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I want to create an extensible app via plugins with C++ and Python

I want to create an app that will be extensible via plugins.
I know that I have 2 options.
I can create my own interpreted language and app with a built-in interpreter for this language.
I can use one of the existing languages such as Python, Lua or another scripting language.
I want to use option 2. And I know that I must create a layer for external language to enable communication between this language and my app. But I don't know how to do it. Maybe I must use interprocess communication or something like that.
Let's assume that I have an application written in C++. In the beginning, it may be even a simple console app that displays a few options. And I want to write a plugin in Python like this:
option = "additional option"
myApp.addOption(option)
And then:
I launch my app
My app loads the plugin
I see my app with this additional option displayed
I want to do this simple thing to understand how it works and then I will be able to do something more complicated.

You could start by looking at the languages' documentation(if you're new):
Python -->https://docs.python.org/3/
Lua --> https://www.lua.org/docs.html
C++ libraries can also be called in C(If you're careful enough),you could look at this too
https://www.teddy.ch/c++_library_in_c/

You should be aware that, with care, a C++ library can be called from a C program, mostly by appropriately using extern "C" to disable name mangling. On Linux, read also the C++ dlopen mini Howto.
Then you need to read the chapter Extending and embedding the Python interpreter
At last, Python is open source, so please study its source code.
I can use one of the existing languages such as Python, Lua or another scripting language.
I strongly recommend considering using GNU Guile or extending Ocaml.
And both TensorFlow or NumPy could inspire you, since they are open source libraries (coded in C and/or C++) usable from Python.

Python support for Qt dll

I have my own C++ library project(with source) written in Qt and it uses QTcpsocket, QUdpSocket, QSerialPort signals and slots.
I would like to support this library in Python as well.
What is the preferred way to do this?
Writing a wrapper in Python, if so does it have obstacles?
Dont know if PyQt is just for this purpose?
Or do you thnink is it better to rewrite the lib in Python by just implementing the logic used in C++ library project.
As this is library is part of a SDK, same applies for supporting QT dll with .NET as well in fact, as a second step after supporting Python.
Example API of Qt.
quint16 SendCommandAsync(CmdBaseSpv1* pcommand,
ConnectionArg connectionArg,
emitLogOptions::emitLogOption logOption,
LogInfo &txLogInfo,
LogInfo &rxLogInfo);
I want to call this function from Python.
Function parameters CmdBaseSpv1, ConnectionArg, emitLogOption, LogInfo are all Qt classes.
Some of these arguments are using the QOBJECT base class.
As you see from the function name; it is an Asynchronous function call. Result will emit a signal so I need to get async result as well.

I'll write down what I know about wrapping C++ libraries and will try to source it, but as a huge disclaimer, I have only used this for something very, very simple myself.
You ask about rewriting the library in Python. I would say this depends. If the code is trivial, then I don't see why not. If it is larger and has to be kept up-to-date with other code (as you imply with .Net), I wouldn't. It makes sense to reuse the same code for both.
 My suggestion
From what I see of your code I would try to wrap it using boost::python or SWIG.
How to wrap
The main trouble is going to be to create CmdBaseSpv1, ConnectionArg, etc. in Python.
If you don't need any Qt-classes to instantiate your classes, this should be straightforward. However, in case you need the Qt types inside of Python (e.g. because the constructor of CmdBaseSpv1 requires a QString), your task is a lot more complicated because you need a way to convert a Python-string into a QString. If you can, you should only use stl-types.
Everything in Python
The simplest way to wrap a small C library is to use the cffi module (or ctypes). You can write the full binding in Python. However, this is a lot of manual work if your API is large and can get difficult.
There is another problem: ctypes is only compatible with C, not C++. So you'd need to change your interface to be compatible with C, internally you could still use C++ and Qt.
Wrap by hand
An alternative is to wrap the library calls yourself. You can either do this by using the Python API. There are also a few libraries that help you create the bindings. Boost::python seems especially promising and works with C++.
Binding generators
If your API is very large, you should use a binding generator which parses the C++ code and generates the bindings itself. For example sip is one of them. It is used to create the bindings for the whole Qt library. There are a few binding generators out there, one mentioned in the Python docs is SWIG. PySide uses Shiboken and also has a nice description of it on their website.
SWIG has the additional advantage, that you can create bindings for multiple languages, including C#.
PyQt
PyQt is a binding generated from Qt using sip. You'll probably not need it, unless you need to access the full power of Qt from inside Python. If this is the case, consider using sipfor generating the bindings, so things like the signal-slot mechanism are compatible between your library and PyQt.
Challenges with bindings
Bindings come with a few challenges because Python and C++ are different in some key areas.
Memory-management
Memory management in Python is almost automatic, in C++ you're required to do it manually. For example
def myfunc():
mywidget = QWidget()
at the end of myfunc() mywidget gets garbage collected. In C++ however
void myfunc() {
auto mywidget = new QWidget();
}
mywidget is still around. This means that even when inside Python, you need to take care of the C++ memory management. The problems I've seen are memory leaks and dangling pointers. Watch out for this when using callbacks, you don't want Python to garbage collect the callback while C++ thinks it's still alive.
Exceptions
Not all programming languages have exceptions or deal with them the same way. For example, it would be nice if an exception inside C++ can be caught inside Python.
Links to related question
How to wrap a c++ library for python? (example of boost::python)
Exposing a C++ API to Python (discussion about boost::python, SWIG and more)
https://stackoverflow.com/a/5686873 (discusses Cython, another choice)

