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The Ultimate Raspberry Pi Handbook 2016 Uk

2017年

The Ultimate Raspberry Pi Handbook 2016 Uk by published by

关键词： capacitive touch code command digital edition explorer hat file gpio gpio pins handbook install latest version linux operating system package manager power supply python python library raspberry raspbian sense hat sudo ultimate

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Nástroj pro práci s Büchi automaty

Nástroj pro práci s Büchi automaty

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作者： Schindler, Petr Brno University of Technology

Tato práce zpracovává problematiku Büchiho automatů a představuje knihovnu, umožňující provádět základní operace nad těmito automaty. V práci jsou uvedeny zákl... 详细信息

Tato práce zpracovává problematiku Büchiho automatů a představuje knihovnu, umožňující provádět základní operace nad těmito automaty. V práci jsou uvedeny základy teorie automatů. Ty jsou použity při popisu konečných automatů, mezi které patří Büchiho automaty, jejichž popis tvoří hlavní část zde uvedené teorie. Znalost jejich vlastností je důležitá pro pochopení algoritmů, které s nimi pracují. Dále jsou tyto algoritmy představeny i s detailním vysvětlením. Následuje návrh datových struktur formátu, v jakém jsou automaty ukládány na disk. Stěžejní část práce se věnuje implementaci knihovny a pomocných skriptů. Detailně jsou zde popsány důležité nebo zajímavé části implementace jednotlivých metod. Závěr práce je věnován testování funkčnosti knihovny.

关键词： Büchiho automat konečný automat knihovna pro python operace nad Büchiho automaty. Büchi automata final state automata python library operations over Büchi automata. Text

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Characterizing and detecting python version incompatibilities caused by inconsistent version specifications☆

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JOURNAL OF SYSTEMS AND SOFTWARE 2024年 222卷

作者： Li, Shuo Gao, Haocheng Chen, Wei Li, Yi Tian, Haoxiang Liu, Chengwei Ye, Dan Univ Chinese Acad Sci Inst Software Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Nanyang Technol Univ Nanyang Singapore Chinese Acad Sci Key Lab Syst Software Beijing Peoples R China Chinese Acad Sci Inst Software State Key Lab Comp Sci Beijing Peoples R China Nanjing Inst Software Technol Nanjing Peoples R China China Singapore Int Joint Res Inst Guangzhou Peoples R China

python interpreter serves as the foundation for developing python projects. Configuring compatible python version is essential for packaging and reusing third-party libraries (TPLs) in the python ecosystem. When the python distribution version range constraint of a third-party library is incorrectly declared, it may lead to installation failures or runtime errors when the library is used by a client project. We call such errors python Distribution Version Specification Error (PDSpecErr). PDSpecErrs are not studied adequately and there still lacks a method to guarantee the correctness of python version specifications. To combat PDSpecErrs and mitigate their adverse impacts on downstream applications, we conducted the first exploratory study on 3000 real-world TPLs to investigate PDSpecErrs about their prevalence, symptom categories, and diagnostic patterns. Based on the comprehensive study, we designed and implemented PyChecker, a tool to automatically detect PDSpecErrs in TPLs, locate root causes, and generate fixing solutions. We evaluated PyChecker on 3000 TPL releases and PyChecker detected 842 PDSpecErrs, reporting their root causes and recommending fixing solutions. We have reported 52 PDSpecErrs to the concerned developers. So far, 32 issues have been confirmed and fixed according to our suggestions.

关键词： python library python version incompatibility Exploratory study

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RocketPy: Six Degree-of-Freedom Rocket Trajectory Simulator

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JOURNAL OF AEROSPACE ENGINEERING 2021年第6期34卷 04021093-04021093页

作者： Ceotto, Giovani H. Schmitt, Rodrigo N. Alves, Guilherme F. Pezente, Lucas A. Carmo, Bruno S. Univ Sao Paulo Escola Politecn Dept Mech Engn Av Prof Mello Moraes 2231 BR-05508030 Sao Paulo Brazil Natl Inst Space Res Av Astronautas 1758 BR-12227010 Sao Jose Dos Campos SP Brazil Univ Sao Paulo Escola Politecn Av Prof Mello Moraes 2231 BR-05508030 Sao Paulo Brazil Univ Sao Paulo Inst Math & Stat R Matao 1010 BR-05508090 Sao Paulo Brazil

