Relevant bibliographies by topics / NumpPy

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'NumpPy'

Author: Grafiati
Published: 28 June 2021
Last updated: 29 July 2025

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Journal articles on the topic "NumpPy"

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Harris, Charles R., K. Jarrod Millman, Stéfan J. van der Walt, et al. "Array programming with NumPy." Nature 585, no. 7825 (2020): 357–62. http://dx.doi.org/10.1038/s41586-020-2649-2.

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AbstractArray programming provides a powerful, compact and expressive syntax for accessing, manipulating and operating on data in vectors, matrices and higher-dimensional arrays. NumPy is the primary array programming library for the Python language. It has an essential role in research analysis pipelines in fields as diverse as physics, chemistry, astronomy, geoscience, biology, psychology, materials science, engineering, finance and economics. For example, in astronomy, NumPy was an important part of the software stack used in the discovery of gravitational waves1 and in the first imaging of
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Ruda, Marta. "Polish Personal Pronouns: [PersPPers [NumPNum [n]]] and [NumP Num [n]]1." Studies in Polish Linguistics 16, no. 1 (2021): 23–40. http://dx.doi.org/10.4467/23005920spl.21.002.13956.

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W niniejszym krótkim artykule argumentujęza przyjęciem założenia, że zaimki osobowe w języku polskim majądo dyspozycji dwa warianty strukturalne: zaimki w pierwszej i drugiej osobie to PersP, a zaimki w trzeciej osobie to PersP lub NumP. Ta różnica strukturalna jest odpowiedzialna za dostępnośćnie tylko określonej, ale także nieokreślonej (w tym nieszczegółowej) interpretacji zaimków osobowych w języku polskim, bez względu na ich złożonośćmorfologiczną(tzn. zarówno formy pełne, jak i zredukowane mogąmiećróżne interpretacje). Wyjaśnia to założenie, że fraza NumP może byćinterpretowana jako anaf
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Li, Yangyang, Yue Shao, and Chunlong Fu. "Exploring Indexing and Slicing in NumPy Arrays." Journal of Physics: Conference Series 2832, no. 1 (2024): 012019. http://dx.doi.org/10.1088/1742-6596/2832/1/012019.

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Abstract This article provides an in-depth analysis of the application differences between indexing and slicing NumPy arrays to provide a clear guide to scientific computing and data analysis. The article first introduces the basics of NumPy arrays, discusses the deep copy and shallow copy mechanisms, and the role of copies and views in memory management. Then it shows the specific application methods of integer indexing, boolean indexing, fancy indexing, and slicing of one- and two-dimensional arrays through examples. It can be concluded from the experiments that slices are views of the origi
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Noel Dawe, Edmund, Piti Ongmongkolkul, and Giordon Stark. "root_numpy: The interface between ROOT and NumPy." Journal of Open Source Software 2, no. 16 (2017): 307. http://dx.doi.org/10.21105/joss.00307.

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Ertek, Muzaffer Kerem. "NumPy-based Calibration of Basic Hypoplastic Constitutive Models." Civil Engineering Beyond Limits 1, no. 1 (2019): 12–16. http://dx.doi.org/10.36937/cebel.2020.001.003.

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Constitutive modeling of soils is a crucial topic in geotechnics. Several constitutive models for soils can be found in material libraries of open-source or commercial geotechnical software packages, and these models can be based on various theories. Hypoplasticity as a relatively young theory is an alternative to elastoplasticity and consistently attracts new researchers. Contrary to elastoplasticity, hypoplasticity does not involve a priori defined yield surface, flow rule and plastic potential and arises from a simple tensorial function of the rate type. An exhaustive review of literature,
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Pannadhitthana Candra, Ariya. "Analisis Data Menggunakan Python: Memperkenalkan Pandas dan NumPy." Journal of Information System and Education Development 3, no. 1 (2025): 11–16. https://doi.org/10.62386/jised.v3i1.118.

