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Relevant bibliographies by topics / Python scripting

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Dissertations / Theses
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Academic literature on the topic 'Python scripting'

Author: Grafiati

Published: 25 June 2021

Last updated: 14 February 2022

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Journal articles on the topic "Python scripting"

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Hinsen, Konrad. "Parallel Scripting with Python." Computing in Science & Engineering 9, no. 6 (November 2007): 82–89. http://dx.doi.org/10.1109/mcse.2007.117.

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Bakker, Mark. "Python Scripting: The Return to Programming." Groundwater 52, no. 6 (September 16, 2014): 821–22. http://dx.doi.org/10.1111/gwat.12269.

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Dixon, Adam P. "Review of GIS Tutorial for Python Scripting." Cartographic Perspectives, no. 80 (August 18, 2015): 51–52. http://dx.doi.org/10.14714/cp80.1306.

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Ayer, Vidya M., Sheila Miguez, and Brian H. Toby. "Why scientists should learn to program in Python." Powder Diffraction 29, S2 (December 2014): S48—S64. http://dx.doi.org/10.1017/s0885715614000931.

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The importance of software continues to grow for all areas of scientific research, no less for powder diffraction. Knowing how to program a computer is a basic and useful skill for scientists. This paper explains the three approaches for programming languages and why scripting languages are preferred for non-expert programmers. The Python-scripting language is extremely efficient for science and its use by scientists is growing. Python is also one of the easiest languages to learn. The language is introduced, as well as a few of the many add-on packages available that extend its capabilities, for example, for numerical computations, scientific graphics, and graphical user interface programming. Resources for learning Python are also provided.

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Bakker, M., V. Post, C. D. Langevin, J. D. Hughes, J. T. White, J. J. Starn, and M. N. Fienen. "Scripting MODFLOW Model Development Using Python and FloPy." Groundwater 54, no. 5 (March 30, 2016): 733–39. http://dx.doi.org/10.1111/gwat.12413.

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Murfitt, M. F., C. E. Meyer, G. Skone, N. Dellby, and O. L. Krivanek. "Open-Source Python Scripting and Analysis with Nion Swift." Microscopy and Microanalysis 19, S2 (August 2013): 782–83. http://dx.doi.org/10.1017/s1431927613005904.

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Myers, Wayne L. "Scalable Shared Scripting for Spatial Structure of Regionalized Ratings." Land 10, no. 8 (August 16, 2021): 859. http://dx.doi.org/10.3390/land10080859.

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Incisive inquiry involving indicators of ecological and environmental integrity entails exploration of spatial structure at selected scales from landscape level to regional regimes. Conventional colorization of digital displays provides perspective but is largely lacking for localization, elaboration, and explication. An overall objective for recent research is explicit extraction of spatial structure as hyper-hills and proximal propensity. Shared scripting as a computational configuration affords analytical advantage, adaptability and availability. Conservation context captures challenges of changing conditions for complex components at several spatial scales. Hyper-hill hypotheses, relativized ratings, and post patterned nucleated networks supporting secondary scaling scenarios are current contributions. Computational concerns in indicant informatics are also addressed. Retrospective results are cogent comparators for change. Shared scripting couples R software with Python as R||Python (R in parallel with Python), which is supplemented by strategic sequencing of compilation capabilities in general GIS (geographic information systems). The specific research question(s) is/are what is the particular pattern of placement and propagation in intensification of an indicant of biodiversity (avian species richness), and how does this relate to some other co-located indicants of environmental effects. This is addressed in a legacy dataset for Pennsylvania, USA. Emergent emphasis is on truncated trees of topology and impaneled indicators. Shareable software has HIDN (hexagonal indicant dual networking) as an aggregate acronym with duly drawn disclaimers.

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Westad, Frank, Lars Gidskehaug, and Chuck Miller. "The best of two worlds." NIR news 31, no. 5-6 (August 19, 2020): 34–39. http://dx.doi.org/10.1177/0960336020944008.

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With the latest release of Unscrambler, Camo Analytics introduced support for Python scripting, giving users the best of two worlds. This Python extension allows users to tap into the vast ecosystem of Data Science tools that are continually being produced in the Python community, while still leveraging the familiar data handling, validation and visualization features of Unscrambler – all contained within a fully compliant framework. This paper discusses the value propositions that the Python extension can provide to Unscrambler users, and follows this up with some specific examples of common workflows that are enabled by this extension: Data Importing, Spectral Preprocessing and Innovative Modeling methods.

