PyDicer: An open-source python library for conversion and analysis of radiotherapy DICOM data

We introduce , an open-source Python library designed for mesh agglomeration in both two- and three-dimensions, based on employing Graph Neural Networks (GNN). serves as a comprehensive solution for training a variety of GNN models, integrating deep learning and other advanced algorithms such as METIS and k-means to facilitate mesh agglomeration and quality metric computation. The library’s introduction is outlined through its code structure and primary features. The GNN framework adopts a graph bisection methodology that capitalizes on connectivity and geometric mesh information via SAGE convolutional layers, in line with the methodology proposed in (Antonietti and Manuzzi in J Comput Phys 452:110900, 2022; Antonietti et al. in Polytopal mesh agglomeration via geometrical deep learning for three-dimensional heterogeneous domains, arXiv:2406.10587 , 2024). Additionally, the proposed library incorporates reinforcement learning to enhance the accuracy and robustness of the model initially suggested in [1, 2] for predicting coarse partitions within a multilevel framework. A detailed tutorial is provided to guide the user through the process of mesh agglomeration and the training of a GNN bisection model. We present several examples of mesh agglomeration conducted by , demonstrating the library’s applicability across various scenarios. Furthermore, the performance of the newly introduced models is contrasted with that of METIS and k-means, illustrating that the proposed GNN models are competitive regarding partition quality and computational efficiency. Finally, we exhibit the versatility of ’s interface through its integration with , an open-source library implementing discontinuous Galerkin methods on polytopal grids for the numerical discretization of multiphysics differential problems.

Since the coining of the term phylodynamics, the use of phylogenies to understand infectious disease dynamics has steadily increased. As methods for phylodynamics and genomic epidemiology have proliferated and grown more computationally expensive, the epidemiological information they extract has also evolved to better complement what can be learned through traditional epidemiological data. However, for genomic epidemiology to continue to grow, and for the accumulating number of pathogen genetic sequences to fulfill their potential widespread utility, the extraction of epidemiological information from phylogenies needs to be simpler and more efficient. Summary statistics provide a straightforward way of extracting information from a phylogenetic tree, but the relationship between these statistics and epidemiological quantities needs to be better understood. In this work we address this need via simulation. Using two different benchmark scenarios, we evaluate 74 tree summary statistics and their relationship to epidemiological quantities. In addition to evaluating the epidemiological information that can be inferred from each summary statistic, we also assess the computational cost of each statistic. This helps us optimize the selection of summary statistics for specific applications. Our study offers guidelines on essential considerations for designing or choosing summary statistics. The evaluated set of summary statistics, along with additional helpful functions for phylogenetic analysis, is accessible through an open-source Python library. Our research not only illuminates the main characteristics of many tree summary statistics but also provides valuable computational tools for real-world epidemiological analyses. These contributions aim to enhance our understanding of disease spread dynamics and advance the broader utilization of genomic epidemiology in public health efforts.Author SummaryOur study focuses on the use of phylogenetic analysis to get valuable epidemiological insights. We conducted a simulation study to evaluate 74 phylogenetic summary statistics and their relationship to epidemiological quantities, shedding light on the potential of each of these statistics to quantify different characteristics of disease spread dynamics. Additionally, we assessed the computational cost of each statistic. This gives us additional information when selecting a statistic for a particular application. Our research is available through an open-source Python library. This work helps us enhance our understanding of phylogenetic tree structures and contributes to the broader application of genomic epidemiology in public health initiatives.

Eye movement analysis is an effective method for research on visual perception and cognition. However, recordings of eye movements present practical difficulties related to the cost of the recording devices and the programming of device controls for use in experiments. GazeParser is an open-source library for low-cost eye tracking and data analysis; it consists of a video-based eyetracker and libraries for data recording and analysis. The libraries are written in Python and can be used in conjunction with PsychoPy and VisionEgg experimental control libraries. Three eye movement experiments are reported on performance tests of GazeParser. These showed that the means and standard deviations for errors in sampling intervals were less than 1 ms. Spatial accuracy ranged from 0.7° to 1.2°, depending on participant. In gap/overlap tasks and antisaccade tasks, the latency and amplitude of the saccades detected by GazeParser agreed with those detected by a commercial eyetracker. These results showed that the GazeParser demonstrates adequate performance for use in psychological experiments.

