Open-source Python Library Research Articles

Formulation of perfect-crystal diffraction from Takagi-Taupin equations: numerical implementation in the crystalpy library.

The Takagi-Taupin equations are solved in their simplest form (zero deformation) to obtain the Bragg-diffracted and transmitted complex amplitudes. The case of plane-parallel crystal plates is discussed using a matrix model. The equations are implemented in an open-source Python library crystalpy adapted for numerical applications such as crystal reflectivity calculations and ray tracing.

Rapid QC-MS: Interactive Dashboard for Synchronous Mass Spectrometry Data Acquisition Quality Control.

Consistently collecting high-quality liquid chromatography-coupled tandem mass spectrometry (LC-MS/MS) data is a time-consuming hurdle for untargeted workflows. Analytical controls such as internal and biological standards are commonly included in high-throughput workflows, helping researchers recognize low-integrity specimens regardless of their biological source. However, evaluating these standards as data are collected has remained a considerable bottleneck─in both person-hours and accuracy. Here we present Rapid QC-MS, an automated, interactive dashboard for assessing LC-MS/MS data quality. Minutes after a new data file is written, a browser-viewable dashboard is updated with quality control results spanning multiple performance dimensions such as instrument sensitivity, in-run retention time shifts, and mass accuracy drift. Rapid QC-MS provides interactive visualizations that help users recognize acute deviations in these performance metrics, as well as gradual drifts over periods of hours, days, months, or years. Rapid QC-MS is open-source, simple to install, and highly configurable. By integrating open-source Python libraries and widely used MS analysis software, it can adapt to any LC-MS/MS workflow. Rapid QC-MS runs locally and offers optional remote quality control by syncing with Google Drive. Furthermore, Rapid QC-MS can operate in a semiautonomous fashion, alerting users to specimens with potentially poor analytical integrity via frequently used messaging applications. Initially developed for integration with Thermo Orbitrap workflows, Rapid QC-MS offers a fast, straightforward approach to help users collect high-quality untargeted LC-MS/MS data by eliminating many of the most time-consuming steps in manual data curation. Download for free: https://github.com/czbiohub-sf/Rapid-QC-MS.

ModelHamiltonian: A Python-scriptable library for generating 0-, 1-, and 2-electron integrals.

ModelHamiltonian is a free, open source, and cross-platform Python library designed to express model Hamiltonians, including spin-based Hamiltonians (Heisenberg and Ising models) and occupation-based Hamiltonians (Pariser-Parr-Pople, Hubbard, and Hückel models) in terms of 1- and 2-electron integrals, so that these systems can be easily treated by traditional quantum chemistry software programs. ModelHamiltonian was originally intended to facilitate the testing of new electronic structure methods using HORTON but emerged as a stand-alone research tool that we recognize has wide utility, even in an educational context. ModelHamiltonian is written in Python and adheres to modern principles of software development, including comprehensive documentation, extensive testing, continuous integration/delivery protocols, and package management. While we anticipate that most users will use ModelHamiltonian as a Python library, we include a graphical user interface so that models can be built without programming, based on connectivity/parameters inferred from, for example, a SMILES string. We also include an interface to ChatGPT so that users can specify a Hamiltonian in plain language (without learning ModelHamiltonian's vocabulary and syntax). This article marks the official release of the ModelHamiltonian library, showcasing its functionality and scope.

PyCI: A Python-scriptable library for arbitrary determinant CI.

PyCI is a free and open-source Python library for setting up and running arbitrary determinant-driven configuration interaction (CI) computations, as well as their generalizations to cases where the coefficients of the determinant are nonlinear functions of optimizable parameters. PyCI also includes functionality for computing the residual correlation energy, along with the ability to compute spin-polarized one- and two-electron (transition) reduced density matrices. PyCI was originally intended to replace the abinitio quantum chemistry functionality in the HORTON library but emerged as a standalone research tool, primarily intended to aid in method development, while maintaining high performance so that it is suitable for practical calculations. To this end, PyCI is written in Python, adopting principles of modern software development, including comprehensive documentation, extensive testing, continuous integration/delivery protocols, and package management. Computationally intensive steps, notably operations related to generating Slater determinants and computing their expectation values, are delegated to low-level C++ code. This article marks the official release of the PyCI library, showcasing its functionality and scope.

