physics

NASA Astrophysics Data System (ADS)

Hughitt, V. K.; Ireland, J.; Christe, S.; Mueller, D.

2012-12-01

We discuss two ongoing software projects in solar physics that have adopted best practices of the open source software community. The first, the Helioviewer Project, is a powerful data visualization tool which includes online and Java interfaces inspired by Google Maps (tm). This effort allows users to find solar features and events of interest, and download the corresponding data. Having found data of interest, the user now has to analyze it. The dominant solar data analysis platform is an open-source library called SolarSoft (SSW). Although SSW itself is open-source, the programming language used is IDL, a proprietary language with licensing costs that are prohibative for many institutions and individuals. SSW is composed of a collection of related scripts written by missions and individuals for solar data processing and analysis, without any consistent data structures or common interfaces. Further, at the time when SSW was initially developed, many of the best software development processes of today (mirrored and distributed version control, unit testing, continuous integration, etc.) were not standard, and have not since been adopted. The challenges inherent in developing SolarSoft led to a second software project known as SunPy. SunPy is an open-source Python-based library which seeks to create a unified solar data analysis environment including a number of core datatypes such as Maps, Lightcurves, and Spectra which have consistent interfaces and behaviors. By taking advantage of the large and sophisticated body of scientific software already available in Python (e.g. SciPy, NumPy, Matplotlib), and by adopting many of the best practices refined in open-source software development, SunPy has been able to develop at a very rapid pace while still ensuring a high level of reliability. The Helioviewer Project and SunPy represent two pioneering technologies in solar physics - simple yet flexible data visualization and a powerful, new data analysis environment. We discuss the development of both these efforts and how they are beginning to influence the solar physics community.

PySpike-A Python library for analyzing spike train synchrony

NASA Astrophysics Data System (ADS)

Mulansky, Mario; Kreuz, Thomas

Understanding how the brain functions is one of the biggest challenges of our time. The analysis of experimentally recorded neural firing patterns (spike trains) plays a crucial role in addressing this problem. Here, the PySpike library is introduced, a Python package for spike train analysis providing parameter-free and time-scale independent measures of spike train synchrony. It allows to compute similarity and dissimilarity profiles, averaged values and distance matrices. Although mainly focusing on neuroscience, PySpike can also be applied in other contexts like climate research or social sciences. The package is available as Open Source on Github and PyPI.

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.

ParFit: A Python-Based Object-Oriented Program for Fitting Molecular Mechanics Parameters to ab Initio Data.

PubMed

Zahariev, Federico; De Silva, Nuwan; Gordon, Mark S; Windus, Theresa L; Dick-Perez, Marilu

2017-03-27

A newly created object-oriented program for automating the process of fitting molecular-mechanics parameters to ab initio data, termed ParFit, is presented. ParFit uses a hybrid of deterministic and stochastic genetic algorithms. ParFit can simultaneously handle several molecular-mechanics parameters in multiple molecules and can also apply symmetric and antisymmetric constraints on the optimized parameters. The simultaneous handling of several molecules enhances the transferability of the fitted parameters. ParFit is written in Python, uses a rich set of standard and nonstandard Python libraries, and can be run in parallel on multicore computer systems. As an example, a series of phosphine oxides, important for metal extraction chemistry, are parametrized using ParFit. ParFit is in an open source program available for free on GitHub ( https://github.com/fzahari/ParFit ).

Psi4NumPy: An Interactive Quantum Chemistry Programming Environment for Reference Implementations and Rapid Development.

PubMed

Smith, Daniel G A; Burns, Lori A; Sirianni, Dominic A; Nascimento, Daniel R; Kumar, Ashutosh; James, Andrew M; Schriber, Jeffrey B; Zhang, Tianyuan; Zhang, Boyi; Abbott, Adam S; Berquist, Eric J; Lechner, Marvin H; Cunha, Leonardo A; Heide, Alexander G; Waldrop, Jonathan M; Takeshita, Tyler Y; Alenaizan, Asem; Neuhauser, Daniel; King, Rollin A; Simmonett, Andrew C; Turney, Justin M; Schaefer, Henry F; Evangelista, Francesco A; DePrince, A Eugene; Crawford, T Daniel; Patkowski, Konrad; Sherrill, C David

2018-06-11

Psi4NumPy demonstrates the use of efficient computational kernels from the open-source Psi4 program through the popular NumPy library for linear algebra in Python to facilitate the rapid development of clear, understandable Python computer code for new quantum chemical methods, while maintaining a relatively low execution time. Using these tools, reference implementations have been created for a number of methods, including self-consistent field (SCF), SCF response, many-body perturbation theory, coupled-cluster theory, configuration interaction, and symmetry-adapted perturbation theory. Furthermore, several reference codes have been integrated into Jupyter notebooks, allowing background, underlying theory, and formula information to be associated with the implementation. Psi4NumPy tools and associated reference implementations can lower the barrier for future development of quantum chemistry methods. These implementations also demonstrate the power of the hybrid C++/Python programming approach employed by the Psi4 program.

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

O'Malley, Daniel; Vesselinov, Velimir V.

MADSpython (Model analysis and decision support tools in Python) is a code in Python that streamlines the process of using data and models for analysis and decision support using the code MADS. MADS is open-source code developed at LANL and written in C/C++ (MADS; http://mads.lanl.gov; LA-CC-11-035). MADS can work with external models of arbitrary complexity as well as built-in models of flow and transport in porous media. The Python scripts in MADSpython facilitate the generation of input and output file needed by MADS as wells as the external simulators which include FEHM and PFLOTRAN. MADSpython enables a number of data-more » and model-based analyses including model calibration, sensitivity analysis, uncertainty quantification, and decision analysis. MADSpython will be released under GPL V3 license. MADSpython will be distributed as a Git repo at gitlab.com and github.com. MADSpython manual and documentation will be posted at http://madspy.lanl.gov.« less

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

NASA Astrophysics Data System (ADS)

Hoyer, S.

2015-12-01

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

A python tool for the implementation of domain-specific languages

NASA Astrophysics Data System (ADS)

Dejanović, Igor; Vaderna, Renata; Milosavljević, Gordana; Simić, Miloš; Vuković, Željko

2017-07-01

In this paper we describe textX, a meta-language and a tool for building Domain-Specific Languages. It is implemented in Python using Arpeggio PEG (Parsing Expression Grammar) parser library. From a single language description (grammar) textX will build a parser and a meta-model (a.k.a. abstract syntax) of the language. The parser is used to parse textual representations of models conforming to the meta-model. As a result of parsing, a Python object graph will be automatically created. The structure of the object graph will conform to the meta-model defined by the grammar. This approach frees a developer from the need to manually analyse a parse tree and transform it to other suitable representation. The textX library is independent of any integrated development environment and can be easily integrated in any Python project. The textX tool works as a grammar interpreter. The parser is configured at run-time using the grammar. The textX tool is a free and open-source project available at GitHub.

PyPathway: Python Package for Biological Network Analysis and Visualization.

PubMed

Xu, Yang; Luo, Xiao-Chun

2018-05-01

Life science studies represent one of the biggest generators of large data sets, mainly because of rapid sequencing technological advances. Biological networks including interactive networks and human curated pathways are essential to understand these high-throughput data sets. Biological network analysis offers a method to explore systematically not only the molecular complexity of a particular disease but also the molecular relationships among apparently distinct phenotypes. Currently, several packages for Python community have been developed, such as BioPython and Goatools. However, tools to perform comprehensive network analysis and visualization are still needed. Here, we have developed PyPathway, an extensible free and open source Python package for functional enrichment analysis, network modeling, and network visualization. The network process module supports various interaction network and pathway databases such as Reactome, WikiPathway, STRING, and BioGRID. The network analysis module implements overrepresentation analysis, gene set enrichment analysis, network-based enrichment, and de novo network modeling. Finally, the visualization and data publishing modules enable users to share their analysis by using an easy web application. For package availability, see the first Reference.

An Inexpensive, Open-Source USB Arduino Data Acquisition Device for Chemical Instrumentation.

PubMed

Grinias, James P; Whitfield, Jason T; Guetschow, Erik D; Kennedy, Robert T

2016-07-12

Many research and teaching labs rely on USB data acquisition devices to collect voltage signals from instrumentation. However, these devices can be cost-prohibitive (especially when large numbers are needed for teaching labs) and require software to be developed for operation. In this article, we describe the development and use of an open-source USB data acquisition device (with 16-bit acquisition resolution) built using simple electronic components and an Arduino Uno that costs under $50. Additionally, open-source software written in Python is included so that data can be acquired using nearly any PC or Mac computer with a simple USB connection. Use of the device was demonstrated for a sophomore-level analytical experiment using GC and a CE-UV separation on an instrument used for research purposes.

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.

Fast and Efficient XML Data Access for Next-Generation Mass Spectrometry.

PubMed

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

2015-01-01

In mass spectrometry-based proteomics, XML formats such as mzML and mzXML provide an open and standardized way to store and exchange the raw data (spectra and chromatograms) of mass spectrometric experiments. These file formats are being used by a multitude of open-source and cross-platform tools which allow the proteomics community to access algorithms in a vendor-independent fashion and perform transparent and reproducible data analysis. Recent improvements in mass spectrometry instrumentation have increased the data size produced in a single LC-MS/MS measurement and put substantial strain on open-source tools, particularly those that are not equipped to deal with XML data files that reach dozens of gigabytes in size. Here we present a fast and versatile parsing library for mass spectrometric XML formats available in C++ and Python, based on the mature OpenMS software framework. Our library implements an API for obtaining spectra and chromatograms under memory constraints using random access or sequential access functions, allowing users to process datasets that are much larger than system memory. For fast access to the raw data structures, small XML files can also be completely loaded into memory. In addition, we have improved the parsing speed of the core mzML module by over 4-fold (compared to OpenMS 1.11), making our library suitable for a wide variety of algorithms that need fast access to dozens of gigabytes of raw mass spectrometric data. Our C++ and Python implementations are available for the Linux, Mac, and Windows operating systems. All proposed modifications to the OpenMS code have been merged into the OpenMS mainline codebase and are available to the community at https://github.com/OpenMS/OpenMS.

Fast and Efficient XML Data Access for Next-Generation Mass Spectrometry

PubMed Central

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

2015-01-01

Motivation In mass spectrometry-based proteomics, XML formats such as mzML and mzXML provide an open and standardized way to store and exchange the raw data (spectra and chromatograms) of mass spectrometric experiments. These file formats are being used by a multitude of open-source and cross-platform tools which allow the proteomics community to access algorithms in a vendor-independent fashion and perform transparent and reproducible data analysis. Recent improvements in mass spectrometry instrumentation have increased the data size produced in a single LC-MS/MS measurement and put substantial strain on open-source tools, particularly those that are not equipped to deal with XML data files that reach dozens of gigabytes in size. Results Here we present a fast and versatile parsing library for mass spectrometric XML formats available in C++ and Python, based on the mature OpenMS software framework. Our library implements an API for obtaining spectra and chromatograms under memory constraints using random access or sequential access functions, allowing users to process datasets that are much larger than system memory. For fast access to the raw data structures, small XML files can also be completely loaded into memory. In addition, we have improved the parsing speed of the core mzML module by over 4-fold (compared to OpenMS 1.11), making our library suitable for a wide variety of algorithms that need fast access to dozens of gigabytes of raw mass spectrometric data. Availability Our C++ and Python implementations are available for the Linux, Mac, and Windows operating systems. All proposed modifications to the OpenMS code have been merged into the OpenMS mainline codebase and are available to the community at https://github.com/OpenMS/OpenMS. PMID:25927999

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

OpenMS: a flexible open-source software platform for mass spectrometry data analysis.

PubMed

Röst, Hannes L; Sachsenberg, Timo; Aiche, Stephan; Bielow, Chris; Weisser, Hendrik; Aicheler, Fabian; Andreotti, Sandro; Ehrlich, Hans-Christian; Gutenbrunner, Petra; Kenar, Erhan; Liang, Xiao; Nahnsen, Sven; Nilse, Lars; Pfeuffer, Julianus; Rosenberger, George; Rurik, Marc; Schmitt, Uwe; Veit, Johannes; Walzer, Mathias; Wojnar, David; Wolski, Witold E; Schilling, Oliver; Choudhary, Jyoti S; Malmström, Lars; Aebersold, Ruedi; Reinert, Knut; Kohlbacher, Oliver

2016-08-30

High-resolution mass spectrometry (MS) has become an important tool in the life sciences, contributing to the diagnosis and understanding of human diseases, elucidating biomolecular structural information and characterizing cellular signaling networks. However, the rapid growth in the volume and complexity of MS data makes transparent, accurate and reproducible analysis difficult. We present OpenMS 2.0 (http://www.openms.de), a robust, open-source, cross-platform software specifically designed for the flexible and reproducible analysis of high-throughput MS data. The extensible OpenMS software implements common mass spectrometric data processing tasks through a well-defined application programming interface in C++ and Python and through standardized open data formats. OpenMS additionally provides a set of 185 tools and ready-made workflows for common mass spectrometric data processing tasks, which enable users to perform complex quantitative mass spectrometric analyses with ease.

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

Stan: A Probabilistic Programming Language for Bayesian Inference and Optimization

ERIC Educational Resources Information Center

Gelman, Andrew; Lee, Daniel; Guo, Jiqiang

2015-01-01

Stan is a free and open-source C++ program that performs Bayesian inference or optimization for arbitrary user-specified models and can be called from the command line, R, Python, Matlab, or Julia and has great promise for fitting large and complex statistical models in many areas of application. We discuss Stan from users' and developers'…

Dheepak Krishnamurthy | NREL

Science.gov Websites

. doi: 10.1109/TPWRS.2015.2399171 D. Krishnamurthy; C. Uckun; Z. Zhou; P. Thimmapuram; A. Botterud Systems, vol.PP, no.99, pp.1-1 doi: 10.1109/TPWRS.2017.2685347 A. Pratt, D. Krishnamurthy, M. Ruth, H. Wu : 10.1109/MELE.2016.2614188 D. Krishnamurthy, psst: An open-source power system simulation toolbox in Python

GALARIO: a GPU accelerated library for analysing radio interferometer observations

NASA Astrophysics Data System (ADS)

Tazzari, Marco; Beaujean, Frederik; Testi, Leonardo

2018-06-01

We present GALARIO, a computational library that exploits the power of modern graphical processing units (GPUs) to accelerate the analysis of observations from radio interferometers like Atacama Large Millimeter and sub-millimeter Array or the Karl G. Jansky Very Large Array. GALARIO speeds up the computation of synthetic visibilities from a generic 2D model image or a radial brightness profile (for axisymmetric sources). On a GPU, GALARIO is 150 faster than standard PYTHON and 10 times faster than serial C++ code on a CPU. Highly modular, easy to use, and to adopt in existing code, GALARIO comes as two compiled libraries, one for Nvidia GPUs and one for multicore CPUs, where both have the same functions with identical interfaces. GALARIO comes with PYTHON bindings but can also be directly used in C or C++. The versatility and the speed of GALARIO open new analysis pathways that otherwise would be prohibitively time consuming, e.g. fitting high-resolution observations of large number of objects, or entire spectral cubes of molecular gas emission. It is a general tool that can be applied to any field that uses radio interferometer observations. The source code is available online at http://github.com/mtazzari/galario under the open source GNU Lesser General Public License v3.

Gpufit: An open-source toolkit for GPU-accelerated curve fitting.

PubMed

Przybylski, Adrian; Thiel, Björn; Keller-Findeisen, Jan; Stock, Bernd; Bates, Mark

2017-11-16

We present a general purpose, open-source software library for estimation of non-linear parameters by the Levenberg-Marquardt algorithm. The software, Gpufit, runs on a Graphics Processing Unit (GPU) and executes computations in parallel, resulting in a significant gain in performance. We measured a speed increase of up to 42 times when comparing Gpufit with an identical CPU-based algorithm, with no loss of precision or accuracy. Gpufit is designed such that it is easily incorporated into existing applications or adapted for new ones. Multiple software interfaces, including to C, Python, and Matlab, ensure that Gpufit is accessible from most programming environments. The full source code is published as an open source software repository, making its function transparent to the user and facilitating future improvements and extensions. As a demonstration, we used Gpufit to accelerate an existing scientific image analysis package, yielding significantly improved processing times for super-resolution fluorescence microscopy datasets.

FreeSASA: An open source C library for solvent accessible surface area calculations.

PubMed

Mitternacht, Simon

2016-01-01

Calculating solvent accessible surface areas (SASA) is a run-of-the-mill calculation in structural biology. Although there are many programs available for this calculation, there are no free-standing, open-source tools designed for easy tool-chain integration. FreeSASA is an open source C library for SASA calculations that provides both command-line and Python interfaces in addition to its C API. The library implements both Lee and Richards' and Shrake and Rupley's approximations, and is highly configurable to allow the user to control molecular parameters, accuracy and output granularity. It only depends on standard C libraries and should therefore be easy to compile and install on any platform. The library is well-documented, stable and efficient. The command-line interface can easily replace closed source legacy programs, with comparable or better accuracy and speed, and with some added functionality.

PyPedia: using the wiki paradigm as crowd sourcing environment for bioinformatics protocols.

PubMed

Kanterakis, Alexandros; Kuiper, Joël; Potamias, George; Swertz, Morris A

2015-01-01

Today researchers can choose from many bioinformatics protocols for all types of life sciences research, computational environments and coding languages. Although the majority of these are open source, few of them possess all virtues to maximize reuse and promote reproducible science. Wikipedia has proven a great tool to disseminate information and enhance collaboration between users with varying expertise and background to author qualitative content via crowdsourcing. However, it remains an open question whether the wiki paradigm can be applied to bioinformatics protocols. We piloted PyPedia, a wiki where each article is both implementation and documentation of a bioinformatics computational protocol in the python language. Hyperlinks within the wiki can be used to compose complex workflows and induce reuse. A RESTful API enables code execution outside the wiki. Initial content of PyPedia contains articles for population statistics, bioinformatics format conversions and genotype imputation. Use of the easy to learn wiki syntax effectively lowers the barriers to bring expert programmers and less computer savvy researchers on the same page. PyPedia demonstrates how wiki can provide a collaborative development, sharing and even execution environment for biologists and bioinformaticians that complement existing resources, useful for local and multi-center research teams. PyPedia is available online at: http://www.pypedia.com. The source code and installation instructions are available at: https://github.com/kantale/PyPedia_server. The PyPedia python library is available at: https://github.com/kantale/pypedia. PyPedia is open-source, available under the BSD 2-Clause License.

pyLIMA : an open source microlensing software

NASA Astrophysics Data System (ADS)

Bachelet, Etienne

2017-01-01

Planetary microlensing is a unique tool to detect cold planets around low-mass stars which is approaching a watershed in discoveries as near-future missions incorporate dedicated surveys. NASA and ESA have decided to complement WFIRST-AFTA and Euclid with microlensing programs to enrich our statistics about this planetary population. Of the nany challenges in- herent in these missions, the data analysis is of primary importance, yet is often perceived as time consuming, complex and daunting barrier to participation in the field. We present the first open source modeling software to conduct a microlensing analysis. This software is written in Python and use as much as possible existing packages.

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.

Disambiguate: An open-source application for disambiguating two species in next generation sequencing data from grafted samples.

PubMed

Ahdesmäki, Miika J; Gray, Simon R; Johnson, Justin H; Lai, Zhongwu

2016-01-01

Grafting of cell lines and primary tumours is a crucial step in the drug development process between cell line studies and clinical trials. Disambiguate is a program for computationally separating the sequencing reads of two species derived from grafted samples. Disambiguate operates on DNA or RNA-seq alignments to the two species and separates the components at very high sensitivity and specificity as illustrated in artificially mixed human-mouse samples. This allows for maximum recovery of data from target tumours for more accurate variant calling and gene expression quantification. Given that no general use open source algorithm accessible to the bioinformatics community exists for the purposes of separating the two species data, the proposed Disambiguate tool presents a novel approach and improvement to performing sequence analysis of grafted samples. Both Python and C++ implementations are available and they are integrated into several open and closed source pipelines. Disambiguate is open source and is freely available at https://github.com/AstraZeneca-NGS/disambiguate.

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

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

NASA Astrophysics Data System (ADS)

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

2014-12-01

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

Landlab: an Open-Source Python Library for Modeling Earth Surface Dynamics

NASA Astrophysics Data System (ADS)

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

2016-12-01

Landlab is an open-source Python modeling library that enables users to easily build unique models to explore earth surface dynamics. The Landlab library provides a number of tools and functionalities that are common to many earth surface models, thus eliminating the need for a user to recode fundamental model elements each time she explores a new problem. For example, Landlab provides a gridding engine so that a user can build a uniform or nonuniform grid in one line of code. The library has tools for setting boundary conditions, adding data to a grid, and performing basic operations on the data, such as calculating gradients and curvature. The library also includes a number of process components, which are numerical implementations of physical processes. To create a model, a user creates a grid and couples together process components that act on grid variables. The current library has components for modeling a diverse range of processes, from overland flow generation to bedrock river incision, from soil wetting and drying to vegetation growth, succession and death. The code is freely available for download (https://github.com/landlab/landlab) or can be installed as a Python package. Landlab models can also be built and run on Hydroshare (www.hydroshare.org), an online collaborative environment for sharing hydrologic data, models, and code. Tutorials illustrating a wide range of Landlab capabilities such as building a grid, setting boundary conditions, reading in data, plotting, using components and building models are also available (https://github.com/landlab/tutorials). The code is also comprehensively documented both online and natively in Python. In this presentation, we illustrate the diverse capabilities of Landlab. We highlight existing functionality by illustrating outcomes from a range of models built with Landlab - including applications that explore landscape evolution and ecohydrology. Finally, we describe the range of resources available for new users.

ObsPy: A Python toolbox for seismology - Sustainability, New Features, and Applications

NASA Astrophysics Data System (ADS)

Krischer, L.; Megies, T.; Sales de Andrade, E.; Barsch, R.; MacCarthy, J.

2016-12-01

ObsPy (https://www.obspy.org) is a community-driven, open-source project dedicated to offer a bridge for seismology into the scientific Python ecosystem. Amongst other things, it provides Read and write support for essentially every commonly used data format in seismology with a unified interface. This includes waveform data as well as station and event meta information. A signal processing toolbox tuned to the specific needs of seismologists. Integrated access to the largest data centers, web services, and databases. Wrappers around third party codes like libmseed and evalresp. Using ObsPy enables users to take advantage of the vast scientific ecosystem that has developed around Python. In contrast to many other programming languages and tools, Python is simple enough to enable an exploratory and interactive coding style desired by many scientists. At the same time it is a full-fledged programming language usable by software engineers to build complex and large programs. This combination makes it very suitable for use in seismology where research code often must be translated to stable and production ready environments, especially in the age of big data. ObsPy has seen constant development for more than six years and enjoys a large rate of adoption in the seismological community with thousands of users. Successful applications include time-dependent and rotational seismology, big data processing, event relocations, and synthetic studies about attenuation kernels and full-waveform inversions to name a few examples. Additionally it sparked the development of several more specialized packages slowly building a modern seismological ecosystem around it. We will present a short overview of the capabilities of ObsPy and point out several representative use cases and more specialized software built around ObsPy. Additionally we will discuss new and upcoming features, as well as the sustainability of open-source scientific software.

On the tradeoffs of programming language choice for numerical modelling in geoscience. A case study comparing modern Fortran, C++/Blitz++ and Python/NumPy.

NASA Astrophysics Data System (ADS)

Jarecka, D.; Arabas, S.; Fijalkowski, M.; Gaynor, A.

2012-04-01

The language of choice for numerical modelling in geoscience has long been Fortran. A choice of a particular language and coding paradigm comes with different set of tradeoffs such as that between performance, ease of use (and ease of abuse), code clarity, maintainability and reusability, availability of open source compilers, debugging tools, adequate external libraries and parallelisation mechanisms. The availability of trained personnel and the scale and activeness of the developer community is of importance as well. We present a short comparison study aimed at identification and quantification of these tradeoffs for a particular example of an object oriented implementation of a parallel 2D-advection-equation solver in Python/NumPy, C++/Blitz++ and modern Fortran. The main angles of comparison will be complexity of implementation, performance of various compilers or interpreters and characterisation of the "added value" gained by a particular choice of the language. The choice of the numerical problem is dictated by the aim to make the comparison useful and meaningful to geoscientists. Python is chosen as a language that traditionally is associated with ease of use, elegant syntax but limited performance. C++ is chosen for its traditional association with high performance but even higher complexity and syntax obscurity. Fortran is included in the comparison for its widespread use in geoscience often attributed to its performance. We confront the validity of these traditional views. We point out how the usability of a particular language in geoscience depends on the characteristics of the language itself and the availability of pre-existing software libraries (e.g. NumPy, SciPy, PyNGL, PyNIO, MPI4Py for Python and Blitz++, Boost.Units, Boost.MPI for C++). Having in mind the limited complexity of the considered numerical problem, we present a tentative comparison of performance of the three implementations with different open source compilers including CPython and PyPy, Clang++ and GNU g++, and GNU gfortran.

RANGER-DTL 2.0: Rigorous Reconstruction of Gene-Family Evolution by Duplication, Transfer, and Loss.

PubMed

Bansal, Mukul S; Kellis, Manolis; Kordi, Misagh; Kundu, Soumya

2018-04-24

RANGER-DTL 2.0 is a software program for inferring gene family evolution using Duplication-Transfer-Loss reconciliation. This new software is highly scalable and easy to use, and offers many new features not currently available in any other reconciliation program. RANGER-DTL 2.0 has a particular focus on reconciliation accuracy and can account for many sources of reconciliation uncertainty including uncertain gene tree rooting, gene tree topological uncertainty, multiple optimal reconciliations, and alternative event cost assignments. RANGER-DTL 2.0 is open-source and written in C ++ and Python. Pre-compiled executables, source code (open-source under GNU GPL), and a detailed manual are freely available from http://compbio.engr.uconn.edu/software/RANGER-DTL/. mukul.bansal@uconn.edu.

ACPYPE - AnteChamber PYthon Parser interfacE.

PubMed

Sousa da Silva, Alan W; Vranken, Wim F

2012-07-23

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

The Particle Accelerator Simulation Code PyORBIT

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

Gorlov, Timofey V; Holmes, Jeffrey A; Cousineau, Sarah M

2015-01-01

The particle accelerator simulation code PyORBIT is presented. The structure, implementation, history, parallel and simulation capabilities, and future development of the code are discussed. The PyORBIT code is a new implementation and extension of algorithms of the original ORBIT code that was developed for the Spallation Neutron Source accelerator at the Oak Ridge National Laboratory. The PyORBIT code has a two level structure. The upper level uses the Python programming language to control the flow of intensive calculations performed by the lower level code implemented in the C++ language. The parallel capabilities are based on MPI communications. The PyORBIT ismore » an open source code accessible to the public through the Google Open Source Projects Hosting service.« less

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/

PyXRF: Python-based X-ray fluorescence analysis package

NASA Astrophysics Data System (ADS)

Li, Li; Yan, Hanfei; Xu, Wei; Yu, Dantong; Heroux, Annie; Lee, Wah-Keat; Campbell, Stuart I.; Chu, Yong S.

2017-09-01

We developed a python-based fluorescence analysis package (PyXRF) at the National Synchrotron Light Source II (NSLS-II) for the X-ray fluorescence-microscopy beamlines, including Hard X-ray Nanoprobe (HXN), and Submicron Resolution X-ray Spectroscopy (SRX). This package contains a high-level fitting engine, a comprehensive commandline/ GUI design, rigorous physics calculations, and a visualization interface. PyXRF offers a method of automatically finding elements, so that users do not need to spend extra time selecting elements manually. Moreover, PyXRF provides a convenient and interactive way of adjusting fitting parameters with physical constraints. This will help us perform quantitative analysis, and find an appropriate initial guess for fitting. Furthermore, we also create an advanced mode for expert users to construct their own fitting strategies with a full control of each fitting parameter. PyXRF runs single-pixel fitting at a fast speed, which opens up the possibilities of viewing the results of fitting in real time during experiments. A convenient I/O interface was designed to obtain data directly from NSLS-II's experimental database. PyXRF is under open-source development and designed to be an integral part of NSLS-II's scientific computation library.

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

Architectures for Rainfall Property Estimation From Polarimetric Radar

NASA Astrophysics Data System (ADS)

Collis, S. M.; Giangrande, S. E.; Helmus, J.; Troemel, S.

2014-12-01

Radars that transmit and receive signals in polarizations aligned both horizontal and vertical to the horizon collect a number of measurements. The relation both between these measurements and between measurements and desired microphysical quantities (such as rainfall rate) is complicated due to a number of scattering mechanisms. The result is that there ends up being an intractable number of often incompatible techniques for extracting geophysical insight. This presentation will discuss methods developed by the Atmospheric Measurement Climate (ARM) Research Facility to streamline the creation of application chains for retrieving rainfall properties for the purposes of fine scale model evaluation. By using a Common Data Model (CDM) approach and working in the popular open source Python scientific environment analysis techniques such as Linear Programming (LP) can be bought to bear on the task of retrieving insight from radar signals. This presentation will outline how we have used these techniques to detangle polarimetric phase signals, estimate a three-dimensional precipitation field and then objectively compare to cloud resolving model derived rainfall fields from the NASA/DoE Mid-Latitude Continental Convective Clouds Experiment (MC3E). All techniques show will be available, open source, in the Python-ARM Radar Toolkit (Py-ART).

Multidisciplinary Tool for Systems Analysis of Planetary Entry, Descent, and Landing

NASA Technical Reports Server (NTRS)

Samareh, Jamshid A.

2011-01-01

Systems analysis of a planetary entry (SAPE), descent, and landing (EDL) is a multidisciplinary activity in nature. SAPE improves the performance of the systems analysis team by automating and streamlining the process, and this improvement can reduce the errors that stem from manual data transfer among discipline experts. SAPE is a multidisciplinary tool for systems analysis of planetary EDL for Venus, Earth, Mars, Jupiter, Saturn, Uranus, Neptune, and Titan. It performs EDL systems analysis for any planet, operates cross-platform (i.e., Windows, Mac, and Linux operating systems), uses existing software components and open-source software to avoid software licensing issues, performs low-fidelity systems analysis in one hour on a computer that is comparable to an average laptop, and keeps discipline experts in the analysis loop. SAPE uses Python, a platform-independent, open-source language, for integration and for the user interface. Development has relied heavily on the object-oriented programming capabilities that are available in Python. Modules are provided to interface with commercial and government off-the-shelf software components (e.g., thermal protection systems and finite-element analysis). SAPE currently includes the following analysis modules: geometry, trajectory, aerodynamics, aerothermal, thermal protection system, and interface for structural sizing.

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

PubMed

Giannakopoulos, Theodoros

2015-01-01

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

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

PubMed Central

Giannakopoulos, Theodoros

2015-01-01

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

Open source tools for ATR development and performance evaluation

NASA Astrophysics Data System (ADS)

Baumann, James M.; Dilsavor, Ronald L.; Stubbles, James; Mossing, John C.

2002-07-01

Early in almost every engineering project, a decision must be made about tools; should I buy off-the-shelf tools or should I develop my own. Either choice can involve significant cost and risk. Off-the-shelf tools may be readily available, but they can be expensive to purchase and to maintain licenses, and may not be flexible enough to satisfy all project requirements. On the other hand, developing new tools permits great flexibility, but it can be time- (and budget-) consuming, and the end product still may not work as intended. Open source software has the advantages of both approaches without many of the pitfalls. This paper examines the concept of open source software, including its history, unique culture, and informal yet closely followed conventions. These characteristics influence the quality and quantity of software available, and ultimately its suitability for serious ATR development work. We give an example where Python, an open source scripting language, and OpenEV, a viewing and analysis tool for geospatial data, have been incorporated into ATR performance evaluation projects. While this case highlights the successful use of open source tools, we also offer important insight into risks associated with this approach.

CMCpy: Genetic Code-Message Coevolution Models in Python

PubMed Central

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

2013-01-01

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

Solvation Structure and Thermodynamic Mapping (SSTMap): An Open-Source, Flexible Package for the Analysis of Water in Molecular Dynamics Trajectories.

PubMed

Haider, Kamran; Cruz, Anthony; Ramsey, Steven; Gilson, Michael K; Kurtzman, Tom

2018-01-09

We have developed SSTMap, a software package for mapping structural and thermodynamic water properties in molecular dynamics trajectories. The package introduces automated analysis and mapping of local measures of frustration and enhancement of water structure. The thermodynamic calculations are based on Inhomogeneous Fluid Solvation Theory (IST), which is implemented using both site-based and grid-based approaches. The package also extends the applicability of solvation analysis calculations to multiple molecular dynamics (MD) simulation programs by using existing cross-platform tools for parsing MD parameter and trajectory files. SSTMap is implemented in Python and contains both command-line tools and a Python module to facilitate flexibility in setting up calculations and for automated generation of large data sets involving analysis of multiple solutes. Output is generated in formats compatible with popular Python data science packages. This tool will be used by the molecular modeling community for computational analysis of water in problems of biophysical interest such as ligand binding and protein function.

Data visualization and analysis tools for the MAVEN mission

NASA Astrophysics Data System (ADS)

Harter, B.; De Wolfe, A. W.; Putnam, B.; Brain, D.; Chaffin, M.

2016-12-01

The Mars Atmospheric and Volatile Evolution (MAVEN) mission has been collecting data at Mars since September 2014. We have developed new software tools for exploring and analyzing the science data. Our open-source Python toolkit for working with data from MAVEN and other missions is based on the widely-used "tplot" IDL toolkit. We have replicated all of the basic tplot functionality in Python, and use the bokeh and matplotlib libraries to generate interactive line plots and spectrograms, providing additional functionality beyond the capabilities of IDL graphics. These Python tools are generalized to work with missions beyond MAVEN, and our software is available on Github. We have also been exploring 3D graphics as a way to better visualize the MAVEN science data and models. We have constructed a 3D visualization of MAVEN's orbit using the CesiumJS library, which not only allows viewing of MAVEN's orientation and position, but also allows the display of selected science data sets and their variation over time.

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.

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

PubMed Central

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

2014-01-01

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

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 Clawpack Community of Codes

NASA Astrophysics Data System (ADS)

Mandli, K. T.; LeVeque, R. J.; Ketcheson, D.; Ahmadia, A. J.

2014-12-01

Clawpack, the Conservation Laws Package, has long been one of the standards for solving hyperbolic conservation laws but over the years has extended well beyond this role. Today a community of open-source codes have been developed that address a multitude of different needs including non-conservative balance laws, high-order accurate methods, and parallelism while remaining extensible and easy to use, largely by the judicious use of Python and the original Fortran codes that it wraps. This talk will present some of the recent developments in projects under the Clawpack umbrella, notably the GeoClaw and PyClaw projects. GeoClaw was originally developed as a tool for simulating tsunamis using adaptive mesh refinement but has since encompassed a large number of other geophysically relevant flows including storm surge and debris-flows. PyClaw originated as a Python version of the original Clawpack algorithms but has since been both a testing ground for new algorithmic advances in the Clawpack framework but also an easily extensible framework for solving hyperbolic balance laws. Some of these extensions include the addition of WENO high-order methods, massively parallel capabilities, and adaptive mesh refinement technologies, made possible largely by the flexibility of the Python language and community libraries such as NumPy and PETSc. Because of the tight integration with Python tecnologies, both packages have benefited also from the focus on reproducibility in the Python community, notably IPython notebooks.

HyDe: a Python Package for Genome-Scale Hybridization Detection.

PubMed

Blischak, Paul D; Chifman, Julia; Wolfe, Andrea D; Kubatko, Laura S

2018-03-19

The analysis of hybridization and gene flow among closely related taxa is a common goal for researchers studying speciation and phylogeography. Many methods for hybridization detection use simple site pattern frequencies from observed genomic data and compare them to null models that predict an absence of gene flow. The theory underlying the detection of hybridization using these site pattern probabilities exploits the relationship between the coalescent process for gene trees within population trees and the process of mutation along the branches of the gene trees. For certain models, site patterns are predicted to occur in equal frequency (i.e., their difference is 0), producing a set of functions called phylogenetic invariants. In this paper we introduce HyDe, a software package for detecting hybridization using phylogenetic invariants arising under the coalescent model with hybridization. HyDe is written in Python, and can be used interactively or through the command line using pre-packaged scripts. We demonstrate the use of HyDe on simulated data, as well as on two empirical data sets from the literature. We focus in particular on identifying individual hybrids within population samples and on distinguishing between hybrid speciation and gene flow. HyDe is freely available as an open source Python package under the GNU GPL v3 on both GitHub (https://github.com/pblischak/HyDe) and the Python Package Index (PyPI: https://pypi.python.org/pypi/phyde).

