Python Package Research Articles

Reservoir assessment tool (RAT): a Python package for monitoring the dynamic state of reservoirs and analyzing dam operations

Reservoir assessment tool (RAT): a Python package for monitoring the dynamic state of reservoirs and analyzing dam operations

Estimating the track-reconstruction efficiency in phenomenological proposals of long-lived-particle searches

Phenomenological proposals of searches for new, long-lived particles face a challenge when estimating the reconstruction efficiency of displaced charged-particle tracks. The efficiency depends not only on the charged-particle’s momentum vector, but also on its production point in relation to the detector boundary and substructure elements. Phenomenological studies generally do not have access to GEANT4-based simulation, which is used for calculating the efficiency in experimental analyses. To address this need, we have developed TrackEff, a python software package for estimating the track-reconstruction efficiency. The default detector geometry within TrackEff is that of the Belle II drift chamber. However, tunable parameters enable modification of the geometry to correspond to other tracker configurations. TrackEff uses a simplified model of the tracker to determine the number of detector hits associated with each track. The user can then decide whether the track would be detected based on the number of hits, potentially in association with any other information.

Lenscat: a public and community-contributed catalogue of known strong gravitational lenses.

We present lenscat, a public and community-contributed catalogue of strong gravitational lenses found by electromagnetic surveys. The main objective of lenscat is to compile a simple, easy-to-access catalogue that can be used in a variety of lensing studies, such as facilitating the search for the host galaxy of a candidate strongly lensed transient event. We also provide a Python package to interact with tools commonly used by the community. This allows end users both with and without lensing expertise to obtain a list of known strong lenses within a given search area and to also rank them by their respective searched probabilities. Here, we exemplify this by cross-matching the gravitational wave joint sky localization region of an interesting pair of events, GW170104-GW170814. Other examples including cross-matching short gamma-ray bursts are given. Thanks to the open and simple infrastructure of lenscat, members of the lensing community can directly add newly found lenses from their own studies to help create a long-lasting catalogue that is as exhaustive and accessible as possible.This article is part of the Theo Murphy meeting issue 'Multi-messenger gravitational lensing (Part 1)'.

A Python package for fast GPU-based proton pencil beam dose calculation.

Open-source GPU-based Monte Carlo (MC) proton dose calculation algorithms provide high speed and unparalleled accuracy but can be complex to integrate with new applications and remain slower than GPU-based pencil beam (PB) methods, which sacrifice some physical accuracy for sub-second plan calculation. We developed and validated a Python package implementing a GPU-based double Gaussian PB algorithm for intensity-modulated proton therapy (IMPT) planning research applications requiring a simple, widely compatible, and ultra-fast proton dose calculation solution. Beam parameters were derived from pristine Bragg peaks generated with MC for 98 energies in our clinical treatment planning system (TPS). We validated the PB approach against measurements by comparing lateral spot profiles (using single-Gaussian sigma) and proton ranges (using R80) for pristine Bragg peaks, as well as spread-out Bragg peaks (SOBPs) in water. Further comparisons of PB and MC from the TPS were performed in a heterogeneous digital phantom and patient plans for four cancer sites using 3D gamma passing rates and dose metrics. The PB algorithm enabled dose calculation following a single Python import statement. Mean±standard deviation (SD) errors in sigma, R80, and SOBP dose were 0.05±0.01, 0.0±0.1mm, and 0.4±1.1%, respectively. Mean±SD patient plan computation time was 0.28±0.07s for PB versus 4.68±2.68s for MC. Mean±SD gamma passing rate at 2%/2mm criteria was 96.0±5.1%, and the mean±SD percent difference in dose metrics was 0.5±3.6%. PB accuracy degraded beyond bone and lung boundaries, characterized by inaccuracies in lateral proton scatter. We developed a GPU-based proton PB algorithm compiled as a Python package, providing simple beam modeling, interface, and fast dose calculation for IMPT plan optimization research and development. Like other PB algorithms, accuracy is limited in highly heterogeneous regions such as the thorax.