PyJedAI: A Library with Resolution-Related Structures and Procedures for Products

This work presents an open-source Python library, named pyJedAI, which provides functionalities supporting the creation of algorithms related to product entity resolution. Building over existing state-of-the-art resolution algorithms, the tool offers a plethora of important tasks required for processing product data collections. It can be easily used by researchers and practitioners for creating algorithms analyzing products, such as real-time ad bidding, sponsored search, or pricing determination. In essence, it allows users to easily import product data from the possible sources, compare products in order to detect either similar or identical products, generate a graph representation using the products and desired relationships, and either visualize or export the outcome in various forms. Our experimental evaluation on data from well-known online retailers illustrates high accuracy and low execution time for the supported tasks. To the best of our knowledge, this is the first Python package to focus on product entities and provide this range of product entity resolution functionalities. History: Accepted by Ted Ralphs, Area Editor for Software Tools. Funding: This was partially funded by the EU project STELAR (Horizon Europe) [Grant 101070122]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0410 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0410 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Code and Data Repository for pyJedAI: A Library with Resolution-Related Structures and Procedures for Products

This work presents an open-source Python library, named pyJedAI, which provides functionalities supporting the creation of algorithms related to Product Entity Resolution. Building over existing state-of-the-art resolution algorithms (Papadakis et al. 2021a), the tool offers a plethora of important tasks required for processing product data collections. It be can easily used by researchers and practitioners for creating algorithms analyzing products, such as real-time ads bidding, sponsored search, or pricing determination. In essence, it allows to easily import product data from the possible sources, compare products in order to detect either similar or identical products, generate a graph representation using the products and desired relationships, and either visualize or export the outcome in various forms. Our experimental evaluation on data from well-known online retailers illustrates high accuracy and low execution time for the supported tasks. To the best of our knowledge this is the first Python package to focus on product entities and provide this range of Product Entity Resolution functionalities.

Synergy of Experiment and Broadened Exploration of Ab Initio Calculations for Understanding of Lanthanide-Pentacyanidocobaltate Molecular Nanomagnets and Their Optical Properties.

We present a synergistic experimental-theoretical methodology for the investigation of lanthanide-based single-molecule magnets (SMMs), demonstrated using the example of novel heterometallic molecules incorporating Nd3+/Ce3+ ions combined with three different, rarely explored, pentacyanidocobaltate(III) metalloligands, [CoIII(CN)5(azido/nitrito-N/iodido)]3-. The theoretical part of our approach broadens the exploration of ab initio calculations for lanthanide(III) complexes toward the convenient simulations of such physical characteristics as directional dependences of Helmholtz energy, magnetization, susceptibility, and their thermal and field evolution, as well as light absorption and emission bands. This work was conducted using newly designed SlothPy software (https://slothpy.org). It is introduced as an open-source Python library for simulating various physical properties from first-principles based on results of electronic structure calculations obtained within popular quantum chemistry packages. The computational results were confronted with spectroscopic and ac/dc-magnetic data, the latter analyzed using previously designed relACs software. The combination of experimental and computational methods gave insight into phonon-assisted magnetic relaxation mechanisms, disentangling them from the temperature-independent quantum tunneling of magnetization and emphasizing the role of local-mode processes. This study provides an understanding of the changes in lanthanide(III) magnetic anisotropy introduced with pentacyanidocobaltates(III) modifications, theoretically exploring also potential applications of reported compounds as anisotropy switches or optical thermometers.

Ichor: A Python library for computational chemistry data management and machine learning force field development.

We present ichor, an open-source Python library that simplifies data management in computational chemistry and streamlines machine learning force field development. Ichor implements many easily extensible file management tools, in addition to a lazy file reading system, allowing efficient management of hundreds of thousands of computational chemistry files. Data from calculations can be readily stored into databases for easy sharing and post-processing. Raw data can be directly processed by ichor to create machine learning-ready datasets. In addition to powerful data-related capabilities, ichor provides interfaces to popular workload management software employed by High Performance Computing clusters, making for effortless submission of thousands of separate calculations with only a single line of Python code. Furthermore, a simple-to-use command line interface has been implemented through a series of menu systems to further increase accessibility and efficiency of common important ichor tasks. Finally, ichor implements general tools for visualization and analysis of datasets and tools for measuring machine-learning model quality both on test set data and in simulations. With the current functionalities, ichor can serve as an end-to-end data procurement, data management, and analysis solution for machine-learning force-field development.

