Python-based Software Research Articles

OpenGridGym: An Open-Source AI-Friendly Toolkit for Distribution Market Simulation

This paper presents OpenGridGym, an open-source Python-based package that allows for seamless integration of distribution market simulation with state-of-the-art artificial intelligence (AI) decision-making algorithms. We present the architecture and design choice for the proposed framework, elaborate on how users interact with OpenGridGym, and highlight its value by providing multiple cases to demonstrate its use. Four modules are used in any simulation: (1) the physical grid, (2) market mechanisms, (3) a set of trainable agents which interact with the former two modules, and (4) environment module that connects and coordinates the above three. We provide templates for each of those four, but they are easily interchangeable with custom alternatives. Several case studies are presented to illustrate the capability and potential of this toolkit in helping researchers address key design and operational questions in distribution electricity markets.

Implementing Open-Source Information Systems for Assessing and Managing the Seismic Vulnerability of Historical Constructions

The characterisation of the seismic vulnerability of historical constructions represents a complex problem in which the typological variability, the difficulty of performing reliable large-scale assessments and dealing with a large database all play a role. Nevertheless, reducing the uncertainty regarding the structural vulnerability of the existing building stock (mostly for small and/or isolated human settlements) is key for risk assessment and management. The present work proposes a novel approach based on the integration of a series of open-source tools for assembling a vulnerability-oriented database that is linked to a series of external services for increasing its capabilities. The database was implemented in a Geographical Information System (GIS) environment and contains the survey of a seismic vulnerability index for masonry constructions based on an adapted version of the GNDT-II approach. A customised Python-based software for reading, managing and editing the database is herein presented. This program allows the execution of the most typical operations with no assistance from the GIS environment, facilitating user interaction. Furthermore, the calculations regarding the vulnerability index and levels of damage have been implemented in this program. Alternatives for distributing the database are implemented and discussed, such as cloud-based distribution and the use of the Transactional Web Feature Service (WFS-T) protocol for its virtual publishing. The entire framework herein presented is a replicable and feasible workflow that can be set even with reduced infrastructure, allowing a progressive enlargement.

Computational control of gene expression in individual yeast using reactive microscopy.

Biotechnology is rapidly improving, largely due to our ability to manipulate genetic material and efficiently measure biological processes. This talk will describe our advances in computational methods and experimental platforms to probe and control stochastic biological processes within individual cells under a microscope. I will describe a novel optogenetics-based approach in which cells are segmented and controlled in real time. An optogenetically regulated gene in each yeast cell under the microscope is modeled using the Chemical Master Equation and controlled independently using a digital micromirror device to send light to only that cell. I will briefly describe the open source python-based software we developed, called MicroMator, that enabled this work, and show how we used it for optogenetically controlled recombination and adaptive illumination of a photobleaching fluorophore. This work brings together stochastic modeling of gene regulation, machine learning based image processing, and automated microscopy to provide methods for understanding basic cellular behavior.

The Deuterium Calculator: An Open-Source Tool for Hydrogen-Deuterium Exchange Mass Spectrometry Analysis.

Hydrogen-deuterium exchange mass spectrometry (HDX-MS) is a powerful protein footprinting technique to study protein dynamics and binding; however, HDX-MS data analysis is often challenging and time-consuming. Moreover, the HDX community is expanding to investigate multiprotein and highly complex protein systems which further complicates data analysis. Thus, a simple, open-source software package designed to analyze large and highly complex protein systems is needed. In this vein, we have developed "The Deuterium Calculator", a Python-based software package for HDX-MS data analysis. The Deuterium Calculator is capable of differential and nondifferential HDX-MS analysis, produces standardized data files according to recommendations from the International Conference on Hydrogen-Exchange Mass Spectrometry (IC-HDX) to increase transparency in data analysis, and generates Woods' plots for statistical analysis and data visualization. This standard output can be used to perform time dependent deuteration studies and for the study of protein folding kinetics or differential uptake. Moreover, The Deuterium Calculator is capable of performing these analyses on large HDX-MS data sets (e.g., LC-HDX-MS from cell lysate digest). The Deuterium Calculator is freely available for download at https://github.com/OUWuLab/TheDeuteriumCalculator.git. Data are available via ProteomeXchange with identifier PXD036813.