Interfacing C/C++ libraries with Python

I have a C++ library that I need to be able to interface with python. I read this question to understand the choice I need to adapt.
I saw SWIG and Cython and wanted to go with SWIG, mainly because my python programming experience is very minimal. However, I realise that with Swig I have to write an interface (.i extensions) for every class. Now, my C++ project is huge and I feel it will take me a lot of time to get the wrappers in place (or maybe I am wrong).
So right now since my application is large I need to make a choice. In the quoted thread I came across Boost Python. Now I can no longer decide and want input from people who can tell me the pros and cons of one over the other. Note my preference is on easy of use and how quickly can it be done. I am willing to compromise system performance for this. I would appreciate immensely if someone could provide me a SWIG implemented project or Boost Python implemented project link (a complete module instead of a sample tutorial would be much better !)

Boost::python provides a nearly wrapper-less interface between C++ and Python. It also allows you to write custom converters and other neat things that make the Python interfaces much nicer. The interfaces are pure C++, but they rely on templates and clever design patterns to make it look all nice and declarative. You also get the benefit of your connector code being checked by the compiler directly.
With Swig, you write interface declarations in Swig's own DSL, which takes a few days to get a hang of. In addition, it always inserts a wrapper layer, so it could be a bit slower. However, it does have the nice feature of automatically converting many things for ya without having to declare anything extra. The wrappers it generates are quite hard to debug though.
IMHO boost::python is the better choice, because you're working pretty directly with CPython's native C interfaces. I use Swig for Java and C++ interaction, because JNI is a bear, Python's C interface is actually quite usable all by itself.
If you already have a bunch of Swig wrappers, I would keep those because you'd have to redo all that work. However, starting a new project, or if you require maximum performance, boost::python all the way!

Is it possible to use re2 from Python?

i just discovered http://code.google.com/p/re2, a promising library that uses a long-neglected way (Thompson NFA) to implement a regular expression engine that can be orders of magnitudes faster than the available engines of awk, Perl, or Python.
so i downloaded the code and did the usual sudo make install thing. however, that action had seemingly done little more than adding /usr/local/include/re2/re2.h to my system. there seemed to be some *.a file in addition, but then what is it with this *.a extension?
i would like to use re2 from Python (preferrably Python 3.1) and was excited to see files like make_unicode_groups.py in the distro (maybe just used during the build process?). those however were not deployed on my machine.
how can i use re2 from Python?
update two friendly people have pointed out that i could try to build DLLs / *.so files from the sources and then use Python’s ctypes library to access those. can anyone give useful pointers how to do just that? i’m pretty much clueless here, especially with the first part (building the *.so files).
update i have also posted this question (earlier) to the re2 developers’ group, without reply till now (it is a small group), and today to the (somewhat more populous) comp.lang.py group [—thread here—]. the hope is that people from various corners can contact each other. my guess is a skilled person can do this in a few hours during their 20% your-free-time-belongs-google-too timeslice; it would tie me for weeks. is there a tool to automatically dumb-down C++ to whatever flavor of C that Python needs to be able to connect? then maybe getting a viable result can be reduced to clever tool chaining.
(rant)why is this so difficult? to think that in 2010 we still cannot have our abundant pieces of software just talk to each other. this is such a roadblock that whenever you want to address some C code from Python you must always cruft these linking bits. this requires a lot of work, but only delivers an extension module that is specific to the version of the C code and the version of Python, so it ages fast.(/rant) would it be possible to run such things in separate processes (say if i had an re2 executable that can produce results for data that comes in on, say, subprocess/Popen/communicate())? (this should not be a pure command-line tool that necessitates the opening of a process each time it is needed, but a single processs that runs continuously; maybe there exist wrappers that sort of ‘demonize’ such C code).

David Reiss has put together a Python wrapper for re2. It doesn't have all of the functionality of Python's re module, but it's a start. It's available here: http://github.com/facebook/pyre2.

