We have previously reported an L2-gradient flow (L2GF) method for cryo-electron tomography and single-particle reconstruction, which has a reasonably good performance. The aim of this paper is to further upgrade both the computational efficiency and accuracy of the L2GF method. In a finite-dimensional space spanned by the radial basis functions, a minimization problem combining a fourth-order geometric flow with an energy decreasing constraint is solved by a bi-gradient method. The bi-gradient method involves a free parameter As β increases from 0 to 1, the structures of the reconstructed function from coarse to fine are captured. The experimental results show that the proposed method yields more desirable results.
We have developed a 2.5D MHD code designed to study how the solar wind influences the evolution of transient events in the solar corona and inner heliosphere. The code includes thermal conduction, coronal heating and radiative cooling. Thermal conduction is assumed to be magnetic field-aligned in the inner corona and transitions to a collisionless formulation in the outer corona. We have developed a stable method to handle field-aligned conduction around magnetic null points. The inner boundary is placed in the upper transition region, and the mass flux across the boundary is determined from 1D field-aligned characteristics and a 'radiative energy balance' condition. The 2.5D nature of this code makes it ideal for parameter studies not yet possible with 3D codes. We have made this code publicly available as a tool for the community. To this end we have developed a graphical interface to aid in the selection of appropriate options and a graphical interface that can process and visualize the data produced by the simulation. As an example, we show a simulation of a dipole field stretched into a helmet streamer by the solar wind. Plasmoids periodically erupt from the streamer, and we perform a parameter study of how the frequency and location of these eruptions changed in response to different levels of coronal heating. As a further example, we show the solar wind stretching a compact multi-polar flux system. This flux system will be used to study breakout coronal mass ejections in the presence of the solar wind.
Sequential model-based optimization (also known as Bayesian optimization) is one of the most efficient methods (per function evaluation) of function minimization. This efficiency makes it appropriate for optimizing the hyperparameters of machine learning algorithms that are slow to train. The Hyperopt library provides algorithms and parallelization infrastructure for performing hyperparameter optimization (model selection) in Python. This paper presents an introductory tutorial on the usage of the Hyperopt library, including the description of search spaces, minimization (in serial and parallel), and the analysis of the results collected in the course of minimization. This paper also gives an overview of Hyperopt-Sklearn, a software project that provides automatic algorithm configuration of the Scikit-learn machine learning library. Following Auto-Weka, we take the view that the choice of classifier and even the choice of preprocessing module can be taken together to represent a single large hyperparameter optimization problem. We use Hyperopt to define a search space that encompasses many standard components (e.g. SVM, RF, KNN, PCA, TFIDF) and common patterns of composing them together. We demonstrate, using search algorithms in Hyperopt and standard benchmarking data sets (MNIST, 20-newsgroups, convex shapes), that searching this space is practical and effective. In particular, we improve on best-known scores for the model space for both MNIST and convex shapes. The paper closes with some discussion of ongoing and future work.
The Hydrodynamic and oil spill modeling system for Python (HyosPy) is presented as an example of a multi-model wrapper that ties together existing models, web access to forecast data and visualization techniques as part of an adaptable operational forecast system. The system is designed to automatically run a continual sequence of hindcast/forecast hydrodynamic models so that multiple predictions of the time-and-space-varying velocity fields are already available when a spill is reported. Once the user provides the estimated spill parameters, the system runs multiple oil spill prediction models using the output from the hydrodynamic models. As new wind and tide data become available, they are downloaded from the web, used as forcing conditions for a new instance of the hydrodynamic model and then applied to a new instance of the oil spill model. The predicted spill trajectories from multiple oil spill models are visualized through Python methods invoking Google MapTM and Google EarthTM functions. HyosPy is designed in modules that allow easy future adaptation to new models, new data sources or new visualization tools.
Graphics processing units (GPUs) have become increasingly powerful in recent years. Programs exploring the advantages of this architecture could achieve large performance gains and this is the aim of new initiatives in high performance computing. The objective of this work is to develop an efficient tool to model 2D elastic wave propagation on parallel computing devices. To this end, we implement the elastodynamic finite integration technique, using the industry open standard open computing language (OpenCL) for cross-platform, parallel programming of modern processors, and an open-source toolkit called [Py]OpenCL. The code written with [Py]OpenCL can run on a wide variety of platforms; it can be used on AMD or NVIDIA GPUs as well as classical multicore CPUs, adapting to the underlying architecture. Our main contribution is its implementation with local and global memory and the performance analysis using five different computing devices (including Kepler, one of the fastest and most efficient high performance computing technologies) with various operating systems.