A newly developed tool for analysing and visualizing data based on python. The main aim of this tool is saving the data analysis time by automating the data analysis process and creating a Graphical User Interface (GUI) to facilitate the usage of this tool regardless how much they experience programming with Python. This paper describes the architecture of the developed software tool and provides a variety of examples to show the developed tool’s advanced features and functionalities. Moreover, in this paper, an industrial problem related to data analysis is discussed, and then a solution of this problem is presented. This tool is developed using open source libraries (Pandas, Matplotlib, Tkinter, and SciPy) to manage multi data files at once using one tool, as well as visualizing and storing these data in one file in a clean and organized way.

Hierarchically clustered heatmaps are a popular visualization technique to display high-dimensional biological datasets as figures in research publications. Most tools developed to visualize hierarchically clustered heatmaps generate static images. Clustergrammer is a web-based matrix visualization tool built, using the D3 JavaScript library, to enable experimental and computational-biologists to easily generate highly interactive heatmaps. Clustergrammer implements many interactive features including: zooming, panning, filtering, sorting, reordering, dimensionality reduction, gene set enrichment analysis, gene description text, and shareable persistent links. Clustergrammer can be used to visualize and analyze a diverse set of high-dimensional biological data including: gene expression, protein-protein interactions, and CyTOF single-cell proteomics data. Clustergrammer provides interactive visualizations for several web-tools including: Enrichr, GEN3VA, GEO2Enrichr, Harmonizome, and L1000CDS2. In addition, Clustergrammer can be embedded within IPython Jupyter Notebooks or as a standalone open-source JavaScript or Python library.

The lack of long-term and consistent meteorological observations limits the application of land-surface simulators (e.g., of phenomena in hydrology, the cryosphere, ecology) at remote locations. For example, most permafrost areas are remote and lacking consistent meteorological time series, models that describe permafrost change over time cannot be driven for comparison with observations or for impact studies. Reanalysis-derived time series are valuable because they are available with global coverage, for a long time period, and for a broad set of physically consistent variables. Multiple reanalyses can be used to provide estimates of uncertainty. Practically, however, this data is difficult to use for several reasons: grid-scale reanalyses must be downscaled and interpolated horizontally (and vertically within the atmospheric column for mountains regions) to the site‑scale, differences in variables, units, and delivery between reanalyses must be reconciled, and large volumes of data need to be handled. Globsim is an open-source python library (available via GitHub) that was developed to handle these challenges and to facilitate a simulation workflow that takes advantage of the multiple reanalysis products available today. It outputs sub-daily meteorological time series that resemble meteorological stations for any location on the planet. Since the release of the first version of Globsim, we have improved usability, refactored code for maintainability and speed, and fixed a number of bugs. We also added support for ERA5 ensemble data, and added more sophisticated heuristic downscaling algorithms, including TOPOscale for elevation-adjusted radiative fluxes. We use Globsim as a core tool in a multi-model permafrost simulation workflow and, as a future step, we intend to use it as part of a debiasing routine to make predictions of permafrost using climate scenarios. We expect this tool to be broadly applicable to climate change impact modelers and other scientists using climate driven simulations working in (remote) locations that lack meteorological data of sufficient quality and duration for their application.

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.

IMDLIB: An open-source library for retrieval, processing and spatiotemporal exploratory assessments of gridded meteorological observation datasets over India

Today online social networks have a high impact in our society as more and more people use them for communicating with each other, express their opinions, participating in public discussions, etc. In particular, Twitter is one of the most popular social network platforms people mainly use for political discussions. This attracted the interest of many research studies that analyzed social phenomena on Twitter, by collecting data, analysing communication patterns, and exploring the structure of user networks. While previous works share many common methodologies for data collection and analysis, these are mainly re-implemented every time by researchers in a custom way. In this paper, we introduce PyPoll an open-source Python library that operationalizes common analysis tasks for Twitter discussions. With PyPoll users can perform Twitter graph mining, calculate the polarization index and generate interactive visualizations without needing third-party tools. We believe that PyPoll can help researchers automate their tasks by giving them methods that are easy to use. Also, we demonstrate the use of the library by presenting two use cases; the PyPoll visualization app, an online application for graph visualizing and sharing, and the Political Lighthouse, a Web portal for displaying the polarization in various political topics on Twitter.