Possible yes, easy no. Looking at the re2.h, this is a C++ library exposed as a class. There are two ways you could use it from Python.
1.) As Tuomas says, compile it as a DLL/so and use ctypes. In order to use it from python, though, you would need to wrap the object init and methods into c style externed functions. I've done this in the past with ctypes by externing functions that pass a pointer to the object around. The "init" function returns a void pointer to the object that gets passed on each subsequent method call. Very messy indeed.
2.) Wrap it into a true python module. Again those functions exposed to python would need to be extern "C". One option is to use Boost.Python, that would ease this work.

SWIG handles C++ (unlike ctypes), so it may be more straightforward to use it.

You could try to build re2 into its own DLL/so and use ctypes to call functions from that DLL/so. You will probably need to define your own entry points in the DLL/so.

You can use the python package https://pypi.org/project/google-re2/. Although look at the bottom, there are a few requirements to install yourself before installing the python package.

Python: SWIG vs ctypes

In python, under what circumstances is SWIG a better choice than ctypes for calling entry points in shared libraries? Let's assume you don't already have the SWIG interface file(s). What are the performance metrics of the two?

I have a rich experience of using swig. SWIG claims that it is a rapid solution for wrapping things. But in real life...
Cons:
SWIG is developed to be general, for everyone and for 20+ languages. Generally, it leads to drawbacks:
- needs configuration (SWIG .i templates), sometimes it is tricky,
- lack of treatment of some special cases (see python properties further),
- lack of performance for some languages.
Python cons:
1) Code style inconsistency. C++ and python have very different code styles (that is obvious, certainly), the possibilities of a swig of making target code more Pythonish is very limited. As an example, it is butt-heart to create properties from getters and setters. See this q&a
2) Lack of broad community. SWIG has some good documentation. But if one caught something that is not in the documentation, there is no information at all. No blogs nor googling helps. So one has to heavily dig SWIG generated code in such cases... That is terrible, I could say...
Pros:
In simple cases, it is really rapid, easy and straight forward
If you produced swig interface files once, you can wrap this C++ code to ANY of other 20+ languages (!!!).
One big concern about SWIG is a performance. Since version 2.04 SWIG includes '-builtin' flag which makes SWIG even faster than other automated ways of wrapping. At least some benchmarks shows this.
When to USE SWIG?
So I concluded for myself two cases when the swig is good to use:
2) If one needs to wrap C++ code for several languages. Or if potentially there could be a time when one needs to distribute the code for several languages. Using SWIG is reliable in this case.
1) If one needs to rapidly wrap just several functions from some C++ library for end use.
Live experience
Update :
It is a year and a half passed as we did a conversion of our library by using SWIG.
First, we made a python version. There were several moments when we experienced troubles with SWIG - it is true. But right now we expanded our library to Java and .NET. So we have 3 languages with 1 SWIG. And I could say that SWIG rocks in terms of saving a LOT of time.
Update 2:
It is two years as we use SWIG for this library. SWIG is integrated into our build system. Recently we had major API change of C++ library. SWIG worked perfectly. The only thing we needed to do is to add several %rename to .i files so our CppCamelStyleFunctions() now looks_more_pythonish in python. First I was concerned about some problems that could arise, but nothing went wrong. It was amazing. Just several edits and everything distributed in 3 languages. Now I am confident that it was a good solution to use SWIG in our case.
Update 3:
It is 3+ years we use SWIG for our library. Major change: python part was totally rewritten in pure python. The reason is that Python is used for the majority of applications of our library now. Even if the pure python version works slower than C++ wrapping, it is more convenient for users to work with pure python, not struggling with native libraries.
SWIG is still used for .NET and Java versions.
The Main question here "Would we use SWIG for python if we started the project from the beginning?". We would! SWIG allowed us to rapidly distribute our product to many languages. It worked for a period of time which gave us the opportunity for better understanding our users requirements.