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

PubMed

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

2014-01-01

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

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.

batman: BAsic Transit Model cAlculatioN in Python

NASA Astrophysics Data System (ADS)

Kreidberg, Laura

2015-11-01

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

Fourier-Bessel Particle-In-Cell (FBPIC) v0.1.0

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

Lehe, Remi; Kirchen, Manuel; Jalas, Soeren

The Fourier-Bessel Particle-In-Cell code is a scientific simulation software for relativistic plasma physics. It is a Particle-In-Cell code whose distinctive feature is to use a spectral decomposition in cylindrical geometry. This decomposition allows to combine the advantages of spectral 3D Cartesian PIC codes (high accuracy and stability) and those of finite-difference cylindrical PIC codes with azimuthal decomposition (orders-of-magnitude speedup when compared to 3D simulations). The code is built on Python and can run both on CPU and GPU (the GPU runs being typically 1 or 2 orders of magnitude faster than the corresponding CPU runs.) The code has the exactmore » same output format as the open-source PIC codes Warp and PIConGPU (openPMD format: openpmd.org) and has a very similar input format as Warp (Python script with many similarities). There is therefore tight interoperability between Warp and FBPIC, and this interoperability will increase even more in the future.« less

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.

Open source tools for the information theoretic analysis of neural data.

PubMed

Ince, Robin A A; Mazzoni, Alberto; Petersen, Rasmus S; Panzeri, Stefano

2010-01-01

The recent and rapid development of open source software tools for the analysis of neurophysiological datasets consisting of simultaneous multiple recordings of spikes, field potentials and other neural signals holds the promise for a significant advance in the standardization, transparency, quality, reproducibility and variety of techniques used to analyze neurophysiological data and for the integration of information obtained at different spatial and temporal scales. In this review we focus on recent advances in open source toolboxes for the information theoretic analysis of neural responses. We also present examples of their use to investigate the role of spike timing precision, correlations across neurons, and field potential fluctuations in the encoding of sensory information. These information toolboxes, available both in MATLAB and Python programming environments, hold the potential to enlarge the domain of application of information theory to neuroscience and to lead to new discoveries about how neurons encode and transmit information.

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

The RAVE/VERTIGO vertex reconstruction toolkit and framework

NASA Astrophysics Data System (ADS)

Waltenberger, W.; Mitaroff, W.; Moser, F.; Pflugfelder, B.; Riedel, H. V.

2008-07-01

A detector-independent toolkit for vertex reconstruction (RAVE1) is being developed, along with a standalone framework (VERTIGO2) 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.

A dynamic regression analysis tool for quantitative assessment of bacterial growth written in Python.

PubMed

Hoeflinger, Jennifer L; Hoeflinger, Daniel E; Miller, Michael J

2017-01-01

Herein, an open-source method to generate quantitative bacterial growth data from high-throughput microplate assays is described. The bacterial lag time, maximum specific growth rate, doubling time and delta OD are reported. Our method was validated by carbohydrate utilization of lactobacilli, and visual inspection revealed 94% of regressions were deemed excellent. Copyright © 2016 Elsevier B.V. All rights reserved.

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.

An integrated, open-source set of tools for urban vulnerability monitoring from Earth observation data

NASA Astrophysics Data System (ADS)

De Vecchi, Daniele; Harb, Mostapha; Dell'Acqua, Fabio; Aurelio Galeazzo, Daniel

2015-04-01

Aim: The paper introduces an integrated set of open-source tools designed to process medium and high-resolution imagery with the aim to extract vulnerability indicators [1]. Problem: In the context of risk monitoring [2], a series of vulnerability proxies can be defined, such as the extension of a built-up area or buildings regularity [3]. Different open-source C and Python libraries are already available for image processing and geospatial information (e.g. OrfeoToolbox, OpenCV and GDAL). They include basic processing tools but not vulnerability-oriented workflows. Therefore, it is of significant importance to provide end-users with a set of tools capable to return information at a higher level. Solution: The proposed set of python algorithms is a combination of low-level image processing and geospatial information handling tools along with high-level workflows. In particular, two main products are released under the GPL license: source code, developers-oriented, and a QGIS plugin. These tools were produced within the SENSUM project framework (ended December 2014) where the main focus was on earthquake and landslide risk. Further development and maintenance is guaranteed by the decision to include them in the platform designed within the FP 7 RASOR project . Conclusion: With the lack of a unified software suite for vulnerability indicators extraction, the proposed solution can provide inputs for already available models like the Global Earthquake Model. The inclusion of the proposed set of algorithms within the RASOR platforms can guarantee support and enlarge the community of end-users. Keywords: Vulnerability monitoring, remote sensing, optical imagery, open-source software tools References [1] M. Harb, D. De Vecchi, F. Dell'Acqua, "Remote sensing-based vulnerability proxies in the EU FP7 project SENSUM", Symposium on earthquake and landslide risk in Central Asia and Caucasus: exploiting remote sensing and geo-spatial information management, 29-30th January 2014, Bishkek, Kyrgyz Republic. [2] UNISDR, "Living with Risk", Geneva, Switzerland, 2004. [3] P. Bisch, E. Carvalho, H. Degree, P. Fajfar, M. Fardis, P. Franchin, M. Kreslin, A. Pecker, "Eurocode 8: Seismic Design of Buildings", Lisbon, 2011. (SENSUM: www.sensum-project.eu, grant number: 312972 ) (RASOR: www.rasor-project.eu, grant number: 606888 )

Dual-polarization phase shift processing with the Python ARM Radar Toolkit

NASA Astrophysics Data System (ADS)

Collis, S. M.; Lang, T. J.; Mühlbauer, K.; Helmus, J.; North, K.

2016-12-01

Weather radars that measure backscatter returns at two orthogonal polarizations can give unique insight into storm macro and microphysics. Phase shift between the two polarizations caused by anisotropy in the liquid water path can be used as a constraint in rainfall rate and drop size distribution retrievals, and has the added benefit of being robust to attenuation and radar calibration. The measurement is complicated, however, by the impact of phase shift on backscatter in the presence of large drops and when the pulse volume is not filled uniformly by scatterers (known as partial beam filling). This has led to a signal processing challenge of separating the underlying desired signal from the transient signal, a challenge that has attracted many diverse solutions. To this end, the Python-ARM Radar Toolkit (Py-ART) [1] becomes increasingly important. By providing an open architecture for implementation of retrieval techniques, Py-ART has attracted three very different approaches to the phase processing problem: a fully variational technique, a finite impulse response filter technique [2], and a technique based on a linear programming [3]. These either exist within the toolkit or in another open source package that uses the Py-ART architecture. This presentation will provide an overview of differential phase and specific differential phase observed at C- and S-band frequencies, the signal processing behind the three aforementioned techniques, and some examples of their application. The goal of this presentation is to highlight the importance of open source architectures such as Py-ART for geophysical retrievals. [1] Helmus, J.J. & Collis, S.M., (2016). The Python ARM Radar Toolkit (Py-ART), a Library for Working with Weather Radar Data in the Python Programming Language. JORS. 4(1), p.e25. DOI: http://doi.org/10.5334/jors.119[2] Timothy J. Lang, David A. Ahijevych, Stephen W. Nesbitt, Richard E. Carbone, Steven A. Rutledge, and Robert Cifelli, 2007: Radar-Observed Characteristics of Precipitating Systems during NAME 2004. J. Climate, 20, 1713-1733. doi: http://dx.doi.org/10.1175/JCLI4082.1[3] Scott E. Giangrande, Robert McGraw, and Lei Lei, 2013: An Application of Linear Programming to Polarimetric Radar Differential Phase Processing. JTECH. 30, 1716-1729, doi: 10.1175/JTECH-D-12-00147.1.

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

Naima: a Python package for inference of particle distribution properties from nonthermal spectra

NASA Astrophysics Data System (ADS)

Zabalza, V.

2015-07-01

The ultimate goal of the observation of nonthermal emission from astrophysical sources is to understand the underlying particle acceleration and evolution processes, and few tools are publicly available to infer the particle distribution properties from the observed photon spectra from X-ray to VHE gamma rays. Here I present naima, an open source Python package that provides models for nonthermal radiative emission from homogeneous distribution of relativistic electrons and protons. Contributions from synchrotron, inverse Compton, nonthermal bremsstrahlung, and neutral-pion decay can be computed for a series of functional shapes of the particle energy distributions, with the possibility of using user-defined particle distribution functions. In addition, naima provides a set of functions that allow to use these models to fit observed nonthermal spectra through an MCMC procedure, obtaining probability distribution functions for the particle distribution parameters. Here I present the models and methods available in naima and an example of their application to the understanding of a galactic nonthermal source. naima's documentation, including how to install the package, is available at http://naima.readthedocs.org.

Building a Snow Data Management System using Open Source Software (and IDL)

NASA Astrophysics Data System (ADS)

Goodale, C. E.; Mattmann, C. A.; Ramirez, P.; Hart, A. F.; Painter, T.; Zimdars, P. A.; Bryant, A.; Brodzik, M.; Skiles, M.; Seidel, F. C.; Rittger, K. E.

2012-12-01

At NASA's Jet Propulsion Laboratory free and open source software is used everyday to support a wide range of projects, from planetary to climate to research and development. In this abstract I will discuss the key role that open source software has played in building a robust science data processing pipeline for snow hydrology research, and how the system is also able to leverage programs written in IDL, making JPL's Snow Data System a hybrid of open source and proprietary software. Main Points: - The Design of the Snow Data System (illustrate how the collection of sub-systems are combined to create a complete data processing pipeline) - Discuss the Challenges of moving from a single algorithm on a laptop, to running 100's of parallel algorithms on a cluster of servers (lesson's learned) - Code changes - Software license related challenges - Storage Requirements - System Evolution (from data archiving, to data processing, to data on a map, to near-real-time products and maps) - Road map for the next 6 months (including how easily we re-used the snowDS code base to support the Airborne Snow Observatory Mission) Software in Use and their Software Licenses: IDL - Used for pre and post processing of data. Licensed under a proprietary software license held by Excelis. Apache OODT - Used for data management and workflow processing. Licensed under the Apache License Version 2. GDAL - Geospatial Data processing library used for data re-projection currently. Licensed under the X/MIT license. GeoServer - WMS Server. Licensed under the General Public License Version 2.0 Leaflet.js - Javascript web mapping library. Licensed under the Berkeley Software Distribution License. Python - Glue code and miscellaneous data processing support. Licensed under the Python Software Foundation License. Perl - Script wrapper for running the SCAG algorithm. Licensed under the General Public License Version 3. PHP - Front-end web application programming. Licensed under the PHP License Version 3.01

SunPy 0.8 - Python for Solar Physics

NASA Astrophysics Data System (ADS)

Inglis, Andrew; Bobra, Monica; Christe, Steven; Hewett, Russell; Ireland, Jack; Mumford, Stuart; Martinez Oliveros, Juan Carlos; Perez-Suarez, David; Reardon, Kevin P.; Savage, Sabrina; Shih, Albert Y.; Ryan, Daniel; Sipocz, Brigitta; Freij, Nabil

2017-08-01

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. Python is one of the top ten most often used programming languages, as such it provides a wide array of software packages, such as numerical computation (NumPy, SciPy), machine learning (scikit-learn), signal processing (scikit-image, statsmodels) to visualization and plotting (matplotlib, mayavi). SunPy aims to provide the software for obtaining and analyzing solar and heliospheric data. This poster introduces a new major release of SunPy (0.8). This release includes two major new functionalities, as well as a number of bug fixes. It is based on 1120 contributions from 34 unique contributors. Fido is the new primary interface to download data. It provides a consistent and powerful search interface to all major data sources provides including VSO, JSOC, as well as individual data sources such as GOES XRS time series and and is fully pluggable to add new data sources, i.e. DKIST. In anticipation of Solar Orbiter and the Parker Solar Probe, SunPy now provides a powerful way of representing coordinates, allowing conversion between coordinate systems and viewpoints of different instruments, including preliminary reprojection capabilities. 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).

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.

Parallel, Distributed Scripting with Python

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

Miller, P J

2002-05-24

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

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.

scraps: An open-source Python-based analysis package for analyzing and plotting superconducting resonator data

DOE PAGES

Carter, Faustin Wirkus; Khaire, Trupti S.; Novosad, Valentyn; ...

2016-11-07

We present "scraps" (SuperConducting Analysis and Plotting Software), a Python package designed to aid in the analysis and visualization of large amounts of superconducting resonator data, specifically complex transmission as a function of frequency, acquired at many different temperatures and driving powers. The package includes a least-squares fitting engine as well as a Monte-Carlo Markov Chain sampler for sampling the posterior distribution given priors, marginalizing over nuisance parameters, and estimating covariances. A set of plotting tools for generating publication-quality figures is also provided in the package. Lastly, we discuss the functionality of the software and provide some examples of itsmore » utility on data collected from a niobium-nitride coplanar waveguide resonator fabricated at Argonne National Laboratory.« less

Visualization and processing of computed solid-state NMR parameters: MagresView and MagresPython.

PubMed

Sturniolo, Simone; Green, Timothy F G; Hanson, Robert M; Zilka, Miri; Refson, Keith; Hodgkinson, Paul; Brown, Steven P; Yates, Jonathan R

2016-09-01

We introduce two open source tools to aid the processing and visualisation of ab-initio computed solid-state NMR parameters. The Magres file format for computed NMR parameters (as implemented in CASTEP v8.0 and QuantumEspresso v5.0.0) is implemented. MagresView is built upon the widely used Jmol crystal viewer, and provides an intuitive environment to display computed NMR parameters. It can provide simple pictorial representation of one- and two-dimensional NMR spectra as well as output a selected spin-system for exact simulations with dedicated spin-dynamics software. MagresPython provides a simple scripting environment to manipulate large numbers of computed NMR parameters to search for structural correlations. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

ROOT.NET: Using ROOT from .NET languages like C# and F#

NASA Astrophysics Data System (ADS)

Watts, G.

2012-12-01

ROOT.NET provides an interface between Microsoft's Common Language Runtime (CLR) and .NET technology and the ubiquitous particle physics analysis tool, ROOT. ROOT.NET automatically generates a series of efficient wrappers around the ROOT API. Unlike pyROOT, these wrappers are statically typed and so are highly efficient as compared to the Python wrappers. The connection to .NET means that one gains access to the full series of languages developed for the CLR including functional languages like F# (based on OCaml). Many features that make ROOT objects work well in the .NET world are added (properties, IEnumerable interface, LINQ compatibility, etc.). Dynamic languages based on the CLR can be used as well, of course (Python, for example). Additionally it is now possible to access ROOT objects that are unknown to the translation tool. This poster will describe the techniques used to effect this translation, along with performance comparisons, and examples. All described source code is posted on the open source site CodePlex.

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

PubMed

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

2014-01-01

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

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).

Cluster-lensing: A Python Package for Galaxy Clusters and Miscentering

NASA Astrophysics Data System (ADS)

Ford, Jes; VanderPlas, Jake

2016-12-01

We describe a new open source package for calculating properties of galaxy clusters, including Navarro, Frenk, and White halo profiles with and without the effects of cluster miscentering. This pure-Python package, cluster-lensing, provides well-documented and easy-to-use classes and functions for calculating cluster scaling relations, including mass-richness and mass-concentration relations from the literature, as well as the surface mass density {{Σ }}(R) and differential surface mass density {{Δ }}{{Σ }}(R) profiles, probed by weak lensing magnification and shear. Galaxy cluster miscentering is especially a concern for stacked weak lensing shear studies of galaxy clusters, where offsets between the assumed and the true underlying matter distribution can lead to a significant bias in the mass estimates if not accounted for. This software has been developed and released in a public GitHub repository, and is licensed under the permissive MIT license. The cluster-lensing package is archived on Zenodo. Full documentation, source code, and installation instructions are available at http://jesford.github.io/cluster-lensing/.

Community interactive webtool to retrieve Greenland glacier data for 1-D geometry

NASA Astrophysics Data System (ADS)

Perrette, Mahé

2015-04-01

Marine-terminating, outlet glaciers are challenging to include in conventional Greenland-wide ice sheet models because of the large variation in scale between model grid size (typically 10 km) and outlet glacier width (typically 1-5km), making it a subgrid scale feature. A possible approach to tackle this problem is to use one-dimensional flowline models for the individual glaciers (e.g. Nick et al., 2013, Nature; Enderlin et al 2013a,b, The Cryosphere). Here we present a python- and javascript- based webtool to prepare data required to feed in or validate a flowline model. It is designed primarily to outline the glacier geometry and returns relevant data averaged over cross-sections. The tool currently allows to: visualize 2-D ice sheet data (zoom/pan), quickly switch between datasets (e.g. ice thickness, bedrock elevation, surface velocity) interpolated / transformed on a common grid. draw flowlines from user-input seeds on the map, calculated from a vector field of surface velocity, as an helpful guide for point 3 interactively draw glacier outline (side and middle lines) on top of the data mesh the outlined glacier domain in the horizontal plane extract relevant data into a 1-D longitudinal profile download the result as a netCDF file The project is hosted on github to encourage collaboration, under the open-source MIT Licence. The server-side is written in python (open-source) using the web-framework flask, and the client-side (javascript) makes use of the d3 library for interactive figures. For now it only works locally in a web browser (start server: "python runserver.py"). Data need to be downloaded separately from the original sources. See the README file in the project for information how to use it. Github projects: https://github.com/perrette/webglacier1d (main) https://github.com/perrette/dimarray (dependency)

Python-Based Applications for Hydrogeological Modeling

NASA Astrophysics Data System (ADS)

Khambhammettu, P.

2013-12-01

Python is a general-purpose, high-level programming language whose design philosophy emphasizes code readability. Add-on packages supporting fast array computation (numpy), plotting (matplotlib), scientific /mathematical Functions (scipy), have resulted in a powerful ecosystem for scientists interested in exploratory data analysis, high-performance computing and data visualization. Three examples are provided to demonstrate the applicability of the Python environment in hydrogeological applications. Python programs were used to model an aquifer test and estimate aquifer parameters at a Superfund site. The aquifer test conducted at a Groundwater Circulation Well was modeled with the Python/FORTRAN-based TTIM Analytic Element Code. The aquifer parameters were estimated with PEST such that a good match was produced between the simulated and observed drawdowns. Python scripts were written to interface with PEST and visualize the results. A convolution-based approach was used to estimate source concentration histories based on observed concentrations at receptor locations. Unit Response Functions (URFs) that relate the receptor concentrations to a unit release at the source were derived with the ATRANS code. The impact of any releases at the source could then be estimated by convolving the source release history with the URFs. Python scripts were written to compute and visualize receptor concentrations for user-specified source histories. The framework provided a simple and elegant way to test various hypotheses about the site. A Python/FORTRAN-based program TYPECURVEGRID-Py was developed to compute and visualize groundwater elevations and drawdown through time in response to a regional uniform hydraulic gradient and the influence of pumping wells using either the Theis solution for a fully-confined aquifer or the Hantush-Jacob solution for a leaky confined aquifer. The program supports an arbitrary number of wells that can operate according to arbitrary schedules. The python wrapper invokes the underlying FORTRAN layer to compute transient groundwater elevations and processes this information to create time-series and 2D plots.

PAL: A Positional Astronomy Library

NASA Astrophysics Data System (ADS)

Jenness, T.; Berry, D. S.

2013-10-01

PAL is a new positional astronomy library written in C that attempts to retain the SLALIB API but is distributed with an open source GPL license. The library depends on the IAU SOFA library wherever a SOFA routine exists and uses the most recent nutation and precession models. Currently about 100 of the 200 SLALIB routines are available. Interfaces are also available from Perl and Python. PAL is freely available via github.

Extending Supernova Spectral Templates for Next Generation Space Telescope Observations

NASA Astrophysics Data System (ADS)

Roberts-Pierel, Justin; Rodney, Steven A.; Steven Rodney

2018-01-01

Widely used empirical supernova (SN) Spectral Energy Distributions (SEDs) have not historically extended meaningfully into the ultraviolet (UV), or the infrared (IR). However, both are critical for current and future aspects of SN research including UV spectra as probes of poorly understood SN Ia physical properties, and expanding our view of the universe with high-redshift James Webb Space Telescope (JWST) IR observations. We therefore present a comprehensive set of SN SED templates that have been extended into the UV and IR, as well as an open-source software package written in Python that enables a user to generate their own extrapolated SEDs. We have taken a sampling of core-collapse (CC) and Type Ia SNe to get a time-dependent distribution of UV and IR colors (U-B,r’-[JHK]), and then generated color curves are used to extrapolate SEDs into the UV and IR. The SED extrapolation process is now easily duplicated using a user’s own data and parameters via our open-source Python package: SNSEDextend. This work develops the tools necessary to explore the JWST’s ability to discriminate between CC and Type Ia SNe, as well as provides a repository of SN SEDs that will be invaluable to future JWST and WFIRST SN studies.

RF Wave Simulation Using the MFEM Open Source FEM Package

NASA Astrophysics Data System (ADS)

Stillerman, J.; Shiraiwa, S.; Bonoli, P. T.; Wright, J. C.; Green, D. L.; Kolev, T.

2016-10-01

A new plasma wave simulation environment based on the finite element method is presented. MFEM, a scalable open-source FEM library, is used as the basis for this capability. MFEM allows for assembling an FEM matrix of arbitrarily high order in a parallel computing environment. A 3D frequency domain RF physics layer was implemented using a python wrapper for MFEM and a cold collisional plasma model was ported. This physics layer allows for defining the plasma RF wave simulation model without user knowledge of the FEM weak-form formulation. A graphical user interface is built on πScope, a python-based scientific workbench, such that a user can build a model definition file interactively. Benchmark cases have been ported to this new environment, with results being consistent with those obtained using COMSOL multiphysics, GENRAY, and TORIC/TORLH spectral solvers. This work is a first step in bringing to bear the sophisticated computational tool suite that MFEM provides (e.g., adaptive mesh refinement, solver suite, element types) to the linear plasma-wave interaction problem, and within more complicated integrated workflows, such as coupling with core spectral solver, or incorporating additional physics such as an RF sheath potential model or kinetic effects. USDoE Awards DE-FC02-99ER54512, DE-FC02-01ER54648.

Using open-source programs to create a web-based portal for hydrologic information

NASA Astrophysics Data System (ADS)

Kim, H.

2013-12-01

Some hydrologic data sets, such as basin climatology, precipitation, and terrestrial water storage, are not easily obtainable and distributable due to their size and complexity. We present a Hydrologic Information Portal (HIP) that has been implemented at the University of California for Hydrologic Modeling (UCCHM) and that has been organized around the large river basins of North America. This portal can be easily accessed through a modern web browser that enables easy access and visualization of such hydrologic data sets. Some of the main features of our HIP include a set of data visualization features so that users can search, retrieve, analyze, integrate, organize, and map data within large river basins. Recent information technologies such as Google Maps, Tornado (Python asynchronous web server), NumPy/SciPy (Scientific Library for Python) and d3.js (Visualization library for JavaScript) were incorporated into the HIP to create ease in navigating large data sets. With such open source libraries, HIP can give public users a way to combine and explore various data sets by generating multiple chart types (Line, Bar, Pie, Scatter plot) directly from the Google Maps viewport. Every rendered object such as a basin shape on the viewport is clickable, and this is the first step to access the visualization of data sets.

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.

Digital beacon receiver for ionospheric TEC measurement developed with GNU Radio

NASA Astrophysics Data System (ADS)

Yamamoto, M.

2008-11-01

A simple digital receiver named GNU Radio Beacon Receiver (GRBR) was developed for the satellite-ground beacon experiment to measure the ionospheric total electron content (TEC). The open-source software toolkit for the software defined radio, GNU Radio, is utilized to realize the basic function of the receiver and perform fast signal processing. The software is written in Python for a LINUX PC. The open-source hardware called Universal Software Radio Peripheral (USRP), which best matches the GNU Radio, is used as a front-end to acquire the satellite beacon signals of 150 and 400 MHz. The first experiment was successful as results from GRBR showed very good agreement to those from the co-located analog beacon receiver. Detailed design information and software codes are open at the URL http://www.rish.kyoto-u.ac.jp/digitalbeacon/.

Tools for open geospatial science

NASA Astrophysics Data System (ADS)

Petras, V.; Petrasova, A.; Mitasova, H.

2017-12-01

Open science uses open source to deal with reproducibility challenges in data and computational sciences. However, just using open source software or making the code public does not make the research reproducible. Moreover, the scientists face the challenge of learning new unfamiliar tools and workflows. In this contribution, we will look at a graduate-level course syllabus covering several software tools which make validation and reuse by a wider professional community possible. For the novices in the open science arena, we will look at how scripting languages such as Python and Bash help us reproduce research (starting with our own work). Jupyter Notebook will be introduced as a code editor, data exploration tool, and a lab notebook. We will see how Git helps us not to get lost in revisions and how Docker is used to wrap all the parts together using a single text file so that figures for a scientific paper or a technical report can be generated with a single command. We will look at examples of software and publications in the geospatial domain which use these tools and principles. Scientific contributions to GRASS GIS, a powerful open source desktop GIS and geoprocessing backend, will serve as an example of why and how to publish new algorithms and tools as part of a bigger open source project.

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

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.

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.

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

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.

The connectome mapper: an open-source processing pipeline to map connectomes with MRI.

PubMed

Daducci, Alessandro; Gerhard, Stephan; Griffa, Alessandra; Lemkaddem, Alia; Cammoun, Leila; Gigandet, Xavier; Meuli, Reto; Hagmann, Patric; Thiran, Jean-Philippe

2012-01-01

Researchers working in the field of global connectivity analysis using diffusion magnetic resonance imaging (MRI) can count on a wide selection of software packages for processing their data, with methods ranging from the reconstruction of the local intra-voxel axonal structure to the estimation of the trajectories of the underlying fibre tracts. However, each package is generally task-specific and uses its own conventions and file formats. In this article we present the Connectome Mapper, a software pipeline aimed at helping researchers through the tedious process of organising, processing and analysing diffusion MRI data to perform global brain connectivity analyses. Our pipeline is written in Python and is freely available as open-source at www.cmtk.org.

Kudi: A free open-source python library for the analysis of properties along reaction paths.

PubMed

Vogt-Geisse, Stefan

2016-05-01

With increasing computational capabilities, an ever growing amount of data is generated in computational chemistry that contains a vast amount of chemically relevant information. It is therefore imperative to create new computational tools in order to process and extract this data in a sensible way. Kudi is an open source library that aids in the extraction of chemical properties from reaction paths. The straightforward structure of Kudi makes it easy to use for users and allows for effortless implementation of new capabilities, and extension to any quantum chemistry package. A use case for Kudi is shown for the tautomerization reaction of formic acid. Kudi is available free of charge at www.github.com/stvogt/kudi.

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.

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".

Detecting Moving Sources in Astronomical Images (Abstract)

NASA Astrophysics Data System (ADS)

Block, A.

2018-06-01

(Abstract only) Source detection in images is an important part of analyzing astronomical data. This project discusses an implementation of image detection in python, as well as processes for performing photometry in python. Application of these tools to looking for moving sources is also discussed.

Information-Theoretical Analysis of EEG Microstate Sequences in Python.

PubMed

von Wegner, Frederic; Laufs, Helmut

2018-01-01

We present an open-source Python package to compute information-theoretical quantities for electroencephalographic data. Electroencephalography (EEG) measures the electrical potential generated by the cerebral cortex and the set of spatial patterns projected by the brain's electrical potential on the scalp surface can be clustered into a set of representative maps called EEG microstates. Microstate time series are obtained by competitively fitting the microstate maps back into the EEG data set, i.e., by substituting the EEG data at a given time with the label of the microstate that has the highest similarity with the actual EEG topography. As microstate sequences consist of non-metric random variables, e.g., the letters A-D, we recently introduced information-theoretical measures to quantify these time series. In wakeful resting state EEG recordings, we found new characteristics of microstate sequences such as periodicities related to EEG frequency bands. The algorithms used are here provided as an open-source package and their use is explained in a tutorial style. The package is self-contained and the programming style is procedural, focusing on code intelligibility and easy portability. Using a sample EEG file, we demonstrate how to perform EEG microstate segmentation using the modified K-means approach, and how to compute and visualize the recently introduced information-theoretical tests and quantities. The time-lagged mutual information function is derived as a discrete symbolic alternative to the autocorrelation function for metric time series and confidence intervals are computed from Markov chain surrogate data. The software package provides an open-source extension to the existing implementations of the microstate transform and is specifically designed to analyze resting state EEG recordings.

Developing a GIS for CO2 analysis using lightweight, open source components

NASA Astrophysics Data System (ADS)

Verma, R.; Goodale, C. E.; Hart, A. F.; Kulawik, S. S.; Law, E.; Osterman, G. B.; Braverman, A.; Nguyen, H. M.; Mattmann, C. A.; Crichton, D. J.; Eldering, A.; Castano, R.; Gunson, M. R.

2012-12-01

There are advantages to approaching the realm of geographic information systems (GIS) using lightweight, open source components in place of a more traditional web map service (WMS) solution. Rapid prototyping, schema-less data storage, the flexible interchange of components, and open source community support are just some of the benefits. In our effort to develop an application supporting the geospatial and temporal rendering of remote sensing carbon-dioxide (CO2) data for the CO2 Virtual Science Data Environment project, we have connected heterogeneous open source components together to form a GIS. Utilizing widely popular open source components including the schema-less database MongoDB, Leaflet interactive maps, the HighCharts JavaScript graphing library, and Python Bottle web-services, we have constructed a system for rapidly visualizing CO2 data with reduced up-front development costs. These components can be aggregated together, resulting in a configurable stack capable of replicating features provided by more standard GIS technologies. The approach we have taken is not meant to replace the more established GIS solutions, but to instead offer a rapid way to provide GIS features early in the development of an application and to offer a path towards utilizing more capable GIS technology in the future.

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

Pgltools: a genomic arithmetic tool suite for manipulation of Hi-C peak and other chromatin interaction data.

PubMed

Greenwald, William W; Li, He; Smith, Erin N; Benaglio, Paola; Nariai, Naoki; Frazer, Kelly A

2017-04-07

Genomic interaction studies use next-generation sequencing (NGS) to examine the interactions between two loci on the genome, with subsequent bioinformatics analyses typically including annotation, intersection, and merging of data from multiple experiments. While many file types and analysis tools exist for storing and manipulating single locus NGS data, there is currently no file standard or analysis tool suite for manipulating and storing paired-genomic-loci: the data type resulting from "genomic interaction" studies. As genomic interaction sequencing data are becoming prevalent, a standard file format and tools for working with these data conveniently and efficiently are needed. This article details a file standard and novel software tool suite for working with paired-genomic-loci data. We present the paired-genomic-loci (PGL) file standard for genomic-interactions data, and the accompanying analysis tool suite "pgltools": a cross platform, pypy compatible python package available both as an easy-to-use UNIX package, and as a python module, for integration into pipelines of paired-genomic-loci analyses. Pgltools is a freely available, open source tool suite for manipulating paired-genomic-loci data. Source code, an in-depth manual, and a tutorial are available publicly at www.github.com/billgreenwald/pgltools , and a python module of the operations can be installed from PyPI via the PyGLtools module.

Detecting Malicious Tweets in Twitter Using Runtime Monitoring With Hidden Information

DTIC Science & Technology

2016-06-01

text mining using Twitter streaming API and python [Online]. Available: http://adilmoujahid.com/posts/2014/07/twitter-analytics/ [22] M. Singh, B...sites with 645,750,000 registered users [3] and has open source public tweets for data mining . 2. Malicious Users and Tweets In the modern world...want to data mine in Twitter, and presents the natural language assertions and corresponding rule patterns. It then describes the steps performed using

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

Conversion of HSPF Legacy Model to a Platform-Independent, Open-Source Language

NASA Astrophysics Data System (ADS)

Heaphy, R. T.; Burke, M. P.; Love, J. T.

2015-12-01

Since its initial development over 30 years ago, the Hydrologic Simulation Program - FORTAN (HSPF) model has been used worldwide to support water quality planning and management. In the United States, HSPF receives widespread endorsement as a regulatory tool at all levels of government and is a core component of the EPA's Better Assessment Science Integrating Point and Nonpoint Sources (BASINS) system, which was developed to support nationwide Total Maximum Daily Load (TMDL) analysis. However, the model's legacy code and data management systems have limitations in their ability to integrate with modern software, hardware, and leverage parallel computing, which have left voids in optimization, pre-, and post-processing tools. Advances in technology and our scientific understanding of environmental processes that have occurred over the last 30 years mandate that upgrades be made to HSPF to allow it to evolve and continue to be a premiere tool for water resource planners. This work aims to mitigate the challenges currently facing HSPF through two primary tasks: (1) convert code to a modern widely accepted, open-source, high-performance computing (hpc) code; and (2) convert model input and output files to modern widely accepted, open-source, data model, library, and binary file format. Python was chosen as the new language for the code conversion. It is an interpreted, object-oriented, hpc code with dynamic semantics that has become one of the most popular open-source languages. While python code execution can be slow compared to compiled, statically typed programming languages, such as C and FORTRAN, the integration of Numba (a just-in-time specializing compiler) has allowed this challenge to be overcome. For the legacy model data management conversion, HDF5 was chosen to store the model input and output. The code conversion for HSPF's hydrologic and hydraulic modules has been completed. The converted code has been tested against HSPF's suite of "test" runs and shown good agreement and similar execution times while using the Numba compiler. Continued verification of the accuracy of the converted code against more complex legacy applications and improvement upon execution times by incorporating an intelligent network change detection tool is currently underway, and preliminary results will be presented.

Instrumentino: An Open-Source Software for Scientific Instruments.

PubMed

Koenka, Israel Joel; Sáiz, Jorge; Hauser, Peter C

2015-01-01

Scientists often need to build dedicated computer-controlled experimental systems. For this purpose, it is becoming common to employ open-source microcontroller platforms, such as the Arduino. These boards and associated integrated software development environments provide affordable yet powerful solutions for the implementation of hardware control of transducers and acquisition of signals from detectors and sensors. It is, however, a challenge to write programs that allow interactive use of such arrangements from a personal computer. This task is particularly complex if some of the included hardware components are connected directly to the computer and not via the microcontroller. A graphical user interface framework, Instrumentino, was therefore developed to allow the creation of control programs for complex systems with minimal programming effort. By writing a single code file, a powerful custom user interface is generated, which enables the automatic running of elaborate operation sequences and observation of acquired experimental data in real time. The framework, which is written in Python, allows extension by users, and is made available as an open source project.

Biomechanical ToolKit: Open-source framework to visualize and process biomechanical data.

PubMed

Barre, Arnaud; Armand, Stéphane

2014-04-01

C3D file format is widely used in the biomechanical field by companies and laboratories to store motion capture systems data. However, few software packages can visualize and modify the integrality of the data in the C3D file. Our objective was to develop an open-source and multi-platform framework to read, write, modify and visualize data from any motion analysis systems using standard (C3D) and proprietary file formats (used by many companies producing motion capture systems). The Biomechanical ToolKit (BTK) was developed to provide cost-effective and efficient tools for the biomechanical community to easily deal with motion analysis data. A large panel of operations is available to read, modify and process data through C++ API, bindings for high-level languages (Matlab, Octave, and Python), and standalone application (Mokka). All these tools are open-source and cross-platform and run on all major operating systems (Windows, Linux, MacOS X). Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Chimenea and other tools: Automated imaging of multi-epoch radio-synthesis data with CASA

NASA Astrophysics Data System (ADS)

Staley, T. D.; Anderson, G. E.

2015-11-01

In preparing the way for the Square Kilometre Array and its pathfinders, there is a pressing need to begin probing the transient sky in a fully robotic fashion using the current generation of radio telescopes. Effective exploitation of such surveys requires a largely automated data-reduction process. This paper introduces an end-to-end automated reduction pipeline, AMIsurvey, used for calibrating and imaging data from the Arcminute Microkelvin Imager Large Array. AMIsurvey makes use of several component libraries which have been packaged separately for open-source release. The most scientifically significant of these is chimenea, which implements a telescope-agnostic algorithm for automated imaging of pre-calibrated multi-epoch radio-synthesis data, of the sort typically acquired for transient surveys or follow-up. The algorithm aims to improve upon standard imaging pipelines by utilizing iterative RMS-estimation and automated source-detection to avoid so called 'Clean-bias', and makes use of CASA subroutines for the underlying image-synthesis operations. At a lower level, AMIsurvey relies upon two libraries, drive-ami and drive-casa, built to allow use of mature radio-astronomy software packages from within Python scripts. While targeted at automated imaging, the drive-casa interface can also be used to automate interaction with any of the CASA subroutines from a generic Python process. Additionally, these packages may be of wider technical interest beyond radio-astronomy, since they demonstrate use of the Python library pexpect to emulate terminal interaction with an external process. This approach allows for rapid development of a Python interface to any legacy or externally-maintained pipeline which accepts command-line input, without requiring alterations to the original code.

