Výsledky vyhledávání - (( python program documentation ) OR ( python property documentation ))

  1. 1

    The RaPID-Ω system: Room and proctor intelligent decider for large scale tests programming Autor Morales, Fernando

    ISSN: 0354-0243, 1820-743X
    Vydáno: University of Belgrade 2021
    “…We present the documentation and mathematical modeling of the open-box system RaPID…”
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  2. 2

    Pickaxe: a Python library for the prediction of novel metabolic reactions Autor Shebek, Kevin M., Strutz, Jonathan, Broadbelt, Linda J., Tyo, Keith E. J.

    ISSN: 1471-2105, 1471-2105
    Vydáno: London BioMed Central 22.03.2023
    Vydáno v BMC bioinformatics (22.03.2023)
    “… for a user’s application due to lack of exhaustive reaction rules, restriction to pre-computed networks, and difficulty in using the software due to lack of documentation…”
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  3. 3

    Metis: a python-based user interface to collect expert feedback for generative chemistry models Autor Menke, Janosch, Nahal, Yasmine, Bjerrum, Esben Jannik, Kabeshov, Mikhail, Kaski, Samuel, Engkvist, Ola

    ISSN: 1758-2946, 1758-2946
    Vydáno: Cham Springer International Publishing 14.08.2024
    Vydáno v Journal of cheminformatics (14.08.2024)
    “… Metis is a Python-based open-source graphical user interface (GUI), designed to solve this and enable the collection of chemists…”
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  4. 4

    Stingray: A Modern Python Library for Spectral Timing Autor Huppenkothen, Daniela, Bachetti, Matteo, Stevens, Abigail L., Migliari, Simone, Balm, Paul, Hammad, Omar, Khan, Usman Mahmood, Mishra, Himanshu, Rashid, Haroon, Sharma, Swapnil, Ribeiro, Evandro Martinez, Blanco, Ricardo Valles

    ISSN: 0004-637X, 1538-4357
    Vydáno: Philadelphia The American Astronomical Society 10.08.2019
    Vydáno v The Astrophysical journal (10.08.2019)
    “…This paper describes the design and implementation of stingray, a library in Python built to perform time series analysis and related tasks on astronomical light curves…”
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  5. 5

    A fast and efficient python library for interfacing with the Biological Magnetic Resonance Data Bank Autor Smelter, Andrey, Astra, Morgan, Moseley, Hunter N. B.

    ISSN: 1471-2105, 1471-2105
    Vydáno: London BioMed Central 17.03.2017
    Vydáno v BMC bioinformatics (17.03.2017)
    “…) spectroscopic data of biological macromolecules. It is an important resource for many researchers using NMR to study structural, biophysical, and biochemical properties of biological macromolecule…”
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  6. 6

    Grid: A Python library for molecular integration, interpolation, differentiation, and more Autor Tehrani, Alireza, Yang, Xiaotian Derrick, Martínez-González, Marco, Pujal, Leila, Hernández-Esparza, Raymundo, Chan, Matthew, Vöhringer-Martinez, Esteban, Verstraelen, Toon, Ayers, Paul W, Heidar-Zadeh, Farnaz

    ISSN: 1089-7690, 1089-7690
    Vydáno: United States 07.05.2024
    Vydáno v The Journal of chemical physics (07.05.2024)
    “…Grid is a free and open-source Python library for constructing numerical grids to integrate, interpolate, and differentiate functions (e.g…”
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  7. 7

    Mordred: a molecular descriptor calculator Autor Moriwaki, Hirotomo, Tian, Yu-Shi, Kawashita, Norihito, Takagi, Tatsuya

    ISSN: 1758-2946, 1758-2946
    Vydáno: Cham Springer International Publishing 06.02.2018
    Vydáno v Journal of cheminformatics (06.02.2018)
    “… Various molecular-descriptor-calculation software programs have been developed. However, users of those programs must contend with several issues, including software bugs, insufficient update frequencies, and software licensing constraints…”
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  8. 8

    TorchIO: A Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning Autor Pérez-García, Fernando, Sparks, Rachel, Ourselin, Sébastien

    ISSN: 0169-2607, 1872-7565, 1872-7565
    Vydáno: Elsevier B.V 01.09.2021
    “…•Open-source Python library for preprocessing, augmentation and sampling of medical images for deep learning…”
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  9. 9

    PyCDT: A Python toolkit for modeling point defects in semiconductors and insulators Autor Broberg, Danny, Medasani, Bharat, Zimmermann, Nils E.R., Yu, Guodong, Canning, Andrew, Haranczyk, Maciej, Asta, Mark, Hautier, Geoffroy

    ISSN: 0010-4655, 1879-2944
    Vydáno: United States Elsevier B.V 01.05.2018
    Vydáno v Computer physics communications (01.05.2018)
    “…), have found widespread use in the calculation of point-defect properties. Here we have developed the Python Charged Defect Toolkit (PyCDT…”
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  10. 10

    ARC 3.0: An expanded Python toolbox for atomic physics calculations Autor Robertson, E.J., Šibalić, N., Potvliege, R.M., Jones, M.P.A.