Thermal Modeling of Li-Ion Cylindrical Cells at Scale

The electric vehicle industry has created a demand for batteries with higher power densities. The current design choice for low-weight lithium-ion battery packs for electric vehicles is to create stacks arranged in parallel or series configurations to match the power demands. The individual cells are separated and placed in a cooling duct to pull heat away from the longitudinal surface convective. The amount of cooling is dependent on the internal temperature of batteries core. The current design of the cylindrical cell is to roll the cathode, anode, and separators into a jelly roll together and attach a tab to the cathode and anode at opposite corners of the sheet. The tabs connect to the positive and negative terminals of the cell to draw current away from the cell. The tabs may act as an agent for drawing heat away from the center of the battery. Currently, commonly used cylindrical cells have a form factor of 18650, 21700, or 26650. The thermal, geometrical, and electrical properties of 18650 and 21700 cylindrical cells are studied and it is found that the 21700 cells showed additional degradation and stronger heating. Recently, companies like Tesla have produced cylindrical cells with a 4680-form factor, which is a decent way to increase the ratio of active material vs. inactive material in a cylindrical cell. However, large diameter thick cylindrical cells suffer from thermal issues. Even large format pouch cells suffer through heat trapped within the stack.Since the thermal conductivity of the anode and cathode electrodes is often near 1 W/m-K, it becomes difficult for the heat to be extracted from the center of a large-diameter cylindrical cell without forming a considerable thermal gradient. PyBaMM, an open-source mathematical modeling software written in Python programming language, has the capability to simulate different electrochemical models such as the Doyle-Fuller-Newman (DFN), Single particle model (SPM), and SPMe, and varying chemistry. The software provides easy access to the cell parameters modification and graphical user interface capabilities. The PyBaMM framework also allows to simulation lumped, isothermal, and base thermal models which are electrochemically coupled. PyBaMM uses CasADi (computer algebra systems algorithmic differentiation). The thermal gradient and heat dynamics within a porous electrode are modeled using PoreSpy and openpnm, two open-source Python libraries used to design porous networks and model physical equations such as governing heat flow, concentration gradient, and movement of water/oil through porous shale media.We have explored and combined heat dynamic models for Li-Ion cylindrical cells at both cell and electrode levels with reduced-order Doyle-Fuller-Newman electro-chemical model frameworks implemented in the PyBaMM python library. Radial temperature gradient at cell and electrode levels were both generated from simulations with a distributed parameter model as a function of electrolyte and separator thickness and thermodynamic properties. Figure 1

PhyloJunction: a computational framework for simulating, developing, and teaching evolutionary models.

We introduce PhyloJunction, a computational framework designed to facilitate the prototyping, test- ing, and characterization of evolutionary models. PhyloJunction is distributed as an open-source Python library that can be used to implement a variety of models, thanks to its flexible graphical modeling architecture and dedicated model specification language. Model design and use are exposed to users via command-line and graphical interfaces, which integrate the steps of simulating, summarizing, and visualizing data. This paper describes the features of PhyloJunction - which include, but are not limited to, a general implementation of a popular family of phylogenetic diversification models - and, moving forward, how it may be expanded to not only include new models, but to also become a platform for conducting and teaching statistical learning.

Open-source hardware and software for the measurement, characterization, reporting, and correction of geometric distortion in MRI.

Geometric distortion is a serious problem in MRI, particularly in MRI guided therapy. A lack of affordable and adaptable tools in this area limits research progress and harmonized quality assurance. To develop and test a suite of open-source hardware and software tools for the measurement, characterization, reporting, and correction of geometric distortion in MRI. An open-source python library was developed, comprising modules for parametric phantom design, data processing, spherical harmonics, distortion correction, and interactive reporting. The code was used to design and manufacture a distortion phantom consisting of 618 oil filled markers covering a sphere of radius 150mm. This phantom was imaged on a CT scanner and a novel split-bore 1.0 T MRI magnet. The CT images provide distortion-free dataset. These data were used to test all modules of the open-source software. All markers were successfully extracted from all images. The distorted MRI markers were mapped to undistorted CT data using an iterative search approach. Spherical harmonics reconstructed the fitted gradient data to 1.0±0.6% of the input data. High resolution data were reconstructed via spherical harmonics and used to generate an interactive report. Finally, distortion correction on an independent data set reduced distortion inside the DSV from 5.5±3.1 to 1.6±0.8mm. Open-source hardware and software for the measurement, characterization, reporting, and correction of geometric distortion in MRI have been developed. The utility of these tools has been demonstrated via their application on a novel 1.0 T split bore magnet.

GBasis: A Python library for evaluating functions, functionals, and integrals expressed with Gaussian basis functions.