Investigating interoperability of the LSST data management software stack with Astropy

NASA Astrophysics Data System (ADS)

Jenness, Tim; Bosch, James; Owen, Russell; Parejko, John; Sick, Jonathan; Swinbank, John; de Val-Borro, Miguel; Dubois-Felsmann, Gregory; Lim, K.-T.; Lupton, Robert H.; Schellart, Pim; Krughoff, K. S.; Tollerud, Erik J.

2016-07-01

The Large Synoptic Survey Telescope (LSST) will be an 8.4m optical survey telescope sited in Chile and capable of imaging the entire sky twice a week. The data rate of approximately 15TB per night and the requirements to both issue alerts on transient sources within 60 seconds of observing and create annual data releases means that automated data management systems and data processing pipelines are a key deliverable of the LSST construction project. The LSST data management software has been in development since 2004 and is based on a C++ core with a Python control layer. The software consists of nearly a quarter of a million lines of code covering the system from fundamental WCS and table libraries to pipeline environments and distributed process execution. The Astropy project began in 2011 as an attempt to bring together disparate open source Python projects and build a core standard infrastructure that can be used and built upon by the astronomy community. This project has been phenomenally successful in the years since it has begun and has grown to be the de facto standard for Python software in astronomy. Astropy brings with it considerable expectations from the community on how astronomy Python software should be developed and it is clear that by the time LSST is fully operational in the 2020s many of the prospective users of the LSST software stack will expect it to be fully interoperable with Astropy. In this paper we describe the overlap between the LSST science pipeline software and Astropy software and investigate areas where the LSST software provides new functionality. We also discuss the possibilities of re-engineering the LSST science pipeline software to build upon Astropy, including the option of contributing affliated packages.

pyhector: A Python interface for the simple climate model Hector

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

N Willner, Sven; Hartin, Corinne; Gieseke, Robert

2017-04-01

Pyhector is a Python interface for the simple climate model Hector (Hartin et al. 2015) developed in C++. Simple climate models like Hector can, for instance, be used in the analysis of scenarios within integrated assessment models like GCAM1, in the emulation of complex climate models, and in uncertainty analyses. Hector is an open-source, object oriented, simple global climate carbon cycle model. Its carbon cycle consists of a one pool atmosphere, three terrestrial pools which can be broken down into finer biomes or regions, and four carbon pools in the ocean component. The terrestrial carbon cycle includes primary production andmore » respiration fluxes. The ocean carbon cycle circulates carbon via a simplified thermohaline circulation, calculating air-sea fluxes as well as the marine carbonate system (Hartin et al. 2016). The model input is time series of greenhouse gas emissions; as example scenarios for these the Pyhector package contains the Representative Concentration Pathways (RCPs)2. These were developed to cover the range of baseline and mitigation emissions scenarios and are widely used in climate change research and model intercomparison projects. Using DataFrames from the Python library Pandas (McKinney 2010) as a data structure for the scenarios simplifies generating and adapting scenarios. Other parameters of the Hector model can easily be modified when running the model. Pyhector can be installed using pip from the Python Package Index.3 Source code and issue tracker are available in Pyhector's GitHub repository4. Documentation is provided through Readthedocs5. Usage examples are also contained in the repository as a Jupyter Notebook (Pérez and Granger 2007; Kluyver et al. 2016). Courtesy of the Mybinder project6, the example Notebook can also be executed and modified without installing Pyhector locally.« less

Advanced functional network analysis in the geosciences: The pyunicorn package

NASA Astrophysics Data System (ADS)

Donges, Jonathan F.; Heitzig, Jobst; Runge, Jakob; Schultz, Hanna C. H.; Wiedermann, Marc; Zech, Alraune; Feldhoff, Jan; Rheinwalt, Aljoscha; Kutza, Hannes; Radebach, Alexander; Marwan, Norbert; Kurths, Jürgen

2013-04-01

Functional networks are a powerful tool for analyzing large geoscientific datasets such as global fields of climate time series originating from observations or model simulations. pyunicorn (pythonic unified complex network and recurrence analysis toolbox) is an open-source, fully object-oriented and easily parallelizable package written in the language Python. It allows for constructing functional networks (aka climate networks) representing the structure of statistical interrelationships in large datasets and, subsequently, investigating this structure using advanced methods of complex network theory such as measures for networks of interacting networks, node-weighted statistics or network surrogates. Additionally, pyunicorn allows to study the complex dynamics of geoscientific systems as recorded by time series by means of recurrence networks and visibility graphs. The range of possible applications of the package is outlined drawing on several examples from climatology.

The digital code driven autonomous synthesis of ibuprofen automated in a 3D-printer-based robot.

PubMed

Kitson, Philip J; Glatzel, Stefan; Cronin, Leroy

2016-01-01

An automated synthesis robot was constructed by modifying an open source 3D printing platform. The resulting automated system was used to 3D print reaction vessels (reactionware) of differing internal volumes using polypropylene feedstock via a fused deposition modeling 3D printing approach and subsequently make use of these fabricated vessels to synthesize the nonsteroidal anti-inflammatory drug ibuprofen via a consecutive one-pot three-step approach. The synthesis of ibuprofen could be achieved on different scales simply by adjusting the parameters in the robot control software. The software for controlling the synthesis robot was written in the python programming language and hard-coded for the synthesis of ibuprofen by the method described, opening possibilities for the sharing of validated synthetic 'programs' which can run on similar low cost, user-constructed robotic platforms towards an 'open-source' regime in the area of chemical synthesis.

Simulation of partially coherent light propagation using parallel computing devices

NASA Astrophysics Data System (ADS)

Magalhães, Tiago C.; Rebordão, José M.

2017-08-01

Light acquires or loses coherence and coherence is one of the few optical observables. Spectra can be derived from coherence functions and understanding any interferometric experiment is also relying upon coherence functions. Beyond the two limiting cases (full coherence or incoherence) the coherence of light is always partial and it changes with propagation. We have implemented a code to compute the propagation of partially coherent light from the source plane to the observation plane using parallel computing devices (PCDs). In this paper, we restrict the propagation in free space only. To this end, we used the Open Computing Language (OpenCL) and the open-source toolkit PyOpenCL, which gives access to OpenCL parallel computation through Python. To test our code, we chose two coherence source models: an incoherent source and a Gaussian Schell-model source. In the former case, we divided into two different source shapes: circular and rectangular. The results were compared to the theoretical values. Our implemented code allows one to choose between the PyOpenCL implementation and a standard one, i.e using the CPU only. To test the computation time for each implementation (PyOpenCL and standard), we used several computer systems with different CPUs and GPUs. We used powers of two for the dimensions of the cross-spectral density matrix (e.g. 324, 644) and a significant speed increase is observed in the PyOpenCL implementation when compared to the standard one. This can be an important tool for studying new source models.

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.

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.

The SAMI2 Open Source Project

NASA Astrophysics Data System (ADS)

Huba, J. D.; Joyce, G.

2001-05-01

In the past decade, the Open Source Model for software development has gained popularity and has had numerous major achievements: emacs, Linux, the Gimp, and Python, to name a few. The basic idea is to provide the source code of the model or application, a tutorial on its use, and a feedback mechanism with the community so that the model can be tested, improved, and archived. Given the success of the Open Source Model, we believe it may prove valuable in the development of scientific research codes. With this in mind, we are `Open Sourcing' the low to mid-latitude ionospheric model that has recently been developed at the Naval Research Laboratory: SAMI2 (Sami2 is Another Model of the Ionosphere). The model is comprehensive and uses modern numerical techniques. The structure and design of SAMI2 make it relatively easy to understand and modify: the numerical algorithms are simple and direct, and the code is reasonably well-written. Furthermore, SAMI2 is designed to run on personal computers; prohibitive computational resources are not necessary, thereby making the model accessible and usable by virtually all researchers. For these reasons, SAMI2 is an excellent candidate to explore and test the open source modeling paradigm in space physics research. We will discuss various topics associated with this project. Research supported by the Office of Naval Research.

MyMolDB: a micromolecular database solution with open source and free components.

PubMed

Xia, Bing; Tai, Zheng-Fu; Gu, Yu-Cheng; Li, Bang-Jing; Ding, Li-Sheng; Zhou, Yan

2011-10-01

To manage chemical structures in small laboratories is one of the important daily tasks. Few solutions are available on the internet, and most of them are closed source applications. The open-source applications typically have limited capability and basic cheminformatics functionalities. In this article, we describe an open-source solution to manage chemicals in research groups based on open source and free components. It has a user-friendly interface with the functions of chemical handling and intensive searching. MyMolDB is a micromolecular database solution that supports exact, substructure, similarity, and combined searching. This solution is mainly implemented using scripting language Python with a web-based interface for compound management and searching. Almost all the searches are in essence done with pure SQL on the database by using the high performance of the database engine. Thus, impressive searching speed has been archived in large data sets for no external Central Processing Unit (CPU) consuming languages were involved in the key procedure of the searching. MyMolDB is an open-source software and can be modified and/or redistributed under GNU General Public License version 3 published by the Free Software Foundation (Free Software Foundation Inc. The GNU General Public License, Version 3, 2007. Available at: http://www.gnu.org/licenses/gpl.html). The software itself can be found at http://code.google.com/p/mymoldb/. Copyright © 2011 Wiley Periodicals, Inc.

Cinfony – combining Open Source cheminformatics toolkits behind a common interface

PubMed Central

O'Boyle, Noel M; Hutchison, Geoffrey R

2008-01-01

Background Open Source cheminformatics toolkits such as OpenBabel, the CDK and the RDKit share the same core functionality but support different sets of file formats and forcefields, and calculate different fingerprints and descriptors. Despite their complementary features, using these toolkits in the same program is difficult as they are implemented in different languages (C++ versus Java), have different underlying chemical models and have different application programming interfaces (APIs). Results We describe Cinfony, a Python module that presents a common interface to all three of these toolkits, allowing the user to easily combine methods and results from any of the toolkits. In general, the run time of the Cinfony modules is almost as fast as accessing the underlying toolkits directly from C++ or Java, but Cinfony makes it much easier to carry out common tasks in cheminformatics such as reading file formats and calculating descriptors. Conclusion By providing a simplified interface and improving interoperability, Cinfony makes it easy to combine complementary features of OpenBabel, the CDK and the RDKit. PMID:19055766

Simulating large atmospheric phase screens using a woofer-tweeter algorithm.

PubMed

Buscher, David F

2016-10-03

We describe an algorithm for simulating atmospheric wavefront perturbations over ranges of spatial and temporal scales spanning more than 4 orders of magnitude. An open-source implementation of the algorithm written in Python can simulate the evolution of the perturbations more than an order-of-magnitude faster than real time. Testing of the implementation using metrics appropriate to adaptive optics systems and long-baseline interferometers show accuracies at the few percent level or better.

Iplt--image processing library and toolkit for the electron microscopy community.

PubMed

Philippsen, Ansgar; Schenk, Andreas D; Stahlberg, Henning; Engel, Andreas

2003-01-01

We present the foundation for establishing a modular, collaborative, integrated, open-source architecture for image processing of electron microscopy images, named iplt. It is designed around object oriented paradigms and implemented using the programming languages C++ and Python. In many aspects it deviates from classical image processing approaches. This paper intends to motivate developers within the community to participate in this on-going project. The iplt homepage can be found at http://www.iplt.org.

Mission Driven Scene Understanding: Candidate Model Training and Validation

DTIC Science & Technology

2016-09-01

driven scene understanding. One of the candidate engines that we are evaluating is a convolutional neural network (CNN) program installed on a Windows 10...Theano-AlexNet6,7) installed on a Windows 10 notebook computer. To the best of our knowledge, an implementation of the open-source, Python-based...AlexNet CNN on a Windows notebook computer has not been previously reported. In this report, we present progress toward the proof-of-principle testing

ISAMBARD: an open-source computational environment for biomolecular analysis, modelling and design.

PubMed

Wood, Christopher W; Heal, Jack W; Thomson, Andrew R; Bartlett, Gail J; Ibarra, Amaurys Á; Brady, R Leo; Sessions, Richard B; Woolfson, Derek N

2017-10-01

The rational design of biomolecules is becoming a reality. However, further computational tools are needed to facilitate and accelerate this, and to make it accessible to more users. Here we introduce ISAMBARD, a tool for structural analysis, model building and rational design of biomolecules. ISAMBARD is open-source, modular, computationally scalable and intuitive to use. These features allow non-experts to explore biomolecular design in silico. ISAMBARD addresses a standing issue in protein design, namely, how to introduce backbone variability in a controlled manner. This is achieved through the generalization of tools for parametric modelling, describing the overall shape of proteins geometrically, and without input from experimentally determined structures. This will allow backbone conformations for entire folds and assemblies not observed in nature to be generated de novo, that is, to access the 'dark matter of protein-fold space'. We anticipate that ISAMBARD will find broad applications in biomolecular design, biotechnology and synthetic biology. A current stable build can be downloaded from the python package index (https://pypi.python.org/pypi/isambard/) with development builds available on GitHub (https://github.com/woolfson-group/) along with documentation, tutorial material and all the scripts used to generate the data described in this paper. d.n.woolfson@bristol.ac.uk or chris.wood@bristol.ac.uk. Supplementary data are available at Bioinformatics online. © The Author(s) 2017. Published by Oxford University Press.

ObsPy: A Python Toolbox for Seismology

NASA Astrophysics Data System (ADS)

Krischer, Lion; Megies, Tobias; Sales de Andrade, Elliott; Barsch, Robert; MacCarthy, Jonathan

2017-04-01

In recent years the Python ecosystem evolved into one of the most powerful and productive scientific environments across disciplines. ObsPy (https://www.obspy.org) is a fully community-driven, open-source project dedicated to providing a bridge for seismology into that ecosystem. It does so by offering Read and write support for essentially every commonly used data format in seismology with a unified interface and automatic format detection. This includes waveform data (MiniSEED, SAC, SEG-Y, Reftek, …) as well as station (SEED, StationXML, …) and event meta information (QuakeML, ZMAP, …). Integrated access to the largest data centers, web services, and real-time data streams (FDSNWS, ArcLink, SeedLink, ...). A powerful signal processing toolbox tuned to the specific needs of seismologists. Utility functionality like travel time calculations with the TauP method, geodetic functions, and data visualizations. ObsPy has been in constant development for more than seven years and is developed and used by scientists around the world with successful applications in all branches of seismology. Additionally it nowadays serves as the foundation for a large number of more specialized packages. This presentation will give a short overview of the capabilities of ObsPy and point out several representative or new use cases. Additionally we will discuss the road ahead as well as the long-term sustainability of open-source scientific software.

Motmot, an open-source toolkit for realtime video acquisition and analysis.

PubMed

Straw, Andrew D; Dickinson, Michael H

2009-07-22

Video cameras sense passively from a distance, offer a rich information stream, and provide intuitively meaningful raw data. Camera-based imaging has thus proven critical for many advances in neuroscience and biology, with applications ranging from cellular imaging of fluorescent dyes to tracking of whole-animal behavior at ecologically relevant spatial scales. Here we present 'Motmot': an open-source software suite for acquiring, displaying, saving, and analyzing digital video in real-time. At the highest level, Motmot is written in the Python computer language. The large amounts of data produced by digital cameras are handled by low-level, optimized functions, usually written in C. This high-level/low-level partitioning and use of select external libraries allow Motmot, with only modest complexity, to perform well as a core technology for many high-performance imaging tasks. In its current form, Motmot allows for: (1) image acquisition from a variety of camera interfaces (package motmot.cam_iface), (2) the display of these images with minimal latency and computer resources using wxPython and OpenGL (package motmot.wxglvideo), (3) saving images with no compression in a single-pass, low-CPU-use format (package motmot.FlyMovieFormat), (4) a pluggable framework for custom analysis of images in realtime and (5) firmware for an inexpensive USB device to synchronize image acquisition across multiple cameras, with analog input, or with other hardware devices (package motmot.fview_ext_trig). These capabilities are brought together in a graphical user interface, called 'FView', allowing an end user to easily view and save digital video without writing any code. One plugin for FView, 'FlyTrax', which tracks the movement of fruit flies in real-time, is included with Motmot, and is described to illustrate the capabilities of FView. Motmot enables realtime image processing and display using the Python computer language. In addition to the provided complete applications, the architecture allows the user to write relatively simple plugins, which can accomplish a variety of computer vision tasks and be integrated within larger software systems. The software is available at http://code.astraw.com/projects/motmot.

Open source Matrix Product States: Opening ways to simulate entangled many-body quantum systems in one dimension

NASA Astrophysics Data System (ADS)

Jaschke, Daniel; Wall, Michael L.; Carr, Lincoln D.

2018-04-01

Numerical simulations are a powerful tool to study quantum systems beyond exactly solvable systems lacking an analytic expression. For one-dimensional entangled quantum systems, tensor network methods, amongst them Matrix Product States (MPSs), have attracted interest from different fields of quantum physics ranging from solid state systems to quantum simulators and quantum computing. Our open source MPS code provides the community with a toolset to analyze the statics and dynamics of one-dimensional quantum systems. Here, we present our open source library, Open Source Matrix Product States (OSMPS), of MPS methods implemented in Python and Fortran2003. The library includes tools for ground state calculation and excited states via the variational ansatz. We also support ground states for infinite systems with translational invariance. Dynamics are simulated with different algorithms, including three algorithms with support for long-range interactions. Convenient features include built-in support for fermionic systems and number conservation with rotational U(1) and discrete Z2 symmetries for finite systems, as well as data parallelism with MPI. We explain the principles and techniques used in this library along with examples of how to efficiently use the general interfaces to analyze the Ising and Bose-Hubbard models. This description includes the preparation of simulations as well as dispatching and post-processing of them.

openPSTD: The open source pseudospectral time-domain method for acoustic propagation

NASA Astrophysics Data System (ADS)

Hornikx, Maarten; Krijnen, Thomas; van Harten, Louis

2016-06-01

An open source implementation of the Fourier pseudospectral time-domain (PSTD) method for computing the propagation of sound is presented, which is geared towards applications in the built environment. Being a wave-based method, PSTD captures phenomena like diffraction, but maintains efficiency in processing time and memory usage as it allows to spatially sample close to the Nyquist criterion, thus keeping both the required spatial and temporal resolution coarse. In the implementation it has been opted to model the physical geometry as a composition of rectangular two-dimensional subdomains, hence initially restricting the implementation to orthogonal and two-dimensional situations. The strategy of using subdomains divides the problem domain into local subsets, which enables the simulation software to be built according to Object-Oriented Programming best practices and allows room for further computational parallelization. The software is built using the open source components, Blender, Numpy and Python, and has been published under an open source license itself as well. For accelerating the software, an option has been included to accelerate the calculations by a partial implementation of the code on the Graphical Processing Unit (GPU), which increases the throughput by up to fifteen times. The details of the implementation are reported, as well as the accuracy of the code.

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.

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

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

Barnette, Daniel W.

2012-01-04

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

OpenClimateGIS - A Web Service Providing Climate Model Data in Commonly Used Geospatial Formats

NASA Astrophysics Data System (ADS)

Erickson, T. A.; Koziol, B. W.; Rood, R. B.

2011-12-01

The goal of the OpenClimateGIS project is to make climate model datasets readily available in commonly used, modern geospatial formats used by GIS software, browser-based mapping tools, and virtual globes.The climate modeling community typically stores climate data in multidimensional gridded formats capable of efficiently storing large volumes of data (such as netCDF, grib) while the geospatial community typically uses flexible vector and raster formats that are capable of storing small volumes of data (relative to the multidimensional gridded formats). OpenClimateGIS seeks to address this difference in data formats by clipping climate data to user-specified vector geometries (i.e. areas of interest) and translating the gridded data on-the-fly into multiple vector formats. The OpenClimateGIS system does not store climate data archives locally, but rather works in conjunction with external climate archives that expose climate data via the OPeNDAP protocol. OpenClimateGIS provides a RESTful API web service for accessing climate data resources via HTTP, allowing a wide range of applications to access the climate data.The OpenClimateGIS system has been developed using open source development practices and the source code is publicly available. The project integrates libraries from several other open source projects (including Django, PostGIS, numpy, Shapely, and netcdf4-python).OpenClimateGIS development is supported by a grant from NOAA's Climate Program Office.

Semi-automated Anatomical Labeling and Inter-subject Warping of High-Density Intracranial Recording Electrodes in Electrocorticography.

PubMed

Hamilton, Liberty S; Chang, David L; Lee, Morgan B; Chang, Edward F

2017-01-01

In this article, we introduce img_pipe, our open source python package for preprocessing of imaging data for use in intracranial electrocorticography (ECoG) and intracranial stereo-EEG analyses. The process of electrode localization, labeling, and warping for use in ECoG currently varies widely across laboratories, and it is usually performed with custom, lab-specific code. This python package aims to provide a standardized interface for these procedures, as well as code to plot and display results on 3D cortical surface meshes. It gives the user an easy interface to create anatomically labeled electrodes that can also be warped to an atlas brain, starting with only a preoperative T1 MRI scan and a postoperative CT scan. We describe the full capabilities of our imaging pipeline and present a step-by-step protocol for users.

Assocplots: a Python package for static and interactive visualization of multiple-group GWAS results.

PubMed

Khramtsova, Ekaterina A; Stranger, Barbara E

2017-02-01

Over the last decade, genome-wide association studies (GWAS) have generated vast amounts of analysis results, requiring development of novel tools for data visualization. Quantile–quantile (QQ) plots and Manhattan plots are classical tools which have been utilized to visually summarize GWAS results and identify genetic variants significantly associated with traits of interest. However, static visualizations are limiting in the information that can be shown. Here, we present Assocplots, a Python package for viewing and exploring GWAS results not only using classic static Manhattan and QQ plots, but also through a dynamic extension which allows to interactively visualize the relationships between GWAS results from multiple cohorts or studies. The Assocplots package is open source and distributed under the MIT license via GitHub (https://github.com/khramts/assocplots) along with examples, documentation and installation instructions. ekhramts@medicine.bsd.uchicago.edu or bstranger@medicine.bsd.uchicago.edu

Pyrcca: Regularized Kernel Canonical Correlation Analysis in Python and Its Applications to Neuroimaging.

PubMed

Bilenko, Natalia Y; Gallant, Jack L

2016-01-01

In this article we introduce Pyrcca, an open-source Python package for performing canonical correlation analysis (CCA). CCA is a multivariate analysis method for identifying relationships between sets of variables. Pyrcca supports CCA with or without regularization, and with or without linear, polynomial, or Gaussian kernelization. We first use an abstract example to describe Pyrcca functionality. We then demonstrate how Pyrcca can be used to analyze neuroimaging data. Specifically, we use Pyrcca to implement cross-subject comparison in a natural movie functional magnetic resonance imaging (fMRI) experiment by finding a data-driven set of functional response patterns that are similar across individuals. We validate this cross-subject comparison method in Pyrcca by predicting responses to novel natural movies across subjects. Finally, we show how Pyrcca can reveal retinotopic organization in brain responses to natural movies without the need for an explicit model.

Pyrcca: Regularized Kernel Canonical Correlation Analysis in Python and Its Applications to Neuroimaging

PubMed Central

Bilenko, Natalia Y.; Gallant, Jack L.

2016-01-01

In this article we introduce Pyrcca, an open-source Python package for performing canonical correlation analysis (CCA). CCA is a multivariate analysis method for identifying relationships between sets of variables. Pyrcca supports CCA with or without regularization, and with or without linear, polynomial, or Gaussian kernelization. We first use an abstract example to describe Pyrcca functionality. We then demonstrate how Pyrcca can be used to analyze neuroimaging data. Specifically, we use Pyrcca to implement cross-subject comparison in a natural movie functional magnetic resonance imaging (fMRI) experiment by finding a data-driven set of functional response patterns that are similar across individuals. We validate this cross-subject comparison method in Pyrcca by predicting responses to novel natural movies across subjects. Finally, we show how Pyrcca can reveal retinotopic organization in brain responses to natural movies without the need for an explicit model. PMID:27920675

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.

PyKE3: data analysis tools for NASA's Kepler, K2, and TESS missions

NASA Astrophysics Data System (ADS)

Hedges, Christina L.; Cardoso, Jose Vinicius De Miranda; Barentsen, Geert; Gully-Santiago, Michael A.; Cody, Ann Marie; Barclay, Thomas; Still, Martin; BAY AREA ENVIRONMENTAL RESEARCH IN

2018-01-01

The PyKE package is a set of easy to use tools for working with Kepler/K2 data. This includes tools to correct light curves for cotrending basis vectors, turn the raw Target Pixel File data into motion corrected light curves, check for exoplanet false positives and run new PSF photometry. We are now releasing PyKE 3, which is compatible with Python 3, is pip installable and no longer depends on PyRAF. Tools are available both as Python routines and from the command line. New tutorials are available and under construction for users to learn about Kepler and K2 data and how to best use it for their science goals. PyKE is open source and welcomes contributions from the community. Routines and more information are available on the PyKE repository on GitHub.

GfaPy: a flexible and extensible software library for handling sequence graphs in Python.

PubMed

Gonnella, Giorgio; Kurtz, Stefan

2017-10-01

GFA 1 and GFA 2 are recently defined formats for representing sequence graphs, such as assembly, variation or splicing graphs. The formats are adopted by several software tools. Here, we present GfaPy, a software package for creating, parsing and editing GFA graphs using the programming language Python. GfaPy supports GFA 1 and GFA 2, using the same interface and allows for interconversion between both formats. The software package provides a simple interface for custom record types, which is an important new feature of GFA 2 (compared to GFA 1). This enables new applications of the format. GfaPy is available open source at https://github.com/ggonnella/gfapy and installable via pip. gonnella@zbh.uni-hamburg.de. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

pyhector: A Python interface for the simple climate model Hector

DOE PAGES

Willner, Sven N.; Hartin, Corinne; Gieseke, Robert

2017-04-01

Here, pyhector is a Python interface for the simple climate model Hector (Hartin et al. 2015) developed in C++. Simple climate models like Hector can, for instance, be used in the analysis of scenarios within integrated assessment models like GCAM1, in the emulation of complex climate models, and in uncertainty analyses. Hector is an open-source, object oriented, simple global climate carbon cycle model. Its carbon cycle consists of a one pool atmosphere, three terrestrial pools which can be broken down into finer biomes or regions, and four carbon pools in the ocean component. The terrestrial carbon cycle includes primary productionmore » and respiration fluxes. The ocean carbon cycle circulates carbon via a simplified thermohaline circulation, calculating air-sea fluxes as well as the marine carbonate system. The model input is time series of greenhouse gas emissions; as example scenarios for these the Pyhector package contains the Representative Concentration Pathways (RCPs)2.« less

Semi-automated Anatomical Labeling and Inter-subject Warping of High-Density Intracranial Recording Electrodes in Electrocorticography

PubMed Central

Hamilton, Liberty S.; Chang, David L.; Lee, Morgan B.; Chang, Edward F.

2017-01-01

In this article, we introduce img_pipe, our open source python package for preprocessing of imaging data for use in intracranial electrocorticography (ECoG) and intracranial stereo-EEG analyses. The process of electrode localization, labeling, and warping for use in ECoG currently varies widely across laboratories, and it is usually performed with custom, lab-specific code. This python package aims to provide a standardized interface for these procedures, as well as code to plot and display results on 3D cortical surface meshes. It gives the user an easy interface to create anatomically labeled electrodes that can also be warped to an atlas brain, starting with only a preoperative T1 MRI scan and a postoperative CT scan. We describe the full capabilities of our imaging pipeline and present a step-by-step protocol for users. PMID:29163118

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

A cross-validation package driving Netica with python

USGS Publications Warehouse

Fienen, Michael N.; Plant, Nathaniel G.

2014-01-01

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

Lenstronomy: Multi-purpose gravitational lens modeling software package

NASA Astrophysics Data System (ADS)

Birrer, Simon; Amara, Adam

2018-04-01

Lenstronomy is a multi-purpose open-source gravitational lens modeling python package. Lenstronomy reconstructs the lens mass and surface brightness distributions of strong lensing systems using forward modelling and supports a wide range of analytic lens and light models in arbitrary combination. The software is also able to reconstruct complex extended sources as well as point sources. Lenstronomy is flexible and numerically accurate, with a clear user interface that could be deployed across different platforms. Lenstronomy has been used to derive constraints on dark matter properties in strong lenses, measure the expansion history of the universe with time-delay cosmography, measure cosmic shear with Einstein rings, and decompose quasar and host galaxy light.

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.

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.

Open-Source Python Tools for Deploying Interactive GIS Dashboards for a Billion Datapoints on a Laptop

NASA Astrophysics Data System (ADS)

Steinberg, P. D.; Bednar, J. A.; Rudiger, P.; Stevens, J. L. R.; Ball, C. E.; Christensen, S. D.; Pothina, D.

2017-12-01

The rich variety of software libraries available in the Python scientific ecosystem provides a flexible and powerful alternative to traditional integrated GIS (geographic information system) programs. Each such library focuses on doing a certain set of general-purpose tasks well, and Python makes it relatively simple to glue the libraries together to solve a wide range of complex, open-ended problems in Earth science. However, choosing an appropriate set of libraries can be challenging, and it is difficult to predict how much "glue code" will be needed for any particular combination of libraries and tasks. Here we present a set of libraries that have been designed to work well together to build interactive analyses and visualizations of large geographic datasets, in standard web browsers. The resulting workflows run on ordinary laptops even for billions of data points, and easily scale up to larger compute clusters when available. The declarative top-level interface used in these libraries means that even complex, fully interactive applications can be built and deployed as web services using only a few dozen lines of code, making it simple to create and share custom interactive applications even for datasets too large for most traditional GIS systems. The libraries we will cover include GeoViews (HoloViews extended for geographic applications) for declaring visualizable/plottable objects, Bokeh for building visual web applications from GeoViews objects, Datashader for rendering arbitrarily large datasets faithfully as fixed-size images, Param for specifying user-modifiable parameters that model your domain, Xarray for computing with n-dimensional array data, Dask for flexibly dispatching computational tasks across processors, and Numba for compiling array-based Python code down to fast machine code. We will show how to use the resulting workflow with static datasets and with simulators such as GSSHA or AdH, allowing you to deploy flexible, high-performance web-based dashboards for your GIS data or simulations without needing major investments in code development or maintenance.

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.

Personalization of structural PDB files.

PubMed

Woźniak, Tomasz; Adamiak, Ryszard W

2013-01-01

PDB format is most commonly applied by various programs to define three-dimensional structure of biomolecules. However, the programs often use different versions of the format. Thus far, no comprehensive solution for unifying the PDB formats has been developed. Here we present an open-source, Python-based tool called PDBinout for processing and conversion of various versions of PDB file format for biostructural applications. Moreover, PDBinout allows to create one's own PDB versions. PDBinout is freely available under the LGPL licence at http://pdbinout.ibch.poznan.pl.

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

Using Kepler for Tool Integration in Microarray Analysis Workflows.

PubMed

Gan, Zhuohui; Stowe, Jennifer C; Altintas, Ilkay; McCulloch, Andrew D; Zambon, Alexander C

Increasing numbers of genomic technologies are leading to massive amounts of genomic data, all of which requires complex analysis. More and more bioinformatics analysis tools are being developed by scientist to simplify these analyses. However, different pipelines have been developed using different software environments. This makes integrations of these diverse bioinformatics tools difficult. Kepler provides an open source environment to integrate these disparate packages. Using Kepler, we integrated several external tools including Bioconductor packages, AltAnalyze, a python-based open source tool, and R-based comparison tool to build an automated workflow to meta-analyze both online and local microarray data. The automated workflow connects the integrated tools seamlessly, delivers data flow between the tools smoothly, and hence improves efficiency and accuracy of complex data analyses. Our workflow exemplifies the usage of Kepler as a scientific workflow platform for bioinformatics pipelines.

The Biological Observation Matrix (BIOM) format or: how I learned to stop worrying and love the ome-ome.

PubMed

McDonald, Daniel; Clemente, Jose C; Kuczynski, Justin; Rideout, Jai Ram; Stombaugh, Jesse; Wendel, Doug; Wilke, Andreas; Huse, Susan; Hufnagle, John; Meyer, Folker; Knight, Rob; Caporaso, J Gregory

2012-07-12

We present the Biological Observation Matrix (BIOM, pronounced "biome") format: a JSON-based file format for representing arbitrary observation by sample contingency tables with associated sample and observation metadata. As the number of categories of comparative omics data types (collectively, the "ome-ome") grows rapidly, a general format to represent and archive this data will facilitate the interoperability of existing bioinformatics tools and future meta-analyses. The BIOM file format is supported by an independent open-source software project (the biom-format project), which initially contains Python objects that support the use and manipulation of BIOM data in Python programs, and is intended to be an open development effort where developers can submit implementations of these objects in other programming languages. The BIOM file format and the biom-format project are steps toward reducing the "bioinformatics bottleneck" that is currently being experienced in diverse areas of biological sciences, and will help us move toward the next phase of comparative omics where basic science is translated into clinical and environmental applications. The BIOM file format is currently recognized as an Earth Microbiome Project Standard, and as a Candidate Standard by the Genomic Standards Consortium.

ObspyDMT: a Python toolbox for retrieving and processing large seismological data sets

NASA Astrophysics Data System (ADS)

Hosseini, Kasra; Sigloch, Karin

2017-10-01

We present obspyDMT, a free, open-source software toolbox for the query, retrieval, processing and management of seismological data sets, including very large, heterogeneous and/or dynamically growing ones. ObspyDMT simplifies and speeds up user interaction with data centers, in more versatile ways than existing tools. The user is shielded from the complexities of interacting with different data centers and data exchange protocols and is provided with powerful diagnostic and plotting tools to check the retrieved data and metadata. While primarily a productivity tool for research seismologists and observatories, easy-to-use syntax and plotting functionality also make obspyDMT an effective teaching aid. Written in the Python programming language, it can be used as a stand-alone command-line tool (requiring no knowledge of Python) or can be integrated as a module with other Python codes. It facilitates data archiving, preprocessing, instrument correction and quality control - routine but nontrivial tasks that can consume much user time. We describe obspyDMT's functionality, design and technical implementation, accompanied by an overview of its use cases. As an example of a typical problem encountered in seismogram preprocessing, we show how to check for inconsistencies in response files of two example stations. We also demonstrate the fully automated request, remote computation and retrieval of synthetic seismograms from the Synthetics Engine (Syngine) web service of the Data Management Center (DMC) at the Incorporated Research Institutions for Seismology (IRIS).

ACQ4: an open-source software platform for data acquisition and analysis in neurophysiology research.

PubMed

Campagnola, Luke; Kratz, Megan B; Manis, Paul B

2014-01-01

The complexity of modern neurophysiology experiments requires specialized software to coordinate multiple acquisition devices and analyze the collected data. We have developed ACQ4, an open-source software platform for performing data acquisition and analysis in experimental neurophysiology. This software integrates the tasks of acquiring, managing, and analyzing experimental data. ACQ4 has been used primarily for standard patch-clamp electrophysiology, laser scanning photostimulation, multiphoton microscopy, intrinsic imaging, and calcium imaging. The system is highly modular, which facilitates the addition of new devices and functionality. The modules included with ACQ4 provide for rapid construction of acquisition protocols, live video display, and customizable analysis tools. Position-aware data collection allows automated construction of image mosaics and registration of images with 3-dimensional anatomical atlases. ACQ4 uses free and open-source tools including Python, NumPy/SciPy for numerical computation, PyQt for the user interface, and PyQtGraph for scientific graphics. Supported hardware includes cameras, patch clamp amplifiers, scanning mirrors, lasers, shutters, Pockels cells, motorized stages, and more. ACQ4 is available for download at http://www.acq4.org.

Bayesian Atmospheric Radiative Transfer (BART)Thermochemical Equilibrium Abundance (TEA) Code and Application to WASP-43b

NASA Astrophysics Data System (ADS)

Blecic, Jasmina; Harrington, Joseph; Bowman, Matthew O.; Cubillos, Patricio E.; Stemm, Madison; Foster, Andrew

2014-11-01

We present a new, open-source, Thermochemical Equilibrium Abundances (TEA) code that calculates the abundances of gaseous molecular species. TEA uses the Gibbs-free-energy minimization method with an iterative Lagrangian optimization scheme. It initializes the radiative-transfer calculation in our Bayesian Atmospheric Radiative Transfer (BART) code. Given elemental abundances, TEA calculates molecular abundances for a particular temperature and pressure or a list of temperature-pressure pairs. The code is tested against the original method developed by White at al. (1958), the analytic method developed by Burrows and Sharp (1999), and the Newton-Raphson method implemented in the open-source Chemical Equilibrium with Applications (CEA) code. TEA is written in Python and is available to the community via the open-source development site GitHub.com. We also present BART applied to eclipse depths of WASP-43b exoplanet, constraining atmospheric thermal and chemical parameters. This work was supported by NASA Planetary Atmospheres grant NNX12AI69G and NASA Astrophysics Data Analysis Program grant NNX13AF38G. JB holds a NASA Earth and Space Science Fellowship.

