Python Functions Research Articles

A Python program to merge Sanger sequences: an update.

Gene cloning is an important step in investigating gene structure and function. To verify gene sequence, Sanger sequencing is used, which may produce several overlapping sequencing files that need to be merged before alignment to the target gene sequence is performed. Previously, we reported the Python program to Merge Sanger sequences (https://peerj.com/articles/11354/), which ran in command line and relied heavily on EMBOSS suite. In this updated version of the program, we have made several remarkable improvements. It provides a graphical user interface (GUI) written with tkinter, which is convenient and stable. It does not require users to rename the input sequences before performing merging. With regard to the implementation, the updated version utilizes Python function (Align.PairwiseAligner) to align adjacent sequences, which is more flexible (can adjust program parameter i.e., the number of first-time consecutive matching bases). The new version of the program makes merging Sanger sequences much more convenient and facilitates gene study.

HiPy: Extracting High-Level Semantics from Python Code for Data Processing

Data science workloads frequently include Python code, but Python's dynamic nature makes efficient execution hard. Traditional approaches either treat Python as a black box, missing out on optimization potential, or are limited to a narrow domain. However, a deep and efficient integration of user-defined Python code into data processing systems requires extracting the semantics of the entire Python code. In this paper, we propose a novel approach for extracting the high-level semantics by transforming general Python functions into program generators that generate a statically-typed IR when executed. The extracted IR then allows for high-level, domain-specific optimizations and the generation of efficient C++ code. With our prototype implementation, HiPy, we achieve single-threaded speedups of 2-20x for many workloads. Furthermore, HiPy is also capable of accelerating Python code in other domains like numerical data, where it can sometimes even outperform specialized compilers.

DetectVul: A statement-level code vulnerability detection for Python

Detecting vulnerabilities in source code using graph neural networks (GNN) has gained significant attention in recent years. However, the detection performance of these approaches relies highly on the graph structure, and constructing meaningful graphs is expensive. Moreover, they often operate at a coarse level of granularity (such as function-level), which limits their applicability to other scripting languages like Python and their effectiveness in identifying vulnerabilities. To address these limitations, we propose DetectVul, a new approach that accurately detects vulnerable patterns in Python source code at the statement level. DetectVul applies self-attention to directly learn patterns and interactions between statements in a raw Python function; thus, it eliminates the complicated graph extraction process without sacrificing model performance. In addition, the information about each type of statement is also leveraged to enhance the model’s detection accuracy. In our experiments, we used two datasets, CVEFixes and Vudenc, with 211,317 Python statements in 21,571 functions from real-world projects on GitHub, covering seven vulnerability types. Our experiments show that DetectVul outperforms GNN-based models using control flow graphs, achieving the best F1 score of 74.47%, which is 25.45% and 18.05% higher than the best GCN and GAT models, respectively.

User guide for Social Determinants of Health Survey data in the All of Us Research Program.

Integration of social determinants of health into health outcomes research will allow researchers to study health inequities. The All of Us Research Program has the potential to be a rich source of social determinants of health data. However, user-friendly recommendations for scoring and interpreting the All of Us Social Determinants of Health Survey are needed to return value to communities through advancing researcher competencies in use of the All of Us Research Hub Researcher Workbench. We created a user guide aimed at providing researchers with an overview of the Social Determinants of Health Survey, recommendations for scoring and interpreting participant responses, and readily executable R and Python functions. This user guide targets registered users of the All of Us Research Hub Researcher Workbench, a cloud-based platform that supports analysis of All of Us data, who are currently conducting or planning to conduct analyses using the Social Determinants of Health Survey. We introduce 14 constructs evaluated as part of the Social Determinants of Health Survey and summarize construct operationalization. We offer 30 literature-informed recommendations for scoring participant responses and interpreting scores, with multiple options available for 8 of the constructs. Then, we walk through example R and Python functions for relabeling responses and scoring constructs that can be directly implemented in Jupyter Notebook or RStudio within the Researcher Workbench. Full source code is available in supplemental files and GitHub. Finally, we discuss psychometric considerations related to the Social Determinants of Health Survey for researchers.

