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python toolbox for android GNSS raw data to RINEX conversion

GPS SOLUTIONS 2024年第2期28卷 95页

作者： Olcina, Jorge Hernandez Julian, Ana B. Anquela Furones, Angel E. Martin Univ Politecn Valencia Dept Cartog Engn Geodesy & Photogrammetry Camino Vera S-N Valencia 46022 Spain

Global navigation satellite system (GNSS) data collected from Android devices have gained increasing importance in various applications, ranging from geospatial positioning to environmental monitoring. However, the lack of standardized tools for converting Android GNSS raw data into receiver independent exchange (RINEX) format poses a significant challenge for researchers and practitioners. In response to this need, we present a comprehensive python toolbox designed to streamline the conversion process and enhance the usability of Android GNSS data. The proposed toolbox leverages python's versatility to provide a user-friendly interface for converting Android GNSS raw data into the widely adopted RINEX format. Key features include robust data parsing algorithms, support for multiple GNSS constellations, and compatibility with diverse Android device configurations. Furthermore, the toolbox's open-source nature encourages community collaboration and allows for continual improvement and adaptation to emerging GNSS technologies. We anticipate that this python toolbox will serve as a valuable resource for researchers and practitioners working with Android GNSS data, facilitating standardized data interchange and promoting reproducibility in GNSS-based studies.

关键词： GNSS Android RINEX format Raw data python toolbox Open source

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pyMechOpt: A python toolbox for optimizing of reaction mechanisms

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SOFTWAREX 2025年 29卷

作者： Di, Sihan Yu, Nanjia Han, Shutao He, Haodong Beihang Univ Sch Astronaut Beijing 100191 Peoples R China

This paper introduces pyMechOpt, an open-source python package designed for the optimization of chemical reaction mechanisms. The package implements a range of optimization methods, including conventional algorithms such as genetic algorithms (GA) and particle swarm optimization (PSO), as well as introducing novel methods such as coordinate descent (CD) and multi-objective optimization algorithms. The optimization of a reduced GRI-Mech 3.0 mechanism for methane combustion is used to demonstrate the capabilities of pyMechOpt. The SILSCD method demonstrated a notable reduction in the objective functions, exceeding the capabilities of other methods. In the context of multi-objective optimization, NSGA-III demonstrated a balanced Pareto front, outperforming both CTAEA and MOEAD. These results serve to illustrate the efficacy of the novel methods implemented in pyMechOpt. The package provides a versatile platform for researchers to customize optimization algorithms and objective functions, supporting detailed analysis of results. This package makes a contribution to the field by introducing innovative optimization methods and a comprehensive software tool for refining chemical reaction mechanisms.

关键词： Reaction mechanisms Optimization python toolbox

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FracAbut: A python toolbox for computing fracture stratigraphy using interface impedance

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COMPUTERS & GEOSCIENCES 2024年 190卷

作者： Soro, Paul Joseph Namongo Lamarche, Juliette Viseur, Sophie Richard, Pascal Messaadi, Fateh Aix Marseille Univ CNRS IRD INRAECEREGE Aix En Prov France PRGeology Paris France

In Naturally Fractured Reservoirs (NFR) diffuse fractures arrangement results from mechanical stratigraphy and tectonic history during failure. Thus, modelling Discrete Fracture Network (DFN) requires to understand and to account for fracture relationships at bed-interface (abutment or crosscutting) in 3D through time (loading path). However, sampling fractures data meaningfully in subsurface has always been a challenge for geologist due to data scarcity. To better understand and forecast fracture networks in stratified rocks, we study outcrops with a focus on geometric relationships between stratigraphic interfaces and fractures. This paper presents an original python toolbox called FracAbut. It is composed of 1 main and 2 auxiliary codes that quantify the geometric relation between fractures and stratigraphic interfaces from 1D (wells, scan-line) and 2D (digital image, photographs data). We calculate the Interface Impedance (II) that accounts for fracture abutment (crossing or not), persistence (single- or multi-bed) and propagation polarity (upward or downward). For each stratigraphic interface FracAbut provides information on fractures (type, number) and interface sensitivity (coupling strength). First, we apply FracAbut on synthetic case studies, then, on naturally fractured and stratified carbonates in Berat, Albania. Using both 1D scan-line and 2D outcrop photograph, we show that i) a mechanical interface can have different coupling above and below based on propagation polarity, ii) FracAbut results can give useful insight on fracture transmissivity, iii) FracAbut is fast and efficient to quantify fracture patterns and classify mechanical interface impact;iv) they are no relation between bed thickness and fracture propagation.