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.

Journal of Open Source Software (JOSS): design and first-year review

NASA Astrophysics Data System (ADS)

Smith, Arfon M.

2018-01-01

JOSS is a free and open-access journal that publishes articles describing research software across all disciplines. It has the dual goals of improving the quality of the software submitted and providing a mechanism for research software developers to receive credit. While designed to work within the current merit system of science, JOSS addresses the dearth of rewards for key contributions to science made in the form of software. JOSS publishes articles that encapsulate scholarship contained in the software itself, and its rigorous peer review targets the software components: functionality, documentation, tests, continuous integration, and the license. A JOSS article contains an abstract describing the purpose and functionality of the software, references, and a link to the software archive. JOSS published more than 100 articles in its first year, many from the scientific python ecosystem (including a number of articles related to astronomy and astrophysics). JOSS is a sponsored project of the nonprofit organization NumFOCUS and is an affiliate of the Open Source Initiative.In this presentation, I'll describes the motivation, design, and progress of the Journal of Open Source Software (JOSS) and how it compares to other avenues for publishing research software in astronomy.

HydroDesktop as a Community Designed and Developed Resource for Hydrologic Data Discovery and Analysis

NASA Astrophysics Data System (ADS)

Ames, D. P.

2013-12-01

As has been seen in other informatics fields, well-documented and appropriately licensed open source software tools have the potential to significantly increase both opportunities and motivation for inter-institutional science and technology collaboration. The CUAHSI HIS (and related HydroShare) projects have aimed to foster such activities in hydrology resulting in the development of many useful community software components including the HydroDesktop software application. HydroDesktop is an open source, GIS-based, scriptable software application for discovering data on the CUAHSI Hydrologic Information System and related resources. It includes a well-defined plugin architecture and interface to allow 3rd party developers to create extensions and add new functionality without requiring recompiling of the full source code. HydroDesktop is built in the C# programming language and uses the open source DotSpatial GIS engine for spatial data management. Capabilities include data search, discovery, download, visualization, and export. An extension that integrates the R programming language with HydroDesktop provides scripting and data automation capabilities and an OpenMI plugin provides the ability to link models. Current revision and updates to HydroDesktop include migration of core business logic to cross platform, scriptable Python code modules that can be executed in any operating system or linked into other software front-end applications.

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).

OpenDrift - an open source framework for ocean trajectory modeling

NASA Astrophysics Data System (ADS)

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

2016-04-01

We will present a new, open source tool for modeling the trajectories and fate of particles or substances (Lagrangian Elements) drifting in the ocean, or even in the atmosphere. The software is named OpenDrift, and has been developed at Norwegian Meteorological Institute in cooperation with Institute of Marine Research. OpenDrift is a generic framework written in Python, and is openly available at https://github.com/knutfrode/opendrift/. The framework is modular with respect to three aspects: (1) obtaining input data, (2) the transport/morphological processes, and (3) exporting of results to file. Modularity is achieved through well defined interfaces between components, and use of a consistent vocabulary (CF conventions) for naming of variables. Modular input implies that it is not necessary to preprocess input data (e.g. currents, wind and waves from Eulerian models) to a particular file format. Instead "reader modules" can be written/used to obtain data directly from any original source, including files or through web based protocols (e.g. OPeNDAP/Thredds). Modularity of processes implies that a model developer may focus on the geophysical processes relevant for the application of interest, without needing to consider technical tasks such as reading, reprojecting, and colocating input data, rotation and scaling of vectors and model output. We will show a few example applications of using OpenDrift for predicting drifters, oil spills, and search and rescue objects.

Open Source Cloud-Based Technologies for Bim

NASA Astrophysics Data System (ADS)

Logothetis, S.; Karachaliou, E.; Valari, E.; Stylianidis, E.

2018-05-01

This paper presents a Cloud-based open source system for storing and processing data from a 3D survey approach. More specifically, we provide an online service for viewing, storing and analysing BIM. Cloud technologies were used to develop a web interface as a BIM data centre, which can handle large BIM data using a server. The server can be accessed by many users through various electronic devices anytime and anywhere so they can view online 3D models using browsers. Nowadays, the Cloud computing is engaged progressively in facilitating BIM-based collaboration between the multiple stakeholders and disciplinary groups for complicated Architectural, Engineering and Construction (AEC) projects. Besides, the development of Open Source Software (OSS) has been rapidly growing and their use tends to be united. Although BIM and Cloud technologies are extensively known and used, there is a lack of integrated open source Cloud-based platforms able to support all stages of BIM processes. The present research aims to create an open source Cloud-based BIM system that is able to handle geospatial data. In this effort, only open source tools will be used; from the starting point of creating the 3D model with FreeCAD to its online presentation through BIMserver. Python plug-ins will be developed to link the two software which will be distributed and freely available to a large community of professional for their use. The research work will be completed by benchmarking four Cloud-based BIM systems: Autodesk BIM 360, BIMserver, Graphisoft BIMcloud and Onuma System, which present remarkable results.

Social.Water--Open Source Citizen Science Software for CrowdHydrology

NASA Astrophysics Data System (ADS)

Fienen, M. N.; Lowry, C.

2013-12-01

CrowdHydrology is a crowd-sourced citizen science project in which passersby near streams are encouraged to read a gage and send an SMS (text) message with the water level to a number indicated on a sign. The project was initially started using free services such as Google Voice, Gmail, and Google Maps to acquire and present the data on the internet. Social.Water is open-source software, using Python and JavaScript, that automates the acquisition, categorization, and presentation of the data. Open-source objectives pervade both the project and the software as the code is hosted at Github, only free scripting codes are used, and any person or organization can install a gage and join the CrowdHydrology network. In the first year, 10 sites were deployed in upstate New York, USA. In the second year, expansion to 44 sites throughout the upper Midwest USA was achieved. Comparison with official USGS and academic measurements have shown low error rates. Citizen participation varies greatly from site to site, so surveys or other social information is sought for insight into why some sites experience higher rates of participation than others.

AstroML: "better, faster, cheaper" towards state-of-the-art data mining and machine learning

NASA Astrophysics Data System (ADS)

Ivezic, Zeljko; Connolly, Andrew J.; Vanderplas, Jacob

2015-01-01

We present AstroML, a Python module for machine learning and data mining built on numpy, scipy, scikit-learn, matplotlib, and astropy, and distributed under an open license. AstroML contains a growing library of statistical and machine learning routines for analyzing astronomical data in Python, loaders for several open astronomical datasets (such as SDSS and other recent major surveys), and a large suite of examples of analyzing and visualizing astronomical datasets. AstroML is especially suitable for introducing undergraduate students to numerical research projects and for graduate students to rapidly undertake cutting-edge research. The long-term goal of astroML is to provide a community repository for fast Python implementations of common tools and routines used for statistical data analysis in astronomy and astrophysics (see http://www.astroml.org).

jSPyDB, an open source database-independent tool for data management

NASA Astrophysics Data System (ADS)

Pierro, Giuseppe Antonio; Cavallari, Francesca; Di Guida, Salvatore; Innocente, Vincenzo

2011-12-01

Nowadays, the number of commercial tools available for accessing Databases, built on Java or .Net, is increasing. However, many of these applications have several drawbacks: usually they are not open-source, they provide interfaces only with a specific kind of database, they are platform-dependent and very CPU and memory consuming. jSPyDB is a free web-based tool written using Python and Javascript. It relies on jQuery and python libraries, and is intended to provide a simple handler to different database technologies inside a local web browser. Such a tool, exploiting fast access libraries such as SQLAlchemy, is easy to install, and to configure. The design of this tool envisages three layers. The front-end client side in the local web browser communicates with a backend server. Only the server is able to connect to the different databases for the purposes of performing data definition and manipulation. The server makes the data available to the client, so that the user can display and handle them safely. Moreover, thanks to jQuery libraries, this tool supports export of data in different formats, such as XML and JSON. Finally, by using a set of pre-defined functions, users are allowed to create their customized views for a better data visualization. In this way, we optimize the performance of database servers by avoiding short connections and concurrent sessions. In addition, security is enforced since we do not provide users the possibility to directly execute any SQL statement.

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.

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.

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

BioXTAS RAW: improvements to a free open-source program for small-angle X-ray scattering data reduction and analysis.

PubMed

Hopkins, Jesse Bennett; Gillilan, Richard E; Skou, Soren

2017-10-01

BioXTAS RAW is a graphical-user-interface-based free open-source Python program for reduction and analysis of small-angle X-ray solution scattering (SAXS) data. The software is designed for biological SAXS data and enables creation and plotting of one-dimensional scattering profiles from two-dimensional detector images, standard data operations such as averaging and subtraction and analysis of radius of gyration and molecular weight, and advanced analysis such as calculation of inverse Fourier transforms and envelopes. It also allows easy processing of inline size-exclusion chromatography coupled SAXS data and data deconvolution using the evolving factor analysis method. It provides an alternative to closed-source programs such as Primus and ScÅtter for primary data analysis. Because it can calibrate, mask and integrate images it also provides an alternative to synchrotron beamline pipelines that scientists can install on their own computers and use both at home and at the beamline.

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

ERIC Educational Resources Information Center

Murrell, Elizabeth

1998-01-01

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

pyro: Python-based tutorial for computational methods for hydrodynamics

NASA Astrophysics Data System (ADS)

Zingale, Michael

2015-07-01

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

Using leap motion to investigate the emergence of structure in speech and language.

PubMed

Eryilmaz, Kerem; Little, Hannah

2017-10-01

 In evolutionary linguistics, experiments using artificial signal spaces are being used to investigate the emergenceof speech structure. These signal spaces need to be continuous, non-discretized spaces from which discrete unitsand patterns can emerge. They need to be dissimilar from-but comparable with-the vocal tract, in order tominimize interference from pre-existing linguistic knowledge, while informing us about language. This is a hardbalance to strike. This article outlines a new approach that uses the Leap Motion, an infrared controller that canconvert manual movement in 3d space into sound. The signal space using this approach is more flexible than signalspaces in previous attempts. Further, output data using this approach is simpler to arrange and analyze. Theexperimental interface was built using free, and mostly open- source libraries in Python. We provide our sourcecode for other researchers as open source.

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

pySeismicDQA: open source post experiment data quality assessment and processing

NASA Astrophysics Data System (ADS)

Polkowski, Marcin

2017-04-01

Seismic Data Quality Assessment is python based, open source set of tools dedicated for data processing after passive seismic experiments. Primary goal of this toolset is unification of data types and formats from different dataloggers necessary for further processing. This process requires additional data checks for errors, equipment malfunction, data format errors, abnormal noise levels, etc. In all such cases user needs to decide (manually or by automatic threshold) if data is removed from output dataset. Additionally, output dataset can be visualized in form of website with data availability charts and waveform visualization with earthquake catalog (external). Data processing can be extended with simple STA/LTA event detection. pySeismicDQA is designed and tested for two passive seismic experiments in central Europe: PASSEQ 2006-2008 and "13 BB Star" (2013-2016). National Science Centre Poland provided financial support for this work via NCN grant DEC-2011/02/A/ST10/00284.

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.

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

NASA Astrophysics Data System (ADS)

Tlp Lavrijsen, Wim

2012-12-01

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

Simulation of Planetary Formation using Python

NASA Astrophysics Data System (ADS)

Bufkin, James; Bixler, David

2015-03-01

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

Coupling West WRF to GSSHA with GSSHApy

NASA Astrophysics Data System (ADS)

Snow, A. D.

2017-12-01

The West WRF output data is in the gridded NetCDF output format containing the required forcing data needed to run a GSSHA simulation. These data include precipitation, pressure, temperature, relative humidity, cloud cover, wind speed, and solar radiation. Tools to reproject, resample, and reformat the data for GSSHA have recently been added to the open source Python library GSSHApy (https://github.com/ci-water/gsshapy). These tools have created a connection that has made it possible to run forecasts using the West WRF forcing data with GSSHA to produce both streamflow and lake level predictions.

Xanthos

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

2017-05-30

Xanthos is a Python package designed to quantify and analyze global water availability in history and in future at 0.5° × 0.5° spatial resolution and a monthly time step under a changing climate. Its performance was also tested through real applications. It is open-source, extendable and convenient to researchers who work on long-term climate data for studies of global water supply, and Global Change Assessment Model (GCAM). This package integrates inherent global gridded data maps, I/O modules, Water-Balance Model modules and diagnostics modules by user-defined configuration.

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

NASA Astrophysics Data System (ADS)

Shiraiwa, S.

2014-10-01

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

Manipulating Environmental Time Series in Python/Numpy: the Scikits.Timeseries Package and its Applications.

NASA Astrophysics Data System (ADS)

Gerard-Marchant, P. G.

2008-12-01

Numpy is a free, open source C/Python interface designed for the fast and convenient manipulation of multidimensional numerical arrays. The base object, ndarray, can also be easily be extended to define new objects meeting specific needs. Thanks to its simplicity, efficiency and modularity, numpy and its companion library Scipy have become increasingly popular in the scientific community over the last few years, with application ranging from astronomy and engineering to finances and statistics. Its capacity to handle missing values is particularly appealing when analyzing environmental time series, where irregular data sampling might be an issue. After reviewing the main characteristics of numpy objects and the mechanism of subclassing, we will present the scikits.timeseries package, developed to manipulate single- and multi-variable arrays indexed in time. We will illustrate some typical applications of this package by introducing climpy, a set of extensions designed to help analyzing the impacts of climate variability on environmental data such as precipitations or streamflows.

The Julia programming language: the future of scientific computing

NASA Astrophysics Data System (ADS)

Gibson, John

2017-11-01

Julia is an innovative new open-source programming language for high-level, high-performance numerical computing. Julia combines the general-purpose breadth and extensibility of Python, the ease-of-use and numeric focus of Matlab, the speed of C and Fortran, and the metaprogramming power of Lisp. Julia uses type inference and just-in-time compilation to compile high-level user code to machine code on the fly. A rich set of numeric types and extensive numerical libraries are built-in. As a result, Julia is competitive with Matlab for interactive graphical exploration and with C and Fortran for high-performance computing. This talk interactively demonstrates Julia's numerical features and benchmarks Julia against C, C++, Fortran, Matlab, and Python on a spectral time-stepping algorithm for a 1d nonlinear partial differential equation. The Julia code is nearly as compact as Matlab and nearly as fast as Fortran. This material is based upon work supported by the National Science Foundation under Grant No. 1554149.

IB2d: a Python and MATLAB implementation of the immersed boundary method.

PubMed

Battista, Nicholas A; Strickland, W Christopher; Miller, Laura A

2017-03-29

The development of fluid-structure interaction (FSI) software involves trade-offs between ease of use, generality, performance, and cost. Typically there are large learning curves when using low-level software to model the interaction of an elastic structure immersed in a uniform density fluid. Many existing codes are not publicly available, and the commercial software that exists usually requires expensive licenses and may not be as robust or allow the necessary flexibility that in house codes can provide. We present an open source immersed boundary software package, IB2d, with full implementations in both MATLAB and Python, that is capable of running a vast range of biomechanics models and is accessible to scientists who have experience in high-level programming environments. IB2d contains multiple options for constructing material properties of the fiber structure, as well as the advection-diffusion of a chemical gradient, muscle mechanics models, and artificial forcing to drive boundaries with a preferred motion.

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.

Cameo: A Python Library for Computer Aided Metabolic Engineering and Optimization of Cell Factories.

PubMed

Cardoso, João G R; Jensen, Kristian; Lieven, Christian; Lærke Hansen, Anne Sofie; Galkina, Svetlana; Beber, Moritz; Özdemir, Emre; Herrgård, Markus J; Redestig, Henning; Sonnenschein, Nikolaus

2018-04-20

Computational systems biology methods enable rational design of cell factories on a genome-scale and thus accelerate the engineering of cells for the production of valuable chemicals and proteins. Unfortunately, the majority of these methods' implementations are either not published, rely on proprietary software, or do not provide documented interfaces, which has precluded their mainstream adoption in the field. In this work we present cameo, a platform-independent software that enables in silico design of cell factories and targets both experienced modelers as well as users new to the field. It is written in Python and implements state-of-the-art methods for enumerating and prioritizing knockout, knock-in, overexpression, and down-regulation strategies and combinations thereof. Cameo is an open source software project and is freely available under the Apache License 2.0. A dedicated Web site including documentation, examples, and installation instructions can be found at http://cameo.bio . Users can also give cameo a try at http://try.cameo.bio .

Modelling and Simulation of the Knee Joint with a Depth Sensor Camera for Prosthetics and Movement Rehabilitation

NASA Astrophysics Data System (ADS)

Risto, S.; Kallergi, M.

2015-09-01

The purpose of this project was to model and simulate the knee joint. A computer model of the knee joint was first created, which was controlled by Microsoft's Kinect for Windows. Kinect created a depth map of the knee and lower leg motion independent of lighting conditions through an infrared sensor. A combination of open source software such as Blender, Python, Kinect SDK and NI_Mate were implemented for the creation and control of the simulated knee based on movements of a live physical model. A physical size model of the knee and lower leg was also created, the movement of which was controlled remotely by the computer model and Kinect. The real time communication of the model and the robotic knee was achieved through programming in Python and Arduino language. The result of this study showed that Kinect in the modelling of human kinematics and can play a significant role in the development of prosthetics and other assistive technologies.

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

Thomas, Gregory S.; Nickless, William K.; Thiede, David R.

Enterprise level cyber security requires the deployment, operation, and monitoring of many sensors across geographically dispersed sites. Communicating with the sensors to gather data and control behavior is a challenging task when the number of sensors is rapidly growing. This paper describes the system requirements, design, and implementation of T3, the third generation of our transport software that performs this task. T3 relies on open source software and open Internet standards. Data is encoded in MIME format messages and transported via NNTP, which provides scalability. OpenSSL and public key cryptography are used to secure the data. Robustness and ease ofmore » development are increased by defining an internal cryptographic API, implemented by modules in C, Perl, and Python. We are currently using T3 in a production environment. It is freely available to download and use for other projects.« less

pySeismicFMM: Python based Travel Time Calculation in Regular 2D and 3D Grids in Cartesian and Geographic Coordinates using Fast Marching Method

NASA Astrophysics Data System (ADS)

Wilde-Piorko, M.; Polkowski, M.

2016-12-01

Seismic wave travel time calculation is the most common numerical operation in seismology. The most efficient is travel time calculation in 1D velocity model - for given source, receiver depths and angular distance time is calculated within fraction of a second. Unfortunately, in most cases 1D is not enough to encounter differentiating local and regional structures. Whenever possible travel time through 3D velocity model has to be calculated. It can be achieved using ray calculation or time propagation in space. While single ray path calculation is quick it is complicated to find the ray path that connects source with the receiver. Time propagation in space using Fast Marching Method seems more efficient in most cases, especially when there are multiple receivers. In this presentation final release of a Python module pySeismicFMM is presented - simple and very efficient tool for calculating travel time from sources to receivers. Calculation requires regular 2D or 3D velocity grid either in Cartesian or geographic coordinates. On desktop class computer calculation speed is 200k grid cells per second. Calculation has to be performed once for every source location and provides travel time to all receivers. pySeismicFMM is free and open source. Development of this tool is a part of authors PhD thesis. Source code of pySeismicFMM will be published before Fall Meeting. National Science Centre Poland provided financial support for this work via NCN grant DEC-2011/02/A/ST10/00284.

Psi4 1.1: An Open-Source Electronic Structure Program Emphasizing Automation, Advanced Libraries, and Interoperability.

PubMed

Parrish, Robert M; Burns, Lori A; Smith, Daniel G A; Simmonett, Andrew C; DePrince, A Eugene; Hohenstein, Edward G; Bozkaya, Uğur; Sokolov, Alexander Yu; Di Remigio, Roberto; Richard, Ryan M; Gonthier, Jérôme F; James, Andrew M; McAlexander, Harley R; Kumar, Ashutosh; Saitow, Masaaki; Wang, Xiao; Pritchard, Benjamin P; Verma, Prakash; Schaefer, Henry F; Patkowski, Konrad; King, Rollin A; Valeev, Edward F; Evangelista, Francesco A; Turney, Justin M; Crawford, T Daniel; Sherrill, C David

2017-07-11

Psi4 is an ab initio electronic structure program providing methods such as Hartree-Fock, density functional theory, configuration interaction, and coupled-cluster theory. The 1.1 release represents a major update meant to automate complex tasks, such as geometry optimization using complete-basis-set extrapolation or focal-point methods. Conversion of the top-level code to a Python module means that Psi4 can now be used in complex workflows alongside other Python tools. Several new features have been added with the aid of libraries providing easy access to techniques such as density fitting, Cholesky decomposition, and Laplace denominators. The build system has been completely rewritten to simplify interoperability with independent, reusable software components for quantum chemistry. Finally, a wide range of new theoretical methods and analyses have been added to the code base, including functional-group and open-shell symmetry adapted perturbation theory, density-fitted coupled cluster with frozen natural orbitals, orbital-optimized perturbation and coupled-cluster methods (e.g., OO-MP2 and OO-LCCD), density-fitted multiconfigurational self-consistent field, density cumulant functional theory, algebraic-diagrammatic construction excited states, improvements to the geometry optimizer, and the "X2C" approach to relativistic corrections, among many other improvements.

The Biological Observation Matrix (BIOM) format or: how I learned to stop worrying and love the ome-ome

PubMed Central

2012-01-01

Background We present the Biological Observation Matrix (BIOM, pronounced “biome”) format: a JSON-based file format for representing arbitrary observation by sample contingency tables with associated sample and observation metadata. As the number of categories of comparative omics data types (collectively, the “ome-ome”) grows rapidly, a general format to represent and archive this data will facilitate the interoperability of existing bioinformatics tools and future meta-analyses. Findings The BIOM file format is supported by an independent open-source software project (the biom-format project), which initially contains Python objects that support the use and manipulation of BIOM data in Python programs, and is intended to be an open development effort where developers can submit implementations of these objects in other programming languages. Conclusions The BIOM file format and the biom-format project are steps toward reducing the “bioinformatics bottleneck” that is currently being experienced in diverse areas of biological sciences, and will help us move toward the next phase of comparative omics where basic science is translated into clinical and environmental applications. The BIOM file format is currently recognized as an Earth Microbiome Project Standard, and as a Candidate Standard by the Genomic Standards Consortium. PMID:23587224

Sirepo for Synchrotron Radiation Workshop

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

Nagler, Robert; Moeller, Paul; Rakitin, Maksim

Sirepo is an open source framework for cloud computing. The graphical user interface (GUI) for Sirepo, also known as the client, executes in any HTML5 compliant web browser on any computing platform, including tablets. The client is built in JavaScript, making use of the following open source libraries: Bootstrap, which is fundamental for cross-platform web applications; AngularJS, which provides a model–view–controller (MVC) architecture and GUI components; and D3.js, which provides interactive plots and data-driven transformations. The Sirepo server is built on the following Python technologies: Flask, which is a lightweight framework for web development; Jinja, which is a secure andmore » widely used templating language; and Werkzeug, a utility library that is compliant with the WSGI standard. We use Nginx as the HTTP server and proxy, which provides a scalable event-driven architecture. The physics codes supported by Sirepo execute inside a Docker container. One of the codes supported by Sirepo is the Synchrotron Radiation Workshop (SRW). SRW computes synchrotron radiation from relativistic electrons in arbitrary magnetic fields and propagates the radiation wavefronts through optical beamlines. SRW is open source and is primarily supported by Dr. Oleg Chubar of NSLS-II at Brookhaven National Laboratory.« less

Is Multitask Deep Learning Practical for Pharma?

PubMed

Ramsundar, Bharath; Liu, Bowen; Wu, Zhenqin; Verras, Andreas; Tudor, Matthew; Sheridan, Robert P; Pande, Vijay

2017-08-28

Multitask deep learning has emerged as a powerful tool for computational drug discovery. However, despite a number of preliminary studies, multitask deep networks have yet to be widely deployed in the pharmaceutical and biotech industries. This lack of acceptance stems from both software difficulties and lack of understanding of the robustness of multitask deep networks. Our work aims to resolve both of these barriers to adoption. We introduce a high-quality open-source implementation of multitask deep networks as part of the DeepChem open-source platform. Our implementation enables simple python scripts to construct, fit, and evaluate sophisticated deep models. We use our implementation to analyze the performance of multitask deep networks and related deep models on four collections of pharmaceutical data (three of which have not previously been analyzed in the literature). We split these data sets into train/valid/test using time and neighbor splits to test multitask deep learning performance under challenging conditions. Our results demonstrate that multitask deep networks are surprisingly robust and can offer strong improvement over random forests. Our analysis and open-source implementation in DeepChem provide an argument that multitask deep networks are ready for widespread use in commercial drug discovery.

ACQ4: an open-source software platform for data acquisition and analysis in neurophysiology research

PubMed Central

Campagnola, Luke; Kratz, Megan B.; Manis, Paul B.

2014-01-01

The complexity of modern neurophysiology experiments requires specialized software to coordinate multiple acquisition devices and analyze the collected data. We have developed ACQ4, an open-source software platform for performing data acquisition and analysis in experimental neurophysiology. This software integrates the tasks of acquiring, managing, and analyzing experimental data. ACQ4 has been used primarily for standard patch-clamp electrophysiology, laser scanning photostimulation, multiphoton microscopy, intrinsic imaging, and calcium imaging. The system is highly modular, which facilitates the addition of new devices and functionality. The modules included with ACQ4 provide for rapid construction of acquisition protocols, live video display, and customizable analysis tools. Position-aware data collection allows automated construction of image mosaics and registration of images with 3-dimensional anatomical atlases. ACQ4 uses free and open-source tools including Python, NumPy/SciPy for numerical computation, PyQt for the user interface, and PyQtGraph for scientific graphics. Supported hardware includes cameras, patch clamp amplifiers, scanning mirrors, lasers, shutters, Pockels cells, motorized stages, and more. ACQ4 is available for download at http://www.acq4.org. PMID:24523692

MicMac GIS application: free open source

NASA Astrophysics Data System (ADS)

Duarte, L.; Moutinho, O.; Teodoro, A.

2016-10-01

The use of Remotely Piloted Aerial System (RPAS) for remote sensing applications is becoming more frequent as the technologies on on-board cameras and the platform itself are becoming a serious contender to satellite and airplane imagery. MicMac is a photogrammetric tool for image matching that can be used in different contexts. It is an open source software and it can be used as a command line or with a graphic interface (for each command). The main objective of this work was the integration of MicMac with QGIS, which is also an open source software, in order to create a new open source tool applied to photogrammetry/remote sensing. Python language was used to develop the application. This tool would be very useful in the manipulation and 3D modelling of a set of images. The main objective was to create a toolbar in QGIS with the basic functionalities with intuitive graphic interfaces. The toolbar is composed by three buttons: produce the points cloud, create the Digital Elevation Model (DEM) and produce the orthophoto of the study area. The application was tested considering 35 photos, a subset of images acquired by a RPAS in the Aguda beach area, Porto, Portugal. They were used in order to create a 3D terrain model and from this model obtain an orthophoto and the corresponding DEM. The code is open and can be modified according to the user requirements. This integration would be very useful in photogrammetry and remote sensing community combined with GIS capabilities.

OpenMSI Arrayed Analysis Toolkit: Analyzing Spatially Defined Samples Using Mass Spectrometry Imaging

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

de Raad, Markus; de Rond, Tristan; Rübel, Oliver

Mass spectrometry imaging (MSI) has primarily been applied in localizing biomolecules within biological matrices. Although well-suited, the application of MSI for comparing thousands of spatially defined spotted samples has been limited. One reason for this is a lack of suitable and accessible data processing tools for the analysis of large arrayed MSI sample sets. In this paper, the OpenMSI Arrayed Analysis Toolkit (OMAAT) is a software package that addresses the challenges of analyzing spatially defined samples in MSI data sets. OMAAT is written in Python and is integrated with OpenMSI (http://openmsi.nersc.gov), a platform for storing, sharing, and analyzing MSI data.more » By using a web-based python notebook (Jupyter), OMAAT is accessible to anyone without programming experience yet allows experienced users to leverage all features. OMAAT was evaluated by analyzing an MSI data set of a high-throughput glycoside hydrolase activity screen comprising 384 samples arrayed onto a NIMS surface at a 450 μm spacing, decreasing analysis time >100-fold while maintaining robust spot-finding. The utility of OMAAT was demonstrated for screening metabolic activities of different sized soil particles, including hydrolysis of sugars, revealing a pattern of size dependent activities. Finally, these results introduce OMAAT as an effective toolkit for analyzing spatially defined samples in MSI. OMAAT runs on all major operating systems, and the source code can be obtained from the following GitHub repository: https://github.com/biorack/omaat.« less

OpenStructure: a flexible software framework for computational structural biology.

PubMed

Biasini, Marco; Mariani, Valerio; Haas, Jürgen; Scheuber, Stefan; Schenk, Andreas D; Schwede, Torsten; Philippsen, Ansgar

2010-10-15

Developers of new methods in computational structural biology are often hampered in their research by incompatible software tools and non-standardized data formats. To address this problem, we have developed OpenStructure as a modular open source platform to provide a powerful, yet flexible general working environment for structural bioinformatics. OpenStructure consists primarily of a set of libraries written in C++ with a cleanly designed application programmer interface. All functionality can be accessed directly in C++ or in a Python layer, meeting both the requirements for high efficiency and ease of use. Powerful selection queries and the notion of entity views to represent these selections greatly facilitate the development and implementation of algorithms on structural data. The modular integration of computational core methods with powerful visualization tools makes OpenStructure an ideal working and development environment. Several applications, such as the latest versions of IPLT and QMean, have been implemented based on OpenStructure-demonstrating its value for the development of next-generation structural biology algorithms. Source code licensed under the GNU lesser general public license and binaries for MacOS X, Linux and Windows are available for download at http://www.openstructure.org. torsten.schwede@unibas.ch Supplementary data are available at Bioinformatics online.

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++.

Owlready: Ontology-oriented programming in Python with automatic classification and high level constructs for biomedical ontologies.

PubMed

Lamy, Jean-Baptiste

2017-07-01

Ontologies are widely used in the biomedical domain. While many tools exist for the edition, alignment or evaluation of ontologies, few solutions have been proposed for ontology programming interface, i.e. for accessing and modifying an ontology within a programming language. Existing query languages (such as SPARQL) and APIs (such as OWLAPI) are not as easy-to-use as object programming languages are. Moreover, they provide few solutions to difficulties encountered with biomedical ontologies. Our objective was to design a tool for accessing easily the entities of an OWL ontology, with high-level constructs helping with biomedical ontologies. From our experience on medical ontologies, we identified two difficulties: (1) many entities are represented by classes (rather than individuals), but the existing tools do not permit manipulating classes as easily as individuals, (2) ontologies rely on the open-world assumption, whereas the medical reasoning must consider only evidence-based medical knowledge as true. We designed a Python module for ontology-oriented programming. It allows access to the entities of an OWL ontology as if they were objects in the programming language. We propose a simple high-level syntax for managing classes and the associated "role-filler" constraints. We also propose an algorithm for performing local closed world reasoning in simple situations. We developed Owlready, a Python module for a high-level access to OWL ontologies. The paper describes the architecture and the syntax of the module version 2. It details how we integrated the OWL ontology model with the Python object model. The paper provides examples based on Gene Ontology (GO). We also demonstrate the interest of Owlready in a use case focused on the automatic comparison of the contraindications of several drugs. This use case illustrates the use of the specific syntax proposed for manipulating classes and for performing local closed world reasoning. Owlready has been successfully used in a medical research project. It has been published as Open-Source software and then used by many other researchers. Future developments will focus on the support of vagueness and additional non-monotonic reasoning feature, and automatic dialog box generation. Copyright © 2017 Elsevier B.V. All rights reserved.

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

TRIPPy: Python-based Trailed Source Photometry

NASA Astrophysics Data System (ADS)

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

2016-05-01

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

Implementation of a near-real time cross-border web-mapping platform on airborne particulate matter (PM) concentration with open-source software

NASA Astrophysics Data System (ADS)

Knörchen, Achim; Ketzler, Gunnar; Schneider, Christoph

2015-01-01

Although Europe has been growing together for the past decades, cross-border information platforms on environmental issues are still scarce. With regard to the establishment of a web-mapping tool on airborne particulate matter (PM) concentration for the Euregio Meuse-Rhine located in the border region of Belgium, Germany and the Netherlands, this article describes the research on methodical and technical backgrounds implementing such a platform. An open-source solution was selected for presenting the data in a Web GIS (OpenLayers/GeoExt; both JavaScript-based), applying other free tools for data handling (Python), data management (PostgreSQL), geo-statistical modelling (Octave), geoprocessing (GRASS GIS/GDAL) and web mapping (MapServer). The multilingual, made-to-order online platform provides access to near-real time data on PM concentration as well as additional background information. In an open data section, commented configuration files for the Web GIS client are being made available for download. Furthermore, all geodata generated by the project is being published under public domain and can be retrieved in various formats or integrated into Desktop GIS as Web Map Services (WMS).

SamuROI, a Python-Based Software Tool for Visualization and Analysis of Dynamic Time Series Imaging at Multiple Spatial Scales.

PubMed

Rueckl, Martin; Lenzi, Stephen C; Moreno-Velasquez, Laura; Parthier, Daniel; Schmitz, Dietmar; Ruediger, Sten; Johenning, Friedrich W

2017-01-01

The measurement of activity in vivo and in vitro has shifted from electrical to optical methods. While the indicators for imaging activity have improved significantly over the last decade, tools for analysing optical data have not kept pace. Most available analysis tools are limited in their flexibility and applicability to datasets obtained at different spatial scales. Here, we present SamuROI (Structured analysis of multiple user-defined ROIs), an open source Python-based analysis environment for imaging data. SamuROI simplifies exploratory analysis and visualization of image series of fluorescence changes in complex structures over time and is readily applicable at different spatial scales. In this paper, we show the utility of SamuROI in Ca 2+ -imaging based applications at three spatial scales: the micro-scale (i.e., sub-cellular compartments including cell bodies, dendrites and spines); the meso-scale, (i.e., whole cell and population imaging with single-cell resolution); and the macro-scale (i.e., imaging of changes in bulk fluorescence in large brain areas, without cellular resolution). The software described here provides a graphical user interface for intuitive data exploration and region of interest (ROI) management that can be used interactively within Jupyter Notebook: a publicly available interactive Python platform that allows simple integration of our software with existing tools for automated ROI generation and post-processing, as well as custom analysis pipelines. SamuROI software, source code and installation instructions are publicly available on GitHub and documentation is available online. SamuROI reduces the energy barrier for manual exploration and semi-automated analysis of spatially complex Ca 2+ imaging datasets, particularly when these have been acquired at different spatial scales.

SamuROI, a Python-Based Software Tool for Visualization and Analysis of Dynamic Time Series Imaging at Multiple Spatial Scales

PubMed Central

Rueckl, Martin; Lenzi, Stephen C.; Moreno-Velasquez, Laura; Parthier, Daniel; Schmitz, Dietmar; Ruediger, Sten; Johenning, Friedrich W.

2017-01-01

The measurement of activity in vivo and in vitro has shifted from electrical to optical methods. While the indicators for imaging activity have improved significantly over the last decade, tools for analysing optical data have not kept pace. Most available analysis tools are limited in their flexibility and applicability to datasets obtained at different spatial scales. Here, we present SamuROI (Structured analysis of multiple user-defined ROIs), an open source Python-based analysis environment for imaging data. SamuROI simplifies exploratory analysis and visualization of image series of fluorescence changes in complex structures over time and is readily applicable at different spatial scales. In this paper, we show the utility of SamuROI in Ca2+-imaging based applications at three spatial scales: the micro-scale (i.e., sub-cellular compartments including cell bodies, dendrites and spines); the meso-scale, (i.e., whole cell and population imaging with single-cell resolution); and the macro-scale (i.e., imaging of changes in bulk fluorescence in large brain areas, without cellular resolution). The software described here provides a graphical user interface for intuitive data exploration and region of interest (ROI) management that can be used interactively within Jupyter Notebook: a publicly available interactive Python platform that allows simple integration of our software with existing tools for automated ROI generation and post-processing, as well as custom analysis pipelines. SamuROI software, source code and installation instructions are publicly available on GitHub and documentation is available online. SamuROI reduces the energy barrier for manual exploration and semi-automated analysis of spatially complex Ca2+ imaging datasets, particularly when these have been acquired at different spatial scales. PMID:28706482

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.

Pydna: a simulation and documentation tool for DNA assembly strategies using python.