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O'Donnell, Jackson H., Robert B. Von Dreele, Maria K. Y. Chan, and Brian H. Toby. "A scripting interface for GSAS-II." Journal of Applied Crystallography 51, no. 4 (July 23, 2018): 1244–50. http://dx.doi.org/10.1107/s1600576718008075.

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The GSAS-II suite is a complete crystallographic analysis package for all types of X-ray and neutron diffraction data, suited for fitting models ranging from simple materials to proteins, and designed around an integrated graphical user interface. This article describes a new module within GSAS-II, GSASIIscriptable, which provides access to the GSAS-II data structures and an extensible mechanism to run a significant fraction of GSAS-II functionality, particularly for powder diffraction applications, from within Python scripts or directly from commands in a shell/batch script. This allows parallelization in a high-performance computing environment; near ideal speedup is seen with up to 240 simultaneous processes.

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Mihăilă, Paul, Titus Bălan, Radu Curpen, and Florin Sandu. "Network Automation and Abstraction using Python Programming Methods." MACRo 2015 2, no. 1 (October 1, 2017): 95–103. http://dx.doi.org/10.1515/macro-2017-0011.

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AbstractNetwork programmability is a trend, enhanced and inspired by Software Defined Networks, that are based on scripting methods and standard programming languages used for controlling and monitoring of network elements. This paper is illustrating some new methods in configuring network devices by using automation, reducing time for equipment configuration and easier maintenance. It also improves network security by recognizing and fixing security vulnerabilities and it increases the network stability. These methods represent the future of networks, allowing the management of an increased number of devices in a unitary way.

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Dissertations / Theses on the topic "Python scripting"

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Tett, Stuart Tosten. "A scripting interface for doubly linked face list based polygonal meshes." [College Station, Tex. : Texas A&M University, 2007. http://hdl.handle.net/1969.1/ETD-TAMU-2121.

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Dogantimur, Erkan. "A method to generate modern city buildings with the aid of Python-scripting." Thesis, University of Gävle, Ämnesavdelningen för datavetenskap, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-4660.

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It takes time to model buildings in a 3D city environment, for example in a game. Time is usually something very constricted in a production stage of anything, whether it is a personal project at home, at school or more occurring; in the 3D industry. This report will bring forth a method to quickly generate detailed buildings with the help of Python scripting, integrated in Maya 2009. The script will be working with modules that will be assembled together to create a modern city type of building. A comparison will be made between this script and a couple other scripts that offer the same solution but in different ways.

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Yost, Morgan. "An Iteration on the Horizon Simulation Framework to Include .NET and Python Scripting." DigitalCommons@CalPoly, 2016. https://digitalcommons.calpoly.edu/theses/1607.

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Modeling and Simulation is a crucial element of the aerospace engineering design pro- cess because it allows designers to thoroughly test their solution before investing in the resources to create it. The Horizon Simulation Framework (HSF) v3.0 is an aerospace modeling and simulation tool that allows the user to verify system level requirements in the early phases of the design process. A low fidelity model of the system that is created by the user is exhaustively tested within the built-in Day-in-the-Life simulator to provide useful information in the form of failed requirements, system bottle necks and leverage points, and potential schedules of operations. The model can be stood up quickly with Extended Markup Language (XML) input files or can be customly created with Python Scripts that interact with the framework at runtime. The goal of the work presented in this thesis is to progress HSF from v2.3 to v3.0 in order to take advantage of current software development technologies. This includes converting the codebase from C++ and Lua scripting to C♯ and Python Scripting. The particulars of the considerations, benefits, and implementation of the new framework are discussed in detail. The simulation data and performance run time of the new framework were compared to that of the old framework. The new framework was found to produce similar data outputs with a faster run time.

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Wang, Lingyun. "Qualitative Analysis of the Usability of Three Contemporary Scripting Languages: Perl, Python and Tcl." [Johnson City, Tenn. : East Tennessee State University], 2001. http://etd-submit.etsu.edu/etd/theses/available/etd-0712101-083723/restricted/Wang7242001.pdf.

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Sacca, Flavio. "Server-side scripting: confronto fra PHP, Python e server-side JS integrato in NodeJs." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2016. http://amslaurea.unibo.it/10302/.

