Deep Research and Development

Once you start developing on Node.js, you'll very soon find out the need for logging. It of course has nothing to do with JavaScript or specifically Node.js, but rather with the need to log activities on production environment, and even on development one for that matter. console.log The most rudimentary type of logging you could do is using console.log and console.error methods. This is better than nothing, but hardly the best solution. Basically they work exactly as they do, when you used them in browsers. However since we're in the server dominion now, an interesting aspect is revealed. That is, the console functions are synchronous, when the destination is a terminal or a file (to avoid lost messages in case of premature exit) and asynchronous when it’s a pipe (to avoid blocking for long periods of time). It is fully manual and you'll have to come up with your own format and basically manage everything yourself. Bunyan Bunyan library makes its mission ...

Following our last article about using Jasmine and Karma together, let's look how we streamline the testing of our server side code written in Node.js. To do this, we'll need to install jasmine-node package. npm install -g jasmine-node If you're not familiar with the tool, please read the JavaScript Developer Toolkit before proceeding any further. The installed version will only support Jasmine 1.3 version. In order to add the 2.0 support, you should install the branched version. npm install -g jasmine-node@2.0.0-beta4 Again as in previous article we'll be using the specs from our previous Jasmine article and the source code can be found in GitHub . After installing the correct version, we'll have to make our Player and Song classes as CommonJS modules. If you don't know how, please read the article about Modular Design Patterns in JavaScript . And of course requiring them in our specs: ... module.exports = Song; ... module.exports = Player; ...

Let's talk about testing again, and will surely again in the future :) Last time I introduced the JavaScript Testing with Jasmine and we ran a few specs, however it seemed some how half baked - you were required to create a special page and open it in the browser to see the results. I you wanted to check in various browsers, you should have iterated the same procedure on each browser again and again. This is not how we do things in 2014! We want to streamline the process! This is where Karma comes. It is built on Node.js and allows you to run the tests of your front end code automatically on various browsers. Installation In order to install Karma on your computer, run the following npm command. If you're not familiar with the tool, please read the JavaScript Developer Toolkit article first: npm install -g karma After you try to call karma though, you'll get unknown command error, which can be easily solved by installing karma command-line interface ( CLI )...

Remember in our first JavaScript Developer Toolkit article I've told that you would need to install Ruby. Today we'll see why and the reason is Sass . Sass is an extension of CSS3 that helps you creating better stylesheets with less effort. Sass frees you from repetition and gives you tools to be creative. Since you can implement changes much faster, you’ll be able to take your design in a more boldly manner. Your stylesheets will be able to keep pace with changes, all while producing standards-based CSS you can use in any environment. As mentioned already, the Sass processor is written in Ruby. However Ruby environment has to be only installed on the development environment, so it doesn't matter how you write your server side. You only deploy the generated CSS files to production environment. gem install sass Sass offers a lot of features such as variables, nesting, importing, mixins and many more. I'll cover the most important of them, but make sure to read ...

Since June has 5 Sundays in it, today I will write a bonus article :) Following our last week's article about Publish and Subscribe Pattern with Postal.js , I'd like to broaden the topic talking about design patterns . The design patterns from the Gang of Four book offer solutions to common problems related to the object-oriented software design. It has been in use for decades and implemented in all server side languages. It's time we use them with JavaScript and start to write some piece of decent code. I'll be writing a series of articles covering all of them. As in the book, we'll be starting with creational patterns  and cover both Abstract Factory and Factory Method patterns. Abstract classes and interfaces enforce consistent interfaces in derived classes. In JavaScript we must ensure this consistency ourselves by making sure that each 'concrete' object has the same interface definition (i.e. properties and methods) as the others. Abstr...

Introduction Having learned some basic packages of Python, you probably started to wonder that working through the python console is not very productive. In R we have RStudio, of which we've already talked in first  articles . Good folks of Python community have developed an IPython - an interactive Python console and web environment. Installation As usual we are using python pip package manager to install the package: pip install ipython Usage Once the package is installed, you can launch the console version by simply typing it's name in the console: ipython Once the application is started, one can simply type python commands and observe the results. Though it doesn't look that far different from the ordinary python console, it provides auto-quoting, code completion, search of previously executed commands, output caching and many more. As I said previously, there are two modes of running the IPython - console and web. To launch the web interface, one...

Introduction Having learnt NumPy and SciPy in previous articles, let's discuss our next package, called pandas. Pandas provides rich data structures and functions designed to make working with structured data fast, easy, and expressive. It is, as you will see, one of the critical ingredients enabling Python to be a powerful and productive data analysis environment. Pandas combines the high performance array-computing features of NumPy with the flexible data manipulation capabilities of spreadsheets and relational databases (such as SQL), mingling DataFrame - a two-dimensional tabular, column-oriented data structure with both row and column labels. It provides sophisticated indexing functionality to make it easy to reshape, slice and dice, perform aggregations, and select subsets of data. For users of the R language for statistical computing, the DataFrame name will be familiar, as the object was named after the similar R data.frame object. However the functionality...