Libepa — A C++/Python library for calculations of cross sections of ultraperipheral collisions

The library provides a set of C++/Python functions for computing cross sections of ultraperipheral collisions of high energy particles under the equivalent photons approximation. Cross sections are represented through multiple integrals over the phase space. The integrals are calculated through recurrent application of algorithms for one dimensional integration. The paper contains an introduction to the theory of ultraperipheral collisions, discusses the library approach and provides a few examples of calculations.

ANALYSES OF EUROPEAN TERMINAL AERODROME WEATHER FORECASTS IN 2022 AND 2023

Terminal Aerodrome Forecasts (TAFs) are essential components of aviation meteorology, providing critical information for flight safety and operational decision-making. This study conducts a comprehensive analysis of TAF for European airports during the years 2022 and 2023, leveraging Python functions accessible via a dedicated GitHub repository. The complexity inherent in TAF, characterized by diverse change groups, header formats, and regional variations, presents challenges for accurate interpretation. The analysis focuses on key parameters within TAF, including the count of corrected messages and the frequency and types of change groups. The count of corrected messages serves as a metric for evaluating the quality of service provided, while the examination of change group utilization reveals distinct patterns and tendencies specific to each airport. The findings underscore the significance of regional regulations, meteorologist decision-making, and adherence to International Civil Aviation Organization (ICAO) standards in shaping TAF. The GitHub repository and associated Python functions presented in this study provide valuable resources for meteorologists, researchers, and aviation personnel to conduct in-depth analyses and derive insights from TAF. Ultimately, this study identifies local differences and inconsistencies in the publication of TAF, laying the groundwork for enhancing their consistency and uniformity.

Xstar atomic database: the PyXstar package

We present a progress report on the development of PyXstar, a Python package to manage the data (input, output, intermediate, atomic database, and model-grids) associated with the xstar code for treating photoionized and collisionally ionized plasmas. The PyXstar modular structure and database retrieval scheme are described, and its functionality is illustrated with Python functions and classes for performing database searches. We briefly compare PyXstar with two other Python spectrum modeling tools: PyNeb and PyAtomDB.Graphic abstract

BioCoder: a benchmark for bioinformatics code generation with large language models.

Pretrained large language models (LLMs) have significantly improved code generation. As these models scale up, there is an increasing need for the output to handle more intricate tasks and to be appropriately specialized to particular domains. Here, we target bioinformatics due to the amount of domain knowledge, algorithms, and data operations this discipline requires. We present BioCoder, a benchmark developed to evaluate LLMs in generating bioinformatics-specific code. BioCoder spans much of the field, covering cross-file dependencies, class declarations, and global variables. It incorporates 1026 Python functions and 1243 Java methods extracted from GitHub, along with 253 examples from the Rosalind Project, all pertaining to bioinformatics. Using topic modeling, we show that the overall coverage of the included code is representative of the full spectrum of bioinformatics calculations. BioCoder incorporates a fuzz-testing framework for evaluation. We have applied it to evaluate various models including InCoder, CodeGen, CodeGen2, SantaCoder, StarCoder, StarCoder+, InstructCodeT5+, GPT-3.5, and GPT-4. Furthermore, we fine-tuned one model (StarCoder), demonstrating that our training dataset can enhance the performance on our testing benchmark (by >15% in terms of Pass@K under certain prompt configurations and always >3%). The results highlight two key aspects of successful models: (i) Successful models accommodate a long prompt (>2600 tokens) with full context, including functional dependencies. (ii) They contain domain-specific knowledge of bioinformatics, beyond just general coding capability. This is evident from the performance gain of GPT-3.5/4 compared to the smaller models on our benchmark (50% versus up to 25%). All datasets, benchmark, Docker images, and scripts required for testing are available at: https://github.com/gersteinlab/biocoder and https://biocoder-benchmark.github.io/.

