Python Interface Research Articles

TMKit: a Python interface for computational analysis of transmembrane proteins.

Transmembrane proteins are receptors, enzymes, transporters and ion channels that are instrumental in regulating a variety of cellular activities, such as signal transduction and cell communication. Despite tremendous progress in computational capacities to support protein research, there is still a significant gap in the availability of specialized computational analysis toolkits for transmembrane protein research. Here, we introduce TMKit, an open-source Python programming interface that is modular, scalable and specifically designed for processing transmembrane protein data. TMKit is a one-stop computational analysis tool for transmembrane proteins, enabling users to perform database wrangling, engineer features at the mutational, domain and topological levels, and visualize protein-protein interaction interfaces. In addition, TMKit includes seqNetRR, a high-performance computing library that allows customized construction of a large number of residue connections. This library is particularly well suited for assigning correlation matrix-based features at a fast speed. TMKit should serve as a useful tool for researchers in assisting the study of transmembrane protein sequences and structures. TMKit is publicly available through https://github.com/2003100127/tmkit and https://tmkit-guide.herokuapp.com/doc/overview.

New MGCAMB tests of gravity with CosmoMC and Cobaya

We present a new version of MGCAMB, a patch for the Einstein-Boltzmann solver CAMB for cosmological tests of gravity. New features include a new cubic-spline parameterization allowing for a simultaneous reconstruction of μ, Σ and the dark energy density fraction Ω X as functions of redshift, the option to work with a direct implementation of μ, Σ (instead of converting to μ, γ first), along with the option to test models with a scalar field coupled only to dark matter, and the option to include dark energy perturbations when working with w ≠ -1 backgrounds, to restore consistency with CAMB in the GR limit. This version of MGCAMB comes with a Python wrapper to run it directly from the Python interface, an implementation in the latest version of CosmoMC, and can be used with Cobaya.

MILK: a Python scripting interface to MAUD for automation of Rietveld analysis.

Modern diffraction experiments (e.g. in situ parametric studies) present scientists with many diffraction patterns to analyze. Interactive analyses via graphical user interfaces tend to slow down obtaining quantitative results such as lattice parameters and phase fractions. Furthermore, Rietveld refinement strategies (i.e. the parameter turn-on-off sequences) tend to be instrument specific or even specific to a given dataset, such that selection of strategies can become a bottleneck for efficient data analysis. Managing multi-histogram datasets such as from multi-bank neutron diffractometers or caked 2D synchrotron data presents additional challenges due to the large number of histogram-specific parameters. To overcome these challenges in the Rietveld software Material Analysis Using Diffraction (MAUD), the MAUD Interface Language Kit (MILK) is developed along with an updated text batch interface for MAUD. The open-source software MILK is computer-platform independent and is packaged as a Python library that interfaces with MAUD. Using MILK, model selection (e.g. various texture or peak-broadening models), Rietveld parameter manipulation and distributed parallel batch computing can be performed through a high-level Python interface. A high-level interface enables analysis workflows to be easily programmed, shared and applied to large datasets, and external tools to be integrated with MAUD. Through modification to the MAUD batch interface, plot and data exports have been improved. The resulting hierarchical folders from Rietveld refinements with MILK are compatible with Cinema: Debye-Scherrer, a tool for visualizing and inspecting the results of multi-parameter analyses of large quantities of diffraction data. In this manuscript, the combined Python scripting and visualization capability of MILK is demonstrated with a quantitative texture and phase analysis of data collected at the HIPPO neutron diffractometer.

Full-waveform inversion by model extension: Practical applications

Producing reliable acoustic subsurface velocity models still remains the main bottleneck of the oil and gas industry’s traditional imaging sequence. In complex geologic settings, the output of conventional ray-based or wave-equation-based tomographic methods may not be sufficiently accurate for full-waveform inversion (FWI) to converge to a geologically satisfactory earth model. We create a new method referred to as full-waveform inversion by model extension (FWIME) in which a wave-equation migration velocity analysis (WEMVA) technique is efficiently paired with a modified version of FWI. We find that our method is more powerful than applying WEMVA and FWI sequentially, and that it can converge to accurate solutions without the use of a good initial guess or low-frequency energy. We determine FWIME’s potential on five realistic and challenging numerical examples that simulate complex geologic scenarios often encountered in hydrocarbon exploration. We guide the reader step by step throughout the optimization process. We find that our method can simultaneously invert all wave types with the same simple mechanism and without the need for a user-intensive hyperparameter tuning process. In an open-source online repository, we provide our C++/compute unified device architecture (CUDA) numerical implementation accelerated with graphics processing units, encapsulated in a Python interface. All the numerical examples developed are accessible through Python notebooks and fully reproducible.