PubMed

Pereira, Filipa; Azevedo, Flávio; Carvalho, Ângela; Ribeiro, Gabriela F; Budde, Mark W; Johansson, Björn

2015-05-02

Recent advances in synthetic biology have provided tools to efficiently construct complex DNA molecules which are an important part of many molecular biology and biotechnology projects. The planning of such constructs has traditionally been done manually using a DNA sequence editor which becomes error-prone as scale and complexity of the construction increase. A human-readable formal description of cloning and assembly strategies, which also allows for automatic computer simulation and verification, would therefore be a valuable tool. We have developed pydna, an extensible, free and open source Python library for simulating basic molecular biology DNA unit operations such as restriction digestion, ligation, PCR, primer design, Gibson assembly and homologous recombination. A cloning strategy expressed as a pydna script provides a description that is complete, unambiguous and stable. Execution of the script automatically yields the sequence of the final molecule(s) and that of any intermediate constructs. Pydna has been designed to be understandable for biologists with limited programming skills by providing interfaces that are semantically similar to the description of molecular biology unit operations found in literature. Pydna simplifies both the planning and sharing of cloning strategies and is especially useful for complex or combinatorial DNA molecule construction. An important difference compared to existing tools with similar goals is the use of Python instead of a specifically constructed language, providing a simulation environment that is more flexible and extensible by the user.

Arc4nix: A cross-platform geospatial analytical library for cluster and cloud computing

NASA Astrophysics Data System (ADS)

Tang, Jingyin; Matyas, Corene J.

2018-02-01

Big Data in geospatial technology is a grand challenge for processing capacity. The ability to use a GIS for geospatial analysis on Cloud Computing and High Performance Computing (HPC) clusters has emerged as a new approach to provide feasible solutions. However, users lack the ability to migrate existing research tools to a Cloud Computing or HPC-based environment because of the incompatibility of the market-dominating ArcGIS software stack and Linux operating system. This manuscript details a cross-platform geospatial library "arc4nix" to bridge this gap. Arc4nix provides an application programming interface compatible with ArcGIS and its Python library "arcpy". Arc4nix uses a decoupled client-server architecture that permits geospatial analytical functions to run on the remote server and other functions to run on the native Python environment. It uses functional programming and meta-programming language to dynamically construct Python codes containing actual geospatial calculations, send them to a server and retrieve results. Arc4nix allows users to employ their arcpy-based script in a Cloud Computing and HPC environment with minimal or no modification. It also supports parallelizing tasks using multiple CPU cores and nodes for large-scale analyses. A case study of geospatial processing of a numerical weather model's output shows that arcpy scales linearly in a distributed environment. Arc4nix is open-source software.

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

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

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

An inexpensive Arduino-based LED stimulator system for vision research.

PubMed

Teikari, Petteri; Najjar, Raymond P; Malkki, Hemi; Knoblauch, Kenneth; Dumortier, Dominique; Gronfier, Claude; Cooper, Howard M

2012-11-15

Light emitting diodes (LEDs) are being used increasingly as light sources in life sciences applications such as in vision research, fluorescence microscopy and in brain-computer interfacing. Here we present an inexpensive but effective visual stimulator based on light emitting diodes (LEDs) and open-source Arduino microcontroller prototyping platform. The main design goal of our system was to use off-the-shelf and open-source components as much as possible, and to reduce design complexity allowing use of the system to end-users without advanced electronics skills. The main core of the system is a USB-connected Arduino microcontroller platform designed initially with a specific emphasis on the ease-of-use creating interactive physical computing environments. The pulse-width modulation (PWM) signal of Arduino was used to drive LEDs allowing linear light intensity control. The visual stimulator was demonstrated in applications such as murine pupillometry, rodent models for cognitive research, and heterochromatic flicker photometry in human psychophysics. These examples illustrate some of the possible applications that can be easily implemented and that are advantageous for students, educational purposes and universities with limited resources. The LED stimulator system was developed as an open-source project. Software interface was developed using Python with simplified examples provided for Matlab and LabVIEW. Source code and hardware information are distributed under the GNU General Public Licence (GPL, version 3). Copyright © 2012 Elsevier B.V. All rights reserved.

Open source software to control Bioflo bioreactors.

PubMed

Burdge, David A; Libourel, Igor G L

2014-01-01

Bioreactors are designed to support highly controlled environments for growth of tissues, cell cultures or microbial cultures. A variety of bioreactors are commercially available, often including sophisticated software to enhance the functionality of the bioreactor. However, experiments that the bioreactor hardware can support, but that were not envisioned during the software design cannot be performed without developing custom software. In addition, support for third party or custom designed auxiliary hardware is often sparse or absent. This work presents flexible open source freeware for the control of bioreactors of the Bioflo product family. The functionality of the software includes setpoint control, data logging, and protocol execution. Auxiliary hardware can be easily integrated and controlled through an integrated plugin interface without altering existing software. Simple experimental protocols can be entered as a CSV scripting file, and a Python-based protocol execution model is included for more demanding conditional experimental control. The software was designed to be a more flexible and free open source alternative to the commercially available solution. The source code and various auxiliary hardware plugins are publicly available for download from https://github.com/LibourelLab/BiofloSoftware. In addition to the source code, the software was compiled and packaged as a self-installing file for 32 and 64 bit windows operating systems. The compiled software will be able to control a Bioflo system, and will not require the installation of LabVIEW.

Open Source Software to Control Bioflo Bioreactors

PubMed Central

Burdge, David A.; Libourel, Igor G. L.

2014-01-01

Bioreactors are designed to support highly controlled environments for growth of tissues, cell cultures or microbial cultures. A variety of bioreactors are commercially available, often including sophisticated software to enhance the functionality of the bioreactor. However, experiments that the bioreactor hardware can support, but that were not envisioned during the software design cannot be performed without developing custom software. In addition, support for third party or custom designed auxiliary hardware is often sparse or absent. This work presents flexible open source freeware for the control of bioreactors of the Bioflo product family. The functionality of the software includes setpoint control, data logging, and protocol execution. Auxiliary hardware can be easily integrated and controlled through an integrated plugin interface without altering existing software. Simple experimental protocols can be entered as a CSV scripting file, and a Python-based protocol execution model is included for more demanding conditional experimental control. The software was designed to be a more flexible and free open source alternative to the commercially available solution. The source code and various auxiliary hardware plugins are publicly available for download from https://github.com/LibourelLab/BiofloSoftware. In addition to the source code, the software was compiled and packaged as a self-installing file for 32 and 64 bit windows operating systems. The compiled software will be able to control a Bioflo system, and will not require the installation of LabVIEW. PMID:24667828

Neo: an object model for handling electrophysiology data in multiple formats

PubMed Central

Garcia, Samuel; Guarino, Domenico; Jaillet, Florent; Jennings, Todd; Pröpper, Robert; Rautenberg, Philipp L.; Rodgers, Chris C.; Sobolev, Andrey; Wachtler, Thomas; Yger, Pierre; Davison, Andrew P.

2014-01-01

Neuroscientists use many different software tools to acquire, analyze and visualize electrophysiological signals. However, incompatible data models and file formats make it difficult to exchange data between these tools. This reduces scientific productivity, renders potentially useful analysis methods inaccessible and impedes collaboration between labs. A common representation of the core data would improve interoperability and facilitate data-sharing. To that end, we propose here a language-independent object model, named “Neo,” suitable for representing data acquired from electroencephalographic, intracellular, or extracellular recordings, or generated from simulations. As a concrete instantiation of this object model we have developed an open source implementation in the Python programming language. In addition to representing electrophysiology data in memory for the purposes of analysis and visualization, the Python implementation provides a set of input/output (IO) modules for reading/writing the data from/to a variety of commonly used file formats. Support is included for formats produced by most of the major manufacturers of electrophysiology recording equipment and also for more generic formats such as MATLAB. Data representation and data analysis are conceptually separate: it is easier to write robust analysis code if it is focused on analysis and relies on an underlying package to handle data representation. For that reason, and also to be as lightweight as possible, the Neo object model and the associated Python package are deliberately limited to representation of data, with no functions for data analysis or visualization. Software for neurophysiology data analysis and visualization built on top of Neo automatically gains the benefits of interoperability, easier data sharing and automatic format conversion; there is already a burgeoning ecosystem of such tools. We intend that Neo should become the standard basis for Python tools in neurophysiology. PMID:24600386

Neo: an object model for handling electrophysiology data in multiple formats.

PubMed

Garcia, Samuel; Guarino, Domenico; Jaillet, Florent; Jennings, Todd; Pröpper, Robert; Rautenberg, Philipp L; Rodgers, Chris C; Sobolev, Andrey; Wachtler, Thomas; Yger, Pierre; Davison, Andrew P

2014-01-01

Neuroscientists use many different software tools to acquire, analyze and visualize electrophysiological signals. However, incompatible data models and file formats make it difficult to exchange data between these tools. This reduces scientific productivity, renders potentially useful analysis methods inaccessible and impedes collaboration between labs. A common representation of the core data would improve interoperability and facilitate data-sharing. To that end, we propose here a language-independent object model, named "Neo," suitable for representing data acquired from electroencephalographic, intracellular, or extracellular recordings, or generated from simulations. As a concrete instantiation of this object model we have developed an open source implementation in the Python programming language. In addition to representing electrophysiology data in memory for the purposes of analysis and visualization, the Python implementation provides a set of input/output (IO) modules for reading/writing the data from/to a variety of commonly used file formats. Support is included for formats produced by most of the major manufacturers of electrophysiology recording equipment and also for more generic formats such as MATLAB. Data representation and data analysis are conceptually separate: it is easier to write robust analysis code if it is focused on analysis and relies on an underlying package to handle data representation. For that reason, and also to be as lightweight as possible, the Neo object model and the associated Python package are deliberately limited to representation of data, with no functions for data analysis or visualization. Software for neurophysiology data analysis and visualization built on top of Neo automatically gains the benefits of interoperability, easier data sharing and automatic format conversion; there is already a burgeoning ecosystem of such tools. We intend that Neo should become the standard basis for Python tools in neurophysiology.

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.

The digital code driven autonomous synthesis of ibuprofen automated in a 3D-printer-based robot

PubMed Central

Kitson, Philip J; Glatzel, Stefan

2016-01-01

An automated synthesis robot was constructed by modifying an open source 3D printing platform. The resulting automated system was used to 3D print reaction vessels (reactionware) of differing internal volumes using polypropylene feedstock via a fused deposition modeling 3D printing approach and subsequently make use of these fabricated vessels to synthesize the nonsteroidal anti-inflammatory drug ibuprofen via a consecutive one-pot three-step approach. The synthesis of ibuprofen could be achieved on different scales simply by adjusting the parameters in the robot control software. The software for controlling the synthesis robot was written in the python programming language and hard-coded for the synthesis of ibuprofen by the method described, opening possibilities for the sharing of validated synthetic ‘programs’ which can run on similar low cost, user-constructed robotic platforms towards an ‘open-source’ regime in the area of chemical synthesis. PMID:28144350

HYDRA Hyperspectral Data Research Application Tom Rink and Tom Whittaker

NASA Astrophysics Data System (ADS)

Rink, T.; Whittaker, T.

2005-12-01

HYDRA is a freely available, easy to install tool for visualization and analysis of large local or remote hyper/multi-spectral datasets. HYDRA is implemented on top of the open source VisAD Java library via Jython - the Java implementation of the user friendly Python programming language. VisAD provides data integration, through its generalized data model, user-display interaction and display rendering. Jython has an easy to read, concise, scripting-like, syntax which eases software development. HYDRA allows data sharing of large datasets through its support of the OpenDAP and OpenADDE server-client protocols. The users can explore and interrogate data, and subset in physical and/or spectral space to isolate key areas of interest for further analysis without having to download an entire dataset. It also has an extensible data input architecture to recognize new instruments and understand different local file formats, currently NetCDF and HDF4 are supported.

Snowflake: A Lightweight Portable Stencil DSL

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

Zhang, Nathan; Driscoll, Michael; Markley, Charles

Stencil computations are not well optimized by general-purpose production compilers and the increased use of multicore, manycore, and accelerator-based systems makes the optimization problem even more challenging. In this paper we present Snowflake, a Domain Specific Language (DSL) for stencils that uses a 'micro-compiler' approach, i.e., small, focused, domain-specific code generators. The approach is similar to that used in image processing stencils, but Snowflake handles the much more complex stencils that arise in scientific computing, including complex boundary conditions, higher-order operators (larger stencils), higher dimensions, variable coefficients, non-unit-stride iteration spaces, and multiple input or output meshes. Snowflake is embedded inmore » the Python language, allowing it to interoperate with popular scientific tools like SciPy and iPython; it also takes advantage of built-in Python libraries for powerful dependence analysis as part of a just-in-time compiler. We demonstrate the power of the Snowflake language and the micro-compiler approach with a complex scientific benchmark, HPGMG, that exercises the generality of stencil support in Snowflake. By generating OpenMP comparable to, and OpenCL within a factor of 2x of hand-optimized HPGMG, Snowflake demonstrates that a micro-compiler can support diverse processor architectures and is performance-competitive whilst preserving a high-level Python implementation.« less

Snowflake: A Lightweight Portable Stencil DSL

DOE PAGES

Zhang, Nathan; Driscoll, Michael; Markley, Charles; ...

2017-05-01

Stencil computations are not well optimized by general-purpose production compilers and the increased use of multicore, manycore, and accelerator-based systems makes the optimization problem even more challenging. In this paper we present Snowflake, a Domain Specific Language (DSL) for stencils that uses a 'micro-compiler' approach, i.e., small, focused, domain-specific code generators. The approach is similar to that used in image processing stencils, but Snowflake handles the much more complex stencils that arise in scientific computing, including complex boundary conditions, higher-order operators (larger stencils), higher dimensions, variable coefficients, non-unit-stride iteration spaces, and multiple input or output meshes. Snowflake is embedded inmore » the Python language, allowing it to interoperate with popular scientific tools like SciPy and iPython; it also takes advantage of built-in Python libraries for powerful dependence analysis as part of a just-in-time compiler. We demonstrate the power of the Snowflake language and the micro-compiler approach with a complex scientific benchmark, HPGMG, that exercises the generality of stencil support in Snowflake. By generating OpenMP comparable to, and OpenCL within a factor of 2x of hand-optimized HPGMG, Snowflake demonstrates that a micro-compiler can support diverse processor architectures and is performance-competitive whilst preserving a high-level Python implementation.« less

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 proposal for an open source graphical environment for simulating x-ray optics

NASA Astrophysics Data System (ADS)

Sanchez del Rio, Manuel; Rebuffi, Luca; Demsar, Janez; Canestrari, Niccolo; Chubar, Oleg

2014-09-01

A new graphic environment to drive X-ray optics simulation packages such as SHADOW and SRW is proposed. The aim is to simulate a virtual experiment, including the description of the electron beam and simulate the emitted radiation, the optics, the scattering by the sample and radiation detection. Python is chosen as common interaction language. The ingredients of the new application, a glossary of variables for optical component, the selection of visualization tools, and the integration of all these components in a high level workflow environment built on Orange are presented.

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.

A-Track: A new approach for detection of moving objects in FITS images

NASA Astrophysics Data System (ADS)

Atay, T.; Kaplan, M.; Kilic, Y.; Karapinar, N.

2016-10-01

We have developed a fast, open-source, cross-platform pipeline, called A-Track, for detecting the moving objects (asteroids and comets) in sequential telescope images in FITS format. The pipeline is coded in Python 3. The moving objects are detected using a modified line detection algorithm, called MILD. We tested the pipeline on astronomical data acquired by an SI-1100 CCD with a 1-meter telescope. We found that A-Track performs very well in terms of detection efficiency, stability, and processing time. The code is hosted on GitHub under the GNU GPL v3 license.

PyGPlates - a GPlates Python library for data analysis through space and deep geological time

NASA Astrophysics Data System (ADS)

Williams, Simon; Cannon, John; Qin, Xiaodong; Müller, Dietmar

2017-04-01

A fundamental consideration for studying the Earth through deep time is that the configurations of the continents, tectonic plates, and plate boundaries are continuously changing. Within a diverse range of fields including geodynamics, paleoclimate, and paleobiology, the importance of considering geodata in their reconstructed context across previous cycles of supercontinent aggregation, dispersal and ocean basin evolution is widely recognised. Open-source software tools such as GPlates provide paleo-geographic information systems for geoscientists to combine a wide variety of geodata and examine them within tectonic reconstructions through time. The availability of such powerful tools also brings new challenges - we want to learn something about the key associations between reconstructed plate motions and the geological record, but the high-dimensional parameter space is difficult for a human being to visually comprehend and quantify these associations. To achieve true spatio-temporal data-mining, new tools are needed. Here, we present a further development of the GPlates ecosystem - a Python-based tool for geotectonic analysis. In contrast to existing GPlates tools that are built around a graphical user interface (GUI) and interactive visualisation, pyGPlates offers a programming interface for the automation of quantitative plate tectonic analysis or arbitrary complexity. The vast array of open-source Python-based tools for data-mining, statistics and machine learning can now be linked to pyGPlates, allowing spatial data to be seamlessly analysed in space and geological "deep time", and with the ability to spread large computations across multiple processors. The presentation will illustrate a range of example applications, both simple and advanced. Basic examples include data querying, filtering, and reconstruction, and file-format conversions. For the innovative study of plate kinematics, pyGPlates has been used to explore the relationships between absolute plate motions, subduction zone kinematics, and mid-ocean ridge migration and orientation through deep time; to investigate the systematics of continental rift velocity evolution during Pangea breakup; and to make connections between kinematics of the Andean subduction zone and ore deposit formation. To support the numerical modelling community, pyGPlates facilitates the connection between tectonic surface boundary conditions contained within plate tectonic reconstructions (plate boundary configurations and plate velocities) and simulations such as thermo-mechanical models of lithospheric deformation and mantle convection. To support the development of web-based applications that can serve the wider geoscience community, we will demonstrate how pyGPlates can be combined with other open-source tools to serve alternative reconstructions together with a diverse array of reconstructed data sets in a self-consistent framework over the internet. PyGPlates is available to the public via the GPlates web site and contains comprehensive documentation covering installation on Windows/Mac/Linux platforms, sample code, tutorials and a detailed reference of pyGPlates functions and classes.

Phonon Calculations Using the Real-Space Multigrid Method (RMG)

NASA Astrophysics Data System (ADS)

Zhang, Jiayong; Lu, Wenchang; Briggs, Emil; Cheng, Yongqiang; Ramirez-Cuesta, A. J.; Bernholc, Jerry

RMG, a DFT-based open-source package using the real-space multigrid method, has proven to work effectively on large scale systems with thousands of atoms. Our recent work has shown its practicability for high accuracy phonon calculations employing the frozen phonon method. In this method, a primary unit cell with a small lattice constant is enlarged to a supercell that is sufficiently large to obtain the force constants matrix by finite displacements of atoms in the supercell. An open-source package PhonoPy is used to determine the necessary displacements by taking symmetry into account. A python script coupling RMG and PhonoPy enables us to perform high-throughput calculations of phonon properties. We have applied this method to many systems, such as silicon, silica glass, ZIF-8, etc. Results from RMG are compared to the experimental spectra measured using the VISION inelastic neutron scattering spectrometer at the Spallation Neutron Source at ORNL, as well as results from other DFT codes. The computing resources were made available through the VirtuES (Virtual Experiments in Spectroscopy) project, funded by Laboratory Directed Research and Development program (LDRD project No. 7739)

Atomicrex—a general purpose tool for the construction of atomic interaction models

NASA Astrophysics Data System (ADS)

Stukowski, Alexander; Fransson, Erik; Mock, Markus; Erhart, Paul

2017-07-01

We introduce atomicrex, an open-source code for constructing interatomic potentials as well as more general types of atomic-scale models. Such effective models are required to simulate extended materials structures comprising many thousands of atoms or more, because electronic structure methods become computationally too expensive at this scale. atomicrex covers a wide range of interatomic potential types and fulfills many needs in atomistic model development. As inputs, it supports experimental property values as well as ab initio energies and forces, to which models can be fitted using various optimization algorithms. The open architecture of atomicrex allows it to be used in custom model development scenarios beyond classical interatomic potentials while thanks to its Python interface it can be readily integrated e.g., with electronic structure calculations or machine learning algorithms.

A new Python library to analyse skeleton images confirms malaria parasite remodelling of the red blood cell membrane skeleton.

PubMed

Nunez-Iglesias, Juan; Blanch, Adam J; Looker, Oliver; Dixon, Matthew W; Tilley, Leann

2018-01-01

We present Skan (Skeleton analysis), a Python library for the analysis of the skeleton structures of objects. It was inspired by the "analyse skeletons" plugin for the Fiji image analysis software, but its extensive Application Programming Interface (API) allows users to examine and manipulate any intermediate data structures produced during the analysis. Further, its use of common Python data structures such as SciPy sparse matrices and pandas data frames opens the results to analysis within the extensive ecosystem of scientific libraries available in Python. We demonstrate the validity of Skan's measurements by comparing its output to the established Analyze Skeletons Fiji plugin, and, with a new scanning electron microscopy (SEM)-based method, we confirm that the malaria parasite Plasmodium falciparum remodels the host red blood cell cytoskeleton, increasing the average distance between spectrin-actin junctions.

A new Python library to analyse skeleton images confirms malaria parasite remodelling of the red blood cell membrane skeleton

PubMed Central

Looker, Oliver; Dixon, Matthew W.; Tilley, Leann

2018-01-01

We present Skan (Skeleton analysis), a Python library for the analysis of the skeleton structures of objects. It was inspired by the “analyse skeletons” plugin for the Fiji image analysis software, but its extensive Application Programming Interface (API) allows users to examine and manipulate any intermediate data structures produced during the analysis. Further, its use of common Python data structures such as SciPy sparse matrices and pandas data frames opens the results to analysis within the extensive ecosystem of scientific libraries available in Python. We demonstrate the validity of Skan’s measurements by comparing its output to the established Analyze Skeletons Fiji plugin, and, with a new scanning electron microscopy (SEM)-based method, we confirm that the malaria parasite Plasmodium falciparum remodels the host red blood cell cytoskeleton, increasing the average distance between spectrin-actin junctions. PMID:29472997

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.

The Ultracool Typing Kit - An Open-Source, Qualitative Spectral Typing GUI for L Dwarfs

NASA Astrophysics Data System (ADS)

Schwab, Ellianna; Cruz, Kelle; Núñez, Alejandro; Burgasser, Adam J.; Rice, Emily; Reid, Neill; Faherty, Jacqueline K.; BDNYC

2018-01-01

The Ultracool Typing Kit (UTK) is an open-source graphical user interface for classifying the NIR spectral types of L dwarfs, including field and low-gravity dwarfs spanning L0-L9. The user is able to input an NIR spectrum and qualitatively compare the input spectrum to a full suite of spectral templates, including low-gravity beta and gamma templates. The user can choose to view the input spectrum as both a band-by-band comparison with the templates and a full bandwidth comparison with NIR spectral standards. Once an optimal qualitative comparison is selected, the user can save their spectral type selection both graphically and to a database. Using UTK to classify 78 previously typed L dwarfs, we show that a band-by-band classification method more accurately agrees with optical spectral typing systems than previous L dwarf NIR classification schemes. UTK is written in python, released on Zenodo with a BSD-3 clause license and publicly available on the BDNYC Github page.

A Platform for Scalable Satellite and Geospatial Data Analysis

NASA Astrophysics Data System (ADS)

Beneke, C. M.; Skillman, S.; Warren, M. S.; Kelton, T.; Brumby, S. P.; Chartrand, R.; Mathis, M.

2017-12-01

At Descartes Labs, we use the commercial cloud to run global-scale machine learning applications over satellite imagery. We have processed over 5 Petabytes of public and commercial satellite imagery, including the full Landsat and Sentinel archives. By combining open-source tools with a FUSE-based filesystem for cloud storage, we have enabled a scalable compute platform that has demonstrated reading over 200 GB/s of satellite imagery into cloud compute nodes. In one application, we generated global 15m Landsat-8, 20m Sentinel-1, and 10m Sentinel-2 composites from 15 trillion pixels, using over 10,000 CPUs. We recently created a public open-source Python client library that can be used to query and access preprocessed public satellite imagery from within our platform, and made this platform available to researchers for non-commercial projects. In this session, we will describe how you can use the Descartes Labs Platform for rapid prototyping and scaling of geospatial analyses and demonstrate examples in land cover classification.

AstroBlend: An astrophysical visualization package for Blender

NASA Astrophysics Data System (ADS)

Naiman, J. P.

2016-04-01

The rapid growth in scale and complexity of both computational and observational astrophysics over the past decade necessitates efficient and intuitive methods for examining and visualizing large datasets. Here, I present AstroBlend, an open-source Python library for use within the three dimensional modeling software, Blender. While Blender has been a popular open-source software among animators and visual effects artists, in recent years it has also become a tool for visualizing astrophysical datasets. AstroBlend combines the three dimensional capabilities of Blender with the analysis tools of the widely used astrophysical toolset, yt, to afford both computational and observational astrophysicists the ability to simultaneously analyze their data and create informative and appealing visualizations. The introduction of this package includes a description of features, work flow, and various example visualizations. A website - www.astroblend.com - has been developed which includes tutorials, and a gallery of example images and movies, along with links to downloadable data, three dimensional artistic models, and various other resources.

PyVCI: A flexible open-source code for calculating accurate molecular infrared spectra

NASA Astrophysics Data System (ADS)

Sibaev, Marat; Crittenden, Deborah L.

2016-06-01

The PyVCI program package is a general purpose open-source code for simulating accurate molecular spectra, based upon force field expansions of the potential energy surface in normal mode coordinates. It includes harmonic normal coordinate analysis and vibrational configuration interaction (VCI) algorithms, implemented primarily in Python for accessibility but with time-consuming routines written in C. Coriolis coupling terms may be optionally included in the vibrational Hamiltonian. Non-negligible VCI matrix elements are stored in sparse matrix format to alleviate the diagonalization problem. CPU and memory requirements may be further controlled by algorithmic choices and/or numerical screening procedures, and recommended values are established by benchmarking using a test set of 44 molecules for which accurate analytical potential energy surfaces are available. Force fields in normal mode coordinates are obtained from the PyPES library of high quality analytical potential energy surfaces (to 6th order) or by numerical differentiation of analytic second derivatives generated using the GAMESS quantum chemical program package (to 4th order).

TEA: A Code Calculating Thermochemical Equilibrium Abundances

NASA Astrophysics Data System (ADS)

Blecic, Jasmina; Harrington, Joseph; Bowman, M. Oliver

2016-07-01

We present an open-source Thermochemical Equilibrium Abundances (TEA) code that calculates the abundances of gaseous molecular species. The code is based on the methodology of White et al. and Eriksson. It applies Gibbs free-energy minimization using an iterative, Lagrangian optimization scheme. Given elemental abundances, TEA calculates molecular abundances for a particular temperature and pressure or a list of temperature-pressure pairs. We tested the code against the method of Burrows & Sharp, the free thermochemical equilibrium code Chemical Equilibrium with Applications (CEA), and the example given by Burrows & Sharp. Using their thermodynamic data, TEA reproduces their final abundances, but with higher precision. We also applied the TEA abundance calculations to models of several hot-Jupiter exoplanets, producing expected results. TEA is written in Python in a modular format. There is a start guide, a user manual, and a code document in addition to this theory paper. TEA is available under a reproducible-research, open-source license via https://github.com/dzesmin/TEA.

LSSGalPy: Interactive Visualization of the Large-scale Environment Around Galaxies

NASA Astrophysics Data System (ADS)

Argudo-Fernández, M.; Duarte Puertas, S.; Ruiz, J. E.; Sabater, J.; Verley, S.; Bergond, G.

2017-05-01

New tools are needed to handle the growth of data in astrophysics delivered by recent and upcoming surveys. We aim to build open-source, light, flexible, and interactive software designed to visualize extensive three-dimensional (3D) tabular data. Entirely written in the Python language, we have developed interactive tools to browse and visualize the positions of galaxies in the universe and their positions with respect to its large-scale structures (LSS). Motivated by a previous study, we created two codes using Mollweide projection and wedge diagram visualizations, where survey galaxies can be overplotted on the LSS of the universe. These are interactive representations where the visualizations can be controlled by widgets. We have released these open-source codes that have been designed to be easily re-used and customized by the scientific community to fulfill their needs. The codes are adaptable to other kinds of 3D tabular data and are robust enough to handle several millions of objects. .

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.

TEA: A CODE CALCULATING THERMOCHEMICAL EQUILIBRIUM ABUNDANCES

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

Blecic, Jasmina; Harrington, Joseph; Bowman, M. Oliver, E-mail: jasmina@physics.ucf.edu

2016-07-01

We present an open-source Thermochemical Equilibrium Abundances (TEA) code that calculates the abundances of gaseous molecular species. The code is based on the methodology of White et al. and Eriksson. It applies Gibbs free-energy minimization using an iterative, Lagrangian optimization scheme. Given elemental abundances, TEA calculates molecular abundances for a particular temperature and pressure or a list of temperature–pressure pairs. We tested the code against the method of Burrows and Sharp, the free thermochemical equilibrium code Chemical Equilibrium with Applications (CEA), and the example given by Burrows and Sharp. Using their thermodynamic data, TEA reproduces their final abundances, but withmore » higher precision. We also applied the TEA abundance calculations to models of several hot-Jupiter exoplanets, producing expected results. TEA is written in Python in a modular format. There is a start guide, a user manual, and a code document in addition to this theory paper. TEA is available under a reproducible-research, open-source license via https://github.com/dzesmin/TEA.« less

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

CellShape: A user-friendly image analysis tool for quantitative visualization of bacterial cell factories inside.

PubMed

Goñi-Moreno, Ángel; Kim, Juhyun; de Lorenzo, Víctor

2017-02-01

Visualization of the intracellular constituents of individual bacteria while performing as live biocatalysts is in principle doable through more or less sophisticated fluorescence microscopy. Unfortunately, rigorous quantitation of the wealth of data embodied in the resulting images requires bioinformatic tools that are not widely extended within the community-let alone that they are often subject to licensing that impedes software reuse. In this context we have developed CellShape, a user-friendly platform for image analysis with subpixel precision and double-threshold segmentation system for quantification of fluorescent signals stemming from single-cells. CellShape is entirely coded in Python, a free, open-source programming language with widespread community support. For a developer, CellShape enhances extensibility (ease of software improvements) by acting as an interface to access and use existing Python modules; for an end-user, CellShape presents standalone executable files ready to open without installation. We have adopted this platform to analyse with an unprecedented detail the tridimensional distribution of the constituents of the gene expression flow (DNA, RNA polymerase, mRNA and ribosomal proteins) in individual cells of the industrial platform strain Pseudomonas putida KT2440. While the CellShape first release version (v0.8) is readily operational, users and/or developers are enabled to expand the platform further. Copyright © 2017 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.

ISMRM Raw Data Format: A Proposed Standard for MRI Raw Datasets

PubMed Central

Inati, Souheil J.; Naegele, Joseph D.; Zwart, Nicholas R.; Roopchansingh, Vinai; Lizak, Martin J.; Hansen, David C.; Liu, Chia-Ying; Atkinson, David; Kellman, Peter; Kozerke, Sebastian; Xue, Hui; Campbell-Washburn, Adrienne E.; Sørensen, Thomas S.; Hansen, Michael S.

2015-01-01

Purpose This work proposes the ISMRM Raw Data (ISMRMRD) format as a common MR raw data format, which promotes algorithm and data sharing. Methods A file format consisting of a flexible header and tagged frames of k-space data was designed. Application Programming Interfaces were implemented in C/C++, MATLAB, and Python. Converters for Bruker, General Electric, Philips, and Siemens proprietary file formats were implemented in C++. Raw data were collected using MRI scanners from four vendors, converted to ISMRMRD format, and reconstructed using software implemented in three programming languages (C++, MATLAB, Python). Results Images were obtained by reconstructing the raw data from all vendors. The source code, raw data, and images comprising this work are shared online, serving as an example of an image reconstruction project following a paradigm of reproducible research. Conclusion The proposed raw data format solves a practical problem for the MRI community. It may serve as a foundation for reproducible research and collaborations. The ISMRMRD format is a completely open and community-driven format, and the scientific community is invited (including commercial vendors) to participate either as users or developers. PMID:26822475

Development of an Optimization Methodology for the Aluminum Alloy Wheel Casting Process

NASA Astrophysics Data System (ADS)

Duan, Jianglan; Reilly, Carl; Maijer, Daan M.; Cockcroft, Steve L.; Phillion, Andre B.

2015-08-01

An optimization methodology has been developed for the aluminum alloy wheel casting process. The methodology is focused on improving the timing of cooling processes in a die to achieve improved casting quality. This methodology utilizes (1) a casting process model, which was developed within the commercial finite element package, ABAQUS™—ABAQUS is a trademark of Dassault Systèms; (2) a Python-based results extraction procedure; and (3) a numerical optimization module from the open-source Python library, Scipy. To achieve optimal casting quality, a set of constraints have been defined to ensure directional solidification, and an objective function, based on the solidification cooling rates, has been defined to either maximize, or target a specific, cooling rate. The methodology has been applied to a series of casting and die geometries with different cooling system configurations, including a 2-D axisymmetric wheel and die assembly generated from a full-scale prototype wheel. The results show that, with properly defined constraint and objective functions, solidification conditions can be improved and optimal cooling conditions can be achieved leading to process productivity and product quality improvements.

Object-oriented numerics with FOSS: comparing PyPy &amp; NumPy, GCC/Clang &amp; Bitz++ and Gfortran

NASA Astrophysics Data System (ADS)

Jarecka, Dorota; Arabas, Sylwester; Fijalkowski, Maciej; Jaruga, Anna; Del Vento, Davide

2013-04-01

Employment of object-oriented programming (OOP) techniques may help to improve code readability, and hence its auditability and maintainability - both being arguably crucial for scientific software. OOP offers, in particular, the possibility to reproduce in the program code the mathematical "blackboard abstractions" used in the literature. There exist a number of free and open-source tools allowing to obtain this goal without sacrificing performance. An OOP implementation of the MPDATA advection algorithm used as a core of weather, ocean and climate modelling systems will serve as an example for briefly highlighting some relevant recent FOSS developments including: - NumPy support in the PyPy just-in-time compiler of Python. - the Blitz++ library coupled with the C++11 support in GCC and Clang; - support for OOP constructs from Fortran 2003/2008 in GFortran; A brief overview of other performance-related packages for Python like Numba and Cython will be also given. This poster will describe and extends key findings presented in http://arxiv.org/abs/1301.1334

Unified functional network and nonlinear time series analysis for complex systems science: The pyunicorn package

NASA Astrophysics Data System (ADS)

Donges, Jonathan F.; Heitzig, Jobst; Beronov, Boyan; Wiedermann, Marc; Runge, Jakob; Feng, Qing Yi; Tupikina, Liubov; Stolbova, Veronika; Donner, Reik V.; Marwan, Norbert; Dijkstra, Henk A.; Kurths, Jürgen

2015-11-01

We introduce the pyunicorn (Pythonic unified complex network and recurrence analysis toolbox) open source software package for applying and combining modern methods of data analysis and modeling from complex network theory and nonlinear time series analysis. pyunicorn is a fully object-oriented and easily parallelizable package written in the language Python. It allows for the construction of functional networks such as climate networks in climatology or functional brain networks in neuroscience representing the structure of statistical interrelationships in large data sets of time series and, subsequently, investigating this structure using advanced methods of complex network theory such as measures and models for spatial networks, networks of interacting networks, node-weighted statistics, or network surrogates. Additionally, pyunicorn provides insights into the nonlinear dynamics of complex systems as recorded in uni- and multivariate time series from a non-traditional perspective by means of recurrence quantification analysis, recurrence networks, visibility graphs, and construction of surrogate time series. The range of possible applications of the library is outlined, drawing on several examples mainly from the field of climatology.

A geometric approach to identify cavities in particle systems

NASA Astrophysics Data System (ADS)

Voyiatzis, Evangelos; Böhm, Michael C.; Müller-Plathe, Florian

2015-11-01

The implementation of a geometric algorithm to identify cavities in particle systems in an open-source python program is presented. The algorithm makes use of the Delaunay space tessellation. The present python software is based on platform-independent tools, leading to a portable program. Its successful execution provides information concerning the accessible volume fraction of the system, the size and shape of the cavities and the group of atoms forming each of them. The program can be easily incorporated into the LAMMPS software. An advantage of the present algorithm is that no a priori assumption on the cavity shape has to be made. As an example, the cavity size and shape distributions in a polyethylene melt system are presented for three spherical probe particles. This paper serves also as an introductory manual to the script. It summarizes the algorithm, its implementation, the required user-defined parameters as well as the format of the input and output files. Additionally, we demonstrate possible applications of our approach and compare its capability with the ones of well documented cavity size estimators.

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.

Feature Geo Analytics and Big Data Processing: Hybrid Approaches for Earth Science and Real-Time Decision Support

NASA Astrophysics Data System (ADS)

Wright, D. J.; Raad, M.; Hoel, E.; Park, M.; Mollenkopf, A.; Trujillo, R.