De-embedding a pair of dipoles to calibrate the mutual impedance plasma density diagnostic

For two dipole antennas in a two-port system, an impedance calibration method is presented for situations where the dipole antennas are inaccessible during testing, but environmental changes require recalibration, such as during mutual-impedance measurements of a plasma inside a vacuum chamber. The antenna system of this study is composed of two dipoles connected by rigid coax cables to their respective calibration boxes. The boxes house surface mounted baluns, for balancing the dipoles, and surface mounted standards, for calibrating the cables. The cables connect the system through the chamber walls to the measurement hardware, a Vector Network Analyzer (VNA). The measured S-parameters are simulated in the python package scikit-rf to de-embed the dipoles. This new virtual approach incorporates the balun common mode and eliminates error from the conventional balun characterization where only the differential mode is considered. The presented calibration method has been validated with direct measurements of known cable loads and a COMSOL simulation for a range of 1 MHz–1 GHz, equating to an electron density range of roughly 1010 – 1016m−3. The calibration method de-embeds the two dipoles without using a “through” measurement, allowing for remote calibration and recalibration during testing. Since the eventual goal of this work is to apply the calibration method to an array of dipoles for plasma impedance tomography, the lack of a “through” measurement provides the following key capabilities that removes uncertainty and significantly reduces the setup time: 1) Calibration uncertainty in the cables due to thermal changes and movement are accounted for, which has previously not been possible during plasma testing. 2) The calibration procedure inside the chamber can be fully automated, greatly speeding up the calibration time for multiple antenna pairs. 3) Without the need for a “through” measurement, the number of calibration measurements within the chamber is reduced to the number of antennas (N) rather than the number of unique antenna pairs (N(N−1)/2). Extending this method to higher plasma densities could be possible with hardware that performs in the respective GHz range.

Data splitting to avoid information leakage with DataSAIL

Information leakage is an increasingly important topic in machine learning research for biomedical applications. When information leakage happens during a model’s training, it risks memorizing the training data instead of learning generalizable properties. This can lead to inflated performance metrics that do not reflect the actual performance at inference time. We present DataSAIL, a versatile Python package to facilitate leakage-reduced data splitting to enable realistic evaluation of machine learning models for biological data that are intended to be applied in out-of-distribution scenarios. DataSAIL is based on formulating the problem to find leakage-reduced data splits as a combinatorial optimization problem. We prove that this problem is NP-hard and provide a scalable heuristic based on clustering and integer linear programming. Finally, we empirically demonstrate DataSAIL’s impact on evaluating biomedical machine learning models.

SwarmRL: building the future of smart active systems

This work introduces SwarmRL, a Python package designed to study intelligent active particles. SwarmRL provides an easy-to-use interface for developing models to control microscopic colloids using classical control and deep reinforcement learning approaches. These models may be deployed in simulations or real-world environments under a common framework. We explain the structure of the software and its key features and demonstrate how it can be used to accelerate research. With SwarmRL, we aim to streamline research into micro-robotic control while bridging the gap between experimental and simulation-driven sciences. SwarmRL is available open-source on GitHub at https://github.com/SwarmRL/SwarmRL.

Open-source phantom with dedicated in-house software for image quality assurance in hybrid PET systems

BackgroundPatients’ diagnosis, treatment and follow-up increasingly rely on multimodality imaging. One of the main limitations for the optimal implementation of hybrid systems in clinical practice is the time and expertise required for applying standardized protocols for equipment quality assurance (QA). Experimental phantoms are commonly used for this purpose, but they are often limited to a single modality and single quality parameter, lacking automated analysis capabilities. In this study, we developed a multimodal 3D-printed phantom and software for QA in positron emission tomography (PET) hybrid systems, with computed tomography (CT) or magnetic resonance (MR), by assessing signal, spatial resolution, radiomic features, co-registration and geometric distortions.ResultsPhantom models and Python software for the proposed QA are available to download, and a user-friendly plugin compatible with the open-source 3D-Slicer software has been developed. The QA viability was proved by characterizing a Philips-Gemini-TF64-PET/CT in terms of signal response (mean, µ), intrinsic variability for three consecutive measurements (daily variation coefficient, CoVd) and reproducibility over time (variation coefficient across 5 months, CoVm). For this system, averaged recovery coefficient for activity concentration was µ = 0.90 ± 0.08 (CoVd = 0.6%, CoVm = 9%) in volumes ranging from 7 to 42 ml. CT calibration-curve averaged over time was HU=(951±12)×density-(944±15) with variability of slope and y-intercept of (CoVd = 0.4%, CoVm = 1.2%) and (CoVd = 0.4%, CoVm = 1.6%), respectively. Radiomics reproducibility resulted in (CoVd = 18%, CoVm = 30%) for PET and (CoVd = 15%, CoVm = 22%) for CT. Co-registration was assessed by Dice-Similarity-Coefficient (DSC) along 37.8 cm in superior-inferior (z) direction (well registered if DSC ≥ 0.91 and Δz ≤ 2 mm), resulting in 3/7 days well co-registered. Applicability to other scanners was additionally proved with Philips-Vereos-PET/CT (V), Siemens-Biograph-Vison-600-PET/CT (S) and GE-SIGNA-PET/MR (G). PET concentration accuracy was (µ = 0.86, CoVd = 0.3%) for V, (µ = 0.87, CoVd = 0.8%) for S, and (µ = 1.10, CoVd = 0.34%) for G. MR(T2) was well co-registered with PET in 3/4 cases, did not show significant distortion within a transaxial diameter of 27.8 cm and along 37 cm in z, and its radiomic variability was CoVd = 13%.ConclusionsOpen-source QA protocol for PET hybrid systems has been presented and its general applicability has been proved. This package facilitates simultaneously simple and semi-automated evaluation for various imaging modalities, providing a complete and efficient QA solution.