RocketPy is a new open-source python library specialized in trajectory simulations of sounding rockets and high-powered rockets. Having a modular structure and being built on top of a six degree-of-freedom flight dynamics model that includes detailed mass variation effects, RocketPy allows for accurate trajectory prediction of different rocket configurations. Additionally, weather data from several meteorological agencies, such as reanalysis, forecasts, and ensembles, are an innovative feature directly integrated into the library, providing precise information about atmospheric conditions for each simulation. The software was successfully validated for three rockets developed by different universities, with apogee predictions showing deviations of the order of 1% when compared to actual flight data. Furthermore, due to its modular nature, RocketPy can be easily employed in optimization studies and Monte Carlo analyses. The latter was a key aspect of this work, enabling the calculation of landing dispersion ellipses based on input uncertainties. Since running several Monte Carlo analyses is computationally expensive, a new algorithm was proposed to significantly speed up results based on rejection sampling. The code described here and made available online enhances the state of the art represented by several widely used software and can serve as a framework for future research on rocket dynamics, control, flight optimization, and dispersion analysis.

关键词： Rocket trajectory simulation Sounding rockets High-powered rockets Open-source software python library Flight dynamics Six degrees of freedom Monte Carlo Multivariate rejection sampling Stochastic simulation

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Algorithm 1004: The lisignature library: Efficient Calculation of Iterated-Integral Signatures and Log Signatures

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ACM TRANSACTIONS ON MATHEMATICAL SOFTWARE 2020年第1期46卷 8-8页

作者： Reizenstein, Jeremy F. Graham, Benjamin Alan Turing Inst London England Univ Warwick Coventry W Midlands England Facebook AI Res Paris France Facebook AI Res 1 Rathbone Sq London W1T 1FB England

Iterated-integral signatures and log signatures are sequences calculated from a path that characterizes its shape. They originate from the work of K. T. Chen and have become important through 'Ferry Lyons's theory of differential equations driven by rough paths, which is an important developing area of stochastic analysis. They have applications in statistics and machine learning, where there can be a need to calculate finite parts of them quickly for many paths. We introduce the signature and the most basic information (displacement and signed areas) that it contains. We present algorithms for efficiently calculating these signatures. For log signatures this requires consideration of the structure of free Lie algebras. We benchmark the performance of the algorithms. The methods are implemented in C++ and released as a python extension package, which also supports differentiation. In combination with a machine learning library (Tensorflow, PyTorch, or Theano), this allows end-to-end learning of neural networks involving signatures.

关键词： Iterated-integral signatures rough paths free Lie algebras machine learning python library

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python Package for the Network Operator Implementation and its Application for Group Control System Synthesis Problem

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Procedia Computer Science 2019年 150卷 679-686页

作者： A.V. Dotsenko A.I. Diveev Yu.A. Malyuk D.A. Ryndin RUDN University 6 Mikluho-Maklaya str. 117198 Moscow Russia Federal Research Center “Computer Science and Control” of Russian Academy of Sciences 44/2 Vavilova str. 119333 Moscow Russia

One of the main problems of symbolic regression methods is how to encode mathematical expressions to make the structure concise and readable. Here we address to the problem of network operator’s matrix scarcity, which we resolve using python’s standard types. Our approach allows to get rid of zero elements bellow the main diagonal. Moreover, we introduce new parametric variation, which helps us to tune parameters during structural evolving. We provide a description of the network operator and algorithms for finding the optimal solution using the principle of small variations of the basis solution. Our new encoding scheme and the complex functional for the group control task then described. In the experimental part we introduce a new way to apply network operator with a variable number of arguments to the group control system synthesis problem. Along with this we use for the first time individual network operators for each robot. From the experiment we can see that provided tricks lead to results that are not inferior in the case of usual network operator shared by all robots.

关键词： control synthesis python library symbolic regression optimal control mobile robot

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DoWhy-GCM: an extension of DoWhy for causal inference in graphical causal models

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2024年第1期25卷 6998-7004页

作者： Patrick Blöbaum Peter Götz Kailash Budhathoki Atalanti A. Mastakouri Dominik Janzing Amazon Web Services

We present DoWhy-GCM, an extension of the DoWhy python library, which leverages graphical causal models. Unlike existing causality libraries, which mainly focus on effect estimation, DoWhy-GCM addresses diverse causal queries, such as identifying the root causes of outliers and distributional changes, attributing causal influences to the data generating process of each node, or diagnosis of causal structures. With DoWhy-GCM, users typically specify cause-effect relations via a causal graph, fit causal mechanisms, and pose causal queries--all with just a few lines of code. The general documentation is available at https://***/dowhy and the DoWhy-GCM specific code at https://***/py-why/dowhy/tree/main/dowhy/gcm.