EXOSpy: A python package to investigate the terrestrial exosphere and its FUV emission

The exosphere is the uppermost layer of the terrestrial atmosphere, mainly composed of atomic hydrogen (H) that resonantly scatters solar far-ultraviolet (FUV) photons at 121.56 nm, also referred to as Lyman-Alpha (Ly-α) emission. Analysis of this emission has been used to determine the global, three-dimensional, and time-dependent exospheric H density structure, which is essential to assess the permanent escape of H to space as well as to determine their role in governing the transient response of terrestrial plasma environment to space weather. Thus, Ly-α emission and its by-product, the H density, are highly desirable to the magnetospheric community. On the other hand, this emission can also be regarded as a significant source of contamination during studies of FUV targets such as O/B-type stars, planetary and exoplanetary atmospheres, and the circumgalactic medium, especially when observations are acquired from Earth-orbiting instruments. In this case, accurate specification of exospheric Ly-α photon flux and its subsequent removal is required by the planetary and astrophysics community studying solar/extra-solar system objects. This work introduces EXOSpy, an open-source python-based package that provides several models of terrestrial exospheric H density and calculates exospheric Ly-α emission with a high potential to contribute to investigations in both communities. We present several examples to demonstrate how EXOSpy can be used to (i) validate current and new exospheric models based on actual Ly-α radiance data, (ii) estimate exospheric contamination for a given instrument’s line-of-sight and spatial location, and (iii) provide support for new space-based FUV instrument design.

MEDUSA©: A novel Python-based software ecosystem to accelerate brain-computer interface and cognitive neuroscience research

Background and objective: Neurotechnologies have great potential to transform our society in ways that are yet to be uncovered. The rate of development in this field has increased significantly in recent years, but there are still barriers that need to be overcome before bringing neurotechnologies to the general public. One of these barriers is the difficulty of performing experiments that require complex software, such as brain-computer interfaces (BCI) or cognitive neuroscience experiments. Current platforms have limitations in terms of functionality and flexibility to meet the needs of researchers, who often need to implement new experimentation settings. This work was aimed to propose a novel software ecosystem, called MEDUSA©, to overcome these limitations.Methods: We followed strict development practices to optimize MEDUSA© for research in BCI and cognitive neuroscience, making special emphasis in the modularity, flexibility and scalability of our solution. Moreover, it was implemented in Python, an open-source programming language that reduces the development cost by taking advantage from its high-level syntax and large number of community packages.Results: MEDUSA© provides a complete suite of signal processing functions, including several deep learning architectures or connectivity analysis, and ready-to-use BCI and neuroscience experiments, making it one of the most complete solutions nowadays. We also put special effort in providing tools to facilitate the development of custom experiments, which can be easily shared with the community through an app market available in our website to promote reproducibility.Conclusions: MEDUSA© is a novel software ecosystem for modern BCI and neurotechnology experimentation that provides state-of-the-art tools and encourages the participation of the community to make a difference for the progress of these fields. Visit the official website at https://www.medusabci.com/ to know more about this project.