Impact of complement C3 levels on the development of healthcare-associated infections in intensive care patients: a retrospective case-control study

Purpose The immune system serves as a critical line of defence against pathogenic microorganisms. To investigate the impact of immune markers, measured within the first 48 h of intensive care unit (ICU) admission, on the incidence of healthcare-associated infections (HAIs) in ICU patients. Methods This case-control study included 364 patients admitted from 1 January 2020 to 30 November 2023, receiving immune marker testing within 48 h of ICU admission. Cox proportional hazard models and propensity score matching evaluated immune markers’ association with HAIs risk. Log-rank tests compared time-to-event by C3 levels. All data processing and analysis were performed using R version 4.2.0 (R Foundation for Statistical Computing, Vienna, Austria) and Python version 3.11 (Python Software Foundation, Wilmington, DE). Results In total, 258 patients without HAIs (mean [SD] age, 67.24 [17.79] years) and 106 patients with HAIs (mean [SD] age, 73.80 [14.93] years) were included in the final analysis. The HAIs group had older age, longer hospital stay, lower Sequential Organ Failure Assessment (SOFA) scores, and a higher rate of comorbid infections than the non-HAIs group. Also, the HAIs group had a higher proportion of basophils, lymphocytes, monocytes and T suppressor cells (CD3 + CD8+), while the proportion of neutrophils and B cells (CD19+) was lower. After Cox regression analysis and propensity score adjustment, we found that C3 complement levels (HR: 0.40; 95%CI, 0.16–0.98; p = .044) influenced the incidence of HAIs. Patients were then divided into high C3 and low C3 groups based on a cut-off value of 0.455 for C3. A time-to-event plot showed that the median time to HAIs occurrence was nine days in the high C3 group and six days in the low C3 group (p = .048). Conclusions Elevated complement C3 levels may associat with a reduced incidence of HAIs in ICU patients.

Real Time Led Scrolling Display with Wifi Connectivity

In this project, a web-based P10 Display system is developed using the ESP8266 micro controller to facilitate real-time text display and brightness control. The system enables users to send text input from a PC via web-based Python software or from a mobile device using MQTT based applications. The Node MCU, equipped with Wi-Fi capability, acts as the core micro controller, interfacing with the P10 display module to process data into a visual representation. The input is transmitted to an MQTT cloud server, from where the ESP8266 retrieves the data and updates the P10 Display accordingly. This approach provides a flexible and efficient method for remote content management and control of P10 Displays. The proposed system finds applications in digital signage, educational institutions, public areas, offices, smart advertisements, and interactive display solutions. KEYWORDS- ESP8266 Node MCU Controller, Arduino IDE, Buzzer, Python Flask MQTT Protocol, P10 Display.

MAR: A Multiband Astronomical Reduction package

MAR: A Multiband Astronomical Reduction package

SHARK-capture identifies functional motifs in intrinsically disordered protein regions.