The Python libraries NumPy and SciPy are extremely powerful tools for numerical processing and analysis well suited to a large variety of applications. We developed ObsPy (http://obspy.org), a Python library for seismology intended to facilitate the development of seismological software packages and workflows, to utilize these abilities and provide a bridge for seismology into the larger scientific Python ecosystem. Scientists in many domains who wish to convert their existing tools and applications to take advantage of a platform like the one Python provides are confronted with several hurdles such as special file formats, unknown terminology, and no suitable replacement for a non-trivial piece of software. We present an approach to implement a domain-specific time series library on top of the scientific NumPy stack. In so doing, we show a realization of an abstract internal representation of time series data permitting I/O support for a diverse collection of file formats. Then we detail the integration and repurposing of well established legacy codes, enabling them to be used in modern workflows composed in Python. Finally we present a case study on how to integrate research code into ObsPy, opening it to the broader community. While the implementations presented in this work are specific to seismology, many of the described concepts and abstractions are directly applicable to other sciences, especially to those with an emphasis on time series analysis.
SunPy is a data analysis toolkit which provides the necessary software for analyzing solar and heliospheric datasets in Python. SunPy aims to provide a free and open-source alternative to the current standard, an IDL- based solar data analysis environment known as SolarSoft (SSW). We present the latest release of SunPy, version 0.3. Though still in active development, SunPy already provides important functionality for solar data analysis. SunPy provides data structures for representing the most common solar data types: images, lightcurves, and spectra. To enable the acquisition of scientific data, SunPy provides integration with the Virtual Solar Observatory (VSO), a single source for accessing most solar data sets, and integration with the Heliophysics Event Knowledgebase (HEK), a database of transient solar events such as solar flares or coronal mass ejections. SunPy utilizes many packages from the greater scientific Python community, including NumPy and SciPy for core data types and analysis routines, PyFITS for opening image files, in FITS format, from major solar missions (e.g., SDO/AIA, SOHO/EIT, SOHO/LASCO, and STEREO) into WCS-aware map objects, and pandas for advanced time-series analysis tools for data from missions such as GOES, SDO/EVE, and Proba2/LYRA, as well as support for radio spectra (e.g., e-Callisto). Future releases will build upon and integrate with current work in the Astropy project and the rest of the scientific python community, to bring greater functionality to SunPy users.
DMTCP (Distributed MultiThreaded CheckPointing) is a mature checkpoint–restart package. It operates in user space without kernel privilege, and adapts to application-specific requirements through plugins. While DMTCP has been able to checkpoint Python and IPython 'from the outside' for many years, a Python module has recently been created to support DMTCP. IPython support is included through a new DMTCP plugin. A checkpoint can be requested interactively within a Python session or under the control of a specific Python program. Further, the Python program can execute specific Python code prior to checkpoint, upon resuming (within the original process) and upon restarting (from a checkpoint image). Applications of DMTCP are demonstrated for: (i) Python-based graphics using virtual network client, (ii) a fast/slow technique to use multiple hosts or cores to check one (Cython Behnel S et al 2011 Comput. Sci. Eng. 13 31–39) computation in parallel, and (iii) a reversible debugger, FReD, with a novel reverse-expression watchpoint feature for locating the cause of a bug.
Machine learning benchmark data sets come in all shapes and sizes, whereas classification algorithms assume sanitized input, such as (x, y) pairs with vector-valued input x and integer class label y. Researchers and practitioners know all too well how tedious it can be to get from the URL of a new data set to a NumPy ndarray suitable for e.g. pandas or sklearn. The SkData library handles that work for a growing number of benchmark data sets (small and large) so that one-off in-house scripts for downloading and parsing data sets can be replaced with library code that is reliable, community-tested, and documented. The SkData library also introduces an open-ended formalization of training and testing protocols that facilitates direct comparison with published research. This paper describes the usage and architecture of the SkData library.
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.