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Penelitian ini membahas pentingnya analisis data di era digital dan memperkenalkan Python sebagai alat yang efektif untuk tujuan ini, dengan fokus pada pustaka Pandas dan NumPy. Dengan volume data global yang diperkirakan mencapai 175 zettabyte pada tahun 2025, kemampuan untuk menganalisis data menjadi krusial. Python, dengan sintaks yang sederhana dan pustaka yang kaya, menjadi pilihan utama dalam data science. Pandas memfasilitasi manipulasi data, sementara NumPy mendukung operasi numerik, menjadikannya esensial bagi analis data. Artikel ini menjelaskan konsep dasar analisis data menggunakan
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Putu Sealtiel Armeza Diandra, Dewa. "MONITORING LAHAN PARKIR MENGGUNAKAN PYTHON, CV2, CVZONE DAN NUMPY." JATI (Jurnal Mahasiswa Teknik Informatika) 9, no. 1 (2024): 157–61. https://doi.org/10.36040/jati.v9i1.12182.

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Sistem monitoring lahan parkir yang ada saat ini masih sangat bergantung pada komunikasi manual antar petugas, yang menyebabkan lambatnya aliran informasi mengenai ketersediaan slot parkir dan berpotensi menimbulkan kemacetan. Penelitian ini bertujuan untuk mengembangkan sistem pemantauan parkir berbasis Python dan teknologi IoT yang dapat mendeteksi ketersediaan tempat parkir secara real-time. Metode yang digunakan meliputi pemrograman dengan pustaka Python seperti OpenCV, CVZone, dan Numpy untuk memproses data visual dari kamera pengawas. Hasil dari pengujian menunjukkan bahwa sistem ini mam
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van der Walt, Stéfan, S. Chris Colbert, and Gaël Varoquaux. "The NumPy Array: A Structure for Efficient Numerical Computation." Computing in Science & Engineering 13, no. 2 (2011): 22–30. http://dx.doi.org/10.1109/mcse.2011.37.

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Eddelbuettel, Dirk, and Wush Wu. "RcppCNPy: Read-Write Support for NumPy Files in R." Journal of Open Source Software 1, no. 5 (2016): 55. http://dx.doi.org/10.21105/joss.00055.

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Dr., M. Parameswari, Priyanka L., R. Swetha G., Uma T., and Vaishnavi E. "AIR CANVAS APPLICATION USING OPENCV AND NUMPY IN PYTHON." International Journal of Engineering Research and Modern Education (IJERME) 7, no. 1 (2022): 10–13. https://doi.org/10.5281/zenodo.6569593.

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On-air writing has become one of the most attractive and challenging areas in the field of image processing and pattern recognition in recent years. It contributes to the development of automation processes and can improve interpersonal and machine interaction across multiple systems. Many research projects focus on new techniques and techniques that can reduce processing time while providing high accuracy of recognition. Tracking an object is considered an important function within the field of Computer Vision. The invention of fast computers, the availability of affordable and high-quality v
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Dissertations / Theses on the topic "NumpPy"

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Kadaňka, Jan. "Vizuální kontrola rozměrů součástí." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2021. http://www.nusl.cz/ntk/nusl-442860.

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This master thesis deals with the comparison of an industrial system for object recognition and measurement with an affordable camera on the Raspberry Pie platform. The theoretical part briefly describes several methods for measuring dimensions, which are commonly used in industry, along with a general description of technical equipment for image recording. The next section describes 3 industrial systems for image analysis. The practical part of the work deals with the design of the measuring stand, the description of the technical equipment, the implementation of measurements using both platf
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OJI, Tatsuo, Sachiko NISHIDA, Saki SHIBATA та ін. "NUMAP活動報告2012 : 2012 annual activity report of NUMAP". 名古屋大学博物館, 2012. http://hdl.handle.net/2237/18207.

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HIRUNAGI, Kanjun, Michiko NIIMI, Sachiko NISHIDA та ін. "NUMAP活動報告2011". 名古屋大学博物館, 2011. http://hdl.handle.net/2237/16663.

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Svensson, Patrik, and Fredrik Galfi. "Performance evaluation of NumPy, SciPy, PyMEL and OpenMaya compared to the C++ API in Autodesk Maya." Thesis, Blekinge Tekniska Högskola, Institutionen för datavetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-21664.