Material selection using knowledge-based expert system for racing bicycle forks

Materials today provide enormous prospects for innovation. However, progress is only possible if a mechanism for making a sensible choice from the materials exists. When choosing a material for a component, one should start with the full menu of materials as an option. When selecting a material for an engineering component, there is usually more than one option. However, the final choice will be a balance between the materials based on the benefits and drawbacks they provide. This paper focuses on material selection for racing bicycle forks utilizing knowledge-based expert systems based on design requirements. Knowledge-based expert systems are computer methods replicating human problem-solving through artificial intelligence to arrive at the best decisive action. A database of 67 materials with its own set of attributes was created. The material index and shape factor were evaluated for all the materials in the database based on the critical selection factors of mass and section shape. Material selection is determined by considering the material index, shape factor, and coupling various design requirements by framing simple IF-THEN rules using the python interface and Jupyter Notebook. According to this method, the final suitable materials for selecting racing bicycle forks are Aluminium alloys and Carbon Fiber Reinforced Plastics (CFRP). As the application demands lower weight by compromising on the cost, Carbon Fiber Reinforced Plastic (CFRP) is the most preferred choice for material selection of racing bicycle forks.

Analysis of Control Element Assembly Withdrawal at Full Power Accident Scenario Using a Hybrid Conservative and BEPU Approach

Reactivity Initiated Accident (RIA) scenarios require special attention using advanced simulation techniques due to their complexity and importance for nuclear power plant (NPP) safety. While the conservative approach has traditionally been used for safety analysis, it may lead to unrealistic results which calls for the use of best estimate plus uncertainty (BEPU) approach, especially with the current advances in computational power which makes the BEPU analysis feasible. In this work an Uncontrolled Control Element Assembly (CEA) Withdrawal at Full Power accident scenario is analyzed using the BEPU approach by loosely coupling the thermal hydraulics best-estimate system code (RELAP5/SCDAPSIM/MOD3.4) to the statistical analysis software (DAKOTA) using a Python interface. Results from the BEPU analysis indicate that a realistic treatment of the accident scenario yields a larger safety margin and is therefore encouraged for accident analysis as it may enable more economic and flexible operation.

MolTwister – a molecular systems construction, manipulation and statistical mechanical calculation tool

To perform molecular dynamics (MD) simulations, Monte Carlo (MC) simulations, quantum mechanical (QM) electronic structure calculations, or similar atomistic calculations, it is necessary to first construct and define the molecular system of interest. This involves creating an initial configuration of atoms, where MD and MC simulations require forcefield assignments, for example in the form of non-bonded, bonded, angular and dihedral potentials. Once simulations or calculations have been performed, large sets of data are available (often several gigabytes). These contain atomic trajectories and other relevant static or dynamic information, from which static and dynamic properties (such as density profiles, vibrational density of states and velocity autocorrelation functions) can be obtained through statistical mechanical calculations. MolTwister is an open source software platform that addresses the construction of molecular systems, basic 3D visualization of these, the generation of input files for selected MD packages, as well as calculation of properties from atomistic simulation data. It also contains a GPU accelerated MD simulator suited for smaller tasks such as molecular thermalization. The software package is written in C++14 and can be used as a basis for further development, where efforts have been made to make access to underlying functionality easy. Moreover, it supports Python, where scripts have access to the majority of program functionality. Program summaryProgram Title: MolTwisterCPC Library link to program files:https://doi.org/10.17632/ph6pwst9mn.1Developer's repository link:http://moltwister.comLicensing provisions: GPLv3 or laterProgramming language: C++14Nature of problem: Lack of SW tools for easy construction of complex molecular systems, as well as tools enabling analysis of long molecular trajectories.Solution method: Developed command line tool with associated 3D view, where molecular construction commands can be executed in complex manners through a Python interface. The program also has commands for analysis of molecular data that process one molecular time-frame at a time, thus enabling analysis of long molecular trajectories. Moreover, the program structure is designed for easy implementation of new commands by other researchers.Additional comments including restrictions and unusual features: Supported file formats: xyz, pdb, mtt (MolTwister trajectory), dcd, xtc, qepos, python, script (MolTwister scripts)Supported MD engines: HOOMD-blue, LAMMPS, MolTwister internal (moldyn)

An adaptive charge control strategy for participation of neighbourhood battery energy storage systems in frequency stability