CoSimPy: An open-source python library for MRI radiofrequency Coil EM/Circuit Cosimulation

A class-specific feature selection and classification approach using neighborhood rough set and K-nearest neighbor theories

Processing DICOM datasets for research and education is challenging due to the format's complexity and frequent patient-specific workflow exceptions. Proper handling demands substantial technical expertise and meticulous care to ensure data fidelity in downstream applications. We developed MedicalDataHandler to streamline the reading, visualization, and processing of DICOM data. By consolidating essential tasks into a user-friendly environment, it minimizes reliance on advanced coding skills and promotes reproducible data handling without custom scripting. Implemented in Python with the third-party Dear PyGui toolkit, MedicalDataHandler organizes DICOM files by patient identifiers and groups each patient's radiation therapy (RT) images, structure sets, plans, and doses based on mutual unique identifiers (UIDs). A comprehensive table of patient data enables metadata inspection, data visualization, and data processing. The GUI supports interactive visualization in axial, coronal, and sagittal views, with intuitive scrolling, zooming, panning, and window width/level adjustments. Segmentation labels, colors, and data orientation can be modified on the fly, and hovering over a voxel reveals its image/dose values and relevant segmented structures. Multithreading and multiprocessing enable rapid data reading and conversion to the deep-learning-friendly Nearly Raw Raster Data (NRRD) format. Additional features include metadata inspection, voxel grid resampling, Hounsfield-Unit-to-Relative-Electron-Density mapping, plan-sum dose creation, and partial or bulk data saving options. We validated MedicalDataHandler with an end-to-end testing approach. DICOM data from 61 radiotherapy patients were processed, and the resulting dataset was used to train a deep-learning-based dose prediction model. MedicalDataHandler streamlined the workflow by eliminating the need for complex, patient-specific code and accelerating the preparation of a research-ready dataset. MedicalDataHandler streamlines DICOM data management and accelerates preprocessing, serving as a valuable tool for researchers and trainees. Its intuitive interface, flexible editing, and rapid data conversion empower a broader audience to manage DICOM data efficiently and consistently in research and education settings.

Expyriment is an open-source and platform-independent lightweight Python library for designing and conducting timing-critical behavioral and neuroimaging experiments. The major goal is to provide a well-structured Python library for script-based experiment development, with a high priority being the readability of the resulting program code. Expyriment has been tested extensively under Linux and Windows and is an all-in-one solution, as it handles stimulus presentation, the recording of input/output events, communication with other devices, and the collection and preprocessing of data. Furthermore, it offers a hierarchical design structure, which allows for an intuitive transition from the experimental design to a running program. It is therefore also suited for students, as well as for experimental psychologists and neuroscientists with little programming experience.

In an age of both big data and increasing strain on water resources, sound management decisions often rely on numerical models. Numerical models provide a physics-based framework for assimilating and making sense of information that by itself only provides a limited description of the hydrologic system. Often, numerical models are the best option for quantifying even intuitively obvious connections between human activities and water resource impacts. However, despite many recent advances in model data assimilation and uncertainty quantification, the process of constructing numerical models remains laborious, expensive, and opaque, often precluding their use in decision making. Modflow-setup aims to provide rapid and consistent construction of MODFLOW groundwater models through robust and repeatable automation. Common model construction tasks are distilled in an open-source, online code base that is tested and extensible through collaborative version control. Input to Modflow-setup consists of a single configuration file that summarizes the workflow for building a model, including source data, construction options, and output packages. Source data providing model structure and parameter information including shapefiles, rasters, NetCDF files, tables, and other (geolocated) sources to MODFLOW models are read in and mapped to the model discretization, using Flopy and other general open-source scientific Python libraries. In a few minutes, an external array-based MODFLOW model amenable to parameter estimation and uncertainty quantification is produced. This paper describes the core functionality of Modflow-setup, including a worked example of a MODFLOW 6 model for evaluating pumping impacts to a lake in central Wisconsin, United States.

In the era of digital media, the ability to accurately identify music from audio input has gained significant importance in areas such as entertainment, copyright enforcement, and media content management. This project presents a robust and efficient system for song recognition based on audio fingerprinting techniques. By capturing real-time audio through a microphone and converting it into a unique digital signature using SHA1 hashing, the system enables accurate and fast identification of songs from a reference database. The implementation leverages open-source tools and Python libraries to ensure scalability, portability, and cost-effectiveness. The methodology involves real-time audio capture using PyAudio, preprocessing and normalization with PyDub, and feature extraction for fingerprint generation. These fingerprints are then matched against a SQLite database containing known song signatures. The system is designed to handle short audio clips and operate reliably even in the presence of background noise or distortions. This enhances its usability in real-world environments, making it a practical tool for mobile apps, music streaming platforms, and media monitoring services