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Background. Autodesk Maya allows scripting through both MEL and Python, and it is also possible to use different Python modules and a C++ API to perform the desired tasks. In theory, the C++ API is the fastest option in Maya, but there are no studies that support this claim. Other studies show that PyMEL is the slowest module in Maya to work with, but it is still the one used most frequently. This thesis has therefore made a speed measurement to determine which of the four selected Python modules and the C++ API is the fastest to use, regarding animation transfer between skeletal hierarchies w
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KADOWAKI, Seiji, 誠二 門脇, Michiko NIIMI та ін. "NUMAP年間活動報告2010". 名古屋大学博物館, 2010. http://hdl.handle.net/2237/14691.

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Кирієнко, Р. О. "Система автоматизованого створення правил на основі списку подій для попередження кібератак". Thesis, Чернігів, 2021. http://ir.stu.cn.ua/123456789/22781.

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Кирієнко, Р. О. Система автоматизованого створення правил на основі списку подій для попередження кібератак : дипломна робота : 121 - Інженерія програмного забезпечення / Р. О. Кирієнко ; керівник роботи М. С. Дорош ; НУ "Чернігівська політехніка", кафедра інформаційних технологій та програмної інженерії. – Чернігів, 2021. – 51 с.<br>Об`єктом розробки виступає система автоматизованого створення правил на основі списку подій для попередження кібератак Результатом роботи має бути реалізація можливостей: – Приймання вхідних данних – Підставлянна даних до шаблону правил – Генерація msg для правил
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Корж, О. А. "Система аналізу подій для виявлення джерел кібератак". Thesis, Чернігів, 2021. http://ir.stu.cn.ua/123456789/22784.

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Корж, О. А. Система аналізу подій для виявлення джерел кібератак : дипломна робота : 121 - Інженерія програмного забезпечення / О. А. Корж ; керівник роботи М. С. Дорош ; НУ "Чернігівська політехніка", кафедра інформаційних технологій та програмної інженерії. – Чернігів, 2021. – 45 с.<br>Об`єктом розробки – система аналізу подій для виявлення джерел кібератак. Результатом роботи є проєктування та розробки системи аналізу подій з використанням програмного забезпечення suricata. Для повноцінного функціонування потрібна suricata та необхідні бібліотеки. Реалізація системи була виконана з
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HIRUNAGI, Kanjun, 観順 蛭薙, Michiko NIIMI та ін. "大学博物館を拠点とした学生によるアウトリーチ活動の実践報告とその展望 : NUMAP活動報告 2007-2009". 名古屋大学博物館, 2009. http://hdl.handle.net/2237/14304.

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Tonelli, Alfredo. "Image Processing e Computer Vision con Python e OpenCV." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/20390/.

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Studio di uno degli ecosistemi software oggi maggiormente utilizzati per Image Processing e Computer Vision. Lo studio affrontato parte dalle discipline di Image Processing e Computer Vision, passando per le principali soluzioni software adottate, tra le quali spiccano il linguaggio di programmazione ad alto livello Python e la libreria di Computer Vision OpenCV, per terminare con esempi pratici di base utili per capire il funzionamento delle tecnologie illustrate.
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Erickson, Xavante. "Acceleration of Machine-Learning Pipeline Using Parallel Computing." Thesis, Uppsala universitet, Signaler och system, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-441722.

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Researchers from Lund have conducted research on classifying images in three different categories, faces, landmarks and objects from EEG data [1]. The researchers used SVMs (Support Vector Machine) to classify between the three different categories [2, 3]. The scripts written to compute this had the potential to be extremely parallelized and could potentially be optimized to complete the computations much faster. The scripts were originally written in MATLAB which is a propriety software and not the most popular language for machine learning. The aim of this project is to translate the MATLAB
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Books on the topic "NumpPy"

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Ewa, Szymańska, ed. Stare numery. "Alfa", 1990.

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Łopieńska, Barbara. Stare numery. Aneks, 1986.

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Zlot, Wiesława. Żywe numery II. Państwowe Muzeum na Majdanku, 2006.

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Bressert, Eli. SciPy and NumPy: Optimizing & boosting your Python programming. O'Reilly, 2012.

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Jan, Przewłocki, Sobolewicz Tadeusz 1923-, Widera Aleksander, and Stochowa Zofia, eds. Numery mówią: Wspomnienia więźniów KL Auschwitz. 3rd ed. Wydawn. "Śląsk", 1986.