GBasis is a free and open-source Python library for molecular property computations based on Gaussian basis functions in quantum chemistry. Specifically, GBasis allows one to evaluate functions expanded in Gaussian basis functions (including molecular orbitals, electron density, and reduced density matrices) and to compute functionals of Gaussian basis functions (overlap integrals, one-electron integrals, and two-electron integrals). Unique features of GBasis include supporting evaluation and analytical integration of arbitrary-order derivatives of the density (matrices), computation of a broad range of (screened) Coulomb interactions, and evaluation of overlap integrals of arbitrary numbers of Gaussians in arbitrarily high dimensions. For circumstances where the flexibility of GBasis is less important than high performance, a seamless Python interface to the Libcint C package is provided. GBasis is designed to be easy to use, maintain, and extend following many standards of sustainable software development, including code-quality assurance through continuous integration protocols, extensive testing, comprehensive documentation, up-to-date package management, and continuous delivery. This article marks the official release of the GBasis library, outlining its features, examples, and development.

3D airway geometry analysis of factors in airway navigation failure for lung nodules

BackgroundThis study aimed to quantitatively reveal contributing factors to airway navigation failure during radial probe endobronchial ultrasound (R-EBUS) by using geometric analysis in a three-dimensional (3D) space and to investigate the clinical feasibility of prediction models for airway navigation failure.MethodsWe retrospectively reviewed patients who underwent R-EBUS between January 2017 and December 2018. Geometric quantification was analyzed using in-house software built with open-source python libraries including the Vascular Modeling Toolkit (http://www.vmtk.org), simple insight toolkit (https://sitk.org), and sci-kit image (https://scikit-image.org). We used a machine learning-based approach to explore the utility of these significant factors.ResultsOf the 491 patients who were eligible for analysis (mean age, 65 years +/- 11 [standard deviation]; 274 men), the target lesion was reached in 434 and was not reached in 57. Twenty-seven patients in the failure group were matched with 27 patients in the success group based on propensity scores. Bifurcation angle at the target branch, the least diameter of the last section, and the curvature of the last section are the most significant and stable factors for airway navigation failure. The support vector machine can predict airway navigation failure with an average area under the curve of 0.803.ConclusionsGeometric analysis in 3D space revealed that a large bifurcation angle and a narrow and tortuous structure of the closest bronchus from the lesion are associated with airway navigation failure during R-EBUS. The models developed using quantitative computer tomography scan imaging show the potential to predict airway navigation failure.

Обзор open-source библиотек для решения задач прогнозирования временных рядов

<p>An overview of various open-source Python libraries for time series analysis and forecasting is presented. It covers such tools as Prophet, Kats, Merlion, as well as ARIMA, LSTM algorithms, which allow to study seasonality, trends and anomalies in time series data. The capabilities of each library, their advantages and applications in time series data analysis are discussed in detail.</p>

Melodia: A Python Library for Protein Structure Analysis.

Analysing protein structure similarities is an important step in protein engineering and drug discovery. Methodologies that are more advanced than simple RMSD are available but often require extensive mathematical or computational knowledge for implementation. Grouping and optimising such tools in an efficient open-source library increases accessibility and encourages the adoption of more advanced metrics. Melodia is a Python library with a complete set of components devised for describing, comparing and analysing the shape of protein structures using differential geometry of three-dimensional curves and knot theory. It can generate robust geometric descriptors for thousands of shapes in just a few minutes. Those descriptors are more sensitive to structural feature variation than RMSD deviation. Melodia also incorporates sequence structural annotation and three-dimensional visualisations. Melodia is an open-source Python library freely available on https://github.com/rwmontalvao/Melodia_py, along with interactive Jupyter Notebook tutorials. Supplementary data are available at Bioinformatics online.

Python Indian Weather Radar Toolkit (pyiwr): An open-source Python library for processing, analyzing and visualizing weather radar data

The Python Indian Weather Radar Toolkit, abbreviated as "pyiwr", is an open-source Python library tailored for the purpose of handling data from the Indian Doppler Weather Radar (DWR). This paper provides a comprehensive overview of the pyiwr, which serves as a toolkit to read, analyze, process, and visualize weather radar data. Apart from this, the toolkit offers a range of robust functions implementing various algorithms covering several aspects of the radar data processing and quality control that facilitate the manipulation and analysis of weather radar data. To demonstrate the practical applicability of pyiwr, various case studies are presented, focusing on processing raw reflectivity data (clutter correction), Quantitative Precipitation Estimation (QPE) using Z-R relationship and time-series analysis of reflectivity and rain intensity, both spatially as well as at a specific location, during various meteorological events. This module enhances the accessibility and compatibility of radar data, enabling researchers, weather forecasters, and hydrologists to efficiently work with DWR data (particularly Indian DWR) that fosters advancements in weather radar research and applications. The open availability of pyiwr's source code on GitHub ensures that researchers and practitioners can not only access the toolkit but also contribute to its ongoing development.

