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Journal articles on the topic 'NumpPy'

Author: Grafiati

Published: 28 June 2021

Last updated: 1 February 2022

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1

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, no. 7825 (September 16, 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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2

Noel Dawe, Edmund, Piti Ongmongkolkul, and Giordon Stark. "root_numpy: The interface between ROOT and NumPy." Journal of Open Source Software 2, no. 16 (August 13, 2017): 307. http://dx.doi.org/10.21105/joss.00307.

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3

Ertek, Muzaffer Kerem. "NumPy-based Calibration of Basic Hypoplastic Constitutive Models." Civil Engineering Beyond Limits 1, no. 1 (December 19, 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, and Gaël Varoquaux. "The NumPy Array: A Structure for Efficient Numerical Computation." Computing in Science & Engineering 13, no. 2 (March 2011): 22–30. http://dx.doi.org/10.1109/mcse.2011.37.

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5

Eddelbuettel, Dirk, and Wush Wu. "RcppCNPy: Read-Write Support for NumPy Files in R." Journal of Open Source Software 1, no. 5 (September 25, 2016): 55. http://dx.doi.org/10.21105/joss.00055.

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6

Phinney, Archie M. "Numipu among the White Settlers." Wicazo Sa Review 17, no. 2 (2002): 21–42. http://dx.doi.org/10.1353/wic.2002.0019.

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7

Klyachin, Vladimir, and Elеna Grigorеva. "Numerical Study of the Stability of Equilibrium Surfaces Using NumPY Package." Vestnik Volgogradskogo gosudarstvennogo universiteta. Serija 1. Mathematica. Physica, no. 2 (June 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, no. 4 (October 2015): 13–23. http://dx.doi.org/10.15688/jvolsu1.2015.4.2.

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9

Pawlik, Aleksandra, Judith Segal, Helen Sharp, and Marian Petre. "Crowdsourcing Scientific Software Documentation: A Case Study of the NumPy Documentation Project." Computing in Science & Engineering 17, no. 1 (January 2015): 28–36. http://dx.doi.org/10.1109/mcse.2014.93.

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10

Abert, Claas, Florian Bruckner, Christoph Vogler, Roman Windl, Raphael Thanhoffer, and 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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11

Farkasovsky, Marian, and Hans Küntzel. "Cortical Num1p Interacts with the Dynein Intermediate Chain Pac11p and Cytoplasmic Microtubules in Budding Yeast." Journal of Cell Biology 152, no. 2 (January 22, 2001): 251–62. http://dx.doi.org/10.1083/jcb.152.2.251.

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Num1p, a cortical 313-kD protein, controls cytoplasmic microtubule (cMT) functions and nuclear migration through the bud neck in anaphase cells. A green fluorescent protein (GFP)-Num1p fusion protein localizes at the bud tip and the distal mother pole of living cells, apparently forming cMT capture sites at late anaphase. In addition, galactose-induced GFP-Num1p is seen at the bud neck and in lateral regions of the mother cortex. The bud tip location of Num1p depends on Bni1p but does not require Kar9p, Dyn1p, or cMTs, whereas cMT contacts with polar Num1p dots are reduced in cells lacking Dyn1p. Num1p associates with the dynein intermediate chain Pac11p in the presence of Dyn1p, and with the α-tubulin Tub3p, as shown by coimmune precipitation of tagged proteins. Num1p also forms a complex with Bni1p and Kar9p, although Num1p is not required for Bni1p- and Kar9p-dependent nuclear migration to the bud neck in preanaphase cells. Our data suggest that Num1p controls nuclear migration during late anaphase by forming dynein-interacting cortical cMT capture sites at both cellular poles. In addition, Num1p may transiently cooperate with an associated Bni1p–Kar9p complex at the bud tip of early anaphase cells.

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12

Boulle, A., and J. Kieffer. "High-performance Python for crystallographic computing." Journal of Applied Crystallography 52, no. 4 (July 24, 2019): 882–97. http://dx.doi.org/10.1107/s1600576719008471.

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The Python programming language, combined with the numerical computing library NumPy and the scientific computing library SciPy, has become the de facto standard for scientific computing in a variety of fields. This popularity is mainly due to the ease with which a Python program can be written and executed (easy syntax, dynamical typing, no compilation etc.), coupled with the existence of a large number of specialized third-party libraries that aim to lift all the limitations of the raw Python language. NumPy introduces vector programming, improving execution speeds, whereas SciPy brings a wealth of highly optimized and reliable scientific functions. There are cases, however, where vector programming alone is not sufficient to reach optimal performance. This issue is addressed with dedicated compilers that aim to translate Python code into native and statically typed code with support for the multi-core architectures of modern processors. In the present article it is shown how these approaches can be efficiently used to tackle different problems, with increasing complexity, that are relevant to crystallography: the 2D Laue function, scattering from a strained 2D crystal, scattering from 3D nanocrystals and, finally, diffraction from films and multilayers. For each case, detailed implementations and explanations of the functioning of the algorithms are provided. Different Python compilers (namely NumExpr, Numba, Pythran and Cython) are used to improve performance and are benchmarked against state-of-the-art NumPy implementations. All examples are also provided as commented and didactic Python (Jupyter) notebooks that can be used as starting points for crystallographers curious to enter the Python ecosystem or wishing to accelerate their existing codes.