2016-12-01

Introduced is a new approach for processing spatiotemporal big data by leveraging distributed analytics and storage. A suite of temporally-aware analysis tools summarizes data nearby or within variable windows, aggregates points (e.g., for various sensor observations or vessel positions), reconstructs time-enabled points into tracks (e.g., for mapping and visualizing storm tracks), joins features (e.g., to find associations between features based on attributes, spatial relationships, temporal relationships or all three simultaneously), calculates point densities, finds hot spots (e.g., in species distributions), and creates space-time slices and cubes (e.g., in microweather applications with temperature, humidity, and pressure, or within human mobility studies). These "feature geo analytics" tools run in both batch and streaming spatial analysis mode as distributed computations across a cluster of servers on typical "big" data sets, where static data exist in traditional geospatial formats (e.g., shapefile) locally on a disk or file share, attached as static spatiotemporal big data stores, or streamed in near-real-time. In other words, the approach registers large datasets or data stores with ArcGIS Server, then distributes analysis across a cluster of machines for parallel processing. Several brief use cases will be highlighted based on a 16-node server cluster at 14 Gb RAM per node, allowing, for example, the buffering of over 8 million points or thousands of polygons in 1 minute. The approach is "hybrid" in that ArcGIS Server integrates open-source big data frameworks such as Apache Hadoop and Apache Spark on the cluster in order to run the analytics. In addition, the user may devise and connect custom open-source interfaces and tools developed in Python or Python Notebooks; the common denominator being the familiar REST API.

ODM2 Admin Pilot Project- a Data Management Application for Observations of the Critical Zone.

NASA Astrophysics Data System (ADS)

Leon, M.; McDowell, W. H.; Mayorga, E.; Setiawan, L.; Hooper, R. P.

2017-12-01

ODM2 Admin is a tool to manage data stored in a relational database using the Observation Data Model 2 (ODM2) information model. Originally developed by the Luquillo Critical Zone Observatory (CZO) to manage a wide range of Earth observations, it has now been deployed at 6 projects: the Catalina Jemez CZO, the Dry Creek Experimental Forest, Au Sable and Manistee River sites managed by Michigan State, Tropical Response to Altered Climate Experiment (TRACE) and the Critical Zone Integrative Microbial Ecology Activity (CZIMEA) EarthCube project; most of these deployments are hosted on a Microsoft Azure cloud server managed by CUAHSI. ODM2 Admin is a web application built on the Python open-source Django framework and available for download from GitHub and DockerHub. It provides tools for data ingestion, editing, QA/QC, data visualization, browsing, mapping and documentation of equipment deployment, methods, and citations. Additional features include the ability to generate derived data values, automatically or manually create data annotations and create datasets from arbitrary groupings of results. Over 22 million time series values for more than 600 time series are being managed with ODM2 Admin across the 6 projects as well as more than 12,000 soil profiles and other measurements. ODM2 Admin links with external identifier systems through DOIs, ORCiDs and IGSNs, so cited works, details about researchers and earth sample meta-data can be accessed directly from ODM2 Admin. This application is part of a growing open source ODM2 application ecosystem under active development. ODM2 Admin can be deployed alongside other tools from the ODM2 ecosystem, including ODM2API and WOFpy, which provide access to the underlying ODM2 data through a Python API and Water One Flow web services.

Sirepo - Warp

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

Nagler, Robert; Moeller, Paul

Sirepo is an open source framework for cloud computing. The graphical user interface (GUI) for Sirepo, also known as the client, executes in any HTML5 compliant web browser on any computing platform, including tablets. The client is built in JavaScript, making use of the following open source libraries: Bootstrap, which is fundamental for cross-platform web applications; AngularJS, which provides a model–view–controller (MVC) architecture and GUI components; and D3.js, which provides interactive plots and data-driven transformations. The Sirepo server is built on the following Python technologies: Flask, which is a lightweight framework for web development; Jin-ja, which is a secure andmore » widely used templating language; and Werkzeug, a utility library that is compliant with the WSGI standard. We use Nginx as the HTTP server and proxy, which provides a scalable event-driven architecture. The physics codes supported by Sirepo execute inside a Docker container. One of the codes supported by Sirepo is Warp. Warp is a particle-in-cell (PIC) code de-signed to simulate high-intensity charged particle beams and plasmas in both the electrostatic and electromagnetic regimes, with a wide variety of integrated physics models and diagnostics. At pre-sent, Sirepo supports a small subset of Warp’s capabilities. Warp is open source and is part of the Berkeley Lab Accelerator Simulation Toolkit.« less

Python Source Code Plagiarism Attacks on Introductory Programming Course Assignments

ERIC Educational Resources Information Center

Karnalim, Oscar

2017-01-01

This paper empirically enlists Python plagiarism attacks that have been found on Introductory Programming course assignments for undergraduate students. According to our observation toward 400 plagiarism-suspected cases, there are 35 plagiarism attacks that have been conducted by students. It starts with comment & whitespace modification as…

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

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

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.

OOSTethys - Open Source Software for the Global Earth Observing Systems of Systems

NASA Astrophysics Data System (ADS)

Bridger, E.; Bermudez, L. E.; Maskey, M.; Rueda, C.; Babin, B. L.; Blair, R.

2009-12-01

An open source software project is much more than just picking the right license, hosting modular code and providing effective documentation. Success in advancing in an open collaborative way requires that the process match the expected code functionality to the developer's personal expertise and organizational needs as well as having an enthusiastic and responsive core lead group. We will present the lessons learned fromOOSTethys , which is a community of software developers and marine scientists who develop open source tools, in multiple languages, to integrate ocean observing systems into an Integrated Ocean Observing System (IOOS). OOSTethys' goal is to dramatically reduce the time it takes to install, adopt and update standards-compliant web services. OOSTethys has developed servers, clients and a registry. Open source PERL, PYTHON, JAVA and ASP tool kits and reference implementations are helping the marine community publish near real-time observation data in interoperable standard formats. In some cases publishing an OpenGeospatial Consortium (OGC), Sensor Observation Service (SOS) from NetCDF files or a database or even CSV text files could take only minutes depending on the skills of the developer. OOSTethys is also developing an OGC standard registry, Catalog Service for Web (CSW). This open source CSW registry was implemented to easily register and discover SOSs using ISO 19139 service metadata. A web interface layer over the CSW registry simplifies the registration process by harvesting metadata describing the observations and sensors from the “GetCapabilities” response of SOS. OPENIOOS is the web client, developed in PERL to visualize the sensors in the SOS services. While the number of OOSTethys software developers is small, currently about 10 around the world, the number of OOSTethys toolkit implementers is larger and growing and the ease of use has played a large role in spreading the use of interoperable standards compliant web services widely in the marine community.

BioBlend: automating pipeline analyses within Galaxy and CloudMan.

PubMed

Sloggett, Clare; Goonasekera, Nuwan; Afgan, Enis

2013-07-01

We present BioBlend, a unified API in a high-level language (python) that wraps the functionality of Galaxy and CloudMan APIs. BioBlend makes it easy for bioinformaticians to automate end-to-end large data analysis, from scratch, in a way that is highly accessible to collaborators, by allowing them to both provide the required infrastructure and automate complex analyses over large datasets within the familiar Galaxy environment. http://bioblend.readthedocs.org/. Automated installation of BioBlend is available via PyPI (e.g. pip install bioblend). Alternatively, the source code is available from the GitHub repository (https://github.com/afgane/bioblend) under the MIT open source license. The library has been tested and is working on Linux, Macintosh and Windows-based systems.

D-GENIES: dot plot large genomes in an interactive, efficient and simple way.

PubMed

Cabanettes, Floréal; Klopp, Christophe

2018-01-01

Dot plots are widely used to quickly compare sequence sets. They provide a synthetic similarity overview, highlighting repetitions, breaks and inversions. Different tools have been developed to easily generated genomic alignment dot plots, but they are often limited in the input sequence size. D-GENIES is a standalone and web application performing large genome alignments using minimap2 software package and generating interactive dot plots. It enables users to sort query sequences along the reference, zoom in the plot and download several image, alignment or sequence files. D-GENIES is an easy-to-install, open-source software package (GPL) developed in Python and JavaScript. The source code is available at https://github.com/genotoul-bioinfo/dgenies and it can be tested at http://dgenies.toulouse.inra.fr/.

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

Damiani, D.; Dubrovin, M.; Gaponenko, I.

Psana(Photon Science Analysis) is a software package that is used to analyze data produced by the Linac Coherent Light Source X-ray free-electron laser at the SLAC National Accelerator Laboratory. The project began in 2011, is written primarily in C++ with some Python, and provides user interfaces in both C++ and Python. Most users use the Python interface. The same code can be run in real time while data are being taken as well as offline, executing on many nodes/cores using MPI for parallelization. It is publicly available and installable on the RHEL5/6/7 operating systems.

IRISpy: Analyzing IRIS Data in Python

NASA Astrophysics Data System (ADS)

Ryan, Daniel; Christe, Steven; Mumford, Stuart; Baruah, Ankit; Timothy, Shelbe; Pereira, Tiago; De Pontieu, Bart

2017-08-01

IRISpy is a new community-developed open-source software library for analysing IRIS level 2 data. It is written in Python, a free, cross-platform, general-purpose, high-level programming language. A wide array of scientific computing software packages have already been developed in Python, from numerical computation (NumPy, SciPy, etc.), to visualization and plotting (matplotlib), to solar-physics-specific data analysis (SunPy). IRISpy is currently under development as a SunPy-affiliated package which means it depends on the SunPy library, follows similar standards and conventions, and is developed with the support of of the SunPy development team. IRISpy’s has two primary data objects, one for analyzing slit-jaw imager data and another for analyzing spectrograph data. Both objects contain basic slicing, indexing, plotting, and animating functionality to allow users to easily inspect, reduce and analyze the data. As part of this functionality the objects can output SunPy Maps, TimeSeries, Spectra, etc. of relevant data slices for easier inspection and analysis. Work is also ongoing to provide additional data analysis functionality including derivation of systematic measurement errors (e.g. readout noise), exposure time correction, residual wavelength calibration, radiometric calibration, and fine scale pointing corrections. IRISpy’s code base is publicly available through github.com and can be contributed to by anyone. In this poster we demonstrate IRISpy’s functionality and future goals of the project. We also encourage interested users to become involved in further developing IRISpy.

PyCoTools: A Python Toolbox for COPASI.

PubMed

Welsh, Ciaran M; Fullard, Nicola; Proctor, Carole J; Martinez-Guimera, Alvaro; Isfort, Robert J; Bascom, Charles C; Tasseff, Ryan; Przyborski, Stefan A; Shanley, Daryl P

2018-05-22

COPASI is an open source software package for constructing, simulating and analysing dynamic models of biochemical networks. COPASI is primarily intended to be used with a graphical user interface but often it is desirable to be able to access COPASI features programmatically, with a high level interface. PyCoTools is a Python package aimed at providing a high level interface to COPASI tasks with an emphasis on model calibration. PyCoTools enables the construction of COPASI models and the execution of a subset of COPASI tasks including time courses, parameter scans and parameter estimations. Additional 'composite' tasks which use COPASI tasks as building blocks are available for increasing parameter estimation throughput, performing identifiability analysis and performing model selection. PyCoTools supports exploratory data analysis on parameter estimation data to assist with troubleshooting model calibrations. We demonstrate PyCoTools by posing a model selection problem designed to show case PyCoTools within a realistic scenario. The aim of the model selection problem is to test the feasibility of three alternative hypotheses in explaining experimental data derived from neonatal dermal fibroblasts in response to TGF-β over time. PyCoTools is used to critically analyse the parameter estimations and propose strategies for model improvement. PyCoTools can be downloaded from the Python Package Index (PyPI) using the command 'pip install pycotools' or directly from GitHub (https://github.com/CiaranWelsh/pycotools). Documentation at http://pycotools.readthedocs.io. Supplementary data are available at Bioinformatics.

Pipe dream? Envisioning a grassroots Python ecosystem of open, common software tools and data access in support of river and coastal biogeochemical research (Invited)

NASA Astrophysics Data System (ADS)

Mayorga, E.

2013-12-01

Practical, problem oriented software developed by scientists and graduate students in domains lacking a strong software development tradition is often balkanized into the scripting environments provided by dominant, typically proprietary tools. In environmental fields, these tools include ArcGIS, Matlab, SAS, Excel and others, and are often constrained to specific operating systems. While this situation is the outcome of rational choices, it limits the dissemination of useful tools and their integration into loosely coupled frameworks that can meet wider needs and be developed organically by groups addressing their own needs. Open-source dynamic languages offer the advantages of an accessible programming syntax, a wealth of pre-existing libraries, multi-platform access, linkage to community libraries developed in lower level languages such as C or FORTRAN, and access to web service infrastructure. Python in particular has seen a large and increasing uptake in scientific communities, as evidenced by the continued growth of the annual SciPy conference. Ecosystems with distinctive physical structures and organization, and mechanistic processes that are well characterized, are both factors that have often led to the grass-roots development of useful code meeting the needs of a range of communities. In aquatic applications, examples include river and watershed analysis tools (River Tools, Taudem, etc), and geochemical modules such as CO2SYS, PHREEQ and LOADEST. I will review the state of affairs and explore the potential offered by a Python tool ecosystem in supporting aquatic biogeochemistry and water quality research. This potential is multi-faceted and broadly involves accessibility to lone grad students, access to a wide community of programmers and problem solvers via online resources such as StackExchange, and opportunities to leverage broader cyberinfrastructure efforts and tools, including those from widely different domains. Collaborative development of such tools can provide the additional advantage of enhancing cohesion and communication across specific research areas, and reducing research obstacles in a range of disciplines.

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

Rapid scoring of genes in microbial pan-genome-wide association studies with Scoary.

PubMed

Brynildsrud, Ola; Bohlin, Jon; Scheffer, Lonneke; Eldholm, Vegard

2016-11-25

Genome-wide association studies (GWAS) have become indispensable in human medicine and genomics, but very few have been carried out on bacteria. Here we introduce Scoary, an ultra-fast, easy-to-use, and widely applicable software tool that scores the components of the pan-genome for associations to observed phenotypic traits while accounting for population stratification, with minimal assumptions about evolutionary processes. We call our approach pan-GWAS to distinguish it from traditional, single nucleotide polymorphism (SNP)-based GWAS. Scoary is implemented in Python and is available under an open source GPLv3 license at https://github.com/AdmiralenOla/Scoary .

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.

Automated Reporting of DXA Studies Using a Custom-Built Computer Program.

PubMed

England, Joseph R; Colletti, Patrick M

2018-06-01

Dual-energy x-ray absorptiometry (DXA) scans are a critical population health tool and relatively simple to interpret but can be time consuming to report, often requiring manual transfer of bone mineral density and associated statistics into commercially available dictation systems. We describe here a custom-built computer program for automated reporting of DXA scans using Pydicom, an open-source package built in the Python computer language, and regular expressions to mine DICOM tags for patient information and bone mineral density statistics. This program, easy to emulate by any novice computer programmer, has doubled our efficiency at reporting DXA scans and has eliminated dictation errors.

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.

Data Cube Visualization with Blender

NASA Astrophysics Data System (ADS)

Kent, Brian R.; Gárate, Matías

2017-06-01

With the increasing data acquisition rates from observational and computational astrophysics, new tools are needed to study and visualize data. We present a methodology for rendering 3D data cubes using the open-source 3D software Blender. By importing processed observations and numerical simulations through the Voxel Data format, we are able use the Blender interface and Python API to create high-resolution animated visualizations. We review the methods for data import, animation, and camera movement, and present examples of this methodology. The 3D rendering of data cubes gives scientists the ability to create appealing displays that can be used for both scientific presentations as well as public outreach.

ABrox-A user-friendly Python module for approximate Bayesian computation with a focus on model comparison.

PubMed

Mertens, Ulf Kai; Voss, Andreas; Radev, Stefan

2018-01-01

We give an overview of the basic principles of approximate Bayesian computation (ABC), a class of stochastic methods that enable flexible and likelihood-free model comparison and parameter estimation. Our new open-source software called ABrox is used to illustrate ABC for model comparison on two prominent statistical tests, the two-sample t-test and the Levene-Test. We further highlight the flexibility of ABC compared to classical Bayesian hypothesis testing by computing an approximate Bayes factor for two multinomial processing tree models. Last but not least, throughout the paper, we introduce ABrox using the accompanied graphical user interface.

pyPcazip: A PCA-based toolkit for compression and analysis of molecular simulation data

NASA Astrophysics Data System (ADS)

Shkurti, Ardita; Goni, Ramon; Andrio, Pau; Breitmoser, Elena; Bethune, Iain; Orozco, Modesto; Laughton, Charles A.

The biomolecular simulation community is currently in need of novel and optimised software tools that can analyse and process, in reasonable timescales, the large generated amounts of molecular simulation data. In light of this, we have developed and present here pyPcazip: a suite of software tools for compression and analysis of molecular dynamics (MD) simulation data. The software is compatible with trajectory file formats generated by most contemporary MD engines such as AMBER, CHARMM, GROMACS and NAMD, and is MPI parallelised to permit the efficient processing of very large datasets. pyPcazip is a Unix based open-source software (BSD licenced) written in Python.

Forward Modeling of Large-scale Structure: An Open-source Approach with Halotools

NASA Astrophysics Data System (ADS)

Hearin, Andrew P.; Campbell, Duncan; Tollerud, Erik; Behroozi, Peter; Diemer, Benedikt; Goldbaum, Nathan J.; Jennings, Elise; Leauthaud, Alexie; Mao, Yao-Yuan; More, Surhud; Parejko, John; Sinha, Manodeep; Sipöcz, Brigitta; Zentner, Andrew

2017-11-01

We present the first stable release of Halotools (v0.2), a community-driven Python package designed to build and test models of the galaxy-halo connection. Halotools provides a modular platform for creating mock universes of galaxies starting from a catalog of dark matter halos obtained from a cosmological simulation. The package supports many of the common forms used to describe galaxy-halo models: the halo occupation distribution, the conditional luminosity function, abundance matching, and alternatives to these models that include effects such as environmental quenching or variable galaxy assembly bias. Satellite galaxies can be modeled to live in subhalos or to follow custom number density profiles within their halos, including spatial and/or velocity bias with respect to the dark matter profile. The package has an optimized toolkit to make mock observations on a synthetic galaxy population—including galaxy clustering, galaxy-galaxy lensing, galaxy group identification, RSD multipoles, void statistics, pairwise velocities and others—allowing direct comparison to observations. Halotools is object-oriented, enabling complex models to be built from a set of simple, interchangeable components, including those of your own creation. Halotools has an automated testing suite and is exhaustively documented on http://halotools.readthedocs.io, which includes quickstart guides, source code notes and a large collection of tutorials. The documentation is effectively an online textbook on how to build and study empirical models of galaxy formation with Python.

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).

BYMUR software: a free and open source tool for quantifying and visualizing multi-risk analyses

NASA Astrophysics Data System (ADS)

Tonini, Roberto; Selva, Jacopo

2013-04-01

The BYMUR software aims to provide an easy-to-use open source tool for both computing multi-risk and managing/visualizing/comparing all the inputs (e.g. hazard, fragilities and exposure) as well as the corresponding results (e.g. risk curves, risk indexes). For all inputs, a complete management of inter-model epistemic uncertainty is considered. The BYMUR software will be one of the final products provided by the homonymous ByMuR project (http://bymur.bo.ingv.it/) funded by Italian Ministry of Education, Universities and Research (MIUR), focused to (i) provide a quantitative and objective general method for a comprehensive long-term multi-risk analysis in a given area, accounting for inter-model epistemic uncertainty through Bayesian methodologies, and (ii) apply the methodology to seismic, volcanic and tsunami risks in Naples (Italy). More specifically, the BYMUR software will be able to separately account for the probabilistic hazard assessment of different kind of hazardous phenomena, the relative (time-dependent/independent) vulnerabilities and exposure data, and their possible (predefined) interactions: the software will analyze these inputs and will use them to estimate both single- and multi- risk associated to a specific target area. In addition, it will be possible to connect the software to further tools (e.g., a full hazard analysis), allowing a dynamic I/O of results. The use of Python programming language guarantees that the final software will be open source and platform independent. Moreover, thanks to the integration of some most popular and rich-featured Python scientific modules (Numpy, Matplotlib, Scipy) with the wxPython graphical user toolkit, the final tool will be equipped with a comprehensive Graphical User Interface (GUI) able to control and visualize (in the form of tables, maps and/or plots) any stage of the multi-risk analysis. The additional features of importing/exporting data in MySQL databases and/or standard XML formats (for instance, the global standards defined in the frame of GEM project for seismic hazard and risk) will grant the interoperability with other FOSS software and tools and, at the same time, to be on hand of the geo-scientific community. An already available example of connection is represented by the BET_VH(**) tool, which probabilistic volcanic hazard outputs will be used as input for BYMUR. Finally, the prototype version of BYMUR will be used for the case study of the municipality of Naples, by considering three different natural hazards (volcanic eruptions, earthquakes and tsunamis) and by assessing the consequent long-term risk evaluation. (**)BET_VH (Bayesian Event Tree for Volcanic Hazard) is probabilistic tool for long-term volcanic hazard assessment, recently re-designed and adjusted to be run on the Vhub cyber-infrastructure, a free web-based collaborative tool in volcanology research (see http://vhub.org/resources/betvh).

pyJac: Analytical Jacobian generator for chemical kinetics

NASA Astrophysics Data System (ADS)

Niemeyer, Kyle E.; Curtis, Nicholas J.; Sung, Chih-Jen

2017-06-01

Accurate simulations of combustion phenomena require the use of detailed chemical kinetics in order to capture limit phenomena such as ignition and extinction as well as predict pollutant formation. However, the chemical kinetic models for hydrocarbon fuels of practical interest typically have large numbers of species and reactions and exhibit high levels of mathematical stiffness in the governing differential equations, particularly for larger fuel molecules. In order to integrate the stiff equations governing chemical kinetics, generally reactive-flow simulations rely on implicit algorithms that require frequent Jacobian matrix evaluations. Some in situ and a posteriori computational diagnostics methods also require accurate Jacobian matrices, including computational singular perturbation and chemical explosive mode analysis. Typically, finite differences numerically approximate these, but for larger chemical kinetic models this poses significant computational demands since the number of chemical source term evaluations scales with the square of species count. Furthermore, existing analytical Jacobian tools do not optimize evaluations or support emerging SIMD processors such as GPUs. Here we introduce pyJac, a Python-based open-source program that generates analytical Jacobian matrices for use in chemical kinetics modeling and analysis. In addition to producing the necessary customized source code for evaluating reaction rates (including all modern reaction rate formulations), the chemical source terms, and the Jacobian matrix, pyJac uses an optimized evaluation order to minimize computational and memory operations. As a demonstration, we first establish the correctness of the Jacobian matrices for kinetic models of hydrogen, methane, ethylene, and isopentanol oxidation (number of species ranging 13-360) by showing agreement within 0.001% of matrices obtained via automatic differentiation. We then demonstrate the performance achievable on CPUs and GPUs using pyJac via matrix evaluation timing comparisons; the routines produced by pyJac outperformed first-order finite differences by 3-7.5 times and the existing analytical Jacobian software TChem by 1.1-2.2 times on a single-threaded basis. It is noted that TChem is not thread-safe, while pyJac is easily parallelized, and hence can greatly outperform TChem on multicore CPUs. The Jacobian matrix generator we describe here will be useful for reducing the cost of integrating chemical source terms with implicit algorithms in particular and algorithms that require an accurate Jacobian matrix in general. Furthermore, the open-source release of the program and Python-based implementation will enable wide adoption.

An open-source wireless sensor stack: from Arduino to SDI-12 to Water One Flow

NASA Astrophysics Data System (ADS)

Hicks, S.; Damiano, S. G.; Smith, K. M.; Olexy, J.; Horsburgh, J. S.; Mayorga, E.; Aufdenkampe, A. K.

2013-12-01

Implementing a large-scale streaming environmental sensor network has previously been limited by the high cost of the datalogging and data communication infrastructure. The Christina River Basin Critical Zone Observatory (CRB-CZO) is overcoming the obstacles to large near-real-time data collection networks by using Arduino, an open source electronics platform, in combination with XBee ZigBee wireless radio modules. These extremely low-cost and easy-to-use open source electronics are at the heart of the new DIY movement and have provided solutions to countless projects by over half a million users worldwide. However, their use in environmental sensing is in its infancy. At present a primary limitation to widespread deployment of open-source electronics for environmental sensing is the lack of a simple, open-source software stack to manage streaming data from heterogeneous sensor networks. Here we present a functioning prototype software stack that receives sensor data over a self-meshing ZigBee wireless network from over a hundred sensors, stores the data locally and serves it on demand as a CUAHSI Water One Flow (WOF) web service. We highlight a few new, innovative components, including: (1) a versatile open data logger design based the Arduino electronics platform and ZigBee radios; (2) a software library implementing SDI-12 communication protocol between any Arduino platform and SDI12-enabled sensors without the need for additional hardware (https://github.com/StroudCenter/Arduino-SDI-12); and (3) 'midStream', a light-weight set of Python code that receives streaming sensor data, appends it with metadata on the fly by querying a relational database structured on an early version of the Observations Data Model version 2.0 (ODM2), and uses the WOFpy library to serve the data as WaterML via SOAP and REST web services.

An Open-source Community Web Site To Support Ground-Water Model Testing

NASA Astrophysics Data System (ADS)

Kraemer, S. R.; Bakker, M.; Craig, J. R.

2007-12-01

A community wiki wiki web site has been created as a resource to support ground-water model development and testing. The Groundwater Gourmet wiki is a repository for user supplied analytical and numerical recipes, howtos, and examples. Members are encouraged to submit analytical solutions, including source code and documentation. A diversity of code snippets are sought in a variety of languages, including Fortran, C, C++, Matlab, Python. In the spirit of a wiki, all contributions may be edited and altered by other users, and open source licensing is promoted. Community accepted contributions are graduated into the library of analytic solutions and organized into either a Strack (Groundwater Mechanics, 1989) or Bruggeman (Analytical Solutions of Geohydrological Problems, 1999) classification. The examples section of the wiki are meant to include laboratory experiments (e.g., Hele Shaw), classical benchmark problems (e.g., Henry Problem), and controlled field experiments (e.g., Borden landfill and Cape Cod tracer tests). Although this work was reviewed by EPA and approved for publication, it may not necessarily reflect official Agency policy. Mention of trade names or commercial products does not constitute endorsement or recommendation for use.

Fast computation of quadrupole and hexadecapole approximations in microlensing with a single point-source evaluation

NASA Astrophysics Data System (ADS)

Cassan, Arnaud

2017-07-01

The exoplanet detection rate from gravitational microlensing has grown significantly in recent years thanks to a great enhancement of resources and improved observational strategy. Current observatories include ground-based wide-field and/or robotic world-wide networks of telescopes, as well as space-based observatories such as satellites Spitzer or Kepler/K2. This results in a large quantity of data to be processed and analysed, which is a challenge for modelling codes because of the complexity of the parameter space to be explored and the intensive computations required to evaluate the models. In this work, I present a method that allows to compute the quadrupole and hexadecapole approximations of the finite-source magnification with more efficiency than previously available codes, with routines about six times and four times faster, respectively. The quadrupole takes just about twice the time of a point-source evaluation, which advocates for generalizing its use to large portions of the light curves. The corresponding routines are available as open-source python codes.

A portable structural analysis library for reaction networks.

PubMed

Bedaso, Yosef; Bergmann, Frank T; Choi, Kiri; Medley, Kyle; Sauro, Herbert M

2018-07-01

The topology of a reaction network can have a significant influence on the network's dynamical properties. Such influences can include constraints on network flows and concentration changes or more insidiously result in the emergence of feedback loops. These effects are due entirely to mass constraints imposed by the network configuration and are important considerations before any dynamical analysis is made. Most established simulation software tools usually carry out some kind of structural analysis of a network before any attempt is made at dynamic simulation. In this paper, we describe a portable software library, libStructural, that can carry out a variety of popular structural analyses that includes conservation analysis, flux dependency analysis and enumerating elementary modes. The library employs robust algorithms that allow it to be used on large networks with more than a two thousand nodes. The library accepts either a raw or fully labeled stoichiometry matrix or models written in SBML format. The software is written in standard C/C++ and comes with extensive on-line documentation and a test suite. The software is available for Windows, Mac OS X, and can be compiled easily on any Linux operating system. A language binding for Python is also available through the pip package manager making it simple to install on any standard Python distribution. The bulk of the source code is licensed under the open source BSD license with other parts using as either the MIT license or more simply public domain. All source is available on GitHub (https://github.com/sys-bio/Libstructural). Copyright © 2018 Elsevier B.V. All rights reserved.

IdentiPy: An Extensible Search Engine for Protein Identification in Shotgun Proteomics.

PubMed

Levitsky, Lev I; Ivanov, Mark V; Lobas, Anna A; Bubis, Julia A; Tarasova, Irina A; Solovyeva, Elizaveta M; Pridatchenko, Marina L; Gorshkov, Mikhail V

2018-06-18

We present an open-source, extensible search engine for shotgun proteomics. Implemented in Python programming language, IdentiPy shows competitive processing speed and sensitivity compared with the state-of-the-art search engines. It is equipped with a user-friendly web interface, IdentiPy Server, enabling the use of a single server installation accessed from multiple workstations. Using a simplified version of X!Tandem scoring algorithm and its novel "autotune" feature, IdentiPy outperforms the popular alternatives on high-resolution data sets. Autotune adjusts the search parameters for the particular data set, resulting in improved search efficiency and simplifying the user experience. IdentiPy with the autotune feature shows higher sensitivity compared with the evaluated search engines. IdentiPy Server has built-in postprocessing and protein inference procedures and provides graphic visualization of the statistical properties of the data set and the search results. It is open-source and can be freely extended to use third-party scoring functions or processing algorithms and allows customization of the search workflow for specialized applications.

GazeParser: an open-source and multiplatform library for low-cost eye tracking and analysis.

PubMed

Sogo, Hiroyuki

2013-09-01

Eye movement analysis is an effective method for research on visual perception and cognition. However, recordings of eye movements present practical difficulties related to the cost of the recording devices and the programming of device controls for use in experiments. GazeParser is an open-source library for low-cost eye tracking and data analysis; it consists of a video-based eyetracker and libraries for data recording and analysis. The libraries are written in Python and can be used in conjunction with PsychoPy and VisionEgg experimental control libraries. Three eye movement experiments are reported on performance tests of GazeParser. These showed that the means and standard deviations for errors in sampling intervals were less than 1 ms. Spatial accuracy ranged from 0.7° to 1.2°, depending on participant. In gap/overlap tasks and antisaccade tasks, the latency and amplitude of the saccades detected by GazeParser agreed with those detected by a commercial eyetracker. These results showed that the GazeParser demonstrates adequate performance for use in psychological experiments.

SPARX, a new environment for Cryo-EM image processing.

PubMed

Hohn, Michael; Tang, Grant; Goodyear, Grant; Baldwin, P R; Huang, Zhong; Penczek, Pawel A; Yang, Chao; Glaeser, Robert M; Adams, Paul D; Ludtke, Steven J

2007-01-01

SPARX (single particle analysis for resolution extension) is a new image processing environment with a particular emphasis on transmission electron microscopy (TEM) structure determination. It includes a graphical user interface that provides a complete graphical programming environment with a novel data/process-flow infrastructure, an extensive library of Python scripts that perform specific TEM-related computational tasks, and a core library of fundamental C++ image processing functions. In addition, SPARX relies on the EMAN2 library and cctbx, the open-source computational crystallography library from PHENIX. The design of the system is such that future inclusion of other image processing libraries is a straightforward task. The SPARX infrastructure intelligently handles retention of intermediate values, even those inside programming structures such as loops and function calls. SPARX and all dependencies are free for academic use and available with complete source.

Leaf-GP: an open and automated software application for measuring growth phenotypes for arabidopsis and wheat.

PubMed

Zhou, Ji; Applegate, Christopher; Alonso, Albor Dobon; Reynolds, Daniel; Orford, Simon; Mackiewicz, Michal; Griffiths, Simon; Penfield, Steven; Pullen, Nick

2017-01-01

Plants demonstrate dynamic growth phenotypes that are determined by genetic and environmental factors. Phenotypic analysis of growth features over time is a key approach to understand how plants interact with environmental change as well as respond to different treatments. Although the importance of measuring dynamic growth traits is widely recognised, available open software tools are limited in terms of batch image processing, multiple traits analyses, software usability and cross-referencing results between experiments, making automated phenotypic analysis problematic. Here, we present Leaf-GP (Growth Phenotypes), an easy-to-use and open software application that can be executed on different computing platforms. To facilitate diverse scientific communities, we provide three software versions, including a graphic user interface (GUI) for personal computer (PC) users, a command-line interface for high-performance computer (HPC) users, and a well-commented interactive Jupyter Notebook (also known as the iPython Notebook) for computational biologists and computer scientists. The software is capable of extracting multiple growth traits automatically from large image datasets. We have utilised it in Arabidopsis thaliana and wheat ( Triticum aestivum ) growth studies at the Norwich Research Park (NRP, UK). By quantifying a number of growth phenotypes over time, we have identified diverse plant growth patterns between different genotypes under several experimental conditions. As Leaf-GP has been evaluated with noisy image series acquired by different imaging devices (e.g. smartphones and digital cameras) and still produced reliable biological outputs, we therefore believe that our automated analysis workflow and customised computer vision based feature extraction software implementation can facilitate a broader plant research community for their growth and development studies. Furthermore, because we implemented Leaf-GP based on open Python-based computer vision, image analysis and machine learning libraries, we believe that our software not only can contribute to biological research, but also demonstrates how to utilise existing open numeric and scientific libraries (e.g. Scikit-image, OpenCV, SciPy and Scikit-learn) to build sound plant phenomics analytic solutions, in a efficient and effective way. Leaf-GP is a sophisticated software application that provides three approaches to quantify growth phenotypes from large image series. We demonstrate its usefulness and high accuracy based on two biological applications: (1) the quantification of growth traits for Arabidopsis genotypes under two temperature conditions; and (2) measuring wheat growth in the glasshouse over time. The software is easy-to-use and cross-platform, which can be executed on Mac OS, Windows and HPC, with open Python-based scientific libraries preinstalled. Our work presents the advancement of how to integrate computer vision, image analysis, machine learning and software engineering in plant phenomics software implementation. To serve the plant research community, our modulated source code, detailed comments, executables (.exe for Windows; .app for Mac), and experimental results are freely available at https://github.com/Crop-Phenomics-Group/Leaf-GP/releases.

ChromaStarPy: A Stellar Atmosphere and Spectrum Modeling and Visualization Lab in Python

NASA Astrophysics Data System (ADS)

Short, C. Ian; Bayer, Jason H. T.; Burns, Lindsey M.

2018-02-01

We announce ChromaStarPy, an integrated general stellar atmospheric modeling and spectrum synthesis code written entirely in python V. 3. ChromaStarPy is a direct port of the ChromaStarServer (CSServ) Java modeling code described in earlier papers in this series, and many of the associated JavaScript (JS) post-processing procedures have been ported and incorporated into CSPy so that students have access to ready-made data products. A python integrated development environment (IDE) allows a student in a more advanced course to experiment with the code and to graphically visualize intermediate and final results, ad hoc, as they are running it. CSPy allows students and researchers to compare modeled to observed spectra in the same IDE in which they are processing observational data, while having complete control over the stellar parameters affecting the synthetic spectra. We also take the opportunity to describe improvements that have been made to the related codes, ChromaStar (CS), CSServ, and ChromaStarDB (CSDB), that, where relevant, have also been incorporated into CSPy. The application may be found at the home page of the OpenStars project: http://www.ap.smu.ca/OpenStars/.

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

Krishnamurthy, Dheepak

This paper is an overview of Power System Simulation Toolbox (psst). psst is an open-source Python application for the simulation and analysis of power system models. psst simulates the wholesale market operation by solving a DC Optimal Power Flow (DCOPF), Security Constrained Unit Commitment (SCUC) and a Security Constrained Economic Dispatch (SCED). psst also includes models for the various entities in a power system such as Generator Companies (GenCos), Load Serving Entities (LSEs) and an Independent System Operator (ISO). psst features an open modular object oriented architecture that will make it useful for researchers to customize, expand, experiment beyond solvingmore » traditional problems. psst also includes a web based Graphical User Interface (GUI) that allows for user friendly interaction and for implementation on remote High Performance Computing (HPCs) clusters for parallelized operations. This paper also provides an illustrative application of psst and benchmarks with standard IEEE test cases to show the advanced features and the performance of toolbox.« less

GeoNotebook: Browser based Interactive analysis and visualization workflow for very large climate and geospatial datasets

NASA Astrophysics Data System (ADS)

Ozturk, D.; Chaudhary, A.; Votava, P.; Kotfila, C.