Neighbourhood Battery Energy Storage System (N-BESS) is a new scale of energy storage that is expected to have a potential role in modern power systems stability. In the literature, there is a lack of studies that proposed a smart engagement of N-BESS in the frequency stability. In this paper, an adaptive charge control strategy for the N-BESS has been proposed to enhance the frequency stability while maintaining a fair State-of-Charge (SOC) levels for N-BESS. Also, a collaborative framework for large-scale Battery Energy Storage System (L-BESS) with N-BESS has been introduced, in which the N-BESS play an auxiliary role in frequency stability. In this study, a number of system uncertainties, including N-BESS SOC, load consumption, wind farm generation, and contingency size and location, have been considered. The simulation has been carried out in the Simplified 14-Generator Model of the South East Australian Power System over one-year simulation using DIgSILENT/PowerFactory software and Python interface. The results have demonstrated that a higher SOC levels of N-BESS under the proposed strategy have been maintained compared to that of the conventional strategy. Moreover, when the L-BESS collaborated with the N-BESS under the proposed framework, the best frequency stability with highest SOC levels of N-BESS have been obtained; at the same time less discharged power by the L-BESS was noted compared to that when the L-BESS participated individually which indicated that a smaller L-BESS size would be required if it collaborated with N-BESS in frequency stability service.

Hydrogeological modeling of the Roussillon coastal aquifer (France): stochastic inversion and analysis of future stresses

Global climate change-induced stresses on coastal water resources include water use restrictions, saline intrusions, and permanently modifying or damaging regional resources. Groundwater in coastal regions is often the only freshwater resource available, so an in-depth understanding of the aquifer, and the aquifer’s response to climate change, is essential for decision-makers. In this study, we focus on the coastal aquifer of Roussillon (southern France) by developing and investigating a steady-state groundwater flow model (MODFLOW 6) and calibrated with PEST++ on a Python interface (FloPy and PyEmu). Model input and boundary conditions are constrained by various scenarios of climate projections by 2080, with model results predicting the aquifer’s response (and associated uncertainty) to these external forcings. Using simple assumptions of intrusion estimates, model results highlight both strong climatic and anthropogenic impacts on the water table. These include aquifer drawdowns reaching several meters locally, and the seawater interface advancing locally several hundred meters inland and rising by several meters. Intrusions of this magnitude risk endangering exploited water wells and their sustainability. Our results demonstrate the critical importance of properly characterizing the geology and its heterogeneity for understanding aquifers at risk because poor predictions may lead to inappropriate decisions, putting critical resources at risk, particularly in coastal environments.

OpenDust: A fast GPU-accelerated code for the calculation of forces acting on microparticles in a plasma flow

We present the first open-source, GPU-based code for complex plasmas. The code, OpenDust, pursues to provide researchers, both experimenters and theorists, a user-friendly and high-performance tool for self-consistent calculation forces, acting on microparticles, and microparticles' charges in a plasma flow. OpenDust performance originates from a highly-optimized Cuda back-end and performs self-consistent calculations of plasma flow around microparticles in seconds. This code outperforms all available codes for self-consistent complex plasma simulation. Moreover, OpenDust can also be used for the simulation of larger systems of dust microparticles, which was previously unavailable. OpenDust interface is written in Python, which provides ease-of-use and simple installation from the Conda repository. Program summaryProgram Title: OpenDustCPC Library link to program files:https://doi.org/10.17632/bs7rthk29w.1Developer's repository link:https://github.com/kolotinsky1998/opendustCode Ocean capsule:https://codeocean.com/capsule/2557151Licensing provisions: MITProgramming language: PythonNature of problem: GPU cards can significantly speedup self-consistent calculations of forces, acting on microparticles in a plasma flow. However, the available codes are CPU-based, or not provided as a package that can also be easily used. Therefore, researchers need to spend much time writing their own codes or use less effective ones.Solution method: Development of a highly-optimized GPU-accelerated library for self-consistent simulations of streaming plasma around microparticles. The functionality of the library is available through a Python interface, which makes it easy to use.

FreiBox: A Versatile Open-Source Behavioral Setup for Investigating the Neuronal Correlates of Behavioral Flexibility via 1-Photon Imaging in Freely Moving Mice.