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13

Klyachin, V. A. "Optimization of Calculus Mesh for Cryobiology Problem Based on Multidimensional Hashing Using NumPy." Izvestiya of Saratov University. New Series. Series: Mathematics. Mechanics. Informatics 14, no. 3 (2014): 355–62. http://dx.doi.org/10.18500/1816-9791-2014-14-3-355-362.

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14

Fasnacht, Laurent. "mmappickle: Python 3 module to store memory-mapped numpy array in pickle format." Journal of Open Source Software 3, no. 26 (June 18, 2018): 651. http://dx.doi.org/10.21105/joss.00651.

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15

Simonenko, V. B., Victor G. Abashin, and A. S. Aleksandrov. "Selflessness of besieged Leningrad: problems of motherhood and childhood (on the occasion of the 72nd anniversary of lifting the siege)." Clinical Medicine (Russian Journal) 94, no. 9 (November 2, 2016): 711–17. http://dx.doi.org/10.18821/0023-2149-2016-94-9-711-717.

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A yeview of documents on the peculiayities of obstetyic and gynecological caye in besieged Leningyad is pyesented with special yefeyence to the data about the numbey of childyen boyn duying blockade and theiy health status in the fiyst months of life, the woyk of maternity hospitals, theiy numbey and peysonnel. The teym «militayy time amenoyyhea» coined duying blockade is discussed.

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16

Borowiec, Natalia, and Anna Pulak. "The use of points cloud processing in supporting oversize transport analysis." AUTOBUSY – Technika, Eksploatacja, Systemy Transportowe 19, no. 6 (June 30, 2018): 804–11. http://dx.doi.org/10.24136/atest.2018.179.

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Paper discussed how to create an algorithm that simulates the movement of large-size vehicle and to detect collision of vehicle path with cloud of points. Simulations and analysis that were rune are based on the transport of wind turbine blade. The proposed algorithm was implemented using Python programming language with PCL and NumPy libraries.

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Roubeyrie, Lionel, and Sébastien Celles. "Windrose: A Python Matplotlib, Numpy library to manage wind and pollution data, draw windrose." Journal of Open Source Software 3, no. 29 (September 4, 2018): 268. http://dx.doi.org/10.21105/joss.00268.

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18

Головін, М., and Н. Головіна. "Фур’є перетворення в якості аплікації спектрального аналізу звуків у курсах комп’ютерної фізики та захисту інформації." COMPUTER-INTEGRATED TECHNOLOGIES: EDUCATION, SCIENCE, PRODUCTION, no. 42 (March 25, 2021): 37–42. http://dx.doi.org/10.36910/6775-2524-0560-2021-42-06.

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Розглянуто та апробовано прості приклади програмного коду в яких реалізуються Фур’є перетворення на мові Python засобами бібліотеки Numpy. Програмний код може бути використаний, як аплікація в багатьох розділах фізики для ілюстрації аналізу хвильових процесів, зокрема, в першу чергу, в акустиці та в оптиці, а також в курсі захисту інформації. Матеріал демонструє чисельні міжпредметні зв’язки математики та інформатики з різними розділами фізики та з курсом захисту інформації.

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19

Dressler, Sven, and Daniel N. Wilke. "PyBONDEM-GPU: A discrete element bonded particle Python research framework – Development and examples." EPJ Web of Conferences 249 (2021): 14009. http://dx.doi.org/10.1051/epjconf/202124914009.

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Discrete element modelling (DEM) is widely used to simulate granular systems, nowadays routinely on graphical processing units. Graphics processing units (GPUs) are inherently designed for parallel computation, and recent advances in the architecture, compiler design and language development are allowing general-purpose computation to be computed on multiple GPUs. Application of DEM to bonded particle systems are much less common, with a number of open research questions remaining. This study outlines a Bonded-Particle Research DEM Framework, PyBONDEM-GPU, written in Python. This framework leverages the parallel nature of GPUs for computational speed-up and the rapid prototype flexibility of Python. Python is faster and easier to learn than classical compiled languages, making computational simulation development accessible to undergraduate and graduate engineers. PyBONDEMGPU leverages the Numba-CUDA module to compile Python syntax for execution on GPUs. The framework enables research of fibre pull-out from fibre-matrix embeddings. Bonds are simulated between all interacting particles. The performance of PyBONDEM-GPU is compared against Python CPU implementations of PyBONDEM using the Numpy and Numba-CPU Python modules. PyBONDEM-GPU was found to be 1000 times faster than the Numpy implementation and 4 times faster than the Numba-CPU implementation to resolve forces and to integrate the equations of motion.