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Lo scopo della tesi è quello di descrivere e mettere a confronto tre diversi linguaggi, e quindi approcci, alla programmazione server-side e di back-end, ovvero il linguaggio PHP, il linguaggio Python ed il linguaggio Javascript, utilizzato però per una programmazione “Server Side”, e quindi associato al framework NodeJS. Questo confronto si pone l’obiettivo di sottolineare le differenti caratteristiche di ogni linguaggio, gli scopi a cui esso maggiormente si addice e di fornire una sorta di guida per far in modo che si possa comprendere al meglio quale dei tre linguaggi maggiormente usati per la programmazione backend si conformi meglio all’obiettivo prepostosi.

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DiCesare, David A. "Automated Identification and Mapping of ODOT Candidate Culverts for Cleaning Operations Using Python Scripting in ArcGIS." University of Akron / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=akron1428329508.

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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 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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Vecchi, Claudio. "Scripting automation e modelli bayesiani: Applicazioni cliniche in radioterapia e sviluppo di tecniche innovative per adaptive radiation therapy." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2014. http://amslaurea.unibo.it/6623/.

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La presente ricerca consiste nel validare ed automatizzare metodiche di Adaptive Radiation Therapy (ART), che hanno come obiettivo la personalizzazione continua del piano di trattamento radioterapico in base alle variazioni anatomiche e dosimetriche del paziente. Tali variazioni (casuali e/o sistematiche) sono identificabili mediante l’utilizzo dell’imaging diagnostico. Il lavoro svolto presso la struttura di Fisica Medica dell’Azienda Ospedaliera Universitaria del Policlinico di Modena, si inserisce in un progetto del Ministero della Salute del bando Giovani Ricercatori dal titolo: “Dose warping methods for IGRT and ADAPTIVERT: dose accumulation based on organ motion and anatomical variations of the patients during radiation therapy treatments”. Questa metodica si sta affermando sempre più come nuova opportunità di trattamento e, per tale motivo, nasce l’esigenza di studiare e automatizzare processi realizzabili nella pratica clinica, con un utilizzo limitato di risorse. Si sono sviluppati script che hanno permesso l’automazione delle operazioni di Adaptive e deformazioni, raccogliendo i dati di 51 pazienti sottoposti a terapia mediante Tomotherapy. L’analisi delle co-registrazioni deformabili delle strutture e delle dosi distribuite, ha evidenziato criticità del software che hanno reso necessario lo sviluppo di sistemi di controllo dei risultati, per facilitare l’utente nella revisione quotidiana dei casi clinici. La letteratura riporta un numero piuttosto limitato di esperienze sulla validazione e utilizzo su larga scala di questi tools, per tale motivo, si è condotto un esame approfondito della qualità degli algoritmi elastici e la valutazione clinica in collaborazione di fisici medici e medici radioterapisti. Sono inoltre stati sviluppati principi di strutturazione di reti Bayesiane, che consentono di predirre la qualità delle deformazioni in diversi ambiti clinici (H&N, Prostata, Polmoni) e coordinare il lavoro quotidiano dei professionisti, identificando i pazienti, per i quali sono apprezzabili variazioni morfo-dosimetriche significative. Da notare come tale attività venga sviluppata automaticamente durante le ore notturne, sfruttando l’automation come strumento avanzato e indipendente dall’operatore. Infine, il forte sviluppo, negli ultimi anni della biomeccanica applicata al movimento degli organi (dimostrato dalla numerosa letteratura al riguardo), ha avuto come effetto lo sviluppo, la valutazione e l’introduzione di algoritmi di deformazione efficaci. In questa direzione, nel presente lavoro, si sono analizzate quantitivamente le variazioni e gli spostamenti delle parotidi, rispetto all’inizio del trattamento, gettando le basi per una proficua linea di ricerca in ambito radioterapico.

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Mahjoub, Musaab. "FE modeling of glulam beams with mechanical slotted-in steel plate connections." Thesis, Linnéuniversitetet, Institutionen för byggteknik (BY), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-105430.