In many communities such as climate science or industrial design, to solve complex coupled problems with high fidelity external coupling of legacy solvers puts a lot of pressure on the tool used for the coupling. The precision of such predictions not only largely depends on simulation resolutions and the use of huge meshes but also on high performance computing to reduce restitution times. In this context, the current work aims at studying the scalability of code coupling on high performance computing architectures for a conjugate heat transfer problem. The flow solver is a Large Eddy Simulation code that has been already ported on massively parallel architectures. The conduction solver is based on the same data structure and thus shares the flow solver scalability properties. Accurately coupling solvers on massively parallel architectures while maintaining their scalability is challenging. It requires exchanging and treating information based on two different computational grids that are partitioned differently on a different number of cores. Such transfers have to be thought to maintain code scalabilities while maintaining numerical accuracy. This raises communication and high performance computing issues: transferring data from a distributed interface to another distributed interface in a parallel way and on a very large number of processors is not straightforward and solutions are not clear. Performance tests have been carried out up to 12 288 cores on the CURIE supercomputer (TGCC/CEA). Results show a good behavior of the coupled model when increasing the number of cores thanks to the fully distributed exchange process implemented in the coupler. Advanced analyses are carried out to draw new paths for future developments for coupled simulations: i.e. optimization of the data transfer protocols through asynchronous communications or coupling-aware preprocessing of the coupled models (mesh partitioning phase).
PythonTeX is a LaTeX package that allows Python code in LaTeX documents to be executed and provides access to the output. This makes possible reproducible documents that combine results with the code required to generate them. Calculations and figures may be next to the code that created them. Since code is adjacent to its output in the document, editing may be more efficient. Since code output may be accessed programmatically in the document, copy-and-paste errors are avoided and output is always guaranteed to be in sync with the code that generated it. This paper provides an introduction to PythonTeX and an overview of major features, including performance optimizations, debugging tools, and dependency tracking. Several complete examples are presented. Finally, advanced features are summarized. Though PythonTeX was designed for Python, it may be extended to support additional languages; support for the Ruby and Julia languages is already included. PythonTeX contains a utility for converting documents into plain LaTeX, suitable for format conversion, sharing, and journal submission.
Nanotechnology ranks among the key technologies that will bring fundamental changes to basic research, many industry sectors and daily life. However, consumers often miss reliable and understandable information on nanomaterials. To create this transparency, the DaNa project collects and evaluates the latest scientific literature before publishing these on the website www.nanoobjects.info. Though all published articles are designed to meet the needs of different recipient groups, a certain knowledge about nanomaterials is presumed due to the nanomaterial-specific structuring of the website. This poses a barrier for interested laymen who usually have a particular nano-related application in mind. These application-oriented facts on nanomaterials already exist on the website but are scattered throughout the different articles. To overcome this, we first analyzed the state of the scattered information and then assessed requirements for a new tool displaying these facts: storage of extracted knowledge in a homogenous way, visualization options and integration of the new database into the existing content management system. We therefore extended our backend to capture knowledge on a semantically higher level in a database called DaNaVis. Based on this database we can increase the accessibility of DaNa's project results by means of interactive visualization components.
Based on a detailed analysis of explosive electron emission in high-current electronic devices, we formulate a system of equations that describes the expansion of cathode plasma and the generation of high-current electron beams. The system underlies the numerical algorithm for the hybrid code which enables the simulation of the charged particles' dynamics in high-current vircators with open resonators. Using the Gabor–Morlet transform, we perform a time-frequency analysis of vircator radiation.
Nanoparticles are potentially powerful therapeutic tools that have the capacity to target drug payloads and imaging agents. However, some nanoparticles can activate complement, a branch of the innate immune system, and cause adverse side-effects. Recently, we employed an in vitro hemolysis assay to measure the serum complement activity of perfluorocarbon nanoparticles that differed by size, surface charge, and surface chemistry, quantifying the nanoparticle-dependent complement activity using a metric called Residual Hemolytic Activity (RHA). In the present work, we have used a decision tree learning algorithm to derive the rules for estimating nanoparticle-dependent complement response based on the data generated from the hemolytic assay studies. Our results indicate that physicochemical properties of nanoparticles, namely, size, polydispersity index, zeta potential, and mole percentage of the active surface ligand of a nanoparticle, can serve as good descriptors for prediction of nanoparticle-dependent complement activation in the decision tree modeling framework.
We consider the inversion of block tridiagonal, block Toeplitz matrices and comment on the behaviour of these inverses as one moves away from the diagonal. Using matrix Möbius transformations, we first present an representation (with respect to the number of block rows and block columns) for the inverse matrix and subsequently use this representation to characterize the inverse matrix. There are four symmetry-distinct cases where the blocks of the inverse matrix (i) decay to zero on both sides of the diagonal, (ii) oscillate on both sides, (iii) decay on one side and oscillate on the other and (iv) decay on one side and grow on the other. This characterization exposes the necessary conditions for the inverse matrix to be numerically banded and may also aid in the design of preconditioners and fast algorithms. Finally, we present numerical examples of these matrix types.