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Willis, Andrew, and Kemal Hasan. "Simple Computer Vision Algorithm Production using OpenCV for “Virtual Ecosystem” project." IJNMT (International Journal of New Media Technology) 7, no. 1 (July 2, 2020): 23–27. http://dx.doi.org/10.31937/ijnmt.v7i1.1660.

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The main purposes of this research is to produce aComputer Vision (CV) algorithm using various library in pythonprogramming language (mainly OpenCV, Numpy, and ZBar) toautomate extraction and process information from an analogdrawing into a digital image to be used in the “Virtual Ecosystem”project. The Computer Vision step will include detecting datawithin the analog drawing using QR-code, determine drawingarea, replace white background with certain threshold withtransparency, and finally save the digital image following requiredratio.

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21

Heil-Chapdelaine, Richard A., Jessica R. Oberle, and John A. Cooper. "The Cortical Protein Num1p Is Essential for Dynein-Dependent Interactions of Microtubules with the Cortex." Journal of Cell Biology 151, no. 6 (December 11, 2000): 1337–44. http://dx.doi.org/10.1083/jcb.151.6.1337.

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In budding yeast, the mitotic spindle moves into the neck between the mother and bud via dynein-dependent sliding of cytoplasmic microtubules along the cortex of the bud. How dynein and microtubules interact with the cortex is unknown. We found that cells lacking Num1p failed to exhibit dynein-dependent microtubule sliding in the bud, resulting in defective mitotic spindle movement and nuclear segregation. Num1p localized to the bud cortex, and that localization was independent of microtubules, dynein, or dynactin. These data are consistent with Num1p being an essential element of the cortical attachment mechanism for dynein-dependent sliding of microtubules in the bud.

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22

Lessa, Leticia De Matos, and Lorena Maria da Silva. "Política Nacional de Enfrentamento à Violência contra as Mulheres." Simpósio Gênero e Políticas Públicas 5, no. 1 (January 18, 2021): 341–54. http://dx.doi.org/10.5433/sgpp.2018v5.p341.

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Redigida em 2011, a Política Nacional de Enfrentamento à Violência contra as Mulheres é fruto de uma militância histórica de movimentos de luta pelos direitos das mulheres. Ela aponta os princípios, diretrizes e objetivos que devem direcionar as ações de enfrentamento as diferentes violências que subjugam mulheres por todo o território brasileiro. Tendo em vista essa Política nosso ensaio objetiva refletir acerca dos possíveis reflexos da aplicação dos princípios e diretrizes da Política na efetividade e integralidade dos atendimentos às mulheres em situação de violência doméstica e de gênero em um Núcleo de Extensão Maria da Penha (NUMAPE). A fim de alcançarmos o objetivo proposto analisaremos os atendimentos realizados no Núcleo Maria da Penha da Universidade Estadual de Maringá (Numape/UEM). Nossa reflexão contará com as lentes da história, para compreendermos tais princípios e diretrizes dispostos na Política e, posteriormente, faremos uma análise do desenvolvimento das ações do Numape/UEM averiguando as possibilidades e desafios da aplicação da Política nas práticas de atenção à mulher em situação de violência doméstica e de gênero. A Política propõe o trabalho interdisciplinar e intersetorial enquanto uma das diretrizes para o enfrentamento à violência. Entendemos assim, que as práticas do Numape/UEM podem favorecer o fortalecimento da rede de atenção as mulheres, por meio do trabalho articulado e interdisciplinar. Com pressupostos que extrapolam o campo jurídico e buscando atender as mulheres integralmente, este Núcleo de Extensão direciona suas ações para além do escopo do judiciário e busca concretizar a Política Nacional convidando diferentes mecanismos da rede para unir esforços a fim de desnaturalizar a violência doméstica e de gênero contra as mulheres.

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23

K S, Srivathsa Bharadwaj. "Predictive Analysis for Real Time Stress Detection." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 20, 2021): 1232–38. http://dx.doi.org/10.22214/ijraset.2021.35177.

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Stress can be characterized as a feeling of either emotional or physical tension. Due to the biology of the human body, it releases some hormones when under stress. These hormones might cause tensed muscles, increase pulse rate or the heart rate, increase brain activity to make the brain more alert to the surrounding. Stress can be predicted well before it happens by constantly measuring the Heart Rate Variability (HRV) parameters obtained using the pulse sensor. In this project a supervised machine learning model is created using the data acquired from Physionet, once the data is acquired it is cleaned and the missing data is filled. This data set is later used to create a random forest classifier and is saved using pickle library. Once the model is created it is used to detect stress in real time. Pulse sensor amped is used to get the required pulse data in the form of a CSV file and a numpy array is created using inter beat interval information got from pulse sensor. Once a numpy array is created neurokit2 library is used to extract the HRV information of the R-R interval. Later these parameters are compared with the created model and checked to see if the subject is stressed, if the model detects the subject as stressed an alerting message is sent to the subject’s smartphone using Twilio.