NiftyPAD - Novel Python Package for Quantitative Analysis of Dynamic PET Data

Current PET datasets are becoming larger, thereby increasing the demand for fast and reproducible processing pipelines. This paper presents a freely available, open source, Python-based software package called NiftyPAD, for versatile analyses of static, full or dual-time window dynamic brain PET data. The key novelties of NiftyPAD are the analyses of dual-time window scans with reference input processing, pharmacokinetic modelling with shortened PET acquisitions through the incorporation of arterial spin labelling (ASL)-derived relative perfusion measures, as well as optional PET data-based motion correction. Results obtained with NiftyPAD were compared with the well-established software packages PPET and QModeling for a range of kinetic models. Clinical data from eight subjects scanned with four different amyloid tracers were used to validate the computational performance. NiftyPAD achieved R^2>0.999 correlation with PPET, with absolute difference sim 10^{-2} for linearised Logan and MRTM2 methods, and R^2>0.999999 correlation with QModeling, with absolute difference sim 10^{-4} for basis function based SRTM and SRTM2 models. For the recently published SRTM ASL method, which is unavailable in existing software packages, high correlations with negligible bias were observed with the full scan SRTM in terms of non-displaceable binding potential (R^2=0.96), indicating reliable model implementation in NiftyPAD. Together, these findings illustrate that NiftyPAD is versatile, flexible, and produces comparable results with established software packages for quantification of dynamic PET data. It is freely available (https://github.com/AMYPAD/NiftyPAD), and allows for multi-platform usage. The modular setup makes adding new functionalities easy, and the package is lightweight with minimal dependencies, making it easy to use and integrate into existing processing pipelines.

Comparison of double-differential cross-section between nuclear data library and experimental data for photoneutron production

In this study, we compared double differential cross-sections (DDXs) between the experimental data obtained by 16.6 MeV of polarized photons and the DDXs from JENDL2004 and JENDL/PD-2016.1 for 197Au, natPb, natCu, and natTi targets. Using Python-based software, we extracted the DDXs from the nuclear data libraries (NDLs), which were subsequently deduced considering the abundances of each target’s isotopes, the width of the photon beam, and the energy resolution of the neutron detectors. For the Ti target, the experimental DDX data were consistent with that of the NDLs. For Pb, Au, and Cu targets, the experimental DDX data at neutron energies higher than 4 MeV were larger than the DDX values obtained from the NDLs. The inconsistency between the DDXs of the experimental data and those of the NDLs indicates the need to improve the physical models to generate the spectrum of photoneutrons.

Defining an Integrated and Computed Methodology Approach for Sentiment and Psychographic Analysis in Tourism Research

High-performance computational resources and artificial intelligence-based tools can enhance tourism research and marketing. However, a formal methodological approach using digital technologies in this field is still missing. This research work presents the preliminary results of defining an integrated computational methodology in tourism research and marketing. In addition, the paper aims to provide guidelines for a methodological approach leveraging technological resources and Big Data. The proposed research method is based on online User-Generated Content (UGC) analysis through a psychographic approach based on the Big Five Model, Sentiment Analysis, and Machine Learning techniques. The study is supported by high-performance computing resources, artificial intelligence-based tools, and open-source Python-based software for data collection, text analysis, and psychographic attribution. Results show a remarkable performance of the BFF prediction model and confirm the role of personality in the tourists’ decision-making and appreciation of a site. Future developments of this project involve using the acquired structured dataset labeled with sentiment and psychographic attribution to create a further prediction model on tourist segments and appreciation as part of a marketing strategy in tourism management. Future research should push forward the development of further integrated and performing computer-based methodology in tourism research and marketing, leveraging the massive amount of data and the potential of high-performance computing techniques. The main contribution of this research effort is twofold: the definition of a general-purpose BFF/Sentiment Analysis methodology and the development of a prediction model from online UGC based on the Big Five personality traits in the tourism research scenario.

UncertainSCI: Uncertainty quantification for computational models in biomedicine and bioengineering