To survive in a complex and changing environment, animals must adapt their behavior. This ability is called behavioral flexibility and is classically evaluated by a reversal learning paradigm. During such a paradigm, the animals adapt their behavior according to a change of the reward contingencies. To study these complex cognitive functions (from outcome evaluation to motor adaptation), we developed a versatile, low-cost, open-source platform, allowing us to investigate the neuronal correlates of behavioral flexibility with 1-photon calcium imaging. This platform consists of FreiBox, a novel low-cost Arduino behavioral setup, as well as further open-source tools, which we developed and integrated into our framework. FreiBox is controlled by a custom Python interface and integrates a new licking sensor (strain gauge lickometer) for controlling spatial licking behavioral tasks. In addition to allowing both discriminative and serial reversal learning, the Arduino can track mouse licking behavior in real time to control task events in a submillisecond timescale. To complete our setup, we also developed and validated an affordable commutator, which is crucial for recording calcium imaging with the Miniscope V4 in freely moving mice. Further, we demonstrated that FreiBox can be associated with 1-photon imaging and other open-source initiatives (e.g., Open Ephys) to form a versatile platform for exploring the neuronal substrates of licking-based behavioral flexibility in mice. The combination of the FreiBox behavioral setup and our low-cost commutator represents a highly competitive and complementary addition to the recently emerging battery of open-source initiatives.

AmberMDrun: A Scripting Tool for Running Amber MD in an Easy Way

MD simulations have been widely applied and become a powerful tool in the field of biomacromolecule simulations and computer-aided drug design, etc., which can estimate binding free energy between receptor and ligand. However, the inputs and force field preparation for performing Amber MD is somewhat complicated, and challenging for beginners. To address this issue, we have developed a script for automatically preparing Amber MD input files, balancing the system, performing Amber MD for production, and predicting receptor-ligand binding free energy. This script is open-source, extensible and can support customization. The core code is written in C++ and has a Python interface, providing both efficient performance and convenience.

ChiLife: An open-source Python package for in silico spin labeling and integrative protein modeling.

Here we introduce chiLife, a Python package for site-directed spin label (SDSL) modeling for electron paramagnetic resonance (EPR) spectroscopy, in particular double electron-electron resonance (DEER). It is based on in silico attachment of rotamer ensemble representations of spin labels to protein structures. chiLife enables the development of custom protein analysis and modeling pipelines using SDSL EPR experimental data. It allows the user to add custom spin labels, scoring functions and spin label modeling methods. chiLife is designed with integration into third-party software in mind, to take advantage of the diverse and rapidly expanding set of molecular modeling tools available with a Python interface. This article describes the main design principles of chiLife and presents a series of examples.

Core Reload Design Using Genetic Optimization for Cost Savings in a Two-Reactor Power Plant With Used Fuel Sharing

A many-reactor power plant with a shared used fuel pool presents significant challenges and a novel opportunity for fuel management optimization. We develop a Python package and interface it with optimization software and core modeling software to automate exploration of the design space for the multireactor multicycle problem. A genetic algorithm is used to search the core reload design space for a two-reactor system, with and without used fuel sharing. With equal computational effort, we find that the fuel-sharing strategy slightly lowers cost.

Visualizing the Implicit Model Selection Tradeoff

The recent rise of machine learning (ML) has been leveraged by practitioners and researchers to provide new solutions to an ever growing number of business problems. As with other ML applications, these solutions rely on model selection, which is typically achieved by evaluating certain metrics on models separately and selecting the model whose evaluations (i.e., accuracy-related loss and/or certain interpretability measures) are optimal. However, empirical evidence suggests that, in practice, multiple models often attain competitive results. Therefore, while models’ overall performance could be similar, they could operate quite differently. This results in an implicit tradeoff in models’ performance throughout the feature space which resolving requires new model selection tools. This paper explores methods for comparing predictive models in an interpretable manner to uncover the tradeoff and help resolve it. To this end, we propose various methods that synthesize ideas from supervised learning, unsupervised learning, dimensionality reduction, and visualization to demonstrate how they can be used to inform model developers about the model selection process. Using various datasets and a simple Python interface, we demonstrate how practitioners and researchers could benefit from applying these approaches to better understand the broader impact of their model selection choices.

Foldcomp: a library and format for compressing and indexing large protein structure sets.