关键词： Mode I fractures python toolbox Mechanical stratigraphy Fracture stratigraphy Interface impedance

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PyTES: A python toolbox for closed-loop transcranial electrical stimulation

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SOFTWAREX 2023年 23卷

作者： Xu, Jiachen Grosse-Wentrup, Moritz Univ Vienna Fac Comp Sci Res Grp Neuroinformat Vienna Austria Uni Vienna Doctoral Sch Comp Sci DoCS Vienna Austria Uni Vienna Res Platform Data Sci Vienna Austria Vienna Cognit Sci Hub Vienna Austria

Closed-loop transcranial electrical stimulation (TES) is attracting increasing interest, but limited hardware and software solutions impede wide adoption. In particular, existing solutions are restricted to specific hardware, operating systems, and commercial software packages. We present a pythonbased toolbox - PyTES - for closed-loop TES. PyTES is open-source, easy to integrate with existing python packages for real-time brain decoding, and is able to interface with a wide range of low-cost hardware solutions. In this paper, we review existing solutions for closed-loop TES, introduce the design principles of PyTES, and demonstrate how to realize a closed-loop TES protocol with PyTES. (c) 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).

关键词： Closed-loop system Transcranial electrical stimulation (TES) Personalized system python toolbox

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A python-based Ringdown Analysis toolbox for Electromechanical Modes Identification

A Python-based Ringdown Analysis Toolbox for Electromechanic...

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International Conference on Smart Grid Synchronized Measurements and Analytics (SGSMA)

作者： Reyes, R. D. de la O, J. A. Paternina, M. R. A. Chow, J. H. Zamora, A. Ortiz, J. UANL Monterrey Mexico Univ Nacl Autonoma Mexico Mexico City DF Mexico RPI Troy NY USA U Michoacana Morelia Michoacan Mexico

ISBN: (纸本)9781665498234

This paper presents a user-friendly graphical user interface (GUI) that is embedded in a python toolbox to identify electromechanical modes emerging after large power system disturbances. This GUI is able to read xslx or csv files that contain the information of several phasor measurement units (PMUs) or a data file with a proper read format. Besides this GUI incorporates three well-known methods that stand out to perform ringdown analysis in power systems such as Prony's method (PM), eigensystem realization algorithm (ERA), and matrix pencil (MP). A straightforward implementation is adopted to capture dynamic parameters by processing single or multiple channels. Numerical and graphical results demonstrate the usability of the GUI, even in real system events(1).

关键词： Electromechanical modes ringdown analysis python toolbox Prony Eigensystem Realization Matrix Pencil

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MTpy: A python toolbox for magnetotellurics

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COMPUTERS & GEOSCIENCES 2014年 72卷 167-175页

作者： Krieger, Lars Peacock, Jared R. Univ Adelaide Sch Earth & Environm Sci Adelaide SA 5005 Australia US Geol Survey Menlo Pk CA 94025 USA