Background:Computational biomedical simulations frequently contain parameters that model physical features, material coefficients, and physiological effects, whose values are typically assumed known a priori. Understanding the effect of variability in those assumed values is currently a topic of great interest. A general-purpose software tool that quantifies how variation in these parameters affects model outputs is not broadly available in biomedicine. For this reason, we developed the ‘UncertainSCI’ uncertainty quantification software suite to facilitate analysis of uncertainty due to parametric variability. Methods:We developed and distributed a new open-source Python-based software tool, UncertainSCI, which employs advanced parameter sampling techniques to build polynomial chaos (PC) emulators that can be used to predict model outputs for general parameter values. Uncertainty of model outputs is studied by modeling parameters as random variables, and model output statistics and sensitivities are then easily computed from the emulator. Our approaches utilize modern, near-optimal techniques for sampling and PC construction based on weighted Fekete points constructed by subsampling from a suitably randomized candidate set. Results:Concentrating on two test cases—modeling bioelectric potentials in the heart and electric stimulation in the brain—we illustrate the use of UncertainSCI to estimate variability, statistics, and sensitivities associated with multiple parameters in these models. Conclusion:UncertainSCI is a powerful yet lightweight tool enabling sophisticated probing of parametric variability and uncertainty in biomedical simulations. Its non-intrusive pipeline allows users to leverage existing software libraries and suites to accurately ascertain parametric uncertainty in a variety of applications.

Robustica: customizable robust independent component analysis

BackgroundIndependent Component Analysis (ICA) allows the dissection of omic datasets into modules that help to interpret global molecular signatures. The inherent randomness of this algorithm can be overcome by clustering many iterations of ICA together to obtain robust components. Existing algorithms for robust ICA are dependent on the choice of clustering method and on computing a potentially biased and large Pearson distance matrix.ResultsWe present robustica, a Python-based package to compute robust independent components with a fully customizable clustering algorithm and distance metric. Here, we exploited its customizability to revisit and optimize robust ICA systematically. Of the 6 popular clustering algorithms considered, DBSCAN performed the best at clustering independent components across ICA iterations. To enable using Euclidean distances, we created a subroutine that infers and corrects the components’ signs across ICA iterations. Our subroutine increased the resolution, robustness, and computational efficiency of the algorithm. Finally, we show the applicability of robustica by dissecting over 500 tumor samples from low-grade glioma (LGG) patients, where we define two new gene expression modules with key modulators of tumor progression upon IDH1 and TP53 mutagenesis.Conclusionrobustica brings precise, efficient, and customizable robust ICA into the Python toolbox. Through its customizability, we explored how different clustering algorithms and distance metrics can further optimize robust ICA. Then, we showcased how robustica can be used to discover gene modules associated with combinations of features of biological interest. Taken together, given the broad applicability of ICA for omic data analysis, we envision robustica will facilitate the seamless computation and integration of robust independent components in large pipelines.

Dnadna: a deep learning framework for population genetics inference.

We present dnadna, a flexible python-based software for deep learning inference in population genetics. It is task-agnostic and aims at facilitating the development, reproducibility, dissemination and re-usability of neural networks designed for population genetic data. dnadna defines multiple user-friendly workflows. First, users can implement new architectures and tasks, while benefiting from dnadna utility functions, training procedure and test environment, which saves time and decreases the likelihood of bugs. Second, the implemented networks can be re-optimized based on user-specified training sets and/or tasks. Newly implemented architectures and pre-trained networks are easily shareable with the community for further benchmarking or other applications. Finally, users can apply pre-trained networks in order to predict evolutionary history from alternative real or simulated genetic datasets, without requiring extensive knowledge in deep learning or coding in general. dnadna comes with a peer-reviewed, exchangeable neural network, allowing demographic inference from SNP data, that can be used directly or retrained to solve other tasks. Toy networks are also available to ease the exploration of the software, and we expect that the range of available architectures will keep expanding thanks to community contributions. dnadna is a Python (≥3.7) package, its repository is available at gitlab.com/mlgenetics/dnadna and its associated documentation at mlgenetics.gitlab.io/dnadna/.