Highly accurate protein structure predictors have generated hundreds of millions of protein structures; these pose a challenge in terms of storage and processing. Here, we present Foldcomp, a novel lossy structure compression algorithm, and indexing system to address this challenge. By using a combination of internal and Cartesian coordinates and a bi-directional NeRF-based strategy, Foldcomp improves the compression ratio by a factor of three compared to the next best method. Its reconstruction error of 0.08 Å is comparable to the best lossy compressor. It is five times faster than the next fastest compressor and competes with the fastest decompressors. With its multi-threading implementation and a Python interface that allows for easy database downloads and efficient querying of protein structures by accession, Foldcomp is a powerful tool for managing and analysing large collections of protein structures. Foldcomp is a free open-source software (GPLv3) and available for Linux, macOS, and Windows at https://foldcomp.foldseek.com. Foldcomp provides the AlphaFold Swiss-Prot (2.9GB), TrEMBL (1.1TB), and ESMatlas HQ (114GB) database ready-for-download.

Deep time spatio‐temporal data analysis using pyGPlates with PlateTectonicTools and GPlately

AbstractPyGPlates is an open‐source Python library to visualize and edit plate tectonic reconstructions created using GPlates. The Python API affords a greater level of flexibility than GPlates to interrogate plate reconstructions and integrate with other Python workflows. GPlately was created to accelerate spatio‐temporal data analysis leveraging pyGPlates and PlateTectonicTools within a simplified Python interface. This object‐oriented package enables the reconstruction of data through deep geologic time (points, lines, polygons and rasters), the interrogation of plate kinematic information (plate velocities, rates of subduction and seafloor spreading), the rapid comparison between multiple plate motion models, and the plotting of reconstructed output data on maps. All tools are designed to be parallel‐safe to accelerate spatio‐temporal analysis over multiple CPU processors.

FeOs: An Open-Source Framework for Equations of State and Classical Density Functional Theory

In this work, we present an open-source software package, referred to as FeOs─Framework for Equations of State and Classical Density Functional Theory. FeOs is a collection of interfaces and data types that can be used (1) to implement thermodynamic equations of state and Helmholtz energy functionals for classical density functional theory, and (2) to compute thermodynamic properties of pure substances and mixtures, phase equilibria, and interfacial properties such as surface tensions and adsorption isotherms. The framework is written in the Rust programming language with a complete Python interface and is designed with a focus on usability and extensibility. It is openly available on GitHub (https://github.com/feos-org/feos). Equations of state can be implemented in Rust, yielding performant code, or as a Python class, which is useful for prototyping and with less emphasis on execution speed. In both cases, the user has to implement a single function: the Helmholtz energy. FeOs then uses generalized (hyper-) dual numbers to evaluate the Helmholtz energy as well as the required exact partial (higher-order) derivatives. Using this type of automatic differentiation delivers performance without the need for implementing any analytical derivatives. The performance is further enhanced by a caching mechanism that avoids duplicate model evaluations. Together with the core interfaces and functionalities for equations of state and classical density functional theory, we provide implementations for multiple models such as the PC-SAFT equation of state (with homo- and heterosegmented group contribution methods) and Helmholtz energy functionals (including segment-based functionals). To showcase a selection of FeOs’ features, an example study of the adsorption of biogas in porous media using the PC-SAFT functional is provided.

Application of a supercomputer Tianhe-II in an electron-positron collider experiment BESIII

Precision measurements and new physics searches require massive computation in high energy physics experiments. Supercomputer remains one of the most powerful computing resources in various areas. Taking the BESIII experiment as an illustration, we deploy the offline software BOSS into the top-tier supercomputer “Tianhe-II” with the help of Singularity. With very limited internet connection bandwidth and without root privilege, we synchronize and maintain the simulation software up to date through CVMFS successfully, and an acceleration rate in a comparison of HPC and HTC is realized for the same large-scale task. We solve two problems of the real-time internet connection and the conflict of loading locker by a deployment of a squid server and using fuse in memory in each computing node. We provide a MPI python interface for high throughput (HT) parallel computation in Tianhe-II. Meanwhile, the program to deal with data output is also specially aligned so that there is no queue issue in the input/output (I/O) task. The acceleration rate in simulation reaches 80%, as we have done the simulation tests up to 15K processes in parallel.

PyMieSim: an open-source library for fast and flexible far-field Mie scattering simulations

Mie scattering simulations are essential to designing sensing and imaging devices for atmospheric and biomedical sciences applications. Current simulators based on Lorenz-Mie theory allow obtaining scattering phase functions for several scatterer geometries. Still, they are not optimized for simulating ranging parameters and do not compute coupling powers into coherent or incoherent detectors. Here, we present PyMieSim: a Python interface wrapping a C++ parallelized core for a fast, flexible, and highly visual simulation package, allowing for the study of ranges of parameters for different source, scatterer, and detector configurations. We demonstrate two applications: flow-cytometry geometry optimization and few-mode optical coherence tomography contrast analysis.