The Gaudi Reflection Tool Crash Course
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J1939 Data Mapping Explained
J1939 Data Mapping Explained Part No. BW2031 Revision: 1.05 May 18, 2018 Pyramid Solutions, Inc. 30200 Telegraph Road, Suite 440 Bingham Farms, MI 48025 www.pyramidsolutions.com | P: 248.549.1200 | F: 248.549.1400 © Pyramid Solutions 1 PUB-BW4031-001 Table of Contents Overview ......................................................................................................................... 2 J1939 Message Terminology ............................................................................................ 2 J1939 PGN Message Definitions ....................................................................................... 3 PGN and Parameter Documentation Examples ........................................................................ 3 J1939 Specification Example ........................................................................................................ 3 Parameter Table Example ............................................................................................................ 5 Determining the BridgeWay Data Table Layout ................................................................ 6 Data Table Layout for Modbus RTU and Modbus TCP (Big Endian) ........................................... 6 Data Table Layout for EtherNet/IP (Little Endian) .................................................................... 7 Configuring the BridgeWay I/O Mapping ......................................................................... 8 Configuration Attributes ........................................................................................................ -
Advanced Software Engineering with C++ Templates Lecture 4: Separate Compilation and Templates III
Advanced Software Engineering with C++ Templates Lecture 4: Separate Compilation and Templates III Thomas Gschwind <thgatzurichdotibmdotcom> Agenda . Separate Compilation • Introduction (C++ versus Java) • Variables • Routines (Functions & Operators) • Types (Structures, Classes) • Makefiles . Templates III • Design and Implementation (C++ versus Java versus C#) • “Dynamic” Static Algorithm Selection • Binders Th. Gschwind. Fortgeschrittene Programmierung in C++. 2 Separate Compilation . Why? • Having only one source file is unrealistic • Break the code up into its logical structure • Reduction of compile time - Only changed parts need to be recompiled . How? • Use multiple source files • Need to know what information about functions and variables “used” from other files Th. Gschwind. Fortgeschrittene Programmierung in C++. 3 Separate Compilation in Java . Each Java file is compiled into a class file . If a Java class invokes a method of another class, the compiler consults that other class file to • Determine whether the class provides the requested method • Determine whether a class file implements a given interface • etc. • Hence, the .class file contains the entire interface . That’s why in Java, the compiler needs the class path . Finally, all class files are loaded by the Java Virtual Machine (and “linked”) . Java source code can be relatively well reconstructed from .class file (see Java Decompiler: jd, jad) Th. Gschwind. Fortgeschrittene Programmierung in C++. 4 Some Java Trivia . Let us write Hello World in Java . In order to simplify the reconfiguration (e.g., translation) • Put all the constants into one Java file • Put the complex application code into another public class Const { public static final String msg="Hello World!"; } public class Cool { public static void main(String[] args) { System.out.println(Const.msg); } } Th. -
P2356R0 Date 2021-04-09 Reply-To Matus Chochlik [email protected] Audience SG7 Overview Details
Overview Details Implementing Factory builder on top of P2320 Document Number P2356R0 Date 2021-04-09 Reply-to Matus Chochlik [email protected] Audience SG7 Overview Details The goal (Semi-)automate the implementation of the Factory design pattern Overview Details What is meant by \Factory" here? • Class that constructs instances of a \Product" type. • From some external representation: • XML, • JSON, • YAML, • a GUI, • a scripting language, • a relational database, • ... Overview Details Factory builder • Framework combining the following parts • Meta-data • obtained from reflection. • Traits • units of code that handle various stages of construction, • specific to a particular external representation, • provided by user. Overview Details Factory builder • Used meta-data • type name strings, • list of meta-objects reflecting constructors, • list of meta-objects reflecting constructor parameters, • parameter type, • parameter name, • ... Overview Details Factory builder • Units handling the stages of construction • selecting the \best" constructor, • invoking the selected constructor, • conversion of parameter values from the external representation, • may recursively use factories for composite parameter types. Overview Details Used reflection features • ^T • [: :]1 • meta::name_of • meta::type_of • meta::members_of • meta::is_constructor • meta::is_default_constructor • meta::is_move_constructor • meta::is_copy_constructor • meta::parameters_of • size(Range) • range iterators 1to get back the reflected type Overview Details Reflection review • The good2 • How extensive and powerful the API is • The bad • Didn't find much3 • The ugly • Some of the syntax4 2great actually! 3some details follow 4but then this is a matter of personal preference Overview Details What is missing • The ability to easily unpack meta-objects from a range into a template, without un-reflecting them. • I used the following workaround + make_index_sequence: template <typename Iterator > consteval auto advance(Iterator it, size_t n) { while (n-- > 0) { ++ it; } return it; } • Details follow. -
