Suchergebnisse - (( (statee OR state:new) python code analysis ) OR ( state python code analysis ))~

From code to reliability: Python-powered Paraconsistent Logic for alarm detection in the power gris/ Do codigo a confiabilidade: Logica Paraconsistente impulsionada por Python para a deteccao de alarmes na rede de energia von de Oliveira, Joseffe Barroso, Gino, Joao Vitor Santa Rosa, de Lima, Carlos Jose, Filho, Joao Inacio da Silva

“… This application enables the comparison of triggered alarms with the resultant logical states obtained during the analysis …”
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Analysis methods and code for very high-precision mass measurements of unstable isotopes von Karthein, J., Atanasov, D., Blaum, K., Lunney, D., Manea, V., Mougeot, M.

“… We present a robust analysis code developed in the Python language and incorporating libraries of the ROOT data analysis framework for the state-of-the-art mass spectrometry method called phase …”
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Identification of Novel Microcystins Using High-Resolution MS and MSn with Python Code von Baliu-Rodriguez, David, Peraino, Nicholas J, Premathilaka, Sanduni H, Birbeck, Johnna A, Baliu-Rodriguez, Tomás, Westrick, Judy A, Isailovic, Dragan

“… ) coupled with high-resolution (HR) Orbitrap mass spectrometry (MS) and a new bottom-up sequencing strategy …”
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GSEApy: a comprehensive package for performing gene set enrichment analysis in Python von Fang, Zhuoqing, Liu, Xinyuan, Peltz, Gary

“… Abstract Motivation Gene set enrichment analysis (GSEA) is a commonly used algorithm for characterizing gene expression changes …”
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Pyteomics—a Python Framework for Exploratory Data Analysis and Rapid Software Prototyping in Proteomics von Goloborodko, Anton A., Levitsky, Lev I., Ivanov, Mark V., Gorshkov, Mikhail V.

“… Pyteomics is a cross-platform, open-source Python library providing a rich set of tools for MS-based proteomics …”
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stk: A python toolkit for supramolecular assembly von Turcani, Lukas, Berardo, Enrico, Jelfs, Kim E.

“… stk is a modular, extensible and open‐source Python library that provides a simple Python API and integration with third party computational codes …”
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Coherent manipulation of the internal state of ultracold 87Rb133Cs molecules with multiple microwave fields von Blackmore, Jacob A, Gregory, Philip D, Bromley, Sarah L, Cornish, Simon L

“… We explore coherent multi-photon processes in 87Rb133Cs molecules using 3-level lambda and ladder configurations of rotational and hyperfine states, and discuss their relevance to future applications …”
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scFates: a scalable python package for advanced pseudotime and bifurcation analysis from single-cell data von Faure, Louis, Soldatov, Ruslan, Kharchenko, Peter V, Adameyko, Igor

“… Abstract Summary scFates provides an extensive toolset for the analysis of dynamic trajectories comprising tree learning, feature association testing, branch differential expression and with a …”
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pyComBat, a Python tool for batch effects correction in high-throughput molecular data using empirical Bayes methods von Behdenna, Abdelkader, Colange, Maximilien, Haziza, Julien, Gema, Aryo, Appé, Guillaume, Azencott, Chloé-Agathe, Nordor, Akpéli

“… Results In this technical note, we present a new Python implementation of ComBat and ComBat-Seq …”
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Short communication: age2exhume – a MATLAB/Python script to calculate steady-state vertical exhumation rates from thermochronometric ages and application to the Himalaya von van der Beek, Peter, Schildgen, Taylor F.

“… datasets in such models remains challenging. Here, we present age2exhume, a thermal model in the form of a MATLAB or Python script, which can be used to rapidly obtain …”
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PHOTONAI—A Python API for rapid machine learning model development von Leenings, Ramona, Winter, Nils Ralf, Plagwitz, Lucas, Holstein, Vincent, Ernsting, Jan, Sarink, Kelvin, Fisch, Lukas, Steenweg, Jakob, Kleine-Vennekate, Leon, Gebker, Julian, Emden, Daniel, Grotegerd, Dominik, Opel, Nils, Risse, Benjamin, Jiang, Xiaoyi, Dannlowski, Udo, Hahn, Tim