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The mechanical behavior of timber beams with a slotted-in steel plate is studied by creating anumerical model that can simulate the global bending behavior and the load carrying capacity aswell as the nonlinear plastic fastener force distribution. Experimental results from Material TestingInstitute (MPA), University of Stuttgart were compared with simulation results done at LinnaeusUniversity. The modeling of the timber beams and the mechanical connections is performed withshell, beam, and nonlinear connector elements. Three models were created, where the first modelwas a single-dowel double shear joint model to study the ability to use structural elements in themodeling of the test beams. It was used to simulate some of the basic failure modes in Eurocode5 (EC5). The second model was a beam model used to simulate the bending of a jointed timberbeam with a slotted-in steel plate, where only two connector elements are used to model the jointbehavior of each dowel group. It can be used to study the global deflection and the load carryingcapacity of the timber beams. The third model was a combined beam-shell model where the beamelements are used for the timber parts outside the connection area and the fasteners, while the shellelements are used for the slotted-in steel plate and the timber parts within the connection area.It uses two nonlinear connectors to connect each dowel to the wood and pure coupling to connectthe dowels to the slotted-in steel plate. This model can simulate same things as model two andalso the development of the elasto-plastic shear force distribution in all the dowels. All the modelswere created using parameterized Python scripts, which makes it possible to easily change differentinput parameters.Most of the modeling results show good agreement with both experimental results and with calculated load carrying capacity results for individual dowels according to EC5. The use of thesestructural elements (beam, shell, and connector elements) was found to result in much less computational time compared to the use of solid elements.

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Baco, Gerond. "Uplift Behaviour of Screw Piles for Offshore Deepwater Wind Turbine Foundations." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017.

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The two main types of foundations used so far for offshore wind energy applications are the conventional solution, the so-called mono-piles and the less conventional suction caissons. In the case of the monopiles cyclic loading is identified as the main issue to be considered, and for suction caissons, a quite large number of design issues are present, but prevalent on the others, is the tension capacity. The idea to extend screw piles to offshore applications is becoming nowadays actual. This type of foundation has a quite large onshore application but not offshore so far. They have excellent promise for the wind turbines in deep water because of the very good combination of compression and tension capacity. The attention in this work is focussed on the tension capacity, this is done through a parametric analysis where the parameters are the pile and flanges diameter, the number of piles, the spacing between piles and the pitch of the helix. The commercial finite element analysis software Abaqus CAE combined with the Python scripting is used for the solution of tension capacity of this problem.

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Books on the topic "Python scripting"

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Python scripting for computational science. 2nd ed. Berlin: Springer, 2006.

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Python scripting for computational science. Berlin: Springer, 2004.

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Python scripting for computational science. 3rd ed. Berlin: Springer, 2009.

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Langtangen, Hans Petter, ed. Python Scripting for Computational Science. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-73916-6.

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Langtangen, Hans Petter. Python Scripting for Computational Science. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-662-05450-5.

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Kak, Avinash C. Scripting with objects: A comparative presentation of scripting with Perl and Python. Hoboken, N.J: John Wiley & Sons, 2008.

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Inc, ebrary, ed. Blender 2.49 scripting: Extend the power and flexibility of Blender with the help of Python, a high-level, easy-to-learn scripting language. Birmingham, U.K: Packt Open Source, 2010.

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Litvin, Maria. Mathematics for the digital age and programming in Python. 2nd ed. Andover, Mass: Skylight Pub., 2010.

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Puri, Gautam. Python scripts for Abaqus: Learn by example. [Charleston, S.C.?: s.n.], 2011.

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Ahlquist, John. Game development essentials: Game artificial intelligence. Clifton Park, NY: Thomson/Delmar Learning, 2008.

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Book chapters on the topic "Python scripting"

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Ferrill, Paul. "Python Scripting Utilities." In Pro Android Python with SL4A, 165–94. Berkeley, CA: Apress, 2011. http://dx.doi.org/10.1007/978-1-4302-3570-5_7.

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Kinser, Jason M. "Scripting in Python." In Image Operators, 21–58. First edition. | Boca Raton, FL: CRC Press/Taylor & Francis Group, [2019] |: CRC Press, 2018. http://dx.doi.org/10.1201/9780429451188-3.

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Hart, William E., Carl D. Laird, Jean-Paul Watson, David L. Woodruff, Gabriel A. Hackebeil, Bethany L. Nicholson, and John D. Siirola. "Scripting." In Pyomo — Optimization Modeling in Python, 235–53. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-58821-6_14.

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Ferrill, Paul. "Background Scripting with Python." In Pro Android Python with SL4A, 139–64. Berkeley, CA: Apress, 2011. http://dx.doi.org/10.1007/978-1-4302-3570-5_6.

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Bynum, Michael L., Gabriel A. Hackebeil, William E. Hart, Carl D. Laird, Bethany L. Nicholson, John D. Siirola, Jean-Paul Watson, and David L. Woodruff. "Scripting Custom Workflows." In Pyomo — Optimization Modeling in Python, 67–81. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68928-5_5.