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Szabłowska, Maria. Znam wszystkie wasze numery: Andrzej Korzyński. Wydawnictwo Agora, 2018.

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Ḥārithī, Yūsuf Muḥammad. Qānūn al-Sharīf Abī Numayy bayna al-wahm wa-al-ḥaqīqah: Dirāsah tārīkhīyah wathāʼiqīyah. Jadāwil lil-Nashr wa-al-Tarjamah wa-al-Tawzīʻ, 2015.

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Halpin, David. Numpty-Rhymes, Numpty-Bys and Numpty-Songs: Poetry from Numpty's Doctor's Brother's Goose. Independently Published, 2019.

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Numpty-Rhymes, Numpty-Bys and Numpty-Songs: Poetry from Numpty's Doctor's Brother's Goose. Independently Published, 2019.

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NumPy Cookbook. Packt Publishing, Limited, 2012.

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Book chapters on the topic "NumpPy"

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Bisong, Ekaba. "NumPy." In Building Machine Learning and Deep Learning Models on Google Cloud Platform. Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-4470-8_10.

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Klein, Bernd. "NumPy." In Einführung in Python 3. Carl Hanser Verlag GmbH & Co. KG, 2013. http://dx.doi.org/10.3139/9783446437173.029.

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Häberlein, Tobias. "Numpy." In Programmieren mit Python. Springer Berlin Heidelberg, 2024. http://dx.doi.org/10.1007/978-3-662-68678-2_5.

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Klein, Bernd. "NumPy." In Einführung in Python 3. Carl Hanser Verlag GmbH & Co. KG, 2014. http://dx.doi.org/10.3139/9783446441514.031.

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Unpingco, José. "Numpy." In Python Programming for Data Analysis. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68952-0_4.

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Klein, Bernd. "NumPy Einführung." In Numerisches Python. Carl Hanser Verlag GmbH & Co. KG, 2019. http://dx.doi.org/10.3139/9783446453630.004.

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Nelli, Fabio. "The NumPy Library." In Python Data Analytics. Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3913-1_3.

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Bernard, Joey. "Numerics and Numpy." In Python Recipes Handbook. Apress, 2016. http://dx.doi.org/10.1007/978-1-4842-0241-8_11.

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Nelli, Fabio. "The NumPy Library." In Python Data Analytics. Apress, 2015. http://dx.doi.org/10.1007/978-1-4842-0958-5_3.

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Pajankar, Ashwin. "Introduction to NumPy." In Raspberry Pi Supercomputing and Scientific Programming. Apress, 2017. http://dx.doi.org/10.1007/978-1-4842-2878-4_10.

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Conference papers on the topic "NumpPy"

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Tabrizi, Seyed Zuhair Bolourchian, Elena Barbera, Wilson Ricardo Leal da Silva, and Fabrizio Bezzo. "A Python/Numpy-based package to support model discrimination and identification." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.192104.

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Addressing challenges in process design and optimisation, especially with complex models and data uncertainties, requires effective tools for model development, selection, and identification. Techniques such as Model-based Design of Experiments (MBDoE) help support this task by screening and discriminating between models and, eventually, calibrating them. Open-source and user-friendly Python packages have implemented some model identification techniques. However, the need for a tool that can couple with various model simulators and account for the steps of model identification as well as physi
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Suneetha, Irala, Ch Prathima, Srinivasulu Sirisala, S. S. D. K. Mahalakshmi, K. Balaji Nanda Kumar Reddy, and Bosubabu Sambana. "A Real Time approach for detecting and counting Vehicles using OpenCV and NumPy." In 2025 International Conference on Pervasive Computational Technologies (ICPCT). IEEE, 2025. https://doi.org/10.1109/icpct64145.2025.10940439.

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Masaev, Sergey, and Andrey Ilgov. "Implementation of a genetic algorithm for two-dimensional arrays using the numpy library for python." In Fourth International Conference on Optics, Computer Applications, and Materials Science (CMSD-IV 2024), edited by Arthur Gibadullin and Ramazon Abdullozoda. SPIE, 2025. https://doi.org/10.1117/12.3060436.

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Bauer, Michael, and Michael Garland. "Legate NumPy." In SC '19: The International Conference for High Performance Computing, Networking, Storage, and Analysis. ACM, 2019. http://dx.doi.org/10.1145/3295500.3356175.