Objects and Classes in Python Documentation Release 0.1
Objects and classes in Python Documentation Release 0.1 Jonathan Fine Sep 27, 2017 Contents 1 Decorators 2 1.1 The decorator syntax.........................................2 1.2 Bound methods............................................3 1.3 staticmethod() .........................................3 1.4 classmethod() ..........................................3 1.5 The call() decorator.......................................4 1.6 Nesting decorators..........................................4 1.7 Class decorators before Python 2.6.................................5 2 Constructing classes 6 2.1 The empty class...........................................6 3 dict_from_class() 8 3.1 The __dict__ of the empty class...................................8 3.2 Is the doc-string part of the body?..................................9 3.3 Definition of dict_from_class() ...............................9 4 property_from_class() 10 4.1 About properties........................................... 10 4.2 Definition of property_from_class() ............................ 11 4.3 Using property_from_class() ................................ 11 4.4 Unwanted keys............................................ 11 5 Deconstructing classes 13 6 type(name, bases, dict) 14 6.1 Constructing the empty class..................................... 14 6.2 Constructing any class........................................ 15 6.3 Specifying __doc__, __name__ and __module__.......................... 15 7 Subclassing int 16 7.1 Mutable and immutable types.................................... 16 7.2 -
Lecture Notes on Types for Part II of the Computer Science Tripos
Q Lecture Notes on Types for Part II of the Computer Science Tripos Prof. Andrew M. Pitts University of Cambridge Computer Laboratory c 2016 A. M. Pitts Contents Learning Guide i 1 Introduction 1 2 ML Polymorphism 6 2.1 Mini-ML type system . 6 2.2 Examples of type inference, by hand . 14 2.3 Principal type schemes . 16 2.4 A type inference algorithm . 18 3 Polymorphic Reference Types 25 3.1 The problem . 25 3.2 Restoring type soundness . 30 4 Polymorphic Lambda Calculus 33 4.1 From type schemes to polymorphic types . 33 4.2 The Polymorphic Lambda Calculus (PLC) type system . 37 4.3 PLC type inference . 42 4.4 Datatypes in PLC . 43 4.5 Existential types . 50 5 Dependent Types 53 5.1 Dependent functions . 53 5.2 Pure Type Systems . 57 5.3 System Fw .............................................. 63 6 Propositions as Types 67 6.1 Intuitionistic logics . 67 6.2 Curry-Howard correspondence . 69 6.3 Calculus of Constructions, lC ................................... 73 6.4 Inductive types . 76 7 Further Topics 81 References 84 Learning Guide These notes and slides are designed to accompany 12 lectures on type systems for Part II of the Cambridge University Computer Science Tripos. The course builds on the techniques intro- duced in the Part IB course on Semantics of Programming Languages for specifying type systems for programming languages and reasoning about their properties. The emphasis here is on type systems for functional languages and their connection to constructive logic. We pay par- ticular attention to the notion of parametric polymorphism (also known as generics), both because it has proven useful in practice and because its theory is quite subtle. -
Csci 658-01: Software Language Engineering Python 3 Reflexive
CSci 658-01: Software Language Engineering Python 3 Reflexive Metaprogramming Chapter 3 H. Conrad Cunningham 4 May 2018 Contents 3 Decorators and Metaclasses 2 3.1 Basic Function-Level Debugging . .2 3.1.1 Motivating example . .2 3.1.2 Abstraction Principle, staying DRY . .3 3.1.3 Function decorators . .3 3.1.4 Constructing a debug decorator . .4 3.1.5 Using the debug decorator . .6 3.1.6 Case study review . .7 3.1.7 Variations . .7 3.1.7.1 Logging . .7 3.1.7.2 Optional disable . .8 3.2 Extended Function-Level Debugging . .8 3.2.1 Motivating example . .8 3.2.2 Decorators with arguments . .9 3.2.3 Prefix decorator . .9 3.2.4 Reformulated prefix decorator . 10 3.3 Class-Level Debugging . 12 3.3.1 Motivating example . 12 3.3.2 Class-level debugger . 12 3.3.3 Variation: Attribute access debugging . 14 3.4 Class Hierarchy Debugging . 16 3.4.1 Motivating example . 16 3.4.2 Review of objects and types . 17 3.4.3 Class definition process . 18 3.4.4 Changing the metaclass . 20 3.4.5 Debugging using a metaclass . 21 3.4.6 Why metaclasses? . 22 3.5 Chapter Summary . 23 1 3.6 Exercises . 23 3.7 Acknowledgements . 23 3.8 References . 24 3.9 Terms and Concepts . 24 Copyright (C) 2018, H. Conrad Cunningham Professor of Computer and Information Science University of Mississippi 211 Weir Hall P.O. Box 1848 University, MS 38677 (662) 915-5358 Note: This chapter adapts David Beazley’s debugly example presentation from his Python 3 Metaprogramming tutorial at PyCon’2013 [Beazley 2013a]. -
