Project 2016 Tutorial

[Prewitt Howells]

Each application you write in Django consists of a Python package that followsa certain convention. Django comes with a utility that automatically generatesthe basic directory structure of an app, so you can focus on writing coderather than creating directories.

The next step is to configure the global URLconf in the mysite project toinclude the URLconf defined in polls.urls. To do this, add an import fordjango.urls.include in mysite/urls.py and insert aninclude() in the urlpatterns list, so you have:

The include() function allows referencing other URLconfs.Whenever Django encounters include(), it chops off whateverpart of the URL matched up to that point and sends the remaining string to theincluded URLconf for further processing.

route is a string that contains a URL pattern. When processing a request,Django starts at the first pattern in urlpatterns and makes its way downthe list, comparing the requested URL against each pattern until it finds onethat matches.

Naming your URL lets you refer to it unambiguously from elsewhere in Django,especially from within templates. This powerful feature allows you to makeglobal changes to the URL patterns of your project while only touching a singlefile.

The goal of this first project is simply to get you used to the process of building something from scratch. You just need to get some practice breaking down a project into smaller pieces. You need to experience that first breath of fresh air as you emerge from the depths of tutorial hell for the first time.

This tutorial walks you through how to package a simple Python project. It willshow you how to add the necessary files and structure to create the package, howto build the package, and how to upload it to the Python Package Index (PyPI).

Creating the file __init__.py is recommended because the existence of an__init__.py file allows users to import the directory as a regular package,even if (as is the case in this tutorial) __init__.py is empty.[1]

Tools like pip and build do not actually convert your sourcesinto a distribution package (like a wheel);that job is performed by a build backend. The build backend determines howyour project will specify its configuration, including metadata (informationabout the project, for example, the name and tags that are displayed on PyPI)and input files. Build backends have different levels of functionality, such aswhether they support building extension modules, andyou should choose one that suits your needs and preferences.

Some build backends are part of larger tools that provide a command-lineinterface with additional features like project initialization and versionmanagement, as well as building, uploading, and installing packages. Thistutorial uses single-purpose tools that work independently.

Additional configuration of the build tool will either be in a tool sectionof the pyproject.toml, or in a special file defined by the build tool. Forexample, when using setuptools as your build backend, additional configurationmay be added to a setup.py or setup.cfg file, and specifyingsetuptools.build_meta in your build allows the tools to locate and use theseautomatically.

readme is a path to a file containing a detailed description of thepackage. This is shown on the package detail page on PyPI.In this case, the description is loaded from README.md (which is acommon pattern). There also is a more advanced table form described in thepyproject.toml guide.

classifiers gives the index and pip some additional metadataabout your package. In this case, the package is only compatible with Python3, is licensed under the MIT license, and is OS-independent. You shouldalways include at least which version(s) of Python your package works on,which license your package is available under, and which operating systemsyour package will work on. For a complete list of classifiers, see

See the pyproject.toml guide for detailson these and other fields that can be defined in the [project]table. Other common fields are keywords to improve discoverabilityand the dependencies that are required to install your package.

The tar.gz file is a source distributionwhereas the .whl file is a built distribution.Newer pip versions preferentially install built distributions, but willfall back to source distributions if needed. You should always upload a sourcedistribution and provide built distributions for the platforms your project iscompatible with. In this case, our example package is compatible with Python onany platform so only one built distribution is needed.

Technically, you can also create Python packages without an __init__.py file,but those are called namespace packagesand considered an advanced topic (not covered in this tutorial).If you are only getting started with Python packaging, it is recommended tostick with regular packages and __init__.py (even if the file is empty).

Important warning: This tutorial would only work in x86 and x86_64 Linux. I don't think it works in OS X and BSD either. (I do think that it is relatively easy to get kcov work in some BSDs like FreeBSD, but I don't have any BSD box.)

You first need to get the basic dependencies: libcurl, libelf and libdw (but no libdwarf). I've confirmed the following command is enough for Ubuntu, use corresponding commands for other Linux distros.

There are some issues with the line number, in particular with macros. Also, sometimes, you will get uncovered lines which should not matter (e.g. #[should_panic] tests will get uncovered lines after the panic); kcov does not know about Rust after all.

I think the line 61's coverage is really for line 60 (that is, Ok(r) technically ends right before the { token). Also, the line 58 has a macro try!, which splits the execution (that is, it will execute self.regex(), then an expanded code for try!, then an assignment) and confuses kcov. This is another caveat from using a generic tool with no access to the language knowledge.

In some cases, there are multiple addresses for a particular line. Sometimes (like the case on line 58), this is probably because the compiler generates two basic blocks for this particular line (part of the line is conditional), so here it simply says that one of the branches has been executed, and the other not. Another case is with inlined code, in which case the same source line can map to many addresses for each of the sites it has been inlined in.

Running kcov on optimized code is of course possible (and advisable where it's applicable), but can sometimes make for confusing results. Of course, looking at the compiler output will also be confusing then

Would it be prudent to file an issue with the travis people to ask them to make kcov part of the standard install for rust projects? It seems unweildy for them to download it on every rust build. Maybe pass them this thread so they see the use case and need.

First of all it is great a pleasure for me to finally join in this illustrious community. I have found out about Platformio project on January of this year and ever since have been using it for many of my arduino projects. I use the Atom Text editor which I found to be extremely easy and intuitive to learn and use.

When I write codes in Arduino IDE, I can split the function bodies from the main ino files into discrete files following a sequential alphabetical naming scheme viz. A.main , B.pid, C.motor etc etc. When I hit the compile button, arduino compiles all these files and their associated header files without any error. Also the other ino files are capable of recognizing public variables, function calls written in libraries by other users installed into the Arduino IDE.

But in platformio, say I make the above files as such, main.cpp, pid.cpp, motor.cpp numerous errors are thrown including missing variable declarations from other libraries, for instance tft declaration of Adafruit GFX library is not recognized in other cpp files EXPECT in the main.cpp file.

Hence though I am not an expert in this subject matter, I strongly believe the platformio community/team should make ONE DEFINITIVE TUTORIAL to teach us less experienced programmers on how to create a multifile arduino project which will do the following

And a header-file (.h) with the function prototype and the struct definition. Arduino.h has to be included so you can use the variable type String. To clean up your project directory you can put the header-file in the include-folder while the source-file (.cpp) should be in the src-folder.
myhelperfunctions.h:

I totally agree. My programming discipline is questionable. I have a lot of multiple .ino projects and I am having a real struggle with getting a clean and manageable structure in Platformio.
At the moment, the Arduino IDE makes them far more readable.
For native C++ programmers, I guess the structure is second nature. For migrants from Arduino, it needs a better explanation - please?

I found that once I had a project working in platformIO that it was easy to navigate to, say a function, because they are all listed. However, I went back to the Arduino IDE for now because I can be get the job done much easier and quicker.
When I next try platformIO I plan to move the code from multiple tabs into one main tab, import that to platform.IO and get it running then maybe break it down into separate .ccp and .h files.

In this tutorial, you will learn how to use Python 3 in Visual Studio Code to create, run, and debug a Python "Roll a dice" application, work with virtual environments, use packages, and more! By using the Python extension, you turn VS Code into a great, lightweight Python editor.

If you are new to programming, check out the Visual Studio Code for Education - Introduction to Python course. This course offers a comprehensive introduction to Python, featuring structured modules in a ready-to-code browser-based development environment.

Note: If you don't have admin access, an additional option for installing Python on Windows is to use the Microsoft Store. The Microsoft Store provides installs of supported Python versions.

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