2016-12-01

Jointly developed by Kitware and NASA Ames, GeoNotebook is an open source tool designed to give the maximum amount of flexibility to analysts, while dramatically simplifying the process of exploring geospatially indexed datasets. Packages like Fiona (backed by GDAL), Shapely, Descartes, Geopandas, and PySAL provide a stack of technologies for reading, transforming, and analyzing geospatial data. Combined with the Jupyter notebook and libraries like matplotlib/Basemap it is possible to generate detailed geospatial visualizations. Unfortunately, visualizations generated is either static or does not perform well for very large datasets. Also, this setup requires a great deal of boilerplate code to create and maintain. Other extensions exist to remedy these problems, but they provide a separate map for each input cell and do not support map interactions that feed back into the python environment. To support interactive data exploration and visualization on large datasets we have developed an extension to the Jupyter notebook that provides a single dynamic map that can be managed from the Python environment, and that can communicate back with a server which can perform operations like data subsetting on a cloud-based cluster.

Mesoscale brain explorer, a flexible python-based image analysis and visualization tool.

PubMed

Haupt, Dirk; Vanni, Matthieu P; Bolanos, Federico; Mitelut, Catalin; LeDue, Jeffrey M; Murphy, Tim H

2017-07-01

Imaging of mesoscale brain activity is used to map interactions between brain regions. This work has benefited from the pioneering studies of Grinvald et al., who employed optical methods to image brain function by exploiting the properties of intrinsic optical signals and small molecule voltage-sensitive dyes. Mesoscale interareal brain imaging techniques have been advanced by cell targeted and selective recombinant indicators of neuronal activity. Spontaneous resting state activity is often collected during mesoscale imaging to provide the basis for mapping of connectivity relationships using correlation. However, the information content of mesoscale datasets is vast and is only superficially presented in manuscripts given the need to constrain measurements to a fixed set of frequencies, regions of interest, and other parameters. We describe a new open source tool written in python, termed mesoscale brain explorer (MBE), which provides an interface to process and explore these large datasets. The platform supports automated image processing pipelines with the ability to assess multiple trials and combine data from different animals. The tool provides functions for temporal filtering, averaging, and visualization of functional connectivity relations using time-dependent correlation. Here, we describe the tool and show applications, where previously published datasets were reanalyzed using MBE.

Calculating the n-point correlation function with general and efficient python code

NASA Astrophysics Data System (ADS)

Genier, Fred; Bellis, Matthew

2018-01-01

There are multiple approaches to understanding the evolution of large-scale structure in our universe and with it the role of baryonic matter, dark matter, and dark energy at different points in history. One approach is to calculate the n-point correlation function estimator for galaxy distributions, sometimes choosing a particular type of galaxy, such as luminous red galaxies. The standard way to calculate these estimators is with pair counts (for the 2-point correlation function) and with triplet counts (for the 3-point correlation function). These are O(n2) and O(n3) problems, respectively and with the number of galaxies that will be characterized in future surveys, having efficient and general code will be of increasing importance. Here we show a proof-of-principle approach to the 2-point correlation function that relies on pre-calculating galaxy locations in coarse “voxels”, thereby reducing the total number of necessary calculations. The code is written in python, making it easily accessible and extensible and is open-sourced to the community. Basic results and performance tests using SDSS/BOSS data will be shown and we discuss the application of this approach to the 3-point correlation function.

SPOTting Model Parameters Using a Ready-Made Python Package

NASA Astrophysics Data System (ADS)

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

2017-04-01

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

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.

New Version of SeismicHandler (SHX) based on ObsPy

NASA Astrophysics Data System (ADS)

Stammler, Klaus; Walther, Marcus

2016-04-01

The command line version of SeismicHandler (SH), a scientific analysis tool for seismic waveform data developed around 1990, has been redesigned in the recent years, based on a project funded by the Deutsche Forschungsgemeinschaft (DFG). The aim was to address new data access techniques, simplified metadata handling and a modularized software design. As a result the program was rewritten in Python in its main parts, taking advantage of simplicity of this script language and its variety of well developed software libraries, including ObsPy. SHX provides an easy access to waveforms and metadata via arclink and FDSN webservice protocols, also access to event catalogs is implemented. With single commands whole networks or stations within a certain area may be read in, the metadata are retrieved from the servers and stored in a local database. For data processing the large set of SH commands is available, as well as the SH scripting language. Via this SH language scripts or additional Python modules the command set of SHX is easily extendable. The program is open source, tested on Linux operating systems, documentation and download is found at URL "https://www.seismic-handler.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

SPOTting Model Parameters Using a Ready-Made Python Package.

PubMed

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

2015-01-01

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

SPOTting Model Parameters Using a Ready-Made Python Package

PubMed Central

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

2015-01-01

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

Sensor Placement Optimization using Chama

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

Klise, Katherine A.; Nicholson, Bethany L.; Laird, Carl Damon

Continuous or regularly scheduled monitoring has the potential to quickly identify changes in the environment. However, even with low - cost sensors, only a limited number of sensors can be deployed. The physical placement of these sensors, along with the sensor technology and operating conditions, can have a large impact on the performance of a monitoring strategy. Chama is an open source Python package which includes mixed - integer, stochastic programming formulations to determine sensor locations and technology that maximize monitoring effectiveness. The methods in Chama are general and can be applied to a wide range of applications. Chama ismore » currently being used to design sensor networks to monitor airborne pollutants and to monitor water quality in water distribution systems. The following documentation includes installation instructions and examples, description of software features, and software license. The software is intended to be used by regulatory agencies, industry, and the research community. It is assumed that the reader is familiar with the Python Programming Language. References are included for addit ional background on software components. Online documentation, hosted at http://chama.readthedocs.io/, will be updated as new features are added. The online version includes API documentation .« 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.

Hierarchical Testing with Automated Document Generation for Amanzi, ASCEM's Subsurface Flow and Reactive Transport Simulator

NASA Astrophysics Data System (ADS)

Moulton, J. D.; Steefel, C. I.; Yabusaki, S.; Castleton, K.; Scheibe, T. D.; Keating, E. H.; Freedman, V. L.

2013-12-01

The Advanced Simulation Capabililty for Environmental Management (ASCEM) program is developing an approach and open-source tool suite for standardized risk and performance assessments at legacy nuclear waste sites. These assessments use a graded and iterative approach, beginning with simplified highly abstracted models, and adding geometric and geologic complexity as understanding is gained. To build confidence in this assessment capability, extensive testing of the underlying tools is needed. Since the tools themselves, such as the subsurface flow and reactive-transport simulator, Amanzi, are under active development, testing must be both hierarchical and highly automated. In this presentation we show how we have met these requirements, by leveraging the python-based open-source documentation system called Sphinx with several other open-source tools. Sphinx builds on the reStructured text tool docutils, with important extensions that include high-quality formatting of equations, and integrated plotting through matplotlib. This allows the documentation, as well as the input files for tests, benchmark and tutorial problems, to be maintained with the source code under a version control system. In addition, it enables developers to build documentation in several different formats (e.g., html and pdf) from a single source. We will highlight these features, and discuss important benefits of this approach for Amanzi. In addition, we'll show that some of ASCEM's other tools, such as the sampling provided by the Uncertainty Quantification toolset, are naturally leveraged to enable more comprehensive testing. Finally, we will highlight the integration of this hiearchical testing and documentation framework with our build system and tools (CMake, CTest, and CDash).

Youpi: A Web-based Astronomical Image Processing Pipeline

NASA Astrophysics Data System (ADS)

Monnerville, M.; Sémah, G.

2010-12-01

Youpi stands for “YOUpi is your processing PIpeline”. It is a portable, easy to use web application providing high level functionalities to perform data reduction on scientific FITS images. It is built on top of open source processing tools that are released to the community by Terapix, in order to organize your data on a computer cluster, to manage your processing jobs in real time and to facilitate teamwork by allowing fine-grain sharing of results and data. On the server side, Youpi is written in the Python programming language and uses the Django web framework. On the client side, Ajax techniques are used along with the Prototype and script.aculo.us Javascript librairies.

Xanthos – A Global Hydrologic Model

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

Li, Xinya; Vernon, Chris R.; Hejazi, Mohamad I.

Xanthos is an open-source hydrologic model, written in Python, designed to quantify and analyse global water availability. Xanthos simulates historical and future global water availability on a monthly time step at a spatial resolution of 0.5 geographic degrees. Xanthos was designed to be extensible and used by scientists that study global water supply and work with the Global Change Assessment Model (GCAM). Xanthos uses a user-defined configuration file to specify model inputs, outputs and parameters. Xanthos has been tested using actual global data sets and the model is able to provide historical observations and future estimates of renewable freshwater resourcesmore » in the form of total runoff.« less

Xanthos – A Global Hydrologic Model

DOE PAGES

Li, Xinya; Vernon, Chris R.; Hejazi, Mohamad I.; ...

2017-09-11

Xanthos is an open-source hydrologic model, written in Python, designed to quantify and analyse global water availability. Xanthos simulates historical and future global water availability on a monthly time step at a spatial resolution of 0.5 geographic degrees. Xanthos was designed to be extensible and used by scientists that study global water supply and work with the Global Change Assessment Model (GCAM). Xanthos uses a user-defined configuration file to specify model inputs, outputs and parameters. Xanthos has been tested using actual global data sets and the model is able to provide historical observations and future estimates of renewable freshwater resourcesmore » in the form of total runoff.« less

Firefly Algorithm for Structural Search.

PubMed

Avendaño-Franco, Guillermo; Romero, Aldo H

2016-07-12

The problem of computational structure prediction of materials is approached using the firefly (FF) algorithm. Starting from the chemical composition and optionally using prior knowledge of similar structures, the FF method is able to predict not only known stable structures but also a variety of novel competitive metastable structures. This article focuses on the strengths and limitations of the algorithm as a multimodal global searcher. The algorithm has been implemented in software package PyChemia ( https://github.com/MaterialsDiscovery/PyChemia ), an open source python library for materials analysis. We present applications of the method to van der Waals clusters and crystal structures. The FF method is shown to be competitive when compared to other population-based global searchers.

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

Zhang, Yuxuan; Bilheux, Jean -Christophe

ImagingReso is an open-source Python library that simulates the neutron resonance signal for neutron imaging measurements. By defining the sample information such as density, thickness in the neutron path, and isotopic ratios of the elemental composition of the material, this package plots the expected resonance peaks for a selected neutron energy range. Various sample types such as layers of single elements (Ag, Co, etc. in solid form), chemical compounds (UO 3, Gd 2O 3, etc.), or even multiple layers of both types can be plotted with this package. As a result, major plotting features include display of the transmission/attenuation inmore » wavelength, energy, and time scale, and show/hide elemental and isotopic contributions in the total resonance signal.« less

Coupled RipCAS-DFLOW (CoRD) Software and Data Management System for Reproducible Floodplain Vegetation Succession Modeling

NASA Astrophysics Data System (ADS)

Turner, M. A.; Miller, S.; Gregory, A.; Cadol, D. D.; Stone, M. C.; Sheneman, L.

2016-12-01

We present the Coupled RipCAS-DFLOW (CoRD) modeling system created to encapsulate the workflow to analyze the effects of stream flooding on vegetation succession. CoRD provides an intuitive command-line and web interface to run DFLOW and RipCAS in succession over many years automatically, which is a challenge because, for our application, DFLOW must be run on a supercomputing cluster via the PBS job scheduler. RipCAS is a vegetation succession model, and DFLOW is a 2D open channel flow model. Data adaptors have been developed to seamlessly connect DFLOW output data to be RipCAS inputs, and vice-versa. CoRD provides automated statistical analysis and visualization, plus automatic syncing of input and output files and model run metadata to the hydrological data management system HydroShare using its excellent Python REST client. This combination of technologies and data management techniques allows the results to be shared with collaborators and eventually published. Perhaps most importantly, it allows results to be easily reproduced via either the command-line or web user interface. This system is a result of collaboration between software developers and hydrologists participating in the Western Consortium for Watershed Analysis, Visualization, and Exploration (WC-WAVE). Because of the computing-intensive nature of this particular workflow, including automating job submission/monitoring and data adaptors, software engineering expertise is required. However, the hydrologists provide the software developers with a purpose and ensure a useful, intuitive tool is developed. Our hydrologists contribute software, too: RipCAS was developed from scratch by hydrologists on the team as a specialized, open-source version of the Computer Aided Simulation Model for Instream Flow and Riparia (CASiMiR) vegetation model; our hydrologists running DFLOW provided numerous examples and help with the supercomputing system. This project is written in Python, a popular language in the geosciences and a good beginner programming language, and is completely open source. It can be accessed at https://github.com/VirtualWatershed/CoRD with documentation available at http://virtualwatershed.github.io/CoRD. These facts enable continued development and use beyond the involvement of the current authors.

OpenMS - A platform for reproducible analysis of mass spectrometry data.

PubMed

Pfeuffer, Julianus; Sachsenberg, Timo; Alka, Oliver; Walzer, Mathias; Fillbrunn, Alexander; Nilse, Lars; Schilling, Oliver; Reinert, Knut; Kohlbacher, Oliver

2017-11-10

In recent years, several mass spectrometry-based omics technologies emerged to investigate qualitative and quantitative changes within thousands of biologically active components such as proteins, lipids and metabolites. The research enabled through these methods potentially contributes to the diagnosis and pathophysiology of human diseases as well as to the clarification of structures and interactions between biomolecules. Simultaneously, technological advances in the field of mass spectrometry leading to an ever increasing amount of data, demand high standards in efficiency, accuracy and reproducibility of potential analysis software. This article presents the current state and ongoing developments in OpenMS, a versatile open-source framework aimed at enabling reproducible analyses of high-throughput mass spectrometry data. It provides implementations of frequently occurring processing operations on MS data through a clean application programming interface in C++ and Python. A collection of 185 tools and ready-made workflows for typical MS-based experiments enable convenient analyses for non-developers and facilitate reproducible research without losing flexibility. OpenMS will continue to increase its ease of use for developers as well as users with improved continuous integration/deployment strategies, regular trainings with updated training materials and multiple sources of support. The active developer community ensures the incorporation of new features to support state of the art research. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

Observations and Thermochemical Calculations for Hot-Jupiter Atmospheres

NASA Astrophysics Data System (ADS)

Blecic, Jasmina; Harrington, Joseph; Bowman, M. Oliver; Cubillos, Patricio; Stemm, Madison

2015-01-01

I present Spitzer eclipse observations for WASP-14b and WASP-43b, an open source tool for thermochemical equilibrium calculations, and components of an open source tool for atmospheric parameter retrieval from spectroscopic data. WASP-14b is a planet that receives high irradiation from its host star, yet, although theory does not predict it, the planet hosts a thermal inversion. The WASP-43b eclipses have signal-to-noise ratios of ~25, one of the largest among exoplanets. To assess these planets' atmospheric composition and thermal structure, we developed an open-source Bayesian Atmospheric Radiative Transfer (BART) code. My dissertation tasks included developing a Thermochemical Equilibrium Abundances (TEA) code, implementing the eclipse geometry calculation in BART's radiative transfer module, and generating parameterized pressure and temperature profiles so the radiative-transfer module can be driven by the statistical module.To initialize the radiative-transfer calculation in BART, TEA calculates the equilibrium abundances of gaseous molecular species at a given temperature and pressure. It uses the Gibbs-free-energy minimization method with an iterative Lagrangian optimization scheme. Given elemental abundances, TEA calculates molecular abundances for a particular temperature and pressure or a list of temperature-pressure pairs. The code is tested against the original method developed by White at al. (1958), the analytic method developed by Burrows and Sharp (1999), and the Newton-Raphson method implemented in the open-source Chemical Equilibrium with Applications (CEA) code. TEA, written in Python, is modular, documented, and available to the community via the open-source development site GitHub.com.Support for this work was provided by NASA Headquarters under the NASA Earth and Space Science Fellowship Program, grant NNX12AL83H, by NASA through an award issued by JPL/Caltech, and through the Science Mission Directorate's Planetary Atmospheres Program, grant NNX12AI69G.

DasPy – Open Source Multivariate Land Data Assimilation Framework with High Performance Computing

NASA Astrophysics Data System (ADS)

Han, Xujun; Li, Xin; Montzka, Carsten; Kollet, Stefan; Vereecken, Harry; Hendricks Franssen, Harrie-Jan

2015-04-01

Data assimilation has become a popular method to integrate observations from multiple sources with land surface models to improve predictions of the water and energy cycles of the soil-vegetation-atmosphere continuum. In recent years, several land data assimilation systems have been developed in different research agencies. Because of the software availability or adaptability, these systems are not easy to apply for the purpose of multivariate land data assimilation research. Multivariate data assimilation refers to the simultaneous assimilation of observation data for multiple model state variables into a simulation model. Our main motivation was to develop an open source multivariate land data assimilation framework (DasPy) which is implemented using the Python script language mixed with C++ and Fortran language. This system has been evaluated in several soil moisture, L-band brightness temperature and land surface temperature assimilation studies. The implementation allows also parameter estimation (soil properties and/or leaf area index) on the basis of the joint state and parameter estimation approach. LETKF (Local Ensemble Transform Kalman Filter) is implemented as the main data assimilation algorithm, and uncertainties in the data assimilation can be represented by perturbed atmospheric forcings, perturbed soil and vegetation properties and model initial conditions. The CLM4.5 (Community Land Model) was integrated as the model operator. The CMEM (Community Microwave Emission Modelling Platform), COSMIC (COsmic-ray Soil Moisture Interaction Code) and the two source formulation were integrated as observation operators for assimilation of L-band passive microwave, cosmic-ray soil moisture probe and land surface temperature measurements, respectively. DasPy is parallelized using the hybrid MPI (Message Passing Interface) and OpenMP (Open Multi-Processing) techniques. All the input and output data flow is organized efficiently using the commonly used NetCDF file format. Online 1D and 2D visualization of data assimilation results is also implemented to facilitate the post simulation analysis. In summary, DasPy is a ready to use open source parallel multivariate land data assimilation framework.

An Open-Source Approach for Catchment's Physiographic Characterization

NASA Astrophysics Data System (ADS)

Di Leo, M.; Di Stefano, M.

2013-12-01

A water catchment's hydrologic response is intimately linked to its morphological shape, which is a signature on the landscape of the particular climate conditions that generated the hydrographic basin over time. Furthermore, geomorphologic structures influence hydrologic regimes and land cover (vegetation). For these reasons, a basin's characterization is a fundamental element in hydrological studies. Physiographic descriptors have been extracted manually for long time, but currently Geographic Information System (GIS) tools ease such task by offering a powerful instrument for hydrologists to save time and improve accuracy of result. Here we present a program combining the flexibility of the Python programming language with the reliability of GRASS GIS, which automatically performing the catchment's physiographic characterization. GRASS (Geographic Resource Analysis Support System) is a Free and Open Source GIS, that today can look back on 30 years of successful development in geospatial data management and analysis, image processing, graphics and maps production, spatial modeling and visualization. The recent development of new hydrologic tools, coupled with the tremendous boost in the existing flow routing algorithms, reduced the computational time and made GRASS a complete toolset for hydrological analysis even for large datasets. The tool presented here is a module called r.basin, based on GRASS' traditional nomenclature, where the "r" stands for "raster", and it is available for GRASS version 6.x and more recently for GRASS 7. As input it uses a Digital Elevation Model and the coordinates of the outlet, and, powered by the recently developed r.stream.* hydrological tools, it performs the flow calculation, delimits the basin's boundaries and extracts the drainage network, returning the flow direction and accumulation, the distance to outlet and the hill slopes length maps. Based on those maps, it calculates hydrologically meaningful shape factors and morphological parameters such as topological diameter, drainage density, Horton's ratios, concentration time, and many more, beside producing statistics on main channel and elevation and geometric features such as centroid's coordinates, rectangle containing the basin, etc. Exploiting Python libraries, such as Numpy and Matplotlib, it produces graphics like the hypsographic and hypsometric curve and the Width Function. The results are exported as a spreadsheet in CSV format and graphics as pngs. The advantages offered by the implementation in Python and GRASS are manifold. Python is a powerful scripting language with huge potential for researchers due to its relative simplicity, high flexibility and thanks to a broad availability of scientific libraries. GRASS, and as a consequence, r.basin, is platform independent, so that it is available for GNU/Linux, MS Windows, Mac, etc. Furthermore, the module is constantly maintained and improved according to users' feedback with the precious help of expert developers. The code is available for review under the official GRASS add-ons repository, allowing hydrologists and researchers to knowingly use, inspect, modify, reuse, and even incorporate it in other projects, such as web services.

gemcWeb: A Cloud Based Nuclear Physics Simulation Software

NASA Astrophysics Data System (ADS)

Markelon, Sam

2017-09-01

gemcWeb allows users to run nuclear physics simulations from the web. Being completely device agnostic, scientists can run simulations from anywhere with an Internet connection. Having a full user system, gemcWeb allows users to revisit and revise their projects, and share configurations and results with collaborators. gemcWeb is based on simulation software gemc, which is based on standard GEant4. gemcWeb requires no C++, gemc, or GEant4 knowledge. Using a simple but powerful GUI allows users to configure their project from geometries and configurations stored on the deployment server. Simulations are then run on the server, with results being posted to the user, and then securely stored. Python based and open-source, the main version of gemcWeb is hosted internally at Jefferson National Labratory and used by the CLAS12 and Electron-Ion Collider Project groups. However, as the software is open-source, and hosted as a GitHub repository, an instance can be deployed on the open web, or any institution's intra-net. An instance can be configured to host experiments specific to an institution, and the code base can be modified by any individual or group. Special thanks to: Maurizio Ungaro, PhD., creator of gemc; Markus Diefenthaler, PhD., advisor; and Kyungseon Joo, PhD., advisor.

Visualization and Quality Control Web Tools for CERES Products

NASA Astrophysics Data System (ADS)

Mitrescu, C.; Doelling, D. R.

2017-12-01

The NASA CERES project continues to provide the scientific communities a wide variety of satellite-derived data products such as observed TOA broadband shortwave and longwave observed fluxes, computed TOA and Surface fluxes, as well as cloud, aerosol, and other atmospheric parameters. They encompass a wide range of temporal and spatial resolutions, suited to specific applications. CERES data is used mostly by climate modeling communities but also by a wide variety of educational institutions. To better serve our users, a web-based Ordering and Visualization Tool (OVT) was developed by using Opens Source Software such as Eclipse, java, javascript, OpenLayer, Flot, Google Maps, python, and others. Due to increased demand by our own scientists, we also implemented a series of specialized functions to be used in the process of CERES Data Quality Control (QC) such as 1- and 2-D histograms, anomalies and differences, temporal and spatial averaging, side-by-side parameter comparison, and others that made the process of QC far easier and faster, but more importantly far more portable. With the integration of ground site observed surface fluxes we further facilitate the CERES project to QC the CERES computed surface fluxes. An overview of the CERES OVT basic functions using Open Source Software, as well as future steps in expanding its capabilities will be presented at the meeting.

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.

Modeling the frequency response of microwave radiometers with QUCS

NASA Astrophysics Data System (ADS)

Zonca, A.; Roucaries, B.; Williams, B.; Rubin, I.; D'Arcangelo, O.; Meinhold, P.; Lubin, P.; Franceschet, C.; Jahn, S.; Mennella, A.; Bersanelli, M.

2010-12-01

Characterization of the frequency response of coherent radiometric receivers is a key element in estimating the flux of astrophysical emissions, since the measured signal depends on the convolution of the source spectral emission with the instrument band shape. Laboratory Radio Frequency (RF) measurements of the instrument bandpass often require complex test setups and are subject to a number of systematic effects driven by thermal issues and impedance matching, particularly if cryogenic operation is involved. In this paper we present an approach to modeling radiometers bandpasses by integrating simulations and RF measurements of individual components. This method is based on QUCS (Quasi Universal Circuit Simulator), an open-source circuit simulator, which gives the flexibility of choosing among the available devices, implementing new analytical software models or using measured S-parameters. Therefore an independent estimate of the instrument bandpass is achieved using standard individual component measurements and validated analytical simulations. In order to automate the process of preparing input data, running simulations and exporting results we developed the Python package python-qucs and released it under GNU Public License. We discuss, as working cases, bandpass response modeling of the COFE and Planck Low Frequency Instrument (LFI) radiometers and compare results obtained with QUCS and with a commercial circuit simulator software. The main purpose of bandpass modeling in COFE is to optimize component matching, while in LFI they represent the best estimation of frequency response, since end-to-end measurements were strongly affected by systematic effects.

Forward Modeling of Large-scale Structure: An Open-source Approach with Halotools

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

Hearin, Andrew P.; Campbell, Duncan; Tollerud, Erik

We present the first stable release of Halotools (v0.2), a community-driven Python package designed to build and test models of the galaxy-halo connection. Halotools provides a modular platform for creating mock universes of galaxies starting from a catalog of dark matter halos obtained from a cosmological simulation. The package supports many of the common forms used to describe galaxy-halo models: the halo occupation distribution, the conditional luminosity function, abundance matching, and alternatives to these models that include effects such as environmental quenching or variable galaxy assembly bias. Satellite galaxies can be modeled to live in subhalos or to follow custommore » number density profiles within their halos, including spatial and/or velocity bias with respect to the dark matter profile. The package has an optimized toolkit to make mock observations on a synthetic galaxy population—including galaxy clustering, galaxy–galaxy lensing, galaxy group identification, RSD multipoles, void statistics, pairwise velocities and others—allowing direct comparison to observations. Halotools is object-oriented, enabling complex models to be built from a set of simple, interchangeable components, including those of your own creation. Halotools has an automated testing suite and is exhaustively documented on http://halotools.readthedocs.io, which includes quickstart guides, source code notes and a large collection of tutorials. The documentation is effectively an online textbook on how to build and study empirical models of galaxy formation with Python.« less

p3d--Python module for structural bioinformatics.

PubMed

Fufezan, Christian; Specht, Michael

2009-08-21

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

interPopula: a Python API to access the HapMap Project dataset

PubMed Central

2010-01-01

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

DendroPy: a Python library for phylogenetic computing.

PubMed

Sukumaran, Jeet; Holder, Mark T

2010-06-15

DendroPy is a cross-platform library for the Python programming language that provides for object-oriented reading, writing, simulation and manipulation of phylogenetic data, with an emphasis on phylogenetic tree operations. DendroPy uses a splits-hash mapping to perform rapid calculations of tree distances, similarities and shape under various metrics. It contains rich simulation routines to generate trees under a number of different phylogenetic and coalescent models. DendroPy's data simulation and manipulation facilities, in conjunction with its support of a broad range of phylogenetic data formats (NEXUS, Newick, PHYLIP, FASTA, NeXML, etc.), allow it to serve a useful role in various phyloinformatics and phylogeographic pipelines. The stable release of the library is available for download and automated installation through the Python Package Index site (http://pypi.python.org/pypi/DendroPy), while the active development source code repository is available to the public from GitHub (http://github.com/jeetsukumaran/DendroPy).

eTOXlab, an open source modeling framework for implementing predictive models in production environments.

PubMed

Carrió, Pau; López, Oriol; Sanz, Ferran; Pastor, Manuel

2015-01-01

Computational models based in Quantitative-Structure Activity Relationship (QSAR) methodologies are widely used tools for predicting the biological properties of new compounds. In many instances, such models are used as a routine in the industry (e.g. food, cosmetic or pharmaceutical industry) for the early assessment of the biological properties of new compounds. However, most of the tools currently available for developing QSAR models are not well suited for supporting the whole QSAR model life cycle in production environments. We have developed eTOXlab; an open source modeling framework designed to be used at the core of a self-contained virtual machine that can be easily deployed in production environments, providing predictions as web services. eTOXlab consists on a collection of object-oriented Python modules with methods mapping common tasks of standard modeling workflows. This framework allows building and validating QSAR models as well as predicting the properties of new compounds using either a command line interface or a graphic user interface (GUI). Simple models can be easily generated by setting a few parameters, while more complex models can be implemented by overriding pieces of the original source code. eTOXlab benefits from the object-oriented capabilities of Python for providing high flexibility: any model implemented using eTOXlab inherits the features implemented in the parent model, like common tools and services or the automatic exposure of the models as prediction web services. The particular eTOXlab architecture as a self-contained, portable prediction engine allows building models with confidential information within corporate facilities, which can be safely exported and used for prediction without disclosing the structures of the training series. The software presented here provides full support to the specific needs of users that want to develop, use and maintain predictive models in corporate environments. The technologies used by eTOXlab (web services, VM, object-oriented programming) provide an elegant solution to common practical issues; the system can be installed easily in heterogeneous environments and integrates well with other software. Moreover, the system provides a simple and safe solution for building models with confidential structures that can be shared without disclosing sensitive information.

NeoAnalysis: a Python-based toolbox for quick electrophysiological data processing and analysis.

PubMed

Zhang, Bo; Dai, Ji; Zhang, Tao

2017-11-13

In a typical electrophysiological experiment, especially one that includes studying animal behavior, the data collected normally contain spikes, local field potentials, behavioral responses and other associated data. In order to obtain informative results, the data must be analyzed simultaneously with the experimental settings. However, most open-source toolboxes currently available for data analysis were developed to handle only a portion of the data and did not take into account the sorting of experimental conditions. Additionally, these toolboxes require that the input data be in a specific format, which can be inconvenient to users. Therefore, the development of a highly integrated toolbox that can process multiple types of data regardless of input data format and perform basic analysis for general electrophysiological experiments is incredibly useful. Here, we report the development of a Python based open-source toolbox, referred to as NeoAnalysis, to be used for quick electrophysiological data processing and analysis. The toolbox can import data from different data acquisition systems regardless of their formats and automatically combine different types of data into a single file with a standardized format. In cases where additional spike sorting is needed, NeoAnalysis provides a module to perform efficient offline sorting with a user-friendly interface. Then, NeoAnalysis can perform regular analog signal processing, spike train, and local field potentials analysis, behavioral response (e.g. saccade) detection and extraction, with several options available for data plotting and statistics. Particularly, it can automatically generate sorted results without requiring users to manually sort data beforehand. In addition, NeoAnalysis can organize all of the relevant data into an informative table on a trial-by-trial basis for data visualization. Finally, NeoAnalysis supports analysis at the population level. With the multitude of general-purpose functions provided by NeoAnalysis, users can easily obtain publication-quality figures without writing complex codes. NeoAnalysis is a powerful and valuable toolbox for users doing electrophysiological experiments.

The JBEI quantitative metabolic modeling library (jQMM): a python library for modeling microbial metabolism.

PubMed

Birkel, Garrett W; Ghosh, Amit; Kumar, Vinay S; Weaver, Daniel; Ando, David; Backman, Tyler W H; Arkin, Adam P; Keasling, Jay D; Martín, Héctor García

2017-04-05

Modeling of microbial metabolism is a topic of growing importance in biotechnology. Mathematical modeling helps provide a mechanistic understanding for the studied process, separating the main drivers from the circumstantial ones, bounding the outcomes of experiments and guiding engineering approaches. Among different modeling schemes, the quantification of intracellular metabolic fluxes (i.e. the rate of each reaction in cellular metabolism) is of particular interest for metabolic engineering because it describes how carbon and energy flow throughout the cell. In addition to flux analysis, new methods for the effective use of the ever more readily available and abundant -omics data (i.e. transcriptomics, proteomics and metabolomics) are urgently needed. The jQMM library presented here provides an open-source, Python-based framework for modeling internal metabolic fluxes and leveraging other -omics data for the scientific study of cellular metabolism and bioengineering purposes. Firstly, it presents a complete toolbox for simultaneously performing two different types of flux analysis that are typically disjoint: Flux Balance Analysis and 13 C Metabolic Flux Analysis. Moreover, it introduces the capability to use 13 C labeling experimental data to constrain comprehensive genome-scale models through a technique called two-scale 13 C Metabolic Flux Analysis (2S- 13 C MFA). In addition, the library includes a demonstration of a method that uses proteomics data to produce actionable insights to increase biofuel production. Finally, the use of the jQMM library is illustrated through the addition of several Jupyter notebook demonstration files that enhance reproducibility and provide the capability to be adapted to the user's specific needs. jQMM will facilitate the design and metabolic engineering of organisms for biofuels and other chemicals, as well as investigations of cellular metabolism and leveraging -omics data. As an open source software project, we hope it will attract additions from the community and grow with the rapidly changing field of metabolic engineering.

The JBEI quantitative metabolic modeling library (jQMM): a python library for modeling microbial metabolism

DOE PAGES

Birkel, Garrett W.; Ghosh, Amit; Kumar, Vinay S.; ...

2017-04-05

Modeling of microbial metabolism is a topic of growing importance in biotechnology. Mathematical modeling helps provide a mechanistic understanding for the studied process, separating the main drivers from the circumstantial ones, bounding the outcomes of experiments and guiding engineering approaches. Among different modeling schemes, the quantification of intracellular metabolic fluxes (i.e. the rate of each reaction in cellular metabolism) is of particular interest for metabolic engineering because it describes how carbon and energy flow throughout the cell. In addition to flux analysis, new methods for the effective use of the ever more readily available and abundant -omics data (i.e. transcriptomics,more » proteomics and metabolomics) are urgently needed. The jQMM library presented here provides an open-source, Python-based framework for modeling internal metabolic fluxes and leveraging other -omics data for the scientific study of cellular metabolism and bioengineering purposes. Firstly, it presents a complete toolbox for simultaneously performing two different types of flux analysis that are typically disjoint: Flux Balance Analysis and 13C Metabolic Flux Analysis. Moreover, it introduces the capability to use 13C labeling experimental data to constrain comprehensive genome-scale models through a technique called two-scale 13C Metabolic Flux Analysis (2S- 13C MFA). In addition, the library includes a demonstration of a method that uses proteomics data to produce actionable insights to increase biofuel production. Finally, the use of the jQMM library is illustrated through the addition of several Jupyter notebook demonstration files that enhance reproducibility and provide the capability to be adapted to the user's specific needs. jQMM will facilitate the design and metabolic engineering of organisms for biofuels and other chemicals, as well as investigations of cellular metabolism and leveraging -omics data. As an open source software project, we hope it will attract additions from the community and grow with the rapidly changing field of metabolic engineering.« less

The JBEI quantitative metabolic modeling library (jQMM): a python library for modeling microbial metabolism

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

Birkel, Garrett W.; Ghosh, Amit; Kumar, Vinay S.

Modeling of microbial metabolism is a topic of growing importance in biotechnology. Mathematical modeling helps provide a mechanistic understanding for the studied process, separating the main drivers from the circumstantial ones, bounding the outcomes of experiments and guiding engineering approaches. Among different modeling schemes, the quantification of intracellular metabolic fluxes (i.e. the rate of each reaction in cellular metabolism) is of particular interest for metabolic engineering because it describes how carbon and energy flow throughout the cell. In addition to flux analysis, new methods for the effective use of the ever more readily available and abundant -omics data (i.e. transcriptomics,more » proteomics and metabolomics) are urgently needed. The jQMM library presented here provides an open-source, Python-based framework for modeling internal metabolic fluxes and leveraging other -omics data for the scientific study of cellular metabolism and bioengineering purposes. Firstly, it presents a complete toolbox for simultaneously performing two different types of flux analysis that are typically disjoint: Flux Balance Analysis and 13C Metabolic Flux Analysis. Moreover, it introduces the capability to use 13C labeling experimental data to constrain comprehensive genome-scale models through a technique called two-scale 13C Metabolic Flux Analysis (2S- 13C MFA). In addition, the library includes a demonstration of a method that uses proteomics data to produce actionable insights to increase biofuel production. Finally, the use of the jQMM library is illustrated through the addition of several Jupyter notebook demonstration files that enhance reproducibility and provide the capability to be adapted to the user's specific needs. jQMM will facilitate the design and metabolic engineering of organisms for biofuels and other chemicals, as well as investigations of cellular metabolism and leveraging -omics data. As an open source software project, we hope it will attract additions from the community and grow with the rapidly changing field of metabolic engineering.« less

IgSimulator: a versatile immunosequencing simulator.

PubMed

Safonova, Yana; Lapidus, Alla; Lill, Jennie

2015-10-01

The recent introduction of next-generation sequencing technologies to antibody studies have resulted in a growing number of immunoinformatics tools for antibody repertoire analysis. However, benchmarking these newly emerging tools remains problematic since the gold standard datasets that are needed to validate these tools are typically not available. Since simulating antibody repertoires is often the only feasible way to benchmark new immunoinformatics tools, we developed the IgSimulator tool that addresses various complications in generating realistic antibody repertoires. IgSimulator's code has modular structure and can be easily adapted to new requirements to simulation. IgSimulator is open source and freely available as a C++ and Python program running on all Unix-compatible platforms. The source code is available from yana-safonova.github.io/ig_simulator. safonova.yana@gmail.com Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

HitWalker2: visual analytics for precision medicine and beyond.

