Thematische Bibliographien / NumpPy

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Auswahl der wissenschaftlichen Literatur zum Thema „NumpPy“

Autor: Grafiati

Veröffentlicht am 28. Juni 2021

Zuletzt aktualisiert am 1. Februar 2022

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Zeitschriftenartikel zum Thema "NumpPy"

Harris, Charles R., K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser et al. „Array programming with NumPy“. Nature 585, Nr. 7825 (16.09.2020): 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 a black hole2. Here we review how a few fundamental array concepts lead to a simple and powerful programming paradigm for organizing, exploring and analysing scientific data. NumPy is the foundation upon which the scientific Python ecosystem is constructed. It is so pervasive that several projects, targeting audiences with specialized needs, have developed their own NumPy-like interfaces and array objects. Owing to its central position in the ecosystem, NumPy increasingly acts as an interoperability layer between such array computation libraries and, together with its application programming interface (API), provides a flexible framework to support the next decade of scientific and industrial analysis.

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Noel Dawe, Edmund, Piti Ongmongkolkul und Giordon Stark. „root_numpy: The interface between ROOT and NumPy“. Journal of Open Source Software 2, Nr. 16 (13.08.2017): 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, Nr. 1 (19.12.2019): 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, however, points to the fact that for the calibration of these models, commercial symbolic mathematics software is mostly referred to and a calibration procedure based upon an open-source software which any individuals can easily make use of is missing. Therefore, an explicit procedure for calibration making use of NumPy, which is the main package for scientific computing with Python, following a concise summary for the theory of hypoplasticity is established. By doing so, it is expected to draw attention to take advantage of open-source packages that almost the majority of the scientific community utilizes increasingly.

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van der Walt, Stéfan, S. Chris Colbert und Gaël Varoquaux. „The NumPy Array: A Structure for Efficient Numerical Computation“. Computing in Science & Engineering 13, Nr. 2 (März 2011): 22–30. http://dx.doi.org/10.1109/mcse.2011.37.

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Eddelbuettel, Dirk, und Wush Wu. „RcppCNPy: Read-Write Support for NumPy Files in R“. Journal of Open Source Software 1, Nr. 5 (25.09.2016): 55. http://dx.doi.org/10.21105/joss.00055.

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Phinney, Archie M. „Numipu among the White Settlers“. Wicazo Sa Review 17, Nr. 2 (2002): 21–42. http://dx.doi.org/10.1353/wic.2002.0019.

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Klyachin, Vladimir, und Elеna Grigorеva. „Numerical Study of the Stability of Equilibrium Surfaces Using NumPY Package“. Vestnik Volgogradskogo gosudarstvennogo universiteta. Serija 1. Mathematica. Physica, Nr. 2 (Juni 2015): 17–30. http://dx.doi.org/10.15688/jvolsu1.2015.2.2.

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Klyachin, Vladimir. „Parallel Algorithm of Geometrical Hashing Based on NumPy Package and Processes Pool“. Vestnik Volgogradskogo gosudarstvennogo universiteta. Serija 1. Mathematica. Physica, Nr. 4 (Oktober 2015): 13–23. http://dx.doi.org/10.15688/jvolsu1.2015.4.2.

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Pawlik, Aleksandra, Judith Segal, Helen Sharp und Marian Petre. „Crowdsourcing Scientific Software Documentation: A Case Study of the NumPy Documentation Project“. Computing in Science & Engineering 17, Nr. 1 (Januar 2015): 28–36. http://dx.doi.org/10.1109/mcse.2014.93.

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Abert, Claas, Florian Bruckner, Christoph Vogler, Roman Windl, Raphael Thanhoffer und Dieter Suess. „A full-fledged micromagnetic code in fewer than 70 lines of NumPy“. Journal of Magnetism and Magnetic Materials 387 (August 2015): 13–18. http://dx.doi.org/10.1016/j.jmmm.2015.03.081.

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Dissertationen zum Thema "NumpPy"

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 platforms and the subsequent evaluation.