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24

Gyulai, Márk. "Numerikus számítások hatékonyságának vizsgálata Python, Matlab és Octave programcsomagokkal." Multidiszciplináris tudományok 10, no. 2 (2020): 338–49. http://dx.doi.org/10.35925/j.multi.2020.2.38.

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A numerikus analízis a matematikai problémák közelítő, számítógéppel is hatékonyan elvégezhető megoldásával foglalkozik. A cikkben bemutatott kutatás célja, hogy összehasonlítja a Python NumPy és SciPy, a Matlab és GNU Octave programcsomagok alapvető numerikus algoritmusainak sebességét. A bemutatásra kerülő sebességtesztek olyan alapvető numerikus műveletekre koncentrálnak, mint a mátrix műveletek, interpolációk, lineáris egyenletrendszer megoldása, valamint a numerikus integrálás és deriválás. Különböző méretű tesztfeladatok esetén, az egyes műveletek elvégzéséhez szükséges időket fogjuk összevetni, valamint a mérés során figyelembe vesszük az egyes implementációk közötti különbségeket is.

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25

Arabas, Sylwester, Dorota Jarecka, Anna Jaruga, and Maciej Fijałkowski. "Formula Translation in Blitz++, NumPy and Modern Fortran: A Case Study of the Language Choice Tradeoffs." Scientific Programming 22, no. 3 (2014): 201–22. http://dx.doi.org/10.1155/2014/870146.

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Three object-oriented implementations of a prototype solver of the advection equation are introduced. The presented programs are based on Blitz++ (C++), NumPy (Python) and Fortran's built-in array containers. The solvers constitute implementations of the Multidimensional Positive-Definite Advective Transport Algorithm (MPDATA). The introduced codes serve as examples for how the application of object-oriented programming (OOP) techniques and new language constructs from C++11 and Fortran 2008 allow to reproduce the mathematical notation used in the literature within the program code. A discussion on the tradeoffs of the programming language choice is presented. The main angles of comparison are code brevity and syntax clarity (and hence maintainability and auditability) as well as performance. All performance tests are carried out using free and open-source compilers. In the case of Python, a significant performance gain is observed when switching from the standard interpreter (CPython) to the PyPy implementation of Python. Entire source code of all three implementations is embedded in the text and is licensed under the terms of the GNU GPL license.

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Chitalov, D. I., and S. T. Kalashnikov. "DRAWING CONVERGENCE GRAPHS IN THE GRAPHICAL USER INTERFACE OF THE OPENFOAM SOFTWARE ENVIRONMENT ON THE BASIS OF THE MATPLOTLIB AND NUMPY LIBRARIES." Современные наукоемкие технологии (Modern High Technologies), no. 11 2017 (2017): 80–85. http://dx.doi.org/10.17513/snt.36849.

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27

Schauss, Astrid C., and Heidi M. McBride. "Mitochondrial Fission: A Non-Nuclear Role for Num1p." Current Biology 17, no. 12 (June 2007): R467—R470. http://dx.doi.org/10.1016/j.cub.2007.04.018.

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28

Anand, Dr Darpan. "Implementation and Analysis of Sentimental Analysis on Facial Expression Using HAAR Cascade Methods." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 2 (April 11, 2021): 2787–93. http://dx.doi.org/10.17762/turcomat.v12i2.2308.

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The sentimental analysis is phenomenon of exploring, analyzing and organizing human feelings. It is a process of extracting feelings of human faro pictures. It involves the separation of image into various characters such as face, background, etc. It uses lips and eye shape for extracting human feelings. It uses numerous of applications such as Pycharm Numpy ,Open CV, Python,etc. Its main objective is to find out the moods of human such as happy , sad ,etc. This report generates the emotional state of human being as well as different emotion of human in different situation.

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Jin, Dawei, and Jun Chen. "Scalarity, degree reading and maximality in a Mandarin numeral construction." Language and Linguistics / 語言暨語言學 20, no. 2 (April 5, 2019): 148–79. http://dx.doi.org/10.1075/lali.00032.jin.

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Abstract This paper investigates the semantics of an understudied Mandarin numeral construction type, here dubbed da-NumPs (i.e. number word < da ‘big’ < noun). Drawing primarily upon evidence from online Mandarin corpora, we argue for a taxonomy of this construction that comprises two distinct interpretations, based on the scalarity of the morpheme da and its composition with the other constituents within the construction. Specifically, one reading of da-NumPs is a degree superlative reading, in which da relates a domain of comparison, denoted by the nominal argument, to a plural group of entities ranked along the upper bound of a contextually determined scale. Second, da-NumPs have a definite description reading, in which da behaves on a par with a maximality-denoting iota operator, such that the construction refers to the maximal group individual that satisfies the property denoted by the nominal argument. We further show that at the discourse level, both readings encode the way the speaker subjectively construes the situation being described, indicating the speaker’s evaluative attitude towards the significance of said situation. This pragmatic condition distinguishes the use of da-NumPs against that of alternative, truth-conditionally identical numeral construction types. We further propose that in cases where the nominal component includes a degree argument, a process of degree intensification enables the definite description reading to verify the same situation as is licensed under a superlative semantics. We show that this process provides a way to make sense of the systematic ambiguity available to da-NumPs, and allows us to capture its polysemy.