The Use of UML for Software Requirements Expression and Management
The Use of UML for Software Requirements Expression and Management Alex Murray Ken Clark Jet Propulsion Laboratory Jet Propulsion Laboratory California Institute of Technology California Institute of Technology Pasadena, CA 91109 Pasadena, CA 91109 818-354-0111 818-393-6258 [email protected] [email protected] Abstract— It is common practice to write English-language 1. INTRODUCTION ”shall” statements to embody detailed software requirements in aerospace software applications. This paper explores the This work is being performed as part of the engineering of use of the UML language as a replacement for the English the flight software for the Laser Ranging Interferometer (LRI) language for this purpose. Among the advantages offered by the of the Gravity Recovery and Climate Experiment (GRACE) Unified Modeling Language (UML) is a high degree of clarity Follow-On (F-O) mission. However, rather than use the real and precision in the expression of domain concepts as well as project’s model to illustrate our approach in this paper, we architecture and design. Can this quality of UML be exploited have developed a separate, ”toy” model for use here, because for the definition of software requirements? this makes it easier to avoid getting bogged down in project details, and instead to focus on the technical approach to While expressing logical behavior, interface characteristics, requirements expression and management that is the subject timeliness constraints, and other constraints on software using UML is commonly done and relatively straight-forward, achiev- of this paper. ing the additional aspects of the expression and management of software requirements that stakeholders expect, especially There is one exception to this choice: we will describe our traceability, is far less so. -
Open C++ Tutorial∗
Open C++ Tutorial∗ Shigeru Chiba Institute of Information Science and Electronics University of Tsukuba [email protected] Copyright c 1998 by Shigeru Chiba. All Rights Reserved. 1 Introduction OpenC++ is an extensible language based on C++. The extended features of OpenC++ are specified by a meta-level program given at compile time. For dis- tinction, regular programs written in OpenC++ are called base-level programs. If no meta-level program is given, OpenC++ is identical to regular C++. The meta-level program extends OpenC++ through the interface called the OpenC++ MOP. The OpenC++ compiler consists of three stages: preprocessor, source-to-source translator from OpenC++ to C++, and the back-end C++ com- piler. The OpenC++ MOP is an interface to control the translator at the second stage. It allows to specify how an extended feature of OpenC++ is translated into regular C++ code. An extended feature of OpenC++ is supplied as an add-on software for the compiler. The add-on software consists of not only the meta-level program but also runtime support code. The runtime support code provides classes and functions used by the base-level program translated into C++. The base-level program in OpenC++ is first translated into C++ according to the meta-level program. Then it is linked with the runtime support code to be executable code. This flow is illustrated by Figure 1. The meta-level program is also written in OpenC++ since OpenC++ is a self- reflective language. It defines new metaobjects to control source-to-source transla- tion. The metaobjects are the meta-level representation of the base-level program and they perform the translation. -
Metaclasses: Generative C++
Metaclasses: Generative C++ Document Number: P0707 R3 Date: 2018-02-11 Reply-to: Herb Sutter ([email protected]) Audience: SG7, EWG Contents 1 Overview .............................................................................................................................................................2 2 Language: Metaclasses .......................................................................................................................................7 3 Library: Example metaclasses .......................................................................................................................... 18 4 Applying metaclasses: Qt moc and C++/WinRT .............................................................................................. 35 5 Alternatives for sourcedefinition transform syntax .................................................................................... 41 6 Alternatives for applying the transform .......................................................................................................... 43 7 FAQs ................................................................................................................................................................. 46 8 Revision history ............................................................................................................................................... 51 Major changes in R3: Switched to function-style declaration syntax per SG7 direction in Albuquerque (old: $class M new: constexpr void M(meta::type target, -