    ISSN: 0010-4655, 1879-2944
    Vydáno: Elsevier B.V 01.04.2021
    Vydáno v Computer physics communications (01.04.2021)
    “…ARC 3.0 is a modular, object-oriented Python library combining data and algorithms to enable the calculation of a range of properties of alkali and divalent atoms…”
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  11. 11

    PyXtal: A Python library for crystal structure generation and symmetry analysis Autor Fredericks, Scott, Parrish, Kevin, Sayre, Dean, Zhu, Qiang

    ISSN: 0010-4655, 1879-2944
    Vydáno: Elsevier B.V 01.04.2021
    Vydáno v Computer physics communications (01.04.2021)
    “…We present PyXtal, a new package based on the Python programming language, used to generate structures with specific symmetry and chemical compositions for both atomic and molecular systems…”
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  12. 12

    Simulation and Exergy Analysis of Energy Conversion Processes Using a Free and Open-Source Framework—Python-Based Object-Oriented Programming for Gas- and Steam Turbine Cycles Autor Zoder, Marius, Balke, Janosch, Hofmann, Mathias, Tsatsaronis, George

    ISSN: 1996-1073, 1996-1073
    Vydáno: Basel MDPI AG 01.10.2018
    Vydáno v Energies (Basel) (01.10.2018)
    “… The procedure to transform a physical model into a Python-based simulation program is shown. The article contains a solving algorithm for a precise gas turbine model with sophisticated equations of state…”
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  13. 13

    PyCharge: An open-source Python package for self-consistent electrodynamics simulations of Lorentz oscillators and moving point charges Autor Filipovich, Matthew J., Hughes, Stephen

    ISSN: 0010-4655, 1879-2944
    Vydáno: Elsevier B.V 01.05.2022
    Vydáno v Computer physics communications (01.05.2022)
    “…PyCharge is a computational electrodynamics Python simulator that can calculate the electromagnetic fields and potentials generated by moving point charges and can self-consistently simulate dipoles…”
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  14. 14

    Flexible and modular virtual scanning probe microscope Autor Tracey, John, Federici Canova, Filippo, Keisanen, Olli, Gao, David Z., Spijker, Peter, Reischl, Bernhard, Foster, Adam S.

    ISSN: 0010-4655, 1879-2944
    Vydáno: Elsevier B.V 01.11.2015
    Vydáno v Computer physics communications (01.11.2015)
    “…-up. Furthermore, the PyVAFM is fully expandable to allow novel and unique set-ups to be simulated, finally the PyVAFM ships with fully developed documentation and tutorial to increase usability. Program title…”
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  15. 15

    ssbio: a Python framework for structural systems biology Autor Mih, Nathan, Brunk, Elizabeth, Chen, Ke, Catoiu, Edward, Sastry, Anand, Kavvas, Erol, Monk, Jonathan M, Zhang, Zhen, Palsson, Bernhard O

    ISSN: 1367-4803, 1367-4811, 1460-2059, 1367-4811
    Vydáno: England Oxford University Press 15.06.2018
    Vydáno v Bioinformatics (15.06.2018)
    “…), wrappers to popular third-party programs to compute associated protein properties, and methods to visualize and annotate structures directly in Jupyter notebooks, thus lowering the barrier…”
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  16. 16

    Spinney: Post-processing of first-principles calculations of point defects in semiconductors with Python Autor Arrigoni, Marco, Madsen, Georg K.H.

    ISSN: 0010-4655, 1879-2944
    Vydáno: Elsevier B.V 01.07.2021
    Vydáno v Computer physics communications (01.07.2021)
    “…Understanding and predicting the thermodynamic properties of point defects in semiconductors and insulators would greatly aid in the design of novel materials and allow tuning the properties of existing ones…”
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  17. 17

    PyLlama: A stable and versatile Python toolkit for the electromagnetic modelling of multilayered anisotropic media Autor Bay, Mélanie M., Vignolini, Silvia, Vynck, Kevin

    ISSN: 0010-4655, 1879-2944
    Vydáno: Elsevier B.V 01.04.2022
    Vydáno v Computer physics communications (01.04.2022)
    “…PyLlama is a handy Python toolkit to compute the electromagnetic reflection and transmission properties of arbitrary multilayered linear media, including the case of anisotropy. Relying on a 4…”
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  18. 18

    A data model of the Climate and Forecast metadata conventions (CF-1.6) with a software implementation (cf-python v2.1) Autor Hassell, David, Gregory, Jonathan, Blower, Jon, Lawrence, Bryan N., Taylor, Karl E.

    ISSN: 1991-9603, 1991-962X, 1991-959X, 1991-9603, 1991-962X
    Vydáno: Katlenburg-Lindau Copernicus GmbH 19.12.2017
    Vydáno v Geoscientific Model Development (19.12.2017)
    “…) files and libraries. The CF conventions provide a description of the physical meaning of data and of their spatial and temporal properties, but they depend on the netCDF file encoding which can currently only be fully…”
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  19. 19

    DScribe: Library of descriptors for machine learning in materials science Autor Himanen, Lauri, Jäger, Marc O.J., Morooka, Eiaki V., Federici Canova, Filippo, Ranawat, Yashasvi S., Gao, David Z., Rinke, Patrick, Foster, Adam S.

    ISSN: 0010-4655, 1879-2944
    Vydáno: Elsevier B.V 01.02.2020
    Vydáno v Computer physics communications (01.02.2020)
    “…”) for atomistic materials simulations. DScribe accelerates the application of machine learning for atomistic property prediction by providing user-friendly, off-the-shelf descriptor implementations…”
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  20. 20

    Digitalization of Legacy Datasets and Machine Learning Regression Yields Insights for Reservoir Property Prediction and Submarine-Fan Evolution: A Subsurface Example From the Lewis Shale, Wyoming Autor Martin, Thomas, Tadla, Jared, Jobe, Zane

    ISSN: 1543-8740, 1543-8740
    Vydáno: 13.07.2022
    Vydáno v The Sedimentary record (13.07.2022)
    “…Machine-learning algorithms have long aided in geologic property prediction from well-log data, but are primarily used to classify lithology, facies, formation, and rock types…”
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