TensorKrowch: Smooth integration of tensor networks in machine learning

Tensor networks are factorizations of high-dimensional tensors into networks of smaller tensors. They have applications in physics and mathematics, and recently have been proposed as promising machine learning architectures. To ease the integration of tensor networks in machine learning pipelines, we introduce TensorKrowch, an open source Python library built on top of PyTorch. Providing a user-friendly interface, TensorKrowch allows users to construct any tensor network, train it, and integrate it as a layer in more intricate deep learning models. In this paper, we describe the main functionality and basic usage of TensorKrowch, and provide technical details on its building blocks and the optimizations performed to achieve efficient operation.

Feature selection using metaheuristics made easy: Open source MAFESE library in Python

Artificial intelligence (AI) often relies on feature selection (FS) to recognize and highlight the most relevant and major features in a dataset. The procedure of training and optimizing AI systems with key data points is decisive for its development and efficacy. To address this challenge, the present study introduces MAFESE, an open-source Python library that employs metaheuristic algorithms for selecting the optimal set of attributes, particularly when dealing with complex and high-dimensional data. MAFESE encompasses a wide range of feature selection techniques, including unsupervised-based, filter-based, embedded-based, and wrapper-based methods. Notably, within the wrapper-based category, MAFESE offers users access to over 200 metaheuristic algorithms, empowering them to choose the most suitable algorithm based on their specific datasets and requirements. Additionally, MAFESE incorporates built-in evaluation metrics that enable efficient comparison among various algorithms. Open-source design of MAFESE encourages cooperation within the data science community, allowing for continuous upgrades. This collaborative environment promotes the sharing of ideas, proposals, and changes, resulting in a stronger and more adaptive feature selection framework. MAFESE distinguishes itself with an easy-to-use Python interface that follows object-oriented programming concepts. It supports both experienced researchers and practitioners. MAFESE offers many resources, including documentation, examples, and test cases, for a smooth user onboarding experience. The modular architecture enables users to enhance features and interface with other tools, such as scikit-learn. MAFESE can be a valuable tool for identifying meaningful features in complicated, high-dimensional datasets, making it a significant addition to feature selection. MAFESE utilizes metaheuristic algorithms to help users tackle complex feature selection problems more efficiently and accurately. Through openness and teamwork, MAFESE may become a comprehensive resource that responds to the evolving demands of the data science community. The source code and materials of MAFESE are publicly available in the official GitHub repository at https://github.com/thieu1995/mafese.

Tstrait: a quantitative trait simulator for ancestral recombination graphs.

Ancestral recombination graphs (ARGs) encode the ensemble of correlated genealogical trees arising from recombination in a compact and efficient structure and are of fundamental importance in population and statistical genetics. Recent breakthroughs have made it possible to simulate and infer ARGs at biobank scale, and there is now intense interest in using ARG-based methods across a broad range of applications, particularly in genome-wide association studies (GWAS). Sophisticated methods exist to simulate ARGs using population genetics models, but there is currently no software to simulate quantitative traits directly from these ARGs. To apply existing quantitative trait simulators users must export genotype data, losing important information about ancestral processes and producing prohibitively large files when applied to the biobank-scale datasets currently of interest in GWAS. We present tstrait, an open-source Python library to simulate quantitative traits on ARGs, and show how this user-friendly software can quickly simulate phenotypes for biobank-scale datasets on a laptop computer. tstrait is available for download on the Python Package Index. Full documentation with examples and workflow templates is available on https://tskit.dev/tstrait/docs/, and the development version is maintained on GitHub (https://github.com/tskit-dev/tstrait).

Grid: A Python library for molecular integration, interpolation, differentiation, and more.

Grid is a free and open-source Python library for constructing numerical grids to integrate, interpolate, and differentiate functions (e.g., molecular properties), with a strong emphasis on facilitating these operations in computational chemistry and conceptual density functional theory. Although designed, maintained, and released as a stand-alone Python library, Grid was originally developed for molecular integration, interpolation, and solving the Poisson equation in the HORTON and ChemTools packages. Grid is designed to be easy to use, extend, and maintain; this is why we use Python and adopt many principles of modern software development, including comprehensive documentation, extensive testing, continuous integration/delivery protocols, and package management. We leverage popular scientific packages, such as NumPy and SciPy, to ensure high efficiency and optimized performance in grid development. This article is the official release note of the Grid library showcasing its unique functionality and scope.