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OJI, Tatsuo, Sachiko NISHIDA, Saki SHIBATA, Sayaka MOCHIZUKI, Kyohei KAWAMOTO, Ukyo SHIMIZU, Takahiro IINO et al. „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, Seiji KADOWAKI, Eri KAJIKAWA, Momotaro NODA, Takeshi SANO et al. „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, und 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 with different numbers of data. Objectives. The aim of this thesis is to measure the performance in terms of speed of the Python modules NumPy, SciPy, OpenMaya and PyMEL, as well as the C++ API, in order to determine which is the fastest. Our objectives are to determine the speed performance of each module by conducting experiments. Methods. To achieve the objectives, an experiment was conducted to compare the speed of each Python module and the C++ API. To perform the experiments, the implementations for each module and the API have been written in the same way, with their own data types and classes. After performing the experiments for each module, the mean time consumption of each program has been compared. Results. The results from the experiments show that there was a noticeable difference in the speed between the C++ API and the Python modules, as the C++ API delivered the highest speed for all the skeletons that took place in the experiments. The OpenMaya module was the fastest Python module that was tested, while PyMEL was the slowest. The C++ API’s measurements show that it took 0,388–1,909 seconds depending on which skeleton was used to perform the experiment, while OpenMaya’s measurements were 0,538–3,119 seconds which show that OpenMaya is 39–68% slower than the C++ API. NumPy, SciPy and PyMEL’s measurements ranged from 689% to 3165% slower than the C++ API. Conclusions. The conclusion of the experiments show that the C++ API is the fastest to use, while PyMEL is the slowest module, as it is 2632–3165 % slower, when used for these animation transfers. This shows that the C++ API can be a better choice for complex calculations, such as animation transfers.

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KADOWAKI, Seiji, 誠二門脇, Michiko NIIMI, 倫子新美, Kanjun HIRUNAGI, 観順蛭薙, Makoto SUGIURA et al. „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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HIRUNAGI, Kanjun, 観順蛭薙, Michiko NIIMI, 倫子新美, Takeshi SANO, 健志佐野, Michiko KIKUCHI et al. „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 code in the aforementioned Lund project to Python and perform code optimization and parallelization, in order to reduce the execution time. With much other data science transitioning into Python as well, it was a key part in this project to understand the differences between MATLAB and Python and how to translate MATLAB code to Python. With the exception of the preprocessing scripts, all the original MATLAB scripts were translated to Python. The translated Python scripts were optimized for speed and parallelized to decrease the execution time even further. Two major parallel implementations of the Python scripts were made. One parallel implementation was made using the Ray framework to compute in the cloud [4]. The other parallel implementation was made using the Accelerator, a framework to compute using local threads[5]. After translation, the code was tested versus the original results and profiled for any key mistakes, for example functions which took unnecessarily long time to execute. After optimization the single thread script was twelve times faster than the original MATLAB script. The final execution times were around 12−15 minutes, compared to the benchmark of 48 hours it is about 200 times faster. The benchmark of the original code used less iterations than the researchers used, decreasing the computational time from a week to 48 hours. The results of the project highlight the importance of learning and teaching basic profiling of slow code. While not entirely considered in this project, doing complexity analysis of code is important as well. Future work includes a deeper complexity analysis on both a high and low level, since a high level language such as Python relies heavily on modules with low level code. Future work also includes an in-depth analysis of the NumPy source code, as the current code relies heavily on NumPy and has shown tobe a bottleneck in this project.
Datorer är en central och oundviklig del av mångas vardag idag. De framsteg som har gjorts inom maskin-inlärning har gjort det nästintill lika viktigt inom mångas vardag som datorer. Med de otroliga framsteg som gjorts inom maskininlärning så har man börjat använda det för att försöka tolka hjärnsignaler, i hopp om att skapa BCI (Brain Computer Interface) eller hjärn dator gränssnitt. Forskare på Lund Universitet genomförde ett experiment där de försökte kategorisera hjärnsignaler med hjälp av maskininlärning. Forskarna försökte kategorisera mellan tre olika saker, objekt, ansikten och landmärken. En av de större utmaningarna med projektet var att det tog väldigt lång tid att beräkna på en vanlig dator, runt en veckas tid. Det här projektet hade som uppgift att försöka förbättra och snabba upp beräkningstiden av koden. Projektet översatte den kod som skulle förbättras från programmeringspråket MATLAB till Python. Projektet använde sig utav profilering, kluster och av ett accelereringsverktyg. Med hjälp av profilering kan man lokalisera delar av kod som körs långsamt och förbättra koden till att vara snabbare, ett optimeringsverktyg helt enkelt. Kluster är en samling av datorer som man kan använda för att kollektivt beräkna större problem med, för att öka beräkningshastigheten. Det här projektet använde sig utav ett ramverk kallat Ray, vilket möjliggjorde beräkningar av koden på ett kluster ägt av Ericsson. Ett accellereringsverktyg kallat the Accelerator implementerades också, separat från Ray implementationen av koden. The Accelerator utnyttjar endast lokala processorer för att parallelisera ett problem gentemot att använda flera datorer. Den största fördelen med the Accelerator är att den kan hålla reda på vad som beräknats och inte och sparar alla resultat automatiskt. När the Accelerator håller reda på allt så kan det återanvända gamla resultat till nya beräkningar ifall gammal kod används. Återanvändningen av gamla resultat betyder att man undviker beräkningstiden det skulle ta att beräkna kod man redan har beräknat. Detta projekt förbättrade beräkningshastigheten till att vara över två hundra gånger snabbare än den var innan. Med både Ray och the Accelerator sågs en förbättring på över två hundra gånger snabbare, med de bästa resultaten från the Accelerator på runt två hundra femtio gånger snabbare. Det skall dock nämnas att de bästa resultaten från the Accelerator gjordes på en bra server processor. En bra server processor är en stor investering medan en klustertjänst endast tar betalt för tiden man använder, vilket kan vara billigare på kort sikt. Om man däremot behöver använda datorkraften mycket kan det vara mer lönsamt i längden att använda en serverprocessor. En förbättring på två hundra gånger kan ha stora konsekvenser, om man kan se en sådan förbättring i hastighet för BCI överlag. Man skulle potentiellt kunna se en tolkning av hjärnsignaler mer i realtid, vilket man kunde använda till att styra apparater eller elektronik med. Resultaten i det här projektet har också visat att NumPy, ett vanligt beräknings bibliotek i Python, har saktat ned koden med de standardinställningar det kommer med. NumPy gjorde kod långsammare genom att använda flera trådar i processorn, även i en flertrådad miljö där manuell parallelisering hade gjorts. Det visade sig att NumPy var långsammare för både den fler och entrådade implementationen, vilket antyder att NumPy kan sakta ned kod generellt, något många är omedvetna om. Efter att manuellt fixat de miljövariabler som NumPy kommer med, så var koden mer än tre gånger så snabb än innan.