We present the software package MTpy that allows handling, processing, and imaging of magnetotelluric (MT) data sets. Written in python, the code is open source, containing sub-packages and modules for various tasks within the standard MT data processing and handling scheme. Besides the independent definition of classes and functions, MTpy provides wrappers and convenience scripts to call standard external data processing and modelling software. In its current state, modules and functions of MTpy work on raw and pre-processed MT data. However, opposite to providing a static compilation of software, we prefer to introduce MTpy as a flexible software toolbox, whose contents can be combined and utilised according to the respective needs of the user. Just as the overall functionality of a mechanical toolbox can be extended by adding new tools, MTpy is a flexible framework, which will be dynamically extended in the future. Furthermore, it can help to unify and extend existing codes and algorithms within the (academic) MT community. In this paper, we introduce the structure and concept of MTpy. Additionally, we show some examples from an everyday work-flow of MT data processing: the generation of standard EDI data files from raw electric (E-) and magnetic flux density (B-) field time series as input, the conversion into MiniSEED data format, as well as the generation of a graphical data representation in the form of a Phase Tensor pseudosection. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Magnetotellurics Data processing python toolbox Software Open source

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SAGEA: A toolbox for comprehensive error assessment of GRACE and GRACE-FO based mass changes

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COMPUTERS & GEOSCIENCES 2025年 196卷

作者： Liu, Shuhao Yang, Fan Forootan, Ehsan Huazhong Univ Sci & Technol Sch Phys Wuhan Peoples R China Aalborg Univ Dept Sustainabil & Planning Aalborg Denmark

The level-2 time-variable gravity fields obtained from Gravity Recovery and Climate Experiment (GRACE) and its follow-on (GRACE-FO) mission are widely used in multi-discipline geoscience studies. However, the post- processing of these gravity fields to obtain a desired signal is rather challenging for users who are not familiar with the level-2 products. In addition, the error assessment/quantification of these derived signals, which is of increasing demand in science application, is still a challenging issue even among professional GRACE(- FO) users. In this paper, we review the known steps of post-processing, along with their implementation strategies. We also make a comprehensive investigation into the error of GRACE(-FO) based mass changes, and for the first time, we define the so-called error into three independent categories. This work, including the post-processing steps and the assessment of each error, is integrated into an open-source python toolbox called SAGEA (SAtellite Gravity Error Assessment). With diverse options, SAGEA provides flexibility to generate signals along with the full error from level-2 products. In addition, a novel in-depth optimization of our post- processing implementation gains a speed-up of similar to 100 times better than traditional method. For verification, a number of case studies are carried out with SAGEA to obtain a comprehensive error assessment of GRACE(-FO) level-2 product at global and local scales.

关键词： GRACE Error assessment Post-processing python toolbox

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OptMSP: A toolbox for designing optimal multi-stage (bio)processes

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JOURNAL OF BIOTECHNOLOGY 2024年 383卷 94-102页

作者： Bauer, Jasmin Klamt, Steffen Max Planck Inst Dynam Complex Tech Syst Sandtorstr 1 Magdeburg Germany

One central goal of bioprocess engineering is to maximize the production of specific chemicals using microbial cell factories. Many bioprocesses are one-stage (batch) processes (OSPs), in which growth and product synthesis are coupled. However, OSPs often exhibit low volumetric productivities due to the competition for substrate for biomass and product synthesis implying trade-offs between biomass and product yields. Two-stage or, more generally, multi-stage processes (MSPs) offer the potential to tackle this trade-off for improved efficiency of bioprocesses, for example, by separating growth and production. MSPs have recently gained much attention, also because of a rapidly growing toolbox for the dynamic control of metabolic fluxes. Despite these promising advancements, computational tools specifically tailored for the optimal design of MSPs in the field of biotechnology are still lacking. Here, we present OptMSP, a new python-based toolbox for identifying optimal MSPs maximizing a userdefined process metrics (such as volumetric productivity, yield, and titer or combinations thereof) under given constraints. In contrast to other methods, our framework starts with a set of well-defined modules representing relevant stages or sub-processes. Experimentally determined parameters (such as growth rates, substrate uptake and product formation rates) are used to build suitable ODE models describing the dynamic behavior of each module. OptMSP finds then the optimal combination of those modules, which, together with the optimal switching time points, maximize a given objective function. We demonstrate the applicability and relevance of the approach with three different case studies, including the example of lactate production by E. coli in a batch setup, where an aerobic growth phase can be combined with anaerobic production phases with or without growth and with or without enhanced ATP turnover.