Increasing insights into how sequence motifs in intrinsically disordered regions (IDRs) provide functions underscore the need for systematic motif detection. Contrary to structured regions where motifs can be readily identified from sequence alignments, the rapid evolution of IDRs limits the usage of alignment-based tools in reliably detecting motifs within. Here, we developed SHARK-capture, an alignment-free motif detection tool designed for difficult-to-align regions. SHARK-capture innovates on word-based methods by flexibly incorporating amino acid physicochemistry to assess motif similarity without requiring rigid definitions of equivalency groups. SHARK-capture offers consistently strong performance in a systematic benchmark, with superior residue-level performance. SHARK-capture identified known functional motifs across orthologs of the microtubule-associated zinc finger protein BuGZ. We also identified a short motif in the IDR of S. cerevisiae RNA helicase Ded1p, which we experimentally verified to be capable of promoting ATPase activity. Our improved performance allows us to systematically calculate 10,889 motifs for 2695 yeast IDRs and provide it as a resource. SHARK-capture offers the most precise tool yet for the systematic identification of conserved regions in IDRs and is freely available as a Python package (https://pypi.org/project/bio-shark/) and on https://git.mpi-cbg.de/tothpetroczylab/shark.

Quantitative characterization of tissue states using multiomics and ecological spatial analysis

The spatial organization of cells in tissues underlies biological function, and recent advances in spatial profiling technologies have enhanced our ability to analyze such arrangements to study biological processes and disease progression. We propose MESA (multiomics and ecological spatial analysis), a framework drawing inspiration from ecological concepts to delineate functional and spatial shifts across tissue states. MESA introduces metrics to systematically quantify spatial diversity and identify hot spots, linking spatial patterns to phenotypic outcomes, including disease progression. Furthermore, MESA integrates spatial and single-cell multiomics data to facilitate an in-depth, molecular understanding of cellular neighborhoods and their spatial interactions within tissue microenvironments. Applying MESA to diverse datasets demonstrates additional insights it brings over prior methods, including newly identified spatial structures and key cell populations linked to disease states. Available as a Python package, MESA offers a versatile framework for quantitative decoding of tissue architectures in spatial omics across health and disease.

Choosing an Analogue to Digital Converter with Data Safety in Mind

Industry 4.0 is built upon the foundations of converting real world analogue effects into digitised binary data suitable for a computer to process. This needs to be done with care, particularly when the data is ingested by a safety critical system. A numerical study probing the limits of a typical analogue to digital converter (ADC) is presented here, highlighting some potential issues that should be identified. Initially a Monte Carlo approach is used to probe the impact of digitisation on an ADC using traditional experimental error analysis. A constant test signal 2.5 ± 0.01 V is used to understand the optimum level of digitisation. The analogue signal is assumed to have Gaussian noise which is then processed by a 5 V ADC. This investigation suggests an optimum digitisation level should be related to the standard error of a measured signal. The use of Bayesian inferencing using the Python package PyMC is then used to gain a better estimate of the underlying standard deviation when the signal has been digitised at the 8-bit level.

Fast simulation of cosmological neutral hydrogen based on the halo model

Cosmological neutral hydrogen (HI) surveys provide a promising tomographic probe of the post-reionization era and of the standard model of cosmology. Simulations of this signal are crucial for maximizing the utility of these surveys. We present a fast method for simulating the cosmological distribution of HI based on a halo model approach. Employing the approximate PINOCCHIO code, we generate the past light cone of dark matter halos. Subsequently, the halos are populated with HI according to a HI-halo mass relation. The nature of 21 cm intensity mapping demands large-volume simulations with a high halo mass resolution. To fulfill both requirements, we simulate a past light cone for declinations between -15° and -35° in the frequency range from 700 to 800 MHz, matching HIRAX, the Hydrogen Intensity and Real-time Analysis eXperiment. We run PINOCCHIO for a 1 h-3 Gpc3 box with 67003 simulation particles. With this configuration, halos with masses as low as M min = 4.3 × 109 M⊙ are simulated, resulting in the recovery of more than 97% of the expected HI density. From the dark matter and HI past light cone, maps with a width of 5 MHz are created. To validate the simulation, we have implemented and present here an analytical dark matter and HI halo model in PyCosmo, a Python package tailored for theoretical cosmological predictions. We perform extensive comparisons between analytical predictions and the simulation for the mass function, mass density, power spectrum, and angular power spectrum for dark matter and HI. We find close agreement in the mass function and mass densities, with discrepancies within a few percent. For the three-dimensional power spectra and angular power spectra, we observe an agreement better than 10%. This approach is broadly applicable for forecasting and forward-modeling of dedicated intensity mapping experiments, such as HIRAX, as well as cosmological surveys with the SKA. Additionally, it is particularly well suited to be extended for cross-correlation studies. The PyCosmo halo model extension and simulation datasets are made publicly available.