Development and implementation of a Python functions for automated chemical reaction balancing

<p>Chemical reaction balancing is a fundamental aspect of chemistry, ensuring the conservation of mass and atoms in reactions. This article introduces a specialized Python functions designed for automating the balancing of chemical reactions. Leveraging the versatility and simplicity of Python, the module employs advanced algorithms to provide an efficient and user-friendly solution for scientists, educators, and industry professionals. This article delves into the design, implementation, features, applications, and future developments of the Python functions for automated chemical reaction balancing. The functions thus developed were tested on some typical chemical reactions and the results are the same as that in the literature.</p>

The application of state feedback principle in turbine adjustment system with Python control library

Abstract This study investigates the utilization of the Python-Control library in contemporary control theory. The Python-Control package, comprising a collection of Python classes and functions, facilitates the comprehensive analysis and design of feedback control systems. Pole placement, a widely utilized method in the automatic control domain for control system design, is particularly emphasized. This paper explores the application of Python-Control in pole assignment, aiming to enhance control system performance. Additionally, the pole assignment method is successfully implemented in a hydro governor model, demonstrating the efficacy of the Python-Control library in practical control system applications.

Exploratory data science on supercomputers for quantum mechanical calculations

Literate programming—the bringing together of program code and natural language narratives—has become a ubiquitous approach in the realm of data science. This methodology is appealing as well for the domain of Density Functional Theory (DFT) calculations, particularly for interactively developing new methodologies and workflows. However, effective use of literate programming is hampered by old programming paradigms and the difficulties associated with using high performance computing (HPC) resources. Here we present two Python libraries that aim to remove these hurdles. First, we describe the PyBigDFT library, which can be used to setup materials or molecular systems and provides high-level access to the wavelet based BigDFT code. We then present the related remotemanager library, which is able to serialize and execute arbitrary Python functions on remote supercomputers. We show how together these libraries enable transparent access to HPC based DFT calculations and can serve as building blocks for rapid prototyping and data exploration.

Simple MATLAB and Python scripts for multi-exponential analysis.

Multi-exponential decay is prevalent in magnetic resonance spectroscopy, relaxation, and imaging. This paper describes simple MATLAB and Python functions and scripts for regularized multi-exponential analysis methods for 1D and 2D data and example test problems and experiments. Regularized least-squares solutions provide production-quality outputs with robust stopping rules in ~5 and ~20 lines of code for 1D and 2D inversions, respectively. The software provides an open-architecture simple solution for transforming exponential decay data to the distribution of their decay lifetimes. Examples from magnetic resonance relaxation of a complex fluid, a Danish North Sea crude oil, and fluid mixtures in porous materials-brine/crude oil mixture in North Sea reservoir chalk-are presented. Developed codes may be incorporated in other software or directly used by other researchers, in magnetic resonance relaxation, diffusion, and imaging or other physical phenomena that require multi-exponential analysis.

Pelatihan dan Pendampingan Pemrograman Python Dalam Meningkatkan Kompetensi Siswa SMKN 5 Palu

Python programming is a multipurpose interpretive programming language with a design philosophy that focuses on code readability. Python is a language that combines capabilities with very clear code syntax and is equipped with the functionality of a large and comprehensive standard library. Python supports multiple programming paradigms. One of the features available in Python is that it is a dynamic programming language equipped with automatic memory management. This Community Service Activity is carried out by providing training material starting from a basic introduction to the Python programming language, basic rules for writing Python syntax, an introduction to variables and data types in Python, operators in Python, and learning structures and functions in Python. The mentoring process is also carried out to provide direct experience to students. This activity went well and received appreciation from the school, and the students hope that this activity can be continued so that students are able to apply Python programming well.

NEUROSYNTH: Enhancing Cognitive Computing using Deep Neural Networks

This study presents an innovative approach to enhance question-answering (QA) systems that utilize a RoBERTa-based architecture and complexity-enhanced input features. The work is divided into four primary parts: training methodology, feature engineering, building models, and data preprocessing. We propose a Python function that uses readability measures and natural language processing techniques to calculate the linguistic difficulty metrics for input sentences. TensorFlow Datasets (TFDS) are then used to load and preprocess the SquAD (Stanford Question Answering Dataset) dataset to enable effective training. Word embeddings from previously trained GloVe vectors are integrated with complexity metrics to prepare input features that add contextual information to the input representation. With the inclusion of the enriched features, a distinctive question-answering model based on the RoBERTa architecture is trained using the AdamW optimizer and CrossEntropyLoss. Iterative epochs are used in the training process to optimize the model's parameters and minimize the loss function. An independent validation dataset is used to evaluate the model's performance, proving the usefulness of the suggested method in improving the accuracy and robustness of the QA system. All in all, this work offers an organized strategy for improving the quality of systems by fusing cutting-edge neural architecture with input properties that are increased by complexity.