关键词： Process optimization Bioprocess engineering Multi-stage bioprocess Escherichia coli python toolbox Volumetric productivity

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PyCoSMoS: An advanced toolbox for simulating real-world hydroclimatic data

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ENVIRONMENTAL MODELLING & SOFTWARE 2024年 178卷

作者： Francesco, Cappelli Papalexiou, Simon Michael Markonis, Yannis Grimaldi, Salvatore Univ Tuscia DIBAF Dept Viterbo Italy Univ Calgary Dept Civil Engn Calgary AB Canada Czech Univ Life Sci Prague Fac Environm Sci Prague Czech Republic

Simulation models are a fundamental tool for investigating hydrological processes and for water resource management. In this study, we introduce PyCoSMoS, a python toolbox that enables researchers to simulate observed univariate time series mimicking hydroclimatic processes. This toolbox preserves arbitrary marginal distribution and autocorrelation functions, while significantly reducing computational burden. PyCoSMoS is built upon the mixed -Uniform CoSMoS method recently proposed by Papalexiou et al. (2023). The toolbox is designed to minimize the user's input, requiring only observed time series, marginal distribution, correlation function, and the number of lags. The output provides both visual and quantitative comparisons between the observed and simulated time series. We evaluate the performance of the package using various synthetic case studies and the results demonstrate satisfactory accuracy. Furthermore, we apply the toolbox to three real case studies: precipitation, temperature, and relative humidity, for which the toolbox can successfully simulate the observed time series in each case.

关键词： Stochastic modeling Hydroclimatic simulations Weather generator python toolbox

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PyHawk: An efficient gravity recovery solver for low-low satellite-to-satellite tracking gravity missions

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COMPUTERS & GEOSCIENCES 2025年 201卷

作者： Wu, Yi Yang, Fan Liu, Shuhao Forootan, Ehsan Huazhong Univ Sci & Technol Sch Phys Wuhan 430074 Peoples R China Aalborg Univ Dept Sustainabil & Planning Geodesy Grp DK-9000 Aalborg Denmark

The low-low satellite-to-satellite tracking (LL-SST) gravity missions, such as the Gravity Recovery and Climate Experiment (GRACE) and its Follow-On (GRACE-FO), provide an important space-based Essential Climate Variable (ECV) to measure changes in the Terrestrial Water Storage (TWS). Due to the high-precision Global Navigation Satellite System (GNSS) receiver, accelerometers, and inter-satellite ranging instrument, these LL-SST missions are able to sense extremely tiny perturbations on both the orbit and inter-satellite ranges, which can project into the Earth's time-variable gravity fields. The measurement systems of these LL-SST missions are highly complex;therefore, a data processing chain is required to exploit the potential of their high-precision measurements, which challenges both general and expert users. In this study, we present an open-source, user-friendly, cross-platform and integrated toolbox "PyHawk", which is the first python-based software in relevant field, to address the complete data processing chain of LL-SST missions including GRACE, GRACE-FO and probably the future gravity missions. This toolbox provides non-expert users an easy access to the payload data pre-processing, background force modeling, orbit integration, ranging calibration, as well as the ability for temporal gravity field recovery using LL-SST measurements. In addition, a series of high-standard benchmark tests have been provided to evaluate PyHawk, confirming its performance to be comparable with those used to provide the official Level-2 time-variable gravity field solutions of GRACE. Researchers working with orbit determination and gravity field modeling can benefit from this toolbox.

关键词： GRACE(-FO) python toolbox Gravity recovery Orbit determination Level-2 gravity solutions Low-low satellite-to-satellite tracking

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