关键词： python library causal inference graphical causal models structural causal models

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An open source library to parse and analyze online collaborative knowledge-building portals

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JOURNAL OF INTERNET SERVICES AND APPLICATIONS 2021年第1期12卷 11-11页

作者： Verma, Amit Arjun Iyengar, S. R. S. Setia, Simran Dubey, Neeru Indian Inst Technol Ropar Social Comp & Collect Intelligence Lab Rupnagar India

With the success of collaborative knowledge-building portals, such as Wikipedia, Stack Overflow, Quora, and GitHub, a class of researchers is driven towards understanding the dynamics of knowledge building on these portals. Even though collaborative knowledge building portals are known to be better than expert-driven knowledge repositories, limited research has been performed to understand the knowledge building dynamics in the former. This is mainly due to two reasons;first, unavailability of the standard data representation format, second, lack of proper tools and libraries to analyze the knowledge building *** describe Knowledge Data Analysis and Processing Platform (KDAP), a programming toolkit that is easy to use and provides high-level operations for analysis of knowledge data. We propose Knowledge Markup Language (Knol-ML), a generic representation format for the data of collaborative knowledge building portals. KDAP can process the massive data of crowdsourced portals like Wikipedia and Stack Overflow efficiently. As a part of this toolkit, a data-dump of various collaborative knowledge building portals is published in Knol-ML format. The combination of Knol-ML and the proposed open-source library will help the knowledge building community to perform benchmark *** of the repository: Verma et al. (2020)Video Tutorial: Verma et al. (2020)Supplementary Material: Verma et al. (2020)

关键词： Knowledge building Wikipedia Stack exchange Open-source python library

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PyBanshee version (1.0): A python implementation of the MATLAB toolbox BANSHEE for Non-Parametric Bayesian Networks with updated features

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SOFTWAREX 2023年 21卷

作者： Koot, Paul Mendoza-Lugo, Miguel Angel Paprotny, Dominik Morales-Napoles, Oswaldo Ragno, Elisa Worm, Daniel T. H. Delft Univ Technol Dept Hydraul Engn Stevinweg 1 NL-2628 CN Delft Netherlands Potsdam Inst Climate Impact Res Res Dept Transformat Pathways D-14473 Potsdam Germany TNO Cyber Secur & Robustness Anna Van Buerenpl 1 NL-2595 DA The Hague Netherlands Nelen & Schuurmans BV Zakkendragershof 34-44 NL-3511 AE Utrecht Netherlands

In this paper we discuss PyBanshee, which is a python-based open-source implementation of the MATLAB toolbox BANSHEE. PyBanshee constitutes the first fully open-source package to quantify, visualize and validate Non-Parametric Bayesian Networks (NPBNs). The architecture of PyBanshee is heavily based on its MATLAB predecessor. It presents the full implementation of existing tools and introduces new modules. Specifically, PyBanshee allows for: (i) choosing fully parametric one-dimensional margins, (ii) choosing different sample sizes for the model-validation tests based on the Hellinger distance, (iii) drawing user-defined sample sizes of the NPBN, (iv) sample-based conditioning sampling (similarly to the closed-source proprietary package UNINET by LightTwist Software) and (v) visualizing the comparison between the histograms of the unconditional and conditional marginal distributions. New detailed examples demonstrating new features are provided.(c) 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).

关键词： Non-Parametric Bayesian Networks BANSHEE python library Copulas

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OBI: A computational tool for the analysis and systematization of the positive selection in proteins

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METHODSX 2022年 9卷 101786页

作者： Calvento, Julian H. Bulgarelli, Franco Leonardo Rueda, Ana Julia Velez Univ Nacl Quilmes Dept Ciencia & Tecnol CONICET Bernal Argentina Mumuki org Buenos Aires Argentina

A B S T R A C T There are multiple tools for positive selection analysis, including vaccine design and detection of variants of circulating drug-resistant pathogens in population selection. However, applying these tools to analyze a large number of protein families or as part of a comprehensive phylogenomics pipeline could be challenging. Since many standard bioinformatics tools are only available as executables, integrating them into complex Bioinformatics pipelines may not be possible. We have developed OBI, an open-source tool aimed to facilitate positive selection analysis on a large scale. It can be used as a stand-alone command-line app that can be easily installed and used as a Conda package. Some advantages of using OBI are: center dot It speeds up the analysis by automating the entire process center dot It allows multiple starting points and customization for the analysis center dot It allows the retrieval and linkage of structural and evolutive data for a protein through We hope to provide with OBI a solution for reliably speeding up large-scale protein evolutionary and structural analysis. (c) 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license ( http://***/licenses/by-nc-nd/4.0/ )

关键词： python library Proteins evolution Structural bioinformatics pipeline

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