Enhancing solar panel efficiency through dual-axis tracking and fresnel lens concentration: an image processing approach

Solar energy is currently utilized as an inexhaustible renewable energy source. Solar panels can convert solar energy into electrical energy that humans can use. The drawback of solar panels is that they cannot always be perpendicular to the sun, causing a decrease in the intensity of incoming light. Therefore, in this research, a solar tracking system with a fresnel lens was designed using image processing to increase the output of solar panels. In this research, programming was done using Python software for image processing using the hue, saturation, value (HSV) color, and space model, which was then connected with Arduino using the PyFirmata library to move the motor. In this research, solar panels with a fresnel lens and solar tracking were implemented. Data collection was performed on the output voltage of the solar panel. The research concludes that solar panels with solar tracker and fresnel lens have a higher average output voltage of 7.53 V than passive solar panels with an average output voltage of 6.38 V. Also, the average output voltage increased by 18.02% after implementing the solar tracking system and adding the fresnel lens.

BOLDsωimsuite: A new software suite for forward modeling of the BOLD fMRI signal

Abstract Many methods for the forward modeling of the blood-oxygenation level-dependent (BOLD) effect have been created and analyzed to elucidate the mechanisms of BOLD functional MRI (fMRI) techniques and to expand on the potential of the transverse relaxation time (T2*) in quantitative MRI. Simulations of this nature can be difficult to implement without prior experience, and differences made by methodological choices can be unclear, which provides a significant barrier of entry into the field. In this paper, we present BOLDsωimsuite, a toolbox for forward modeling of the BOLD effect, which collects many of the principal methods used in the literature into a single coherent package. Implemented as a Python package, simulations are made using scripts by combining various simulation components, thereby providing flexibility in methodological choices. The goal of this toolbox is to provide an open-source, reproducible simulation software suite that is adaptable for different MRI applications, and to which additional features can be added by the user with relative ease. This paper first provides an overview of the methods available in the package and how these methods can be constructed from the toolbox’s modular code components. Then, a brief theoretical explanation of each simulation component is given, supported by the relevant contributors. Next, sample simulations and analyzes that can be created using the package are presented to display its features. Finally, recommendations regarding computational requirements are included to help users choose the best simulation methods to fit their needs. This package has many use cases and significantly reduces methodological barriers to forward modeling. It can also be a good learning tool for MR physics as well as a powerful tool to promote reproducible science.

Natural Language-Based Synthetic Data Generation for Cluster Analysis

Abstract Cluster analysis relies on effective benchmarks for evaluating and comparing different algorithms. Simulation studies on synthetic data are popular because important features of the data sets, such as the overlap between clusters, or the variation in cluster shapes, can be effectively varied. Unfortunately, creating evaluation scenarios is often laborious, as practitioners must translate higher-level scenario descriptions like “clusters with very different shapes” into lower-level geometric parameters such as cluster centers, covariance matrices, etc. To make benchmarks more convenient and informative, we propose synthetic data generation based on direct specification of high-level scenarios, either through verbal descriptions or high-level geometric parameters. Our open-source Python package https://repliclust.org implements this workflow, making it easy to set up interpretable and reproducible benchmarks for cluster analysis. A demo of data generation from verbal inputs is available at https://demo.repliclust.org.

SOAPy: a Python package to dissect spatial architecture, dynamics, and communication

Advances in spatial omics enable deeper insights into tissue microenvironments while posing computational challenges. Therefore, we developed SOAPy, a comprehensive tool for analyzing spatial omics data, which offers methods for spatial domain identification, spatial expression tendency, spatiotemporal expression pattern, cellular co-localization, multi-cellular niches, cell–cell communication, and so on. SOAPy can be applied to diverse spatial omics technologies and multiple areas in physiological and pathological contexts, such as tumor biology and developmental biology. Its versatility and robust performance make it a universal platform for spatial omics analysis, providing diverse insights into the dynamics and architecture of tissue microenvironments.

Katsu: A Python package for Mueller and Stokes simulation and polarimetry

Katsu: A Python package for Mueller and Stokes simulation and polarimetry