AugLiChem: data augmentation library of chemical structures for machine learning

Machine learning (ML) has demonstrated the promise for accurate and efficient property prediction of molecules and crystalline materials. To develop highly accurate ML models for chemical structure property prediction, datasets with sufficient samples are required. However, obtaining clean and sufficient data of chemical properties can be expensive and time-consuming, which greatly limits the performance of ML models. Inspired by the success of data augmentations in computer vision and natural language processing, we developed AugLiChem: the data augmentation library for chemical structures. Augmentation methods for both crystalline systems and molecules are introduced, which can be utilized for fingerprint-based ML models and graph neural networks (GNNs). We show that using our augmentation strategies significantly improves the performance of ML models, especially when using GNNs. In addition, the augmentations that we developed can be used as a direct plug-in module during training and have demonstrated the effectiveness when implemented with different GNN models through the AugliChem library. The Python-based package for our implementation of Auglichem: Data augmentation library for chemical structures, is publicly available at: https://github.com/BaratiLab/AugLiChem.

Modeling geomagnetic induction in submarine cables

Submarine cables have become a vital component of modern infrastructure, but past submarine cable natural hazard studies have mostly focused on potential cable damage from landslides and tsunamis. A handful of studies examine the possibility of space weather effects in submarine cables. The main purpose of this study is to develop a computational model, using Python, of geomagnetic induction on submarine cables. The model is used to estimate the induced voltage in the submarine cables in response to geomagnetic disturbances. It also utilizes newly acquired knowledge from magnetotelluric studies and associated investigations of geomagnetically induced currents in power systems. We describe the Python-based software, its working principle, inputs/outputs based on synthetic geomagnetic field data, and compare its operational capabilities against analytical solutions. We present the results for different model inputs, and find: 1) the seawater layer acts as a shield in the induction process: the greater the ocean depth, the smaller the seafloor geoelectric field; and 2) the model is sensitive to the Ocean-Earth layered conductivity structure.

MetPy: A Meteorological Python Library for Data Analysis and Visualization

Abstract MetPy is an open-source, Python-based package for meteorology, providing domain-specific functionality built extensively on top of the robust scientific Python software stack, which includes libraries like NumPy, SciPy, Matplotlib, and xarray. The goal of the project is to bring the weather analysis capabilities of GEMPAK (and similar software tools) into a modern computing paradigm. MetPy strives to employ best practices in its development, including software tests, continuous integration, and automated publishing of web-based documentation. As such, MetPy represents a sustainable, long-term project that fills a need for the meteorological community. MetPy’s development is substantially driven by its user community, both through feedback on a variety of open, public forums like Stack Overflow, and through code contributions facilitated by the GitHub collaborative software development platform. MetPy has recently seen the release of version 1.0, with robust functionality for analyzing and visualizing meteorological datasets. While previous versions of MetPy have already seen extensive use, the 1.0 release represents a significant milestone in terms of completeness and a commitment to long-term support for the programming interfaces. This article provides an overview of MetPy’s suite of capabilities, including its use of labeled arrays and physical unit information as its core data model, unit-aware calculations, cross sections, skew T and GEMPAK-like plotting, station model plots, and support for parsing a variety of meteorological data formats. The general road map for future planned development for MetPy is also discussed.

SBbadger: biochemical reaction networks with definable degree distributions.

An essential step in developing computational tools for the inference, optimization and simulation of biochemical reaction networks is gauging tool performance against earlier efforts using an appropriate set of benchmarks. General strategies for the assembly of benchmark models include collection from the literature, creation via subnetwork extraction and de novo generation. However, with respect to biochemical reaction networks, these approaches and their associated tools are either poorly suited to generate models that reflect the wide range of properties found in natural biochemical networks or to do so in numbers that enable rigorous statistical analysis. In this work, we present SBbadger, a python-based software tool for the generation of synthetic biochemical reaction or metabolic networks with user-defined degree distributions, multiple available kinetic formalisms and a host of other definable properties. SBbadger thus enables the creation of benchmark model sets that reflect properties of biological systems and generate the kinetics and model structures typically targeted by computational analysis and inference software. Here, we detail the computational and algorithmic workflow of SBbadger, demonstrate its performance under various settings, provide sample outputs and compare it to currently available biochemical reaction network generation software. SBbadger is implemented in Python and is freely available at https://github.com/sys-bio/SBbadger and via PyPI at https://pypi.org/project/SBbadger/. Documentation can be found at https://SBbadger.readthedocs.io. Supplementary data are available at Bioinformatics online.