Implementation of a Real-Time Brain-to-Brain Synchrony Estimation Algorithm for Neuroeducation Applications.

This study centers on creating a real-time algorithm to estimate brain-to-brain synchronization during social interactions, specifically in collaborative and competitive scenarios. This type of algorithm can provide useful information in the educational context, for instance, during teacher-student or student-student interactions. Positioned within the context of neuroeducation and hyperscanning, this research addresses the need for biomarkers as metrics for feedback, a missing element in current teaching methods. Implementing the bispectrum technique with multiprocessing functions in Python, the algorithm effectively processes electroencephalography signals and estimates brain-to-brain synchronization between pairs of subjects during (competitive and collaborative) activities that imply specific cognitive processes. Noteworthy differences, such as higher bispectrum values in collaborative tasks compared to competitive ones, emerge with reliability, showing a total of 33.75% of significant results validated through a statistical test. While acknowledging progress, this study identifies areas of opportunity, including embedded operations, wider testing, and improved result visualization. Beyond academia, the algorithm's utility extends to classrooms, industries, and any setting involving human interactions. Moreover, the presented algorithm is shared openly, to facilitate implementations by other researchers, and is easily adjustable to other electroencephalography devices. This research not only bridges a technological gap but also contributes insights into the importance of interactions in educational contexts.

ARTEMIS: An alarm threshold and policy mining system for the intensive care unit

BackgroundAlarm fatigue is a major technology-induced hazard for patients and staff in intensive care units. Too many – mostly unnecessary – alarms cause desensitisation and lack of response in medical staff. Unsuitable alarm policies are one reason for alarm fatigue. But changing alarm policies is a delicate issue since it concerns patient safety. ObjectiveWe present ARTEMIS, a novel, computer-aided clinical decision support system for policy makers that can help to considerably improve alarm policies using data from hospital information systems. MethodsPolicy makers can use different policy components from ARTEMIS' internal library to assemble tailor-made alarm policies for their intensive care units. Alternatively, policy makers can provide even more highly customised policy components as Python functions using data the hospital information systems. This can even include machine learning models – for example for setting alarm thresholds. Finally, policy makers can evaluate their system of policies and compare the resulting alarm loads. ResultsARTEMIS reports and compares numbers of alarms caused by different alarm policies for an easily adaptable target population. ARTEMIS can compare policies side-by-side and provides grid comparisons and heat maps for parameter optimisation. For example, we found that the utility of alarm delays varies based on target population. Furthermore, policy makers can introduce virtual parameters that are not in the original data by providing a formula to compute them. Virtual parameters help measuring and alarming on the right metric, even if the patient monitors do not directly measure this metric. ConclusionARTEMIS does not release the policy maker from assessing the policy from a medical standpoint. But as a knowledge discovery and clinical decision support system, it provides a strong quantitative foundation for medical decisions. At comparatively low cost of implementation, ARTEMIS can have a substantial impact on patients and staff alike – with organisational, economic, and clinical benefits for the implementing hospital.

Inter-compartment concentration monitoring of key species in a vanadium redox flow cell operating with and without polarization: Experiments and construction of a predictive model

The usually encountered phenomenon of vanadium crossover in Vanadium Redox Flow Batteries (VRFB), leading to energy capacity losses, is specifically studied without (diffusion measurements) and with the application of an electric current (battery operating conditions). The experimental set-up is an electrochemical filter-press reactor integrating a NafionTM 424 membrane and two storage tanks. The electrolytes composition on both sides was determined using potentiometric titration (VO2+ and H+) and UV–Vis analysis (V2+, V3+ and VO2+). First, experiment is conducted without polarization and a model based on mass balance equation incorporating diffusion flux and electrolyte volumes change is derived to predict electrolytes composition on both sides. The effective diffusion coefficient of VO2+ in the membrane is obtained by fitting the model with experimental data. The experiment is then carried out under galvanostatic polarization (10 mA/cm2) and a model embedding, simultaneously, electrolyte volumes variation, diffusion and migration across the membrane and the electrochemical and self-discharge reactions, is proposed to predict electrolytes composition. The partitioning of the applied current between all possible electrochemical reactions is addressed using a specific algorithm based on logical tests on limiting currents. The resulting system of coupled ordinary differential equations is solved numerically using standard python functions. The results indicate a satisfactory agreement between predicted and experimental data and highlight the requirement of considering all phenomena for an accurate prediction of electrolytes composition in VRFB. The interest of applying the developed model to simulate and predict composition evolution in VRFB (State of Charge (SoC) and acidity) is discussed.