The calculation of reactive azeotropes is an important task in the preliminary design and simulation of reactive distillation columns. Classically, homogeneous nonreactive azeotropes are vapor–liquid coexistence conditions where phase compositions are equal. For homogeneous reactive azeotropes, simultaneous phase and chemical equilibria occur concomitantly with equality of compositions (in the Ung–Doherty transformed space). The modeling of reactive azeotrope calculation is represented by a nonlinear algebraic system with phase equilibrium, chemical equilibrium and azeotropy equations. This nonlinear system can exhibit more than one solution, corresponding to a double reactive azeotrope. In a previous paper (Platt et al 2013 J. Phys.: Conf. Ser. 410 012020), we investigated some numerical aspects of the calculation of reactive azeotropes in the isobutene + methanol + methyl-tert-butyl-ether (with two reactive azeotropes) system using two metaheuristics: the Luus–Jaakola adaptive random search and the Firefly algorithm. Here, we use a hybrid structure (stochastic + deterministic) in order to produce accurate results for both azeotropes. After identifying the neighborhood of the reactive azeotrope, the nonlinear algebraic system is solved using Newton's method. The results indicate that using metaheuristics and some techniques devoted to the calculation of multiple minima allows both azeotropic coordinates in this reactive system to be obtains. In this sense, we provide a comprehensive analysis of a useful framework devoted to solving nonlinear systems, particularly in phase equilibrium problems.
Most traditional continuum molecular modeling adopted finite difference or finite volume methods which were based on a structured mesh (grid). Unstructured meshes were only occasionally used, but an increased number of applications emerge in molecular simulations. To facilitate the continuum modeling of biomolecular systems based on unstructured meshes, we are developing a software platform with tools which are particularly beneficial to those approaches. This work describes the software system specifically for the simulation of a typical, complex molecular procedure: ion transport through a three-dimensional channel system that consists of a protein and a membrane. The platform contains three parts: a meshing tool chain for ion channel systems, a parallel finite element solver for the Poisson–Nernst–Planck equations describing the electrodiffusion process of ion transport, and a visualization program for continuum molecular modeling. The meshing tool chain in the platform, which consists of a set of mesh generation tools, is able to generate high-quality surface and volume meshes for ion channel systems. The parallel finite element solver in our platform is based on the parallel adaptive finite element package PHG which wass developed by one of the authors [1]. As a featured component of the platform, a new visualization program, VCMM, has specifically been developed for continuum molecular modeling with an emphasis on providing useful facilities for unstructured mesh-based methods and for their output analysis and visualization. VCMM provides a graphic user interface and consists of three modules: a molecular module, a meshing module and a numerical module. A demonstration of the platform is provided with a study of two real proteins, the connexin 26 and hemolysin ion channels.
Aspartic acid (Asp) residues in peptides and proteins (l-Asp) are known to undergo spontaneous nonenzymatic reactions to form l-β-Asp, d-Asp, and d-β-Asp residues. The formation of these abnormal Asp residues in proteins may affect their three-dimensional structures and hence their properties and functions. Indeed, the reactions have been thought to contribute to aging and pathologies. Most of the above reactions of the l-Asp residues proceed via a cyclic succinimide intermediate. In this paper, a novel three-water-assisted mechanism is proposed for cyclization of an Asp residue (forming a gem-diol precursor of the succinimide) by the B3LYP/6-31 + G(d,p) density functional theory calculations carried out for an Asp-containing model compound (Ace−Asp−Nme, where Ace = acetyl and Nme = NHCH3). The three water molecules act as catalysts by mediating 'long-range' proton transfers. In the proposed mechanism, the amide group on the C-terminal side of the Asp residue is first converted to the tautomeric iminol form (iminolization). Then, reorientation of a water molecule and a conformational change occur successively, followed by the nucleophilic attack of the iminol nitrogen on the carboxyl carbon of the Asp side chain to form the gem-diol species. A satisfactory agreement was obtained between the calculated and experimental energetics.
We develop a new implicit symplectic integrator and derive the first and second order integration algorithms for it. We combine this integrator with a Yoshida composition and with a Suzuki composition to get third order hybrid symplectic integrators. We test the algorithms on an exactly solvable example from McMillan (1950 Phys. Rev. 79 498) of a particle in an electromagnetic wave and find very good results, including long term stability and exact preservation of constants associated with symmetries. We discuss the circumstances in which the new algorithms may be useful.