“… PHOTONAI is a high-level Python API designed to simplify and accelerate machine learning model development …”
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SciPy 1.0: fundamental algorithms for scientific computing in Python von Virtanen, Pauli, Gommers, Ralf, Oliphant, Travis E., Haberland, Matt, Reddy, Tyler, Cournapeau, David, Burovski, Evgeni, Peterson, Pearu, Weckesser, Warren, Bright, Jonathan, van der Walt, Stéfan J., Brett, Matthew, Wilson, Joshua, Millman, K. Jarrod, Mayorov, Nikolay, Nelson, Andrew R. J., Jones, Eric, Kern, Robert, Larson, Eric, Carey, C J, Polat, İlhan, Feng, Yu, Moore, Eric W., VanderPlas, Jake, Laxalde, Denis, Perktold, Josef, Cimrman, Robert, Henriksen, Ian, Quintero, E. A., Harris, Charles R., Archibald, Anne M., Ribeiro, Antônio H., Pedregosa, Fabian, van Mulbregt, Paul

“… Since its initial release in 2001, SciPy has become a de facto standard for leveraging scientific algorithms in Python, with over 600 unique code contributors, thousands of dependent packages …”
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PyNoetic: A modular python framework for no-code development of EEG brain-computer interfaces von Singh, Gursimran, Chharia, Aviral, Upadhyay, Rahul, Kumar, Vinay, Longo, Luca

“… PyNoetic is one of the very few frameworks in Python that encompasses the entire BCI design pipeline, from stimulus presentation and data …”
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GPAW: An open Python package for electronic structure calculations von Mortensen, Jens Jørgen, Larsen, Ask Hjorth, Kuisma, Mikael, Ivanov, Aleksei V, Taghizadeh, Alireza, Peterson, Andrew, Haldar, Anubhab, Dohn, Asmus Ougaard, Schäfer, Christian, Jónsson, Elvar Örn, Hermes, Eric D, Nilsson, Fredrik Andreas, Kastlunger, Georg, Levi, Gianluca, Jónsson, Hannes, Häkkinen, Hannu, Fojt, Jakub, Kangsabanik, Jiban, Sødequist, Joachim, Lehtomäki, Jouko, Heske, Julian, Enkovaara, Jussi, Winther, Kirsten Trøstrup, Dulak, Marcin, Melander, Marko M, Ovesen, Martin, Louhivuori, Martti, Walter, Michael, Gjerding, Morten, Lopez-Acevedo, Olga, Erhart, Paul, Warmbier, Robert, Würdemann, Rolf, Kaappa, Sami, Latini, Simone, Boland, Tara Maria, Bligaard, Thomas, Skovhus, Thorbjørn, Susi, Toma, Maxson, Tristan, Rossi, Tuomas, Chen, Xi, Schmerwitz, Yorick Leonard A, Schiøtz, Jakob, Olsen, Thomas, Jacobsen, Karsten Wedel, Thygesen, Kristian Sommer

“… This multi-basis feature renders GPAW highly versatile and unique among similar codes. By virtue of its modular structure, the GPAW code constitutes an ideal platform for the implementation of new features and methodologies …”
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MEG and EEG data analysis with MNE-Python von Gramfort, Alexandre

“… As part of the MNE software suite, MNE-Python is an open-source software package that addresses this challenge by providing state-of-the-art algorithms implemented in Python that cover multiple …”
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Tensorpac: An open-source Python toolbox for tensor-based phase-amplitude coupling measurement in electrophysiological brain signals von Combrisson, Etienne, Nest, Timothy, Brovelli, Andrea, Ince, Robin A. A., Soto, Juan L. P., Guillot, Aymeric, Jerbi, Karim

“… Current research in the field is not only hampered by the absence of a gold standard for PAC analysis, but also by the computational costs of running exhaustive computations on large and high …”
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galpy: A python LIBRARY FOR GALACTIC DYNAMICS von Bovy, Jo

“… I describe the design, implementation, and usage of galpy, a python package for galactic-dynamics calculations …”
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libRoadRunner 2.0: a high performance SBML simulation and analysis library von Welsh, Ciaran, Xu, Jin, Smith, Lucian, König, Matthias, Choi, Kiri, Sauro, Herbert M

“… Abstract Motivation This article presents libRoadRunner 2.0, an extensible, high-performance, cross-platform, open-source software library for the simulation and analysis of models expressed using the systems biology markup language (SBML …”
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AMADAR: a python-based package for large scale prediction of Diels–Alder transition state geometries and IRC path analysis von Isamura, Bienfait K., Lobb, Kevin A.

“… Predicting transition state geometries is one of the most challenging tasks in computational chemistry, which often requires expert-based knowledge and permanent human intervention …”
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GPT-4.1 Sets the Standard in Automated Experiment Design Using Novel Python Libraries von Fachada, Nuno, Fernandes, Daniel, Fernandes, Carlos M., Ferreira-Saraiva, Bruno D., Matos-Carvalho, João P.

“… unfamiliar Python APIs for complex computational experiments remains poorly characterized. This study systematically benchmarks a selection of state-of-the-art LLMs in generating functional Python code for two increasingly challenging scenarios …”
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