EPyDGGA: automatic configuration for fitting epidemic curves

Many epidemiological models and algorithms are used to fit the parameters of a given epidemic curve. On many occasions, fitting algorithms are interleaved with the actual epidemic models, which yields combinations of model-parameters that are hard to compare among themselves. Here, we provide a model-agnostic framework for epidemic parameter fitting that can (fairly) compare different epidemic models without jeopardizing the quality of the fitted parameters. Briefly, we have developed a Python framework that expects a Python function (epidemic model) and epidemic data and performs parameter fitting using automatic configuration. Our framework is capable of fitting parameters for any type of epidemic model, as long as it is provided as a Python function (or even in a different programming language). Moreover, we provide the code for different types of models, as well as the implementation of 4 concrete models with data to fit them. Documentation, code and examples can be found at https://ulog.udl.cat/static/doc/epidemic-gga/html/index.html.

Adaptive data reduction workflows for astronomy: The ESO Data Processing System (EDPS)

Context. Astronomical data reduction is usually done via processing pipelines that consist of a series of individual processing steps that can be executed one at a time. These processing steps are then strung together into workflows and fed with data to address a particular processing goal. Examples of such pipeline processing goals are the quality control of incoming data from telescopes, unsupervised production of science and calibration products for an archive, and supervised data reduction to serve the specific science goals of a scientist. For each of these goals, individual workflows need to be developed. These workflows need to evolve when the pipeline, observing strategies, or calibration plans change. Writing and maintaining such a collection of workflows is therefore a complex and expensive task. Aims. In this paper we propose a data processing system that automatically derives processing workflows for different use cases from a single specification of a cascade of processing steps. Methods. The system works by using formalised descriptions of data processing pipelines that specify the input and output of each processing step. Inputs can be existing data or the output of a previous step. The rules for selecting the most appropriate input data are directly attached to the description. Results. A version of the proposed system has been implemented as the ESO Data Processing System (EDPS) in the Python language. The specification of processing cascades and data organisation rules use a restrictive set of Python classes, attributes, and functions. Conclusions. The EDPS implementation of the proposed system was used to demonstrate that it is possible to automatically derive from a single specification of a pipeline processing cascade the workflows that the European Southern Observatory uses for quality control, archive production, and specialised science reduction. The EDPS will be used to replace all data reduction systems using different workflow specifications that are currently in use at the European Southern Observatory.

Defining Bacterial RNA-RNA Interactomes Using CLASH.

Methicillin-resistant Staphylococcus aureus (MRSA) is a bacterial pathogen accounting for high mortality rates among infected patients. Transcriptomic regulation by small RNAs (sRNAs) has been shown to regulate networks promoting antibiotic resistance and virulence in S. aureus. Yet, the biological role of most sRNAs during MRSA host infection remains unknown. To fill this gap, in collaboration with the lab of Jai Tree, we performed comprehensive RNA-RNA interactome analyses in MRSA using CLASH under conditions that mimic the host environment. Here we present a detailed version of this optimized CLASH (cross-linking, ligation, and sequencing of hybrids) protocol we recently developed, which has been tailored to explore the RNA interactome in S. aureus as well as other Gram-positive bacteria. Alongside, we introduce a compilation of helpful Python functions for analyzing folding energies of putative RNA-RNA interactions and streamlining sRNA and mRNA seed discovery in CLASH data. In the accompanying computational demonstration, we aim to establish a standardized strategy to evaluate the likelihood that observed chimeras arise from true RNA-RNA interactions.