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Lisboa, Antônio Chicharo Prata, Claudia Mazza Dias, and Julio Henrique Lopes De Almeida. "Análise Energética e Exergética para o Ciclo Dual em Motores de Alta Rotação." Pesquisa e Ensino em Ciências Exatas e da Natureza 2, no. 1.1 (November 26, 2018): 15. http://dx.doi.org/10.29215/pecen.v2i2.1037.

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<p>Este trabalho tem como propósito fazer uma análise do Ciclo Dual, adequado a motores de alta rotação, com o objetivo de obter suas eficiências Energética e Exergética, bem como quantificar a destruição de exergia nos processos. Os resultados são obtidos através de estudos paramétricos que permitem a avaliação do comportamento dos parâmetros de interesse. Todos os cálculos são desenvolvidos em um código aberto escrito em Python® com o suporte das bibliotecas numpy e scipy.</p><p><strong>Palavras chave</strong>: Ciclo Dual, Modelagem Computacional, Eficiência Exergética.</p>

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Zelichenok, I., and R. Pirmagomedov. "Tutorial on Using Machine Learning for Activity Recognition Via a Smartphone." Telecom IT 8, no. 2 (June 2020): 92–108. http://dx.doi.org/10.31854/2307-1303-2020-8-2-92-108.

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This article provides a tutorial for developing a simple machine learning application in Python. More spe-cifically, the paper considers daily activity recognition using sensors of a smartphone. For development, we used TensorFlow, Skikit learn, NumPy, Pandas, and Matplotlib. The paper explains in detail the main steps of the application development, including data collection and pre-processing, design of the neural network, learning process, and use of a trained model. The overall accuracy of the developed application when recognizing the activity is about 95 %. This paper can be useful for students and specialists who want to start work on machine learning.

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Дмитренко, Т. "Методика обробки аудіо-сигналів за допомогою алгоритмів на базі мови програмування Python." COMPUTER-INTEGRATED TECHNOLOGIES: EDUCATION, SCIENCE, PRODUCTION, no. 41 (December 23, 2020): 152–58. http://dx.doi.org/10.36910/6775-2524-0560-2020-41-24.

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Проведено аналіз можливості застосування інтерпретовано об'єктно‑орієнтованої мови програмування при обробці масивів аудіоданих як цифрового способу представлення звукових сигналів. Продемонстровано принцип використання з зазначеною метою мови програмування високого рівня зі строгою динамічною типізацією Python. Визначено особливості застосування у даній галузі таких модулів (python‑бібліотек)як: NumPy, SciPy та Matplotlib. Наведено методи обробки та модифікації масивів аудіоданих з метою їх подальшого застосування у мультимедійних комп’ютерних мережах. Побудовано математичну модель обробки аудіо-даних, ефективність якої перевірено на базі відповідних програмнихалгоритмів. Показано можливість вирішення актуальних задач та дослідження теоретичних аспектів проблему області обробки аудіо-даних шляхом використання інтерпретовано об'єктно‑орієнтованої мови програмування і спеціалізованих бібліотек з відкритим кодом.

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Farkasovsky, M., and H. Küntzel. "Yeast Num1p associates with the mother cell cortex during S/G2 phase and affects microtubular functions." Journal of Cell Biology 131, no. 4 (November 15, 1995): 1003–14. http://dx.doi.org/10.1083/jcb.131.4.1003.

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The NUM1 gene is involved in the control of nuclear migration in Saccharomyces cerevisiae. The content of NUM1 mRNA fluctuates during the cell cycle, reaching a maximum at S/G2 phase, and the translation product Num1p associates with the cortex of mother cells mainly during S, G2, and mitosis, as seen by indirect immunofluorescence. The nuclear spindle in NUM1-deficient large-budded cells often fails to align along the mother/bud axis, while abnormally elongated astral microtubules emanate from both spindle pole bodies. A num1 null mutation confers temperature sensitivity to the cold-sensitive alpha-tubulin mutant tub1-1, and shows synthetic lethality with the beta-tubulin mutant alleles tub2-402, tub2-403, tub2-404, and tub2-405. Deletion mapping has defined three functionally important Num1p regions: a potential EF hand Ca2+ binding site, a cluster of potential phosphorylation sites and a pleckstrin homology domain. The latter domain appears to be involved in targeting Num1p to the mother cell cortex. Our data suggest that the periodically expressed NUM1 gene product controls nuclear migration by affecting astral microtubule functions.

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Alharbi, Bader A., Thamir H. Alshammari, Nathan L. Felton, Victor B. Zhurkin, and Feng Cui. "nuMap: A Web Platform for Accurate Prediction of Nucleosome Positioning." Genomics, Proteomics & Bioinformatics 12, no. 5 (October 2014): 249–53. http://dx.doi.org/10.1016/j.gpb.2014.08.001.

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Pivarsk, Jim, Jaydeep Nandi, David Lange, and Peter Elmer. "Columnar data processing for HEP analysis." EPJ Web of Conferences 214 (2019): 06026. http://dx.doi.org/10.1051/epjconf/201921406026.

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In the last stages of data analysis, physicists are often forced to choose between simplicity and execution speed. In High Energy Physics (HEP), high-level languages like Python are known for ease of use but also very slow execution. However, Python is used in speed-critical data analysis in other fields of science and industry. In those fields, most operations are performed on Numpy arrays in an array programming style; this style can be adopted for HEP by introducing variable-sized, nested data structures. We describe how array programming may be extended for HEP use-cases and an implementation known as awkward-array. We also present integration with ROOT, Apache Arrow, and Parquet, as well as preliminary performance results.

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Spotz, William F. "PyTrilinos: Recent Advances in the Python Interface to Trilinos." Scientific Programming 20, no. 3 (2012): 311–25. http://dx.doi.org/10.1155/2012/965812.

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PyTrilinos is a set of Python interfaces to compiled Trilinos packages. This collection supports serial and parallel dense linear algebra, serial and parallel sparse linear algebra, direct and iterative linear solution techniques, algebraic and multilevel preconditioners, nonlinear solvers and continuation algorithms, eigensolvers and partitioning algorithms. Also included are a variety of related utility functions and classes, including distributed I/O, coloring algorithms and matrix generation. PyTrilinos vector objects are compatible with the popular NumPy Python package. As a Python front end to compiled libraries, PyTrilinos takes advantage of the flexibility and ease of use of Python, and the efficiency of the underlying C++, C and Fortran numerical kernels. This paper covers recent, previously unpublished advances in the PyTrilinos package.

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Kumar, Rakesh. "FUTURE FOR SCIENTIFIC COMPUTING USING PYTHON." International Journal of Engineering Technologies and Management Research 2, no. 1 (January 29, 2020): 30–41. http://dx.doi.org/10.29121/ijetmr.v2.i1.2015.28.

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Computational science (scientific computing or scientific computation) is concerned with constructing mathematical models as well as quantitative analysis techniques and using computers to analyze as well as solve scientific problems. In practical use, it is basically the application of computer simulation as well as other forms of computation from numerical analysis and theoretical computer science to problems in different scientific disciplines. The scientific computing approach is to gain understanding, basically through the analysis of mathematical models implemented on computers. Python is frequently used for highperformance scientific applications and widely used in academia as well as scientific projects because it is easy to write and performs well. Due to its high performance nature, scientific computing in Python often utilizes external libraries like NumPy, SciPy and Matplotlib etc.

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Javidi, G., E. Sheybani, and D. Mason. "Visualization of Real-Time Radar Data by Integration of X-Band Software." International Journal of Interdisciplinary Telecommunications and Networking 6, no. 4 (October 2014): 73–82. http://dx.doi.org/10.4018/ijitn.2014100108.

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To have a fully functional FMCW X-band radar for the SMARTLabs ACHIEVE trailer, it is necessary to produce code to retrieve data from an FPGA board linked to the radar, calculate Fourier transforms and display the power spectrum in near-real time using a computer code based on freely available scientific development tools. In order for the communication between the FPGA board and the computer to be reliable and accurate, developing a specific format through the use of C was an initial step. This was followed by the development of a method to visualize data efficiently. In this case, Python, along with its matplotlib, SciPy, and NumPy modules, were used. Both programs were then integrated together within a graphical user interface.

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Rubint, Jakub. "Effects of meshing density of 1D structural members with non-uniform cross-section along the length on the calculation of eigenfrequencies." MATEC Web of Conferences 313 (2020): 00004. http://dx.doi.org/10.1051/matecconf/202031300004.

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Density of division in finite element method does not affect only the accuracy of calculation, but also the necessary calculation time. This is directly influenced by the power of the used hardware, the efficiency of the algorithm used to assemble global stiffness and mass matrices and finally, by the method used to find eigenvalues of matrices for determination of eigenfrequencies. In engineering practice, when commercially available software is used, it is necessary to look for the optimum between the accuracy of the calculation and the length of the calculation. This paper deals with solution of eigenfrequencies of 1D elements with nonuniform cross section using Python 3.7.4 with libraries "numpy 1.18.1" for finding eigenvalues of matrices and "scipy 1.4.1" for finding solution for system of nonlinear equations.

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Shukla, Pushkal Kumar. "Learning Application for Kids based on Artificial Intelligence." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (July 15, 2021): 600–603. http://dx.doi.org/10.22214/ijraset.2021.36355.

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In this fast-moving technological era, a child gets access to mobile phones rather than "Toys" in its childhood. This early exposure to technology gives rise to the demand for a mobile phone app that will help the child to learn new things for the upcoming educational phases of their life. Artificial intelligence is making computers "intelligent", which is helping us in pragmatic ways; thus, it will help our descendants similarly. The main objective is to develop an application using artificial intelligence in android for learning purposes. OpenCV library, Android Studio, Numpy and Game Studio have been used by the authors of this paper. Some memory games and free drawing space have also been added to make the learning enjoyable.

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Killoran, Nathan, Josh Izaac, Nicolás Quesada, Ville Bergholm, Matthew Amy, and Christian Weedbrook. "Strawberry Fields: A Software Platform for Photonic Quantum Computing." Quantum 3 (March 11, 2019): 129. http://dx.doi.org/10.22331/q-2019-03-11-129.

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We introduce Strawberry Fields, an open-source quantum programming architecture for light-based quantum computers, and detail its key features. Built in Python, Strawberry Fields is a full-stack library for design, simulation, optimization, and quantum machine learning of continuous-variable circuits. The platform consists of three main components: (i) an API for quantum programming based on an easy-to-use language named Blackbird; (ii) a suite of three virtual quantum computer backends, built in NumPy and TensorFlow, each targeting specialized uses; and (iii) an engine which can compile Blackbird programs on various backends, including the three built-in simulators, and - in the near future - photonic quantum information processors. The library also contains examples of several paradigmatic algorithms, including teleportation, (Gaussian) boson sampling, instantaneous quantum polynomial, Hamiltonian simulation, and variational quantum circuit optimization.

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Pasanisi, F., and C. Tebano. "A QGIS Plugin for Offshore Wave Hindcasting Based on Geographic Transposition of Wave Gauge Data." Journal of Applied Engineering Sciences 6, no. 2 (December 1, 2016): 33–40. http://dx.doi.org/10.1515/jaes-2016-0015.

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Abstract The paper presents a first experimental version of the original QGIS plugin QWaveTransposition that numerically implements the geographic transposition of wave gauge data method proposed by Contini and De Girolamo (1998) for offshore wave hindcasting. The method allows one to transfer wave data measured at a given gauging station to a virtual station located offshore the area of interest, by comparing the effective fetches at both stations. The QWaveTransposition plugin was implemented in Python programming language, including the NumPy package for numerical computations. A graphical user interface was developed to manage the input/output data and model parameters. The fetch geometry at real and virtual stations can be imported by selecting appropriate vector layers from the QGIS map. An application to a sample site in southern Italy is presented for example purposes.

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Pereira, Bruna Karla. "Nump and Possp in Dialectal Brazilian Portuguese." Revista Diadorim 19 (October 30, 2017): 72–98. http://dx.doi.org/10.35520/diadorim.2017.v19n0a13578.

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In standard Brazilian Portuguese (BP), as well as in other Romance languages, possessives have uninterpretable number features, which are valued via nominal agreement. However, dialects of BP, especially the one spoken in Minas Gerais, have shown that 2nd person possessives, in postnominal position, do not have number agreement with the noun. In order to account for these facts, I will argue that, in this grammar, number features on 2nd person possessives are reanalyzed as being: (i) associated with the person (rather than the noun) and (ii) valued. From the frst postulation, ‘seu' is expected to be the possessive for 2nd person singular, and ‘seus' for 2nd person plural. From the second postulation, no number concord is expected to be triggered on the possessive. In addition, based on Danon (2011) and Norris (2014), I will argue that cardinals divide BP DPs into two domains in that phrases located above NumP are marked with the plural morpheme, while phrases below it are unmarked. In this sense, because prenominal possessives precede cardinals (NumP), they must be marked with the plural morpheme for nominal agreement; whereas postnominal possessives, which follow NumP, must be unmarked. Free from the plural marking associated with nominal agreement, postnominal 2nd person possessives favor the reanalysis of the morpheme ‘-s' as indicating the number associated with person features.

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Smith, Nicholas, Lindsey Gray, Matteo Cremonesi, Bo Jayatilaka, Oliver Gutsche, Allison Hall, Kevin Pedro, et al. "Coffea Columnar Object Framework For Effective Analysis." EPJ Web of Conferences 245 (2020): 06012. http://dx.doi.org/10.1051/epjconf/202024506012.

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The coffea framework provides a new approach to High-Energy Physics analysis, via columnar operations, that improves time-to-insight, scalability, portability, and reproducibility of analysis. It is implemented with the Python programming language, the scientific python package ecosystem, and commodity big data technologies. To achieve this suite of improvements across many use cases, coffea takes a factorized approach, separating the analysis implementation and data delivery scheme. All analysis operations are implemented using the NumPy or awkward-array packages which are wrapped to yield user code whose purpose is quickly intuited. Various data delivery schemes are wrapped into a common front-end which accepts user inputs and code, and returns user defined outputs. We will discuss our experience in implementing analysis of CMS data using the coffea framework along with a discussion of the user experience and future directions.

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Grohmann, Peter, and Michael S. J. Walter. "Speeding up Statistical Tolerance Analysis to Real Time." Applied Sciences 11, no. 9 (May 5, 2021): 4207. http://dx.doi.org/10.3390/app11094207.

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Statistical tolerance analysis based on Monte Carlo simulation can be applied to obtain a cost-optimized tolerance specification that satisfies both the cost and quality requirements associated with manufacturing. However, this process requires time-consuming computations. We found that an implementation that uses the graphics processing unit (GPU) for vector-chain-based statistical tolerance analysis scales better with increasing sample size than a similar implementation on the central processing unit (CPU). Furthermore, we identified a significant potential for reducing runtime by using array vectorization with NumPy, the proper selection of row- and column- major order, and the use of single precision floating-point numbers for the GPU implementation. In conclusion, we present open source statistical tolerance analysis and statistical tolerance synthesis approaches with Python that can be used to improve existing workflows to real time on regular desktop computers.

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Pires, Sandra Regina de abreu. "Uma experiência em monitoramento de penas e medidas alternativas: o caso do NUMOPA/CEAPA/NUAPA." Serviço Social em Revista 16, no. 2 (June 30, 2014): 204. http://dx.doi.org/10.5433/1679-4842.2014v16n2p204.

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Toby, Brian H., and Robert B. Von Dreele. "GSAS-II: the genesis of a modern open-source all purpose crystallography software package." Journal of Applied Crystallography 46, no. 2 (March 14, 2013): 544–49. http://dx.doi.org/10.1107/s0021889813003531.

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The newly developedGSAS-IIsoftware is a general purpose package for data reduction, structure solution and structure refinement that can be used with both single-crystal and powder diffraction data from both neutron and X-ray sources, including laboratory and synchrotron sources, collected on both two- and one-dimensional detectors. It is intended thatGSAS-IIwill eventually replace both theGSASand theEXPGUIpackages, as well as many other utilities.GSAS-IIis open source and is written largely in object-oriented Python but offers speeds comparable to compiled code because of its reliance on the Python NumPy and SciPy packages for computation. It runs on all common computer platforms and offers highly integrated graphics, both for a user interface and for interpretation of parameters. The package can be applied to all stages of crystallographic analysis for constant-wavelength X-ray and neutron data. Plans for considerable additional development are discussed.

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Han, Wenyan, Guolin Yu, and Tiantian Gong. "Optimality Conditions for a Nonsmooth Uncertain Multiobjective Programming Problem." Complexity 2020 (July 25, 2020): 1–8. http://dx.doi.org/10.1155/2020/9849636.

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This note is devoted to the investigation of optimality conditions for robust approximate quasi weak efficient solutions to a nonsmooth uncertain multiobjective programming problem (NUMP). Firstly, under the extended nonsmooth Mangasarian–Fromovitz constrained qualification assumption, the optimality necessary conditions of robust approximate quasi weak efficient solutions are given by using an alternative theorem. Secondly, a class of generalized convex functions is introduced to the problem (NUMP), which is called the pseudoquasi-type-I function, and its existence is illustrated by a concrete example. Finally, under the pseudopseudo-type-I generalized convexity hypothesis, the optimality sufficient conditions for robust approximate quasi weak efficient solutions to the problem (NUMP) are established.

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황준길, ByungSeop Song, and 곽수원. "A Study on Development and Verification of One-handed Keyboard Software Using Numpad of General Keyboard." Korean Journal of Physical, Multiple, & Health Disabilities 53, no. 2 (April 2010): 99–118. http://dx.doi.org/10.20971/kcpmd.2010.53.2.99.

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Arias Hernández, Jesús Daniel, Andrés Fernando Jiménez López, and Hernán Oswaldo Porras Castro. "Desarrollo de aplicaciones en python para el aprendizaje de física computacional (Development of Python applications for learning computational physics)." Ingeniería Investigación y Desarrollo 16, no. 1 (January 11, 2016): 72. http://dx.doi.org/10.19053/1900771x.5122.

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Este artículo describe una aplicación desarrollada para el aprendizaje de algoritmos de simulación basados en conceptos de mecánica clásica. Los estudiantes de Ingeniería Electrónica y de Ciencias de la Computación de la Universidad de los Llanos estudian la física computacional usando cinemática de partículas (CP), como una de las actividades del grupo de investigación Sistemas Dinámicos. Python, el lenguaje de programación seleccionado, facilita portabilidad y el acceso a las librerías necesarias para la representación de partículas. Las principales librerías de Python usadas en este ejercicio son: matplotlib, numpy, PyQt4, scipy, Tkinter y VPython. Estas librerías permiten la simulación de movimiento uniforme,movimiento lineal acelerado, caída libre y movimiento de proyectiles. Además, son útiles para la generación de interfaces gráficas de usuario para mostrar los datos en tablas y gráficos. Las GUIs fueron implementadas usando las librerías Tkinter y PyQt4, donde esta última facilita el desarrollo con la ayuda de herramientas del software Qt Designer.

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