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Aldrovandi, Lorenzo. „Depth estimation algorithm for light field data“. Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018.

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Basauri, Mondragon Gabriela, Fuentes Katyuska D’yaku Cabrera und Villamonte Brenda Melissa Ramirez. „Clima organizacional y desempeño laboral del personal de la generación Y: caso de empresa del sector de venta mayorista de combustible NUMAY S.A., Lima Metropolitana en el periodo 2018- 2019“. Bachelor's thesis, Pontificia Universidad Católica del Perú, 2020. http://hdl.handle.net/20.500.12404/18163.

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El objetivo del presente trabajo de investigación fue analizar el clima organizacional y su influencia en el desempeño laboral de los trabajadores de la generación Y de NUMAY S.A. en Lima Metropolitana durante el periodo 2018-2019. La investigación tuvo un enfoque mixto que combinó el estudio cuantitativo y el cualitativo. Para conseguir este objetivo, se hizo uso de dos instrumentos elaborados (encuesta de clima organizacional y de desempeño laboral), respetando cada una de las variables, se aplicó a 68 personas entre hombres y mujeres que trabajan en la empresa NUMAY S.A. que se encuentran dentro de un rango de edades estimado de 18 a 35 años, la denominada generación Y. Todos fueron evaluados con dos encuestas; una que mide el clima organizacional y otra de desempeño laboral, ambas de tipo Likert para ver la relación de las dos variables mencionadas. Además, la investigación se complementó de entrevistas semiestructuras aplicadas a especialistas de recursos humanos y a los principales representantes del caso de estudio. Se trabajó con el método descriptivo y un diseño descriptivo correlacional. A partir de ello se muestra que existe una relación entre el clima organizacional y desempeño laboral en la empresa NUMAY S.A., demostrada a través de la correlación de Pearson.

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Bücher zum Thema "NumpPy"

NumPy Cookbook. Birmingham: Packt Publishing, Limited, 2012.

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Łopieńska, Barbara. Stare numery. Londyn: Aneks, 1986.

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Ewa, Szymańska, Hrsg. Stare numery. Warszawa: "Alfa", 1990.

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

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

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NumPy 1.5: Beginner's guide : an action-packed guide for the easy-to-use, high performance, Python based free open source NumPy mathematical library using real-world examples. Birmingham, U.K: Packt Publishing, 2011.

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Finance, United States Congress Senate Committee on. Nomination of Michael H. Moskow and David M. Nummy: Hearing before the Committee on Finance, United States Senate, One Hundred Second Congress, first session, on the nomination of Michael H. Moskow to be Deputy U.S. Trade Representative and David M. Nummy to be an Assistant Secretary of the Treasury, October 29, 1991. Washington: U.S. G.P.O., 1992.

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Makabrrryczne numery. Warszawa, Poland: Muza, 2002.