SWIG generates (rather ugly) C or C++ code. It is straightforward to use for simple functions (things that can be translated directly) and reasonably easy to use for more complex functions (such as functions with output parameters that need an extra translation step to represent in Python.) For more powerful interfacing you often need to write bits of C as part of the interface file. For anything but simple use you will need to know about CPython and how it represents objects -- not hard, but something to keep in mind.
ctypes allows you to directly access C functions, structures and other data, and load arbitrary shared libraries. You do not need to write any C for this, but you do need to understand how C works. It is, you could argue, the flip side of SWIG: it doesn't generate code and it doesn't require a compiler at runtime, but for anything but simple use it does require that you understand how things like C datatypes, casting, memory management and alignment work. You also need to manually or automatically translate C structs, unions and arrays into the equivalent ctypes datastructure, including the right memory layout.
It is likely that in pure execution, SWIG is faster than ctypes -- because the management around the actual work is done in C at compiletime rather than in Python at runtime. However, unless you interface a lot of different C functions but each only a few times, it's unlikely the overhead will be really noticeable.
In development time, ctypes has a much lower startup cost: you don't have to learn about interface files, you don't have to generate .c files and compile them, you don't have to check out and silence warnings. You can just jump in and start using a single C function with minimal effort, then expand it to more. And you get to test and try things out directly in the Python interpreter. Wrapping lots of code is somewhat tedious, although there are attempts to make that simpler (like ctypes-configure.)
SWIG, on the other hand, can be used to generate wrappers for multiple languages (barring language-specific details that need filling in, like the custom C code I mentioned above.) When wrapping lots and lots of code that SWIG can handle with little help, the code generation can also be a lot simpler to set up than the ctypes equivalents.

CTypes is very cool and much easier than SWIG, but it has the drawback that poorly or malevolently-written python code can actually crash the python process. You should also consider boost python. IMHO it's actually easier than swig while giving you more control over the final python interface. If you are using C++ anyway, you also don't add any other languages to your mix.

In my experience, ctypes does have a big disadvantage: when something goes wrong (and it invariably will for any complex interfaces), it's a hell to debug.
The problem is that a big part of your stack is obscured by ctypes/ffi magic and there is no easy way to determine how did you get to a particular point and why parameter values are what they are..

You can also use Pyrex, which can act as glue between high-level Python code and low-level C code. lxml is written in Pyrex, for instance.

ctypes is great, but does not handle C++ classes. I've also found ctypes is about 10% slower than a direct C binding, but that will highly depend on what you are calling.
If you are going to go with ctypes, definitely check out the Pyglet and Pyopengl projects, that have massive examples of ctype bindings.

I'm going to be contrarian and suggest that, if you can, you should write your extension library using the standard Python API. It's really well-integrated from both a C and Python perspective... if you have any experience with the Perl API, you will find it a very pleasant surprise.
Ctypes is nice too, but as others have said, it doesn't do C++.
How big is the library you're trying to wrap? How quickly does the codebase change? Any other maintenance issues? These will all probably affect the choice of the best way to write the Python bindings.

Just wanted to add a few more considerations that I didn't see mentioned yet.
[EDIT: Ooops, didn't see Mike Steder's answer]
If you want to try using a non Cpython implementation (like PyPy, IronPython or Jython), then ctypes is about the only way to go. PyPy doesn't allow writing C-extensions, so that rules out pyrex/cython and Boost.python. For the same reason, ctypes is the only mechanism that will work for IronPython and (eventually, once they get it all working) jython.
As someone else mentioned, no compilation is required. This means that if a new version of the .dll or .so comes out, you can just drop it in, and load that new version. As long as the none of the interfaces changed, it's a drop in replacement.

Something to keep in mind is that SWIG targets only the CPython implementation. Since ctypes is also supported by the PyPy and IronPython implementations it may be worth writing your modules with ctypes for compatibility with the wider Python ecosystem.

I have found SWIG to be be a little bloated in its approach (in general, not just Python) and difficult to implement without having to cross the sore point of writing Python code with an explicit mindset to be SWIG friendly, rather than writing clean well-written Python code. It is, IMHO, a much more straightforward process to write C bindings to C++ (if using C++) and then use ctypes to interface to any C layer.
If the library you are interfacing to has a C interface as part of the library, another advantage of ctypes is that you don't have to compile a separate python-binding library to access third-party libraries. This is particularly nice in formulating a pure-python solution that avoids cross-platform compilation issues (for those third-party libs offered on disparate platforms). Having to embed compiled code into a package you wish to deploy on something like PyPi in a cross-platform friendly way is a pain; one of my most irritating points about Python packages using SWIG or underlying explicit C code is their general inavailability cross-platform. So consider this if you are working with cross-platform available third party libraries and developing a python solution around them.
As a real-world example, consider PyGTK. This (I believe) uses SWIG to generate C code to interface to the GTK C calls. I used this for the briefest time only to find it a real pain to set up and use, with quirky odd errors if you didn't do things in the correct order on setup and just in general. It was such a frustrating experience, and when I looked at the interace definitions provided by GTK on the web I realized what a simple excercise it would be to write a translator of those interface to python ctypes interface. A project called PyGGI was born, and in ONE day I was able to rewrite PyGTK to be a much more functiona and useful product that matches cleanly to the GTK C-object-oriented interfaces. And it required no compilation of C-code making it cross-platform friendly. (I was actually after interfacing to webkitgtk, which isn't so cross-platform). I can also easily deploy PyGGI to any platform supporting GTK.