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Blum, Troels, Mads R. B. Kristensen, and Brian Vinter. "Transparent GPU Execution of NumPy Applications." In 2014 IEEE International Parallel & Distributed Processing Symposium Workshops (IPDPSW). IEEE, 2014. http://dx.doi.org/10.1109/ipdpsw.2014.114.

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Harrington, Joseph. "The SciPy Documentation Project." In Python in Science Conference. SciPy, 2008. http://dx.doi.org/10.25080/mjhw7647.

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The SciPy Documentation Project seeks to provide NumPy and SciPy with professional documentation and documentation access tools in a variety of formats. As our first effort, we have sponsored the SciPy Documentation Marathon 2008, whose goal is to produce reference documentation for the most-used portions of NumPy. I present an overview of current efforts and future plans.
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Jones, Nathaniel. "Speeding up Annual Daylighting Simulation Using NumPy." In 2022 Building Performance Analysis Conference and SimBuild. ASHRAE and IBPSA-USA, 2022. http://dx.doi.org/10.26868/25746308.2022.c002.

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Jones, Nathaniel. "Speeding up Annual Daylighting Simulation Using NumPy." In 2022 Building Performance Analysis Conference and SimBuild. ASHRAE and IBPSA-USA, 2022. http://dx.doi.org/10.26868/25746308.2022.simbuild2022_c002.

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Garg, Rahul, and Jose Nelson Amaral. "unPython: Converting Python Numerical Programs into C." In Python in Science Conference. SciPy, 2008. http://dx.doi.org/10.25080/xgyx8633.

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unPython is a Python-to-C compiler intended for numerical Python programs. The compiler takes as input type-annotated Python source and produces C source code for an equivalent extension module. The compiler is NumPy-aware and can convert most NumPy indexing or slicing operations into C array accesses. Furthermore the compiler also allows annotating certain for-loops as parallel and can generate OpenMP code thus providing an easy way to take advantage of multicore architectures.
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Pivarski, Jim, Ianna Osborne, Pratyush Das, Anish Biswas, and Peter Elmer. "Awkward Array: JSON-like data, NumPy-like idioms." In Python in Science Conference. SciPy, 2020. http://dx.doi.org/10.25080/majora-342d178e-00b.

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Reports on the topic "NumpPy"

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Daily, Jeffrey, and Dan Berghofer. Efficient Memory Access with NumPy Global Arrays using Local Memory Access. Office of Scientific and Technical Information (OSTI), 2013. http://dx.doi.org/10.2172/1136617.

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Соловйов, Володимир Миколайович, Олександр Ілліч Теплицький та Р. С. Забєйда. Об’єктно-орієнтовані динамічні моделі в курсі комп’ютерного моделювання. Міністерство регіонального розвитку та будівництва України, 2008. http://dx.doi.org/10.31812/0564/1124.

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Метод молекулярної динаміки є потужним засобом дослідження найрізноманітніших систем; його теоретичні основи, маючи корені у класичній механіці, легко засвоюються студентами. Нажаль, реалізація методу часто викликає утруднення через необхідність програмування чисельних алгоритмів та інтерфейсу користувача, що призводило до стрімкого росту розміру програм та утруднювало їх налагодження. Тому, пропонуючи студентам ознайомлення з методом у лабораторному практикумі з моделювання, ми або пропонували шаблони програм, як автори [1], або застосовували спеціалізовані середовища для моделювання. Застосу
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Scavuzzo, Sebastian, Jonathan Cedeño, and Jaroslava Miksovska. In Silico Calculation of Interhelical Angles in NCS1. Florida International University, 2025. https://doi.org/10.25148/fiuurj.3.1.16.

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Proteins are important macromolecules responsible for a variety of processes in living organisms. One of the most important features of proteins is their ability to respond to environmental stimuli, such as changes in intracellular metal concentration by binding metal ions, which in turns triggers structural changes within the protein that can modify its function or allow the protein to participate in a signaling pathway. One such signaling protein is the so-called neuronal calcium sensor 1 protein or NCS1, which binds Ca2+ along with other abiogenic metals such as Li+, and the metal binding r
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