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Cakmak, Umit Mert, und Mert Cuhadaroglu. Mastering Numerical Computing with NumPy: Master scientific computing and perform complex operations with ease. Packt Publishing, 2018.

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Idris, Ivan. NumPy Beginner's Guide - Second Edition. Packt Publishing, 2013.

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Buchteile zum Thema "NumpPy"

Klein, Bernd. „NumPy“. In Einführung in Python 3, 377–98. München: Carl Hanser Verlag GmbH & Co. KG, 2014. http://dx.doi.org/10.3139/9783446441514.031.

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Bisong, Ekaba. „NumPy“. In Building Machine Learning and Deep Learning Models on Google Cloud Platform, 91–113. Berkeley, CA: 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, 323–44. München: Carl Hanser Verlag GmbH & Co. KG, 2013. http://dx.doi.org/10.3139/9783446437173.029.

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Unpingco, José. „Numpy“. In Python Programming for Data Analysis, 103–26. Cham: 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, 41–50. München: Carl Hanser Verlag GmbH & Co. KG, 2019. http://dx.doi.org/10.3139/9783446453630.004.

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Porcu, Valentina. „SciPy and NumPy“. In Python for Data Mining Quick Syntax Reference, 177–200. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-4113-4_9.

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Bernard, Joey. „Numerics and Numpy“. In Python Recipes Handbook, 81–90. Berkeley, CA: 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, 35–61. Berkeley, CA: 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, 109–28. Berkeley, CA: Apress, 2017. http://dx.doi.org/10.1007/978-1-4842-2878-4_10.

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Nelli, Fabio. „The NumPy Library“. In Python Data Analytics, 49–85. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3913-1_3.

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Konferenzberichte zum Thema "NumpPy"

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

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Blum, Troels, Mads R. B. Kristensen und 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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Higuchi, Tomokazu, Naoki Yoshifuji, Tomoya Sakai, Yoriyuki Kitta, Ryousei Takano, Tsutomu Ikegami und Kenjiro Taura. „ClPy: A NumPy-Compatible Library Accelerated with OpenCL“. In 2019 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW). IEEE, 2019. http://dx.doi.org/10.1109/ipdpsw.2019.00159.

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Pivarski, Jim, Ianna Osborne, Pratyush Das, Anish Biswas und 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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Smith, Ross. „Performance of MPI Codes Written in Python with NumPy and mpi4py“. In 2016 6th Workshop on Python for High-Performance and Scientific Computing (PyHPC). IEEE, 2016. http://dx.doi.org/10.1109/pyhpc.2016.010.

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Daily, Jeff, und Robert Lewis. „Using the Global Arrays Toolkit to Reimplement NumPy for Distributed Computation“. In Python in Science Conference. SciPy, 2011. http://dx.doi.org/10.25080/majora-ebaa42b7-004.

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Patitsas, Elizabeth. „A Numpy-First Approach to Teaching CS1 to Natural Science Students“. In ITICSE '15: Innovation and Technology in Computer Science Education Conference 2015. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2729094.2754861.

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Buriol, Tiago Martinuzzi, und César Beneti. „Experimentando Processar e Visualizar Dados de Radares Meteorológicos Usando NumPy e Pygame“. In CMAC Sul – Congresso de Matemática Aplicada e Computacional. SBMAC, 2014. http://dx.doi.org/10.5540/03.2014.002.01.0016.

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Eduardo Beluzo, Carlos, Lucas Rodrigues Pimentel und Tiago José de Carvalho. „Big Data Visualization Methods Applied in the Context of Neonatal Mortality“. In Computer on the Beach. Itajaí: Universidade do Vale do Itajaí, 2020. http://dx.doi.org/10.14210/cotb.v11n1.p592-595.

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Neonatal mortality is a worldwide problem and its reduction isof common international interest. Around 46% of all deaths inthe world happen to infants up to five years of age, and most ofthose deaths is concentrated in the first few days after birth. InBrazil, although having data sources such as IBGE and DATASUS,there is no platform that provides an intuitive way of visualizingthis data. Thus, this work proposes the use of specialized visualanalysis tools for big data, such as Python libraries Dash by Plotly,NumPy and Pandas, in order to facilitate the understanding ofpublic information related to neonatal mortality, which can lead tothe mitigation of the problem.

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Grout, Ian. „Realization of NumPy Tensordot using the Field Programmable Gate Array for Embedded Machine Learning Applications“. In 2020 8th International Electrical Engineering Congress (iEECON). IEEE, 2020. http://dx.doi.org/10.1109/ieecon48109.2020.229523.

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Berichte der Organisationen zum Thema "NumpPy"

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

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