PubMed

Bottomly, Daniel; McWeeney, Shannon K; Wilmot, Beth

2016-04-15

The lack of visualization frameworks to guide interpretation and facilitate discovery is a potential bottleneck for precision medicine, systems genetics and other studies. To address this we have developed an interactive, reproducible, web-based prioritization approach that builds on our earlier work. HitWalker2 is highly flexible and can utilize many data types and prioritization methods based upon available data and desired questions, allowing it to be utilized in a diverse range of studies such as cancer, infectious disease and psychiatric disorders. Source code is freely available at https://github.com/biodev/HitWalker2 and implemented using Python/Django, Neo4j and Javascript (D3.js and jQuery). We support major open source browsers (e.g. Firefox and Chromium/Chrome). wilmotb@ohsu.edu Supplementary data are available at Bioinformatics online. Additional information/instructions are available at https://github.com/biodev/HitWalker2/wiki. © The Author 2015. Published by Oxford University Press.

13Check_RNA: A tool to evaluate 13C chemical shifts assignments of RNA.

PubMed

Icazatti, A A; Martin, O A; Villegas, M; Szleifer, I; Vila, J A

2018-06-19

Chemical shifts (CS) are an important source of structural information of macromolecules such as RNA. In addition to the scarce availability of CS for RNA, the observed values are prone to errors due to a wrong re-calibration or miss assignments. Different groups have dedicated their efforts to correct CS systematic errors on RNA. Despite this, there are not automated and freely available algorithms for correct assignments of RNA 13C CS before their deposition to the BMRB or re-reference already deposited CS with systematic errors. Based on an existent method we have implemented an open source python module to correct 13C CS (from here on 13Cexp) systematic errors of RNAs and then return the results in 3 formats including the nmrstar one. This software is available on GitHub at https://github.com/BIOS-IMASL/13Check_RNA under a MIT license. Supplementary data are available at Bioinformatics online.

ePMV embeds molecular modeling into professional animation software environments.

PubMed

Johnson, Graham T; Autin, Ludovic; Goodsell, David S; Sanner, Michel F; Olson, Arthur J

2011-03-09

Increasingly complex research has made it more difficult to prepare data for publication, education, and outreach. Many scientists must also wade through black-box code to interface computational algorithms from diverse sources to supplement their bench work. To reduce these barriers we have developed an open-source plug-in, embedded Python Molecular Viewer (ePMV), that runs molecular modeling software directly inside of professional 3D animation applications (hosts) to provide simultaneous access to the capabilities of these newly connected systems. Uniting host and scientific algorithms into a single interface allows users from varied backgrounds to assemble professional quality visuals and to perform computational experiments with relative ease. By enabling easy exchange of algorithms, ePMV can facilitate interdisciplinary research, smooth communication between broadly diverse specialties, and provide a common platform to frame and visualize the increasingly detailed intersection(s) of cellular and molecular biology. Copyright © 2011 Elsevier Ltd. All rights reserved.

Standardizing Exoplanet Analysis with the Exoplanet Characterization Tool Kit (ExoCTK)

NASA Astrophysics Data System (ADS)

Fowler, Julia; Stevenson, Kevin B.; Lewis, Nikole K.; Fraine, Jonathan D.; Pueyo, Laurent; Bruno, Giovanni; Filippazzo, Joe; Hill, Matthew; Batalha, Natasha; Wakeford, Hannah; Bushra, Rafia

2018-06-01

Exoplanet characterization depends critically on analysis tools, models, and spectral libraries that are constantly under development and have no single source nor sense of unified style or methods. The complexity of spectroscopic analysis and initial time commitment required to become competitive is prohibitive to new researchers entering the field, as well as a remaining obstacle for established groups hoping to contribute in a comparable manner to their peers. As a solution, we are developing an open-source, modular data analysis package in Python and a publicly facing web interface including tools that address atmospheric characterization, transit observation planning with JWST, JWST corongraphy simulations, limb darkening, forward modeling, and data reduction, as well as libraries of stellar, planet, and opacity models. The foundation of these software tools and libraries exist within pockets of the exoplanet community, but our project will gather these seedling tools and grow a robust, uniform, and well-maintained exoplanet characterization toolkit.

ePMV Embeds Molecular Modeling into Professional Animation Software Environments

PubMed Central

Johnson, Graham T.; Autin, Ludovic; Goodsell, David S.; Sanner, Michel F.; Olson, Arthur J.

2011-01-01

SUMMARY Increasingly complex research has made it more difficult to prepare data for publication, education, and outreach. Many scientists must also wade through black-box code to interface computational algorithms from diverse sources to supplement their bench work. To reduce these barriers, we have developed an open-source plug-in, embedded Python Molecular Viewer (ePMV), that runs molecular modeling software directly inside of professional 3D animation applications (hosts) to provide simultaneous access to the capabilities of these newly connected systems. Uniting host and scientific algorithms into a single interface allows users from varied backgrounds to assemble professional quality visuals and to perform computational experiments with relative ease. By enabling easy exchange of algorithms, ePMV can facilitate interdisciplinary research, smooth communication between broadly diverse specialties and provide a common platform to frame and visualize the increasingly detailed intersection(s) of cellular and molecular biology. PMID:21397181

Helioviewer.org: Enhanced Solar & Heliospheric Data Visualization

NASA Astrophysics Data System (ADS)

Stys, J. E.; Ireland, J.; Hughitt, V. K.; Mueller, D.

2013-12-01

Helioviewer.org enables the simultaneous exploration of multiple heterogeneous solar data sets. In the latest iteration of this open-source web application, Hinode XRT and Yohkoh SXT join SDO, SOHO, STEREO, and PROBA2 as supported data sources. A newly enhanced user-interface expands the utility of Helioviewer.org by adding annotations backed by data from the Heliospheric Events Knowledgebase (HEK). Helioviewer.org can now overlay solar feature and event data via interactive marker pins, extended regions, data labels, and information panels. An interactive time-line provides enhanced browsing and visualization to image data set coverage and solar events. The addition of a size-of-the-Earth indicator provides a sense of the scale to solar and heliospheric features for education and public outreach purposes. Tight integration with the Virtual Solar Observatory and SDO AIA cutout service enable solar physicists to seamlessly import science data into their SSW/IDL or SunPy/Python data analysis environments.

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).

STSE: Spatio-Temporal Simulation Environment Dedicated to Biology.

PubMed

Stoma, Szymon; Fröhlich, Martina; Gerber, Susanne; Klipp, Edda

2011-04-28

Recently, the availability of high-resolution microscopy together with the advancements in the development of biomarkers as reporters of biomolecular interactions increased the importance of imaging methods in molecular cell biology. These techniques enable the investigation of cellular characteristics like volume, size and geometry as well as volume and geometry of intracellular compartments, and the amount of existing proteins in a spatially resolved manner. Such detailed investigations opened up many new areas of research in the study of spatial, complex and dynamic cellular systems. One of the crucial challenges for the study of such systems is the design of a well stuctured and optimized workflow to provide a systematic and efficient hypothesis verification. Computer Science can efficiently address this task by providing software that facilitates handling, analysis, and evaluation of biological data to the benefit of experimenters and modelers. The Spatio-Temporal Simulation Environment (STSE) is a set of open-source tools provided to conduct spatio-temporal simulations in discrete structures based on microscopy images. The framework contains modules to digitize, represent, analyze, and mathematically model spatial distributions of biochemical species. Graphical user interface (GUI) tools provided with the software enable meshing of the simulation space based on the Voronoi concept. In addition, it supports to automatically acquire spatial information to the mesh from the images based on pixel luminosity (e.g. corresponding to molecular levels from microscopy images). STSE is freely available either as a stand-alone version or included in the linux live distribution Systems Biology Operational Software (SB.OS) and can be downloaded from http://www.stse-software.org/. The Python source code as well as a comprehensive user manual and video tutorials are also offered to the research community. We discuss main concepts of the STSE design and workflow. We demonstrate it's usefulness using the example of a signaling cascade leading to formation of a morphological gradient of Fus3 within the cytoplasm of the mating yeast cell Saccharomyces cerevisiae. STSE is an efficient and powerful novel platform, designed for computational handling and evaluation of microscopic images. It allows for an uninterrupted workflow including digitization, representation, analysis, and mathematical modeling. By providing the means to relate the simulation to the image data it allows for systematic, image driven model validation or rejection. STSE can be scripted and extended using the Python language. STSE should be considered rather as an API together with workflow guidelines and a collection of GUI tools than a stand alone application. The priority of the project is to provide an easy and intuitive way of extending and customizing software using the Python language.

VideoHacking: Automated Tracking and Quantification of Locomotor Behavior with Open Source Software and Off-the-Shelf Video Equipment.

PubMed

Conklin, Emily E; Lee, Kathyann L; Schlabach, Sadie A; Woods, Ian G

2015-01-01

Differences in nervous system function can result in differences in behavioral output. Measurements of animal locomotion enable the quantification of these differences. Automated tracking of animal movement is less labor-intensive and bias-prone than direct observation, and allows for simultaneous analysis of multiple animals, high spatial and temporal resolution, and data collection over extended periods of time. Here, we present a new video-tracking system built on Python-based software that is free, open source, and cross-platform, and that can analyze video input from widely available video capture devices such as smartphone cameras and webcams. We validated this software through four tests on a variety of animal species, including larval and adult zebrafish (Danio rerio), Siberian dwarf hamsters (Phodopus sungorus), and wild birds. These tests highlight the capacity of our software for long-term data acquisition, parallel analysis of multiple animals, and application to animal species of different sizes and movement patterns. We applied the software to an analysis of the effects of ethanol on thigmotaxis (wall-hugging) behavior on adult zebrafish, and found that acute ethanol treatment decreased thigmotaxis behaviors without affecting overall amounts of motion. The open source nature of our software enables flexibility, customization, and scalability in behavioral analyses. Moreover, our system presents a free alternative to commercial video-tracking systems and is thus broadly applicable to a wide variety of educational settings and research programs.

SU-G-BRB-02: An Open-Source Software Analysis Library for Linear Accelerator Quality Assurance

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

Kerns, J; Yaldo, D

Purpose: Routine linac quality assurance (QA) tests have become complex enough to require automation of most test analyses. A new data analysis software library was built that allows physicists to automate routine linear accelerator quality assurance tests. The package is open source, code tested, and benchmarked. Methods: Images and data were generated on a TrueBeam linac for the following routine QA tests: VMAT, starshot, CBCT, machine logs, Winston Lutz, and picket fence. The analysis library was built using the general programming language Python. Each test was analyzed with the library algorithms and compared to manual measurements taken at the timemore » of acquisition. Results: VMAT QA results agreed within 0.1% between the library and manual measurements. Machine logs (dynalogs & trajectory logs) were successfully parsed; mechanical axis positions were verified for accuracy and MLC fluence agreed well with EPID measurements. CBCT QA measurements were within 10 HU and 0.2mm where applicable. Winston Lutz isocenter size measurements were within 0.2mm of TrueBeam’s Machine Performance Check. Starshot analysis was within 0.2mm of the Winston Lutz results for the same conditions. Picket fence images with and without a known error showed that the library was capable of detecting MLC offsets within 0.02mm. Conclusion: A new routine QA software library has been benchmarked and is available for use by the community. The library is open-source and extensible for use in larger systems.« less

PRIMED: PRIMEr Database for Deleting and Tagging All Fission and Budding Yeast Genes Developed Using the Open-Source Genome Retrieval Script (GRS)

PubMed Central

Cummings, Michael T.; Joh, Richard I.; Motamedi, Mo

2015-01-01

The fission (Schizosaccharomyces pombe) and budding (Saccharomyces cerevisiae) yeasts have served as excellent models for many seminal discoveries in eukaryotic biology. In these organisms, genes are deleted or tagged easily by transforming cells with PCR-generated DNA inserts, flanked by short (50-100bp) regions of gene homology. These PCR reactions use especially designed long primers, which, in addition to the priming sites, carry homology for gene targeting. Primer design follows a fixed method but is tedious and time-consuming especially when done for a large number of genes. To automate this process, we developed the Python-based Genome Retrieval Script (GRS), an easily customizable open-source script for genome analysis. Using GRS, we created PRIMED, the complete PRIMEr D atabase for deleting and C-terminal tagging genes in the main S. pombe and five of the most commonly used S. cerevisiae strains. Because of the importance of noncoding RNAs (ncRNAs) in many biological processes, we also included the deletion primer set for these features in each genome. PRIMED are accurate and comprehensive and are provided as downloadable Excel files, removing the need for future primer design, especially for large-scale functional analyses. Furthermore, the open-source GRS can be used broadly to retrieve genome information from custom or other annotated genomes, thus providing a suitable platform for building other genomic tools by the yeast or other research communities. PMID:25643023

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

Ingargiola, A.; Laurence, T. A.; Boutelle, R.

We introduce Photon-HDF5, an open and efficient file format to simplify exchange and long term accessibility of data from single-molecule fluorescence experiments based on photon-counting detectors such as single-photon avalanche diode (SPAD), photomultiplier tube (PMT) or arrays of such detectors. The format is based on HDF5, a widely used platform- and language-independent hierarchical file format for which user-friendly viewers are available. Photon-HDF5 can store raw photon data (timestamp, channel number, etc) from any acquisition hardware, but also setup and sample description, information on provenance, authorship and other metadata, and is flexible enough to include any kind of custom data. Themore » format specifications are hosted on a public website, which is open to contributions by the biophysics community. As an initial resource, the website provides code examples to read Photon-HDF5 files in several programming languages and a reference python library (phconvert), to create new Photon-HDF5 files and convert several existing file formats into Photon-HDF5. As a result, to encourage adoption by the academic and commercial communities, all software is released under the MIT open source license.« less

Photon-HDF5: An Open File Format for Timestamp-Based Single-Molecule Fluorescence Experiments.

PubMed

Ingargiola, Antonino; Laurence, Ted; Boutelle, Robert; Weiss, Shimon; Michalet, Xavier

2016-01-05

We introduce Photon-HDF5, an open and efficient file format to simplify exchange and long-term accessibility of data from single-molecule fluorescence experiments based on photon-counting detectors such as single-photon avalanche diode, photomultiplier tube, or arrays of such detectors. The format is based on HDF5, a widely used platform- and language-independent hierarchical file format for which user-friendly viewers are available. Photon-HDF5 can store raw photon data (timestamp, channel number, etc.) from any acquisition hardware, but also setup and sample description, information on provenance, authorship and other metadata, and is flexible enough to include any kind of custom data. The format specifications are hosted on a public website, which is open to contributions by the biophysics community. As an initial resource, the website provides code examples to read Photon-HDF5 files in several programming languages and a reference Python library (phconvert), to create new Photon-HDF5 files and convert several existing file formats into Photon-HDF5. To encourage adoption by the academic and commercial communities, all software is released under the MIT open source license. Copyright © 2016 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Photon-HDF5: An Open File Format for Timestamp-Based Single-Molecule Fluorescence Experiments

PubMed Central

Ingargiola, Antonino; Laurence, Ted; Boutelle, Robert; Weiss, Shimon; Michalet, Xavier

2016-01-01

We introduce Photon-HDF5, an open and efficient file format to simplify exchange and long-term accessibility of data from single-molecule fluorescence experiments based on photon-counting detectors such as single-photon avalanche diode, photomultiplier tube, or arrays of such detectors. The format is based on HDF5, a widely used platform- and language-independent hierarchical file format for which user-friendly viewers are available. Photon-HDF5 can store raw photon data (timestamp, channel number, etc.) from any acquisition hardware, but also setup and sample description, information on provenance, authorship and other metadata, and is flexible enough to include any kind of custom data. The format specifications are hosted on a public website, which is open to contributions by the biophysics community. As an initial resource, the website provides code examples to read Photon-HDF5 files in several programming languages and a reference Python library (phconvert), to create new Photon-HDF5 files and convert several existing file formats into Photon-HDF5. To encourage adoption by the academic and commercial communities, all software is released under the MIT open source license. PMID:26745406

Photon-HDF5: an open file format for single-molecule fluorescence experiments using photon-counting detectors

DOE PAGES

Ingargiola, A.; Laurence, T. A.; Boutelle, R.; ...

2015-12-23

We introduce Photon-HDF5, an open and efficient file format to simplify exchange and long term accessibility of data from single-molecule fluorescence experiments based on photon-counting detectors such as single-photon avalanche diode (SPAD), photomultiplier tube (PMT) or arrays of such detectors. The format is based on HDF5, a widely used platform- and language-independent hierarchical file format for which user-friendly viewers are available. Photon-HDF5 can store raw photon data (timestamp, channel number, etc) from any acquisition hardware, but also setup and sample description, information on provenance, authorship and other metadata, and is flexible enough to include any kind of custom data. Themore » format specifications are hosted on a public website, which is open to contributions by the biophysics community. As an initial resource, the website provides code examples to read Photon-HDF5 files in several programming languages and a reference python library (phconvert), to create new Photon-HDF5 files and convert several existing file formats into Photon-HDF5. As a result, to encourage adoption by the academic and commercial communities, all software is released under the MIT open source license.« less

Compare Gene Calls

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

Ecale Zhou, Carol L.

2016-07-05

Compare Gene Calls (CGC) is a Python code used for combining and comparing gene calls from any number of gene callers. A gene caller is a computer program that predicts the extends of open reading frames within genomes of biological organisms.

Linking descriptive geology and quantitative machine learning through an ontology of lithological concepts

NASA Astrophysics Data System (ADS)

Klump, J. F.; Huber, R.; Robertson, J.; Cox, S. J. D.; Woodcock, R.

2014-12-01

Despite the recent explosion of quantitative geological data, geology remains a fundamentally qualitative science. Numerical data only constitute a certain part of data collection in the geosciences. In many cases, geological observations are compiled as text into reports and annotations on drill cores, thin sections or drawings of outcrops. The observations are classified into concepts such as lithology, stratigraphy, geological structure, etc. These descriptions are semantically rich and are generally supported by more quantitative observations using geochemical analyses, XRD, hyperspectral scanning, etc, but the goal is geological semantics. In practice it has been difficult to bring the different observations together due to differing perception or granularity of classification in human observation, or the partial observation of only some characteristics using quantitative sensors. In the past years many geological classification schemas have been transferred into ontologies and vocabularies, formalized using RDF and OWL, and published through SPARQL endpoints. Several lithological ontologies were compiled by stratigraphy.net and published through a SPARQL endpoint. This work is complemented by the development of a Python API to integrate this vocabulary into Python-based text mining applications. The applications for the lithological vocabulary and Python API are automated semantic tagging of geochemical data and descriptions of drill cores, machine learning of geochemical compositions that are diagnostic for lithological classifications, and text mining for lithological concepts in reports and geological literature. This combination of applications can be used to identify anomalies in databases, where composition and lithological classification do not match. It can also be used to identify lithological concepts in the literature and infer quantitative values. The resulting semantic tagging opens new possibilities for linking these diverse sources of data.

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

NASA Astrophysics Data System (ADS)

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

2016-04-01

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

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

NASA Astrophysics Data System (ADS)

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

2015-12-01

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

An open source software tool to assign the material properties of bone for ABAQUS finite element simulations.

PubMed

Pegg, Elise C; Gill, Harinderjit S

2016-09-06

A new software tool to assign the material properties of bone to an ABAQUS finite element mesh was created and compared with Bonemat, a similar tool originally designed to work with Ansys finite element models. Our software tool (py_bonemat_abaqus) was written in Python, which is the chosen scripting language for ABAQUS. The purpose of this study was to compare the software packages in terms of the material assignment calculation and processing speed. Three element types were compared (linear hexahedral (C3D8), linear tetrahedral (C3D4) and quadratic tetrahedral elements (C3D10)), both individually and as part of a mesh. Comparisons were made using a CT scan of a hemi-pelvis as a test case. A small difference, of -0.05kPa on average, was found between Bonemat version 3.1 (the current version) and our Python package. Errors were found in the previous release of Bonemat (version 3.0 downloaded from www.biomedtown.org) during calculation of the quadratic tetrahedron Jacobian, and conversion of the apparent density to modulus when integrating over the Young׳s modulus field. These issues caused up to 2GPa error in the modulus assignment. For these reasons, we recommend users upgrade to the most recent release of Bonemat. Processing speeds were assessed for the three different element types. Our Python package took significantly longer (110s on average) to perform the calculations compared with the Bonemat software (10s). Nevertheless, the workflow advantages of the package and added functionality makes 'py_bonemat_abaqus' a useful tool for ABAQUS users. Copyright © 2016 Elsevier Ltd. All rights reserved.

NPTFit: A Code Package for Non-Poissonian Template Fitting

NASA Astrophysics Data System (ADS)

Mishra-Sharma, Siddharth; Rodd, Nicholas L.; Safdi, Benjamin R.

2017-06-01

We present NPTFit, an open-source code package, written in Python and Cython, for performing non-Poissonian template fits (NPTFs). The NPTF is a recently developed statistical procedure for characterizing the contribution of unresolved point sources (PSs) to astrophysical data sets. The NPTF was first applied to Fermi gamma-ray data to provide evidence that the excess of ˜GeV gamma-rays observed in the inner regions of the Milky Way likely arises from a population of sub-threshold point sources, and the NPTF has since found additional applications studying sub-threshold extragalactic sources at high Galactic latitudes. The NPTF generalizes traditional astrophysical template fits to allow for the ability to search for populations of unresolved PSs that may follow a given spatial distribution. NPTFit builds upon the framework of the fluctuation analyses developed in X-ray astronomy, thus it likely has applications beyond those demonstrated with gamma-ray data. The NPTFit package utilizes novel computational methods to perform the NPTF efficiently. The code is available at http://github.com/bsafdi/NPTFit and up-to-date and extensive documentation may be found at http://nptfit.readthedocs.io.

ImagingReso: A Tool for Neutron Resonance Imaging

DOE PAGES

Zhang, Yuxuan; Bilheux, Jean -Christophe

2017-11-01

ImagingReso is an open-source Python library that simulates the neutron resonance signal for neutron imaging measurements. By defining the sample information such as density, thickness in the neutron path, and isotopic ratios of the elemental composition of the material, this package plots the expected resonance peaks for a selected neutron energy range. Various sample types such as layers of single elements (Ag, Co, etc. in solid form), chemical compounds (UO 3, Gd 2O 3, etc.), or even multiple layers of both types can be plotted with this package. As a result, major plotting features include display of the transmission/attenuation inmore » wavelength, energy, and time scale, and show/hide elemental and isotopic contributions in the total resonance signal.« less

World of intelligence defense object detection-machine learning (artificial intelligence)

NASA Astrophysics Data System (ADS)

Gupta, Anitya; Kumar, Akhilesh; Bhushan, Vinayak

2018-04-01

This paper proposes a Quick Locale based Convolutional System strategy (Quick R-CNN) for question recognition. Quick R-CNN expands on past work to effectively characterize ob-ject recommendations utilizing profound convolutional systems. Com-pared to past work, Quick R-CNN utilizes a few in-novations to enhance preparing and testing speed while likewise expanding identification precision. Quick R-CNN trains the profound VGG16 arrange 9 quicker than R-CNN, is 213 speedier at test-time, and accomplishes a higher Guide on PASCAL VOC 2012. Contrasted with SPPnet, Quick R-CNN trains VGG16 3 quicker, tests 10 speedier, and is more exact. Quick R-CNN is actualized in Python and C++ (utilizing Caffe) and is accessible under the open-source MIT Permit.

An open source GIS-based tool to integrate the fragmentation mechanism in rockfall propagation

NASA Astrophysics Data System (ADS)

Matas, Gerard; Lantada, Nieves; Gili, Josep A.; Corominas, Jordi

2015-04-01

Rockfalls are frequent instability processes in road cuts, open pit mines and quarries, steep slopes and cliffs. Even though the stability of rock slopes can be determined using analytical approaches, the assessment of large rock cliffs require simplifying assumptions due to the difficulty of working with a large amount of joints, the scattering of both the orientations and strength parameters. The attitude and persistency of joints within the rock mass define the size of kinematically unstable rock volumes. Furthermore the rock block will eventually split in several fragments during its propagation downhill due its impact with the ground surface. Knowledge of the size, energy, trajectory… of each block resulting from fragmentation is critical in determining the vulnerability of buildings and protection structures. The objective of this contribution is to present a simple and open source tool to simulate the fragmentation mechanism in rockfall propagation models and in the calculation of impact energies. This tool includes common modes of motion for falling boulders based on the previous literature. The final tool is being implemented in a GIS (Geographic Information Systems) using open source Python programming. The tool under development will be simple, modular, compatible with any GIS environment, open source, able to model rockfalls phenomena correctly. It could be used in any area susceptible to rockfalls with a previous adjustment of the parameters. After the adjustment of the model parameters to a given area, a simulation could be performed to obtain maps of kinetic energy, frequency, stopping density and passing heights. This GIS-based tool and the analysis of the fragmentation laws using data collected from recent rockfall have being developed within the RockRisk Project (2014-2016). This project is funded by the Spanish Ministerio de Economía y Competitividad and entitled "Rockfalls in cliffs: risk quantification and its prevention"(BIA2013-42582-P).

Pilot Study of an Open-source Image Analysis Software for Automated Screening of Conventional Cervical Smears.

PubMed

Sanyal, Parikshit; Ganguli, Prosenjit; Barui, Sanghita; Deb, Prabal

2018-01-01

The Pap stained cervical smear is a screening tool for cervical cancer. Commercial systems are used for automated screening of liquid based cervical smears. However, there is no image analysis software used for conventional cervical smears. The aim of this study was to develop and test the diagnostic accuracy of a software for analysis of conventional smears. The software was developed using Python programming language and open source libraries. It was standardized with images from Bethesda Interobserver Reproducibility Project. One hundred and thirty images from smears which were reported Negative for Intraepithelial Lesion or Malignancy (NILM), and 45 images where some abnormality has been reported, were collected from the archives of the hospital. The software was then tested on the images. The software was able to segregate images based on overall nuclear: cytoplasmic ratio, coefficient of variation (CV) in nuclear size, nuclear membrane irregularity, and clustering. 68.88% of abnormal images were flagged by the software, as well as 19.23% of NILM images. The major difficulties faced were segmentation of overlapping cell clusters and separation of neutrophils. The software shows potential as a screening tool for conventional cervical smears; however, further refinement in technique is required.

A collection of open source applications for mass spectrometry data mining.

PubMed

Gallardo, Óscar; Ovelleiro, David; Gay, Marina; Carrascal, Montserrat; Abian, Joaquin

2014-10-01

We present several bioinformatics applications for the identification and quantification of phosphoproteome components by MS. These applications include a front-end graphical user interface that combines several Thermo RAW formats to MASCOT™ Generic Format extractors (EasierMgf), two graphical user interfaces for search engines OMSSA and SEQUEST (OmssaGui and SequestGui), and three applications, one for the management of databases in FASTA format (FastaTools), another for the integration of search results from up to three search engines (Integrator), and another one for the visualization of mass spectra and their corresponding database search results (JsonVisor). These applications were developed to solve some of the common problems found in proteomic and phosphoproteomic data analysis and were integrated in the workflow for data processing and feeding on our LymPHOS database. Applications were designed modularly and can be used standalone. These tools are written in Perl and Python programming languages and are supported on Windows platforms. They are all released under an Open Source Software license and can be freely downloaded from our software repository hosted at GoogleCode. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

a Free and Open Source Tool to Assess the Accuracy of Land Cover Maps: Implementation and Application to Lombardy Region (italy)

NASA Astrophysics Data System (ADS)

Bratic, G.; Brovelli, M. A.; Molinari, M. E.

2018-04-01

The availability of thematic maps has significantly increased over the last few years. Validation of these maps is a key factor in assessing their suitability for different applications. The evaluation of the accuracy of classified data is carried out through a comparison with a reference dataset and the generation of a confusion matrix from which many quality indexes can be derived. In this work, an ad hoc free and open source Python tool was implemented to automatically compute all the matrix confusion-derived accuracy indexes proposed by literature. The tool was integrated into GRASS GIS environment and successfully applied to evaluate the quality of three high-resolution global datasets (GlobeLand30, Global Urban Footprint, Global Human Settlement Layer Built-Up Grid) in the Lombardy Region area (Italy). In addition to the most commonly used accuracy measures, e.g. overall accuracy and Kappa, the tool allowed to compute and investigate less known indexes such as the Ground Truth and the Classification Success Index. The promising tool will be further extended with spatial autocorrelation analysis functions and made available to researcher and user community.

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

Open source libraries and frameworks for biological data visualisation: a guide for developers.

PubMed

Wang, Rui; Perez-Riverol, Yasset; Hermjakob, Henning; Vizcaíno, Juan Antonio

2015-04-01

Recent advances in high-throughput experimental techniques have led to an exponential increase in both the size and the complexity of the data sets commonly studied in biology. Data visualisation is increasingly used as the key to unlock this data, going from hypothesis generation to model evaluation and tool implementation. It is becoming more and more the heart of bioinformatics workflows, enabling scientists to reason and communicate more effectively. In parallel, there has been a corresponding trend towards the development of related software, which has triggered the maturation of different visualisation libraries and frameworks. For bioinformaticians, scientific programmers and software developers, the main challenge is to pick out the most fitting one(s) to create clear, meaningful and integrated data visualisation for their particular use cases. In this review, we introduce a collection of open source or free to use libraries and frameworks for creating data visualisation, covering the generation of a wide variety of charts and graphs. We will focus on software written in Java, JavaScript or Python. We truly believe this software offers the potential to turn tedious data into exciting visual stories. © 2014 The Authors. PROTEOMICS published by Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Open source libraries and frameworks for biological data visualisation: A guide for developers

PubMed Central

Wang, Rui; Perez-Riverol, Yasset; Hermjakob, Henning; Vizcaíno, Juan Antonio

2015-01-01

Recent advances in high-throughput experimental techniques have led to an exponential increase in both the size and the complexity of the data sets commonly studied in biology. Data visualisation is increasingly used as the key to unlock this data, going from hypothesis generation to model evaluation and tool implementation. It is becoming more and more the heart of bioinformatics workflows, enabling scientists to reason and communicate more effectively. In parallel, there has been a corresponding trend towards the development of related software, which has triggered the maturation of different visualisation libraries and frameworks. For bioinformaticians, scientific programmers and software developers, the main challenge is to pick out the most fitting one(s) to create clear, meaningful and integrated data visualisation for their particular use cases. In this review, we introduce a collection of open source or free to use libraries and frameworks for creating data visualisation, covering the generation of a wide variety of charts and graphs. We will focus on software written in Java, JavaScript or Python. We truly believe this software offers the potential to turn tedious data into exciting visual stories. PMID:25475079

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

OASYS (OrAnge SYnchrotron Suite): an open-source graphical environment for x-ray virtual experiments

NASA Astrophysics Data System (ADS)

Rebuffi, Luca; Sanchez del Rio, Manuel

2017-08-01

The evolution of the hardware platforms, the modernization of the software tools, the access to the codes of a large number of young people and the popularization of the open source software for scientific applications drove us to design OASYS (ORange SYnchrotron Suite), a completely new graphical environment for modelling X-ray experiments. The implemented software architecture allows to obtain not only an intuitive and very-easy-to-use graphical interface, but also provides high flexibility and rapidity for interactive simulations, making configuration changes to quickly compare multiple beamline configurations. Its purpose is to integrate in a synergetic way the most powerful calculation engines available. OASYS integrates different simulation strategies via the implementation of adequate simulation tools for X-ray Optics (e.g. ray tracing and wave optics packages). It provides a language to make them to communicate by sending and receiving encapsulated data. Python has been chosen as main programming language, because of its universality and popularity in scientific computing. The software Orange, developed at the University of Ljubljana (SLO), is the high level workflow engine that provides the interaction with the user and communication mechanisms.

PyPLIF: Python-based Protein-Ligand Interaction Fingerprinting.

PubMed

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

2013-01-01

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

ISMRM Raw data format: A proposed standard for MRI raw datasets.

PubMed

Inati, Souheil J; Naegele, Joseph D; Zwart, Nicholas R; Roopchansingh, Vinai; Lizak, Martin J; Hansen, David C; Liu, Chia-Ying; Atkinson, David; Kellman, Peter; Kozerke, Sebastian; Xue, Hui; Campbell-Washburn, Adrienne E; Sørensen, Thomas S; Hansen, Michael S

2017-01-01

This work proposes the ISMRM Raw Data format as a common MR raw data format, which promotes algorithm and data sharing. A file format consisting of a flexible header and tagged frames of k-space data was designed. Application Programming Interfaces were implemented in C/C++, MATLAB, and Python. Converters for Bruker, General Electric, Philips, and Siemens proprietary file formats were implemented in C++. Raw data were collected using magnetic resonance imaging scanners from four vendors, converted to ISMRM Raw Data format, and reconstructed using software implemented in three programming languages (C++, MATLAB, Python). Images were obtained by reconstructing the raw data from all vendors. The source code, raw data, and images comprising this work are shared online, serving as an example of an image reconstruction project following a paradigm of reproducible research. The proposed raw data format solves a practical problem for the magnetic resonance imaging community. It may serve as a foundation for reproducible research and collaborations. The ISMRM Raw Data format is a completely open and community-driven format, and the scientific community is invited (including commercial vendors) to participate either as users or developers. Magn Reson Med 77:411-421, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

BuddySuite: Command-Line Toolkits for Manipulating Sequences, Alignments, and Phylogenetic Trees.

PubMed

Bond, Stephen R; Keat, Karl E; Barreira, Sofia N; Baxevanis, Andreas D

2017-06-01

The ability to manipulate sequence, alignment, and phylogenetic tree files has become an increasingly important skill in the life sciences, whether to generate summary information or to prepare data for further downstream analysis. The command line can be an extremely powerful environment for interacting with these resources, but only if the user has the appropriate general-purpose tools on hand. BuddySuite is a collection of four independent yet interrelated command-line toolkits that facilitate each step in the workflow of sequence discovery, curation, alignment, and phylogenetic reconstruction. Most common sequence, alignment, and tree file formats are automatically detected and parsed, and over 100 tools have been implemented for manipulating these data. The project has been engineered to easily accommodate the addition of new tools, is written in the popular programming language Python, and is hosted on the Python Package Index and GitHub to maximize accessibility. Documentation for each BuddySuite tool, including usage examples, is available at http://tiny.cc/buddysuite_wiki. All software is open source and freely available through http://research.nhgri.nih.gov/software/BuddySuite. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution 2017. This work is written by US Government employees and is in the public domain in the US.

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.

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

PubMed

Hermann, Gunter; Pohl, Vincent; Tremblay, Jean Christophe; Paulus, Beate; Hege, Hans-Christian; Schild, Axel

2016-06-15

ORBKIT is a toolbox for postprocessing electronic structure calculations based on a highly modular and portable Python architecture. The program allows computing a multitude of electronic properties of molecular systems on arbitrary spatial grids from the basis set representation of its electronic wavefunction, as well as several grid-independent properties. The required data can be extracted directly from the standard output of a large number of quantum chemistry programs. ORBKIT can be used as a standalone program to determine standard quantities, for example, the electron density, molecular orbitals, and derivatives thereof. The cornerstone of ORBKIT is its modular structure. The existing basic functions can be arranged in an individual way and can be easily extended by user-written modules to determine any other derived quantity. ORBKIT offers multiple output formats that can be processed by common visualization tools (VMD, Molden, etc.). Additionally, ORBKIT possesses routines to order molecular orbitals computed at different nuclear configurations according to their electronic character and to interpolate the wavefunction between these configurations. The program is open-source under GNU-LGPLv3 license and freely available at https://github.com/orbkit/orbkit/. This article provides an overview of ORBKIT with particular focus on its capabilities and applicability, and includes several example calculations. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

A comparative study of programming languages for next-generation astrodynamics systems

NASA Astrophysics Data System (ADS)

Eichhorn, Helge; Cano, Juan Luis; McLean, Frazer; Anderl, Reiner

2018-03-01

Due to the computationally intensive nature of astrodynamics tasks, astrodynamicists have relied on compiled programming languages such as Fortran for the development of astrodynamics software. Interpreted languages such as Python, on the other hand, offer higher flexibility and development speed thereby increasing the productivity of the programmer. While interpreted languages are generally slower than compiled languages, recent developments such as just-in-time (JIT) compilers or transpilers have been able to close this speed gap significantly. Another important factor for the usefulness of a programming language is its wider ecosystem which consists of the available open-source packages and development tools such as integrated development environments or debuggers. This study compares three compiled languages and three interpreted languages, which were selected based on their popularity within the scientific programming community and technical merit. The three compiled candidate languages are Fortran, C++, and Java. Python, Matlab, and Julia were selected as the interpreted candidate languages. All six languages are assessed and compared to each other based on their features, performance, and ease-of-use through the implementation of idiomatic solutions to classical astrodynamics problems. We show that compiled languages still provide the best performance for astrodynamics applications, but JIT-compiled dynamic languages have reached a competitive level of speed and offer an attractive compromise between numerical performance and programmer productivity.