Meta-Class Features for Large-Scale Object Categorization on a Budget
Meta-Class Features for Large-Scale Object Categorization on a Budget Alessandro Bergamo Lorenzo Torresani Dartmouth College Hanover, NH, U.S.A. faleb, [email protected] Abstract cation accuracy over a predefined set of classes, and without consideration of the computational costs of the recognition. In this paper we introduce a novel image descriptor en- We believe that these two assumptions do not meet the abling accurate object categorization even with linear mod- requirements of modern applications of large-scale object els. Akin to the popular attribute descriptors, our feature categorization. For example, test-recognition efficiency is a vector comprises the outputs of a set of classifiers evaluated fundamental requirement to be able to scale object classi- on the image. However, unlike traditional attributes which fication to Web photo repositories, such as Flickr, which represent hand-selected object classes and predefined vi- are growing at rates of several millions new photos per sual properties, our features are learned automatically and day. Furthermore, while a fixed set of object classifiers can correspond to “abstract” categories, which we name meta- be used to annotate pictures with a set of predefined tags, classes. Each meta-class is a super-category obtained by the interactive nature of searching and browsing large im- grouping a set of object classes such that, collectively, they age collections calls for the ability to allow users to define are easy to distinguish from other sets of categories. By us- their own personal query categories to be recognized and ing “learnability” of the meta-classes as criterion for fea- retrieved from the database, ideally in real-time. -
Metaclass Functions: Generative C++
Metaclass functions: Generative C++ Document Number: P0707 R4 Date: 2019-06-16 Reply-to: Herb Sutter ([email protected]) Audience: SG7, EWG Contents 1 Overview .............................................................................................................................................................2 2 Language: “Metaclass” functions .......................................................................................................................6 3 Library: Example metaclasses .......................................................................................................................... 18 4 Applying metaclasses: Qt moc and C++/WinRT .............................................................................................. 35 5 FAQs ................................................................................................................................................................. 41 R4: • Updated notes in §1.3 to track the current prototype, which now also has consteval support. • Added using metaclass function in the class body. Abstract The only way to make a language more powerful, but also make its programs simpler, is by abstraction: adding well-chosen abstractions that let programmers replace manual code patterns with saying directly what they mean. There are two major categories: Elevate coding patterns/idioms into new abstractions built into the language. For example, in current C++, range-for lets programmers directly declare “for each” loops with compiler support and enforcement. -
Generic Programming
Generic Programming July 21, 1998 A Dagstuhl Seminar on the topic of Generic Programming was held April 27– May 1, 1998, with forty seven participants from ten countries. During the meeting there were thirty seven lectures, a panel session, and several problem sessions. The outcomes of the meeting include • A collection of abstracts of the lectures, made publicly available via this booklet and a web site at http://www-ca.informatik.uni-tuebingen.de/dagstuhl/gpdag.html. • Plans for a proceedings volume of papers submitted after the seminar that present (possibly extended) discussions of the topics covered in the lectures, problem sessions, and the panel session. • A list of generic programming projects and open problems, which will be maintained publicly on the World Wide Web at http://www-ca.informatik.uni-tuebingen.de/people/musser/gp/pop/index.html http://www.cs.rpi.edu/˜musser/gp/pop/index.html. 1 Contents 1 Motivation 3 2 Standards Panel 4 3 Lectures 4 3.1 Foundations and Methodology Comparisons ........ 4 Fundamentals of Generic Programming.................. 4 Jim Dehnert and Alex Stepanov Automatic Program Specialization by Partial Evaluation........ 4 Robert Gl¨uck Evaluating Generic Programming in Practice............... 6 Mehdi Jazayeri Polytypic Programming........................... 6 Johan Jeuring Recasting Algorithms As Objects: AnAlternativetoIterators . 7 Murali Sitaraman Using Genericity to Improve OO Designs................. 8 Karsten Weihe Inheritance, Genericity, and Class Hierarchies.............. 8 Wolf Zimmermann 3.2 Programming Methodology ................... 9 Hierarchical Iterators and Algorithms................... 9 Matt Austern Generic Programming in C++: Matrix Case Study........... 9 Krzysztof Czarnecki Generative Programming: Beyond Generic Programming........ 10 Ulrich Eisenecker Generic Programming Using Adaptive and Aspect-Oriented Programming .