![]() Make sure your Doxyfile contains GENERATE_XML = YES and XML_OUTPUT = xml.Document your code so that Doxygen can pick it up.You only need Doxygen, doxybook2 from this repository, and some markdown static site generator. build -target install -config MinSizeRel DCMAKE_TOOLCHAIN_FILE=/usr/local/share/vcpkg/scripts/buildsystems/vcpkg.cmakeĬmake -build. Vcpkg install -triplet 圆4-linux $(cat vcpkg.txt ) # Configure the project and use vcpkg toolchain # Install dependencies via vcpkg # The 'vcpkg.txt' file contains the list of dependencies to install # ensure you are using C++17 compiler # Linux: export CC=/usr/bin/gcc-9 C++17 compiler (for example: GCC-9 or Visual Studio 2017).To install from source, simply clone the repository, install the dependencies listed in the vcpkg.txt file, and use CMake + vcpkg toolchain to build it. The binary file doxybook2.exe is located in the bin folder in the zip file, put it somewhere in your system and add it to the OS PATH environment variable. The windows release needs Microsoft Visual C++ Redistributable for Visual Studio 2015, 20. Go to and download the precompiled binary in the zip file for your target platform. If using Windows, you will need Microsoft Visual C++ Redistributable for Visual Studio 2015, 20. No extra OS dependencies needed (everything is done via git vcpkg as linked statically), simply download the executable file from the GitHub release page. Windows arm64 is not tested and not supported at this moment. Doxygen 1.8.15 is supported but I do not recommend it. You will also need Doxygen 1.8.16 or newer. Using any other architecture, such as power PC, is not guaranteed to work. This tool has been compiled and tested on Windows (win32 and win64), Linux (amd64 and arm64), and OSX (amd64). Source markdown files for these examples above: Creating examples locallyįirst, compile the doxybook2 and then run examples.bat or examples.sh in the root folder of this repository. ![]() Feel free to submit an issue here on GitHub to let me know if you have found something. I can't catch all of those cases on my own. There will be some weird edge cases in which the markdown will not be properly generated. This project is not perfect and I will never claim it will be. If you don't want to bother with the templates, you can siply generate JSON only output (which contains partial Markdown for some things such as brief and detailed description), and use your own tool to create documentation you want. If you don't prefer the Markdown output generated by this tool, you can always make your own templates and supply them into this tool via the command line. The config file will help you to acomplish that by specifying the behavior or file names, relative links, etc. This project is not limited to only the static site generators listed here, you can use any other one. I have decided to created this next version (doxybook2) in C++ in order to get better memory usage, templating, and overall better customization. This project is a successor of doxybook which was a Python based tool that did the exact same thing. You can use the generated Markdown files to create beautiful C++ documentation using with MkDocs, GitBook, VuePress, Hugo, Docsify, or any other static site generator that supports markdown. ![]() This is a command line tool that converts Doxygen generated XML files into markdown files (or JSON). Screenshot taken from here Table of contents Also comes with an optional templating mechanism and extensive configuration file. Generate beautiful C++ documentation by converting Doxygen XML output into markdown pages via MkDocs, Hugo, VuePress, GitBook, Docsify, or your custom generator.
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![]() The supervisors were also responsible for the coding of industry and occupation textual description. Each enumerator was responsible for two (2) EAs in which he/she listed and enumerated households. Each supervisor was assigned 3 enumerators to work with in order to collect and edit the data. Field work was conducted between November and December in 2014 in all the provinces. ![]() Other sessions during training were for classroom role plays in which participants demonstrated how an interview should be conducted. The Master Trainers (MTs) facilitated the training of supervisors and enumerators using the enumerators' manual and survey instruments. A combined training of supervisors and enumerators was conducted in October 2014 in all the provinces and lasted for 12 days. A total of 96 supervisors and 288 enumerators were trained in data collection practices. The pre-test experiences formed the basis for finalizing the survey instruments. In 2014 LFS, the primary objective of the pre-test was to finalize the survey instruments and to introduce them to trainers of trainers for subsequent series of training. The main LFS is preceded by a pre-test on some selected households drawn from a rural and urban areas. The sample was then selected using a stratified two-stage cluster design. The disproportionate allocation is based on the optimal square root allocation method designed by Leslie Kish. Therefore, disproportionate allocation was adopted, for the purpose of maximizing the precision of survey estimates. Adjustments to the proportional allocation of the sample were made to allow for reasonable comparison to be achieved between strata or domains. The proportional allocation does not however allow for reliable estimates at lower domains like district, constituency or ward. The total sample of 11,520 households was first allocated between rural, urban and the provincial domains in proportion to the population of each domain according to the 2010 Census frame. In the second stage, households in each of the selected EAs were first listed followed by the selection of 20 households for enumeration. In the first stage, 576 Enumeration Areas (EAs) were selected from the 2010 Census sampling frame. A representative sample of 11, 520 households was selected at two stages. A larger proportion of the total population accounting for 58.4 percent was in rural areas while 41.6 percent was in urban areas. The population shows an increase of 4.2 percent from the population of 14,375,601 recorded in 2012. This population was spread across 2,934,096 households. The total population of Zambia was estimated at 14,983,315 in 2014, of which 49.1 percent were male and 50.9 percent were female. Assessing the incidence and prevalence of child labour. Measuring income levels among Paid employees, Self employed and Employers. Assessing levels of unemployment so that job creation efforts could be intensified. ![]() Measuring the size of the employed population both in the Formal and Informal sectors. The specific objectives of the LFS included: The main purpose of the 2014 LFS was to measure the size of the labour force and its characteristics with the view to providing guidance in the formulation and implementation of labour market policies and programmes. The LFS provides Key Indicators of Labour Market (KILM) namely: labour force participation rate, employment-population ratio, status in employment, employment by sector, employment by occupation, part time work, hours of work, unemployment, youth unemployment, time-related underemployment, informal sector employment, income, inactivity. Successive labour force surveys were conducted in 2005, 20. The first Zambia Labour Force Survey was conducted in 1986 to satisfy a need for reliable and timely data on the labour market. Since its inception in 1986, the major objective of the LFS has been to measure the size of the labour force and its characteristics (age, sex, industry, sector of employment, education, e.t.c). The Labour Force Survey (LFS) is a household survey designed to be carried out every two years by the Central Statistical Office in collaboration with the Ministry of Labour and Social Security. Intel® Xeon® and Intel Atom® processors and Intel® FPGAs power its network and edge infrastructure, Intel® Edge Controls and Intel® Edge Insights for Industrial software further improve data insights, and Intel® OpenVINO™ Toolkit is used for quality inspection.
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