New Software for DQE Calculation in Digital Mammography Compliant with IEC 62220–1-2

Significant improvements in mammography systems have been achieved with the introduction of active matrix flat-panel digital detectors. The advent of this technology also makes it possible to implement computational methods for quantitative image analysis. This study describes new software created to perform detective quantum efficiency (DQE) calculations fully compliant with the IEC 62220–1-2 standard. Python-based software was developed that contains modules to calculate inverse conversion function, modulation transfer function (MTF), noise power spectrum (NPS), and DQE itself. A graphical user interface (GUI) and further add-ons make this software more user-friendly. Results are immediately displayed diagrammatically, and complete output data are exported to a .csv file. The code is available freely, as a compiled, executable file (.exe). The program was successfully tested using DICOM images obtained from mammography units from different manufacturers. This study also includes validation of the new software, based on comparisons of results obtained for the same set of data with two other, freely available programs.

Curation of BIDS (CuBIDS): A workflow and software package for streamlining reproducible curation of large BIDS datasets

The Brain Imaging Data Structure (BIDS) is a specification accompanied by a software ecosystem that was designed to create reproducible and automated workflows for processing neuroimaging data. BIDS Apps flexibly build workflows based on the metadata detected in a dataset. However, even BIDS valid metadata can include incorrect values or omissions that result in inconsistent processing across sessions. Additionally, in large-scale, heterogeneous neuroimaging datasets, hidden variability in metadata is difficult to detect and classify. To address these challenges, we created a Python-based software package titled “Curation of BIDS” (CuBIDS), which provides an intuitive workflow that helps users validate and manage the curation of their neuroimaging datasets. CuBIDS includes a robust implementation of BIDS validation that scales to large samples and incorporates DataLad––a version control software package for data––as an optional dependency to ensure reproducibility and provenance tracking throughout the entire curation process. CuBIDS provides tools to help users perform quality control on their images’ metadata and identify unique combinations of imaging parameters. Users can then execute BIDS Apps on a subset of participants that represent the full range of acquisition parameters that are present, accelerating pipeline testing on large datasets.

Toise: a framework to describe the performance of high-energy neutrino detectors

Neutrinos offer a unique window to the distant, high-energy universe. Several next-generation instruments are being designed and proposed to characterize the flux of TeV–EeV neutrinos. The projected physics reach of the detectors is often quantified with simulation studies. However, a complete Monte Carlo estimate of detector performance is costly from a computational perspective, restricting the number of detector configurations considered when designing the instruments. In this paper, we present a new Python-based software framework, toise, which forecasts the performance of a high-energy neutrino detector using parameterizations of the detector performance, such as the effective areas, angular and energy resolutions, etc. The framework can be used to forecast performance of a variety of physics analyses, including sensitivities to diffuse fluxes of neutrinos and sensitivity to both transient and steady state point sources. This parameterized approach reduces the need for extensive simulation studies in order to estimate detector performance, and allows the user to study the influence of single performance metrics, like the angular resolution, in isolation. The framework is designed to allow for multiple detector components, each with different responses and exposure times, and supports paramterization of both optical- and radio-Cherenkov (Askaryan) neutrino telescopes. In the paper, we describe the mathematical concepts behind toise and introduce the reader to the use of the framework.

BERT Probe: A python package for probing attention based robustness evaluation of BERT models

<h2>Abstract</h2> Transformer models based on attention-based architectures have been significantly successful in establishing state-of-the-art results in natural language processing (NLP). However, recent work about adversarial robustness of attention-based models show that their robustness is susceptible to adversarial inputs causing spurious outputs thereby raising questions about trustworthiness of such models. In this paper, we present BERT Probe which is a python-based package for evaluating robustness to attention attribution based on character-level and word-level evasion attacks and empirically quantifying potential vulnerabilities for sequence classification tasks. Additionally, BERT Probe also provides two out-of-the-box defenses against character-level attention attribution-based evasion attacks.