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Hosmer, Chet. "PowerShell Scripting Targeting Investigation." In PowerShell and Python Together, 45–87. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-4504-0_3.

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Langtangen, Hans Petter. "Getting Started with Python Scripting." In Texts in Computational Science and Engineering, 27–64. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-662-05450-5_2.

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Hart, William E., Carl Laird, Jean-Paul Watson, and David L. Woodruff. "Scripting and Algorithm Development." In Pyomo – Optimization Modeling in Python, 165–203. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/978-1-4614-3226-5_10.

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López, Claudio David, and José Luis Rueda Torres. "Python Scripting for DIgSILENT PowerFactory: Leveraging the Python API for Scenario Manipulation and Analysis of Large Datasets." In Green Energy and Technology, 19–48. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-50532-9_2.

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Kent, Brian R. "Python scripting." In 3D Scientific Visualization with Blender. Morgan & Claypool Publishers, 2014. http://dx.doi.org/10.1088/978-1-6270-5612-0ch7.

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Conference papers on the topic "Python scripting"

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Martin, Joe, and Amanda H. Schmidt. "TEACHING PYTHON SCRIPTING THROUGH ARCGIS." In Joint 52nd Northeastern Annual Section and 51st North-Central Annual GSA Section Meeting - 2017. Geological Society of America, 2017. http://dx.doi.org/10.1130/abs/2017ne-291544.

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Calleja, A. C. "Scripting a large Fortran code with Python." In "Software Engineering for High Performance Computing System (HPCS) Applications" W3S Workshop - 26th International Conference on Software Engineering. IEE, 2004. http://dx.doi.org/10.1049/ic:20040419.

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Kukuev, Ivan Yu, Ilya O. Starodumov, Evgeniy V. Pavlyuk, and Sergey I. Osipov. "Analysis of MPFC dynamics using ParaView Python scripting." In 28TH RUSSIAN CONFERENCE ON MATHEMATICAL MODELLING IN NATURAL SCIENCES. AIP Publishing, 2020. http://dx.doi.org/10.1063/5.0003424.

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McNamara, Ann. "An Introduction to Python Scripting in Autodesk Maya." In SIGGRAPH '21: Special Interest Group on Computer Graphics and Interactive Techniques Conference. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3450549.3464409.

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Chaves, Juan Carlos, John Nehrbass, Brian Guilfoos, Judy Gardiner, Stanley Ahalt, Ashok Krishnamurthy, Jose Unpingco, Alan Chalker, Andy Warnock, and Siddharth Samsi. "Octave and Python: High-Level Scripting Languages Productivity and Performance Evaluation." In 2006 HPCMP Users Group Conference (HPCMP-UGC'06). IEEE, 2006. http://dx.doi.org/10.1109/hpcmp-ugc.2006.55.

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Musthyala, Harish, and P. Nagarjuna Reddy. "Hacking wireless network credentials by performing phishing attack using Python Scripting." In 2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS). IEEE, 2021. http://dx.doi.org/10.1109/iciccs51141.2021.9432155.

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Dai, Yitian, Mathaios Panteli, and Robin Preece. "Python Scripting for DIgSILENT PowerFactory: Enhancing Dynamic Modelling of Cascading Failures." In 2021 IEEE Madrid PowerTech. IEEE, 2021. http://dx.doi.org/10.1109/powertech46648.2021.9494872.

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Dietl, Karin, Stephanie Gallardo Yances, Anna Johnsson, Johan Åkesson, Kilian Link, and Stéphane Velut. "Industrial application of optimization with Modelica and Optimica using intelligent Python scripting." In the 10th International Modelica Conference, March 10-12, 2014, Lund, Sweden. Linköping University Electronic Press, 2014. http://dx.doi.org/10.3384/ecp14096777.

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Schatten, Markus. "Reasonable Python or how to Integrate F-Logic into an Object-Oriented Scripting Language." In 2007 11th International Conference on Intelligent Engineering Systems. IEEE, 2007. http://dx.doi.org/10.1109/ines.2007.4283715.

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Manhães, Musa. "pcg_gazebo_pkgs: A Python library for scripting and rapid-prototyping of simulated Gazebo models and worlds." In ROSCon2019. Mountain View, CA: Open Robotics, 2019. http://dx.doi.org